experimental design x1

A contract was soon awarded to Bell for the construction of three XS-1 (experimental supersonic - 1) aircraft, though the 'S' portion of the designation was later dropped. The fuselage was patterned after a .50 caliber bullet to reduce drag. The portly shape also provided significant internal volume for a powerful rocket motor, fuel, and data collection equipment.

Though the X-1 had originally been designed for conventional takeoffs, all flights but one were carried aloft by a B-29 or B-50 Superfortress mother plane. The X-1 was lifted to an altitude of 20,000 ft (6,100 m) before being released to ignite its rocket engines. This technique was advantageous since it improved safety in ground operations and also vastly increased the aircraft's performance.

The flight test program began with a few test glides and powered flights, but the most important flight of the X-1 came on 14 October 1947. It was on this date that Capt. Charles Yeager became the first pilot to break the "sound barrier" when he reached Mach 1.06 at 43,000 ft (13,120 m) over the Mojave Desert near Muroc Dry Lake, California. A few days later, the X-1-1 also set an altitude record by reaching 71,900 ft (21,935 m).

Following the loss of the X-1-3 in a ground accident, NASA ordered a further three examples called the X-1A, X-1B, and X-1D to explore flight at Mach 2. Chuck Yeager set a new speed record of Mach 2.44 aboard the X-1A in 1953, but both this model and the X-1D were lost following propulsion explosions. Despite these dangers, the X-1-2 was rebuilt as the X-1E to conduct further experiments at Mach 2 and beyond. This model became one of the fastest and highest flying of the series thanks to its reduced weight and drag.

The X-1 program was completed in 1958, but its impact on aviation history is considerable. The three surviving X-1 models, including the historic X-1-1, have been preserved at sites across the country.

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The Bell X-1: Program changed history, established research aircraft concept

When most people think of the Bell X-1, they think of then-Capt. Chuck Yeager breaking the sound barrier on Oct. 14, 1947.

However, the X-1 program involved seven different variants of the aircraft, and multiple flights from the program’s first flight on Jan. 19, 1946, through 1958.

The first generation X-1 aircraft changed aviation history in numerous ways, and not simply because they were the first aircraft to fly faster than the speed of sound.

Rather, they established the concept of the research aircraft, built solely for experimental purposes, and unhampered by any military or commercial requirements. Although subsequent X-planes were built for a wide range of purposes — technology or concept demonstrators, unmanned test missiles, and even as prototypes in all but name — the X-1s were built to go faster than an aircraft had ever flown before.

The X-1 resulted from technological challenges facing aircraft designers in the late 1930s and early 1940s. Aircraft had begun to experience both subsonic and supersonic airflow over their wings. This created a range of undesirable characteristics: compressibility, increased drag, trim changes, severe turbulence, and loss of control effectiveness. Wind tunnels were affected by the same aerodynamic problems, and their data proved to be unreliable in this regime. As a result, a few individuals — John Stack of NACA, Ezra Kotchner of the Army Air Forces, and Walter Diehl of the Navy — realized a specialized research aircraft offered the only feasible means of getting supersonic aeronautical data.

The Army Air Forces selected Bell Aircraft to build three X-1 aircraft.

The fuselage was the same shape as a 0.50 caliber machine gun bullet, which was known to be stable at supersonic speeds. The X-1 wings were straight, rather than swept back, and relatively thin for the time. The X-1-1 (serial number 46-062) had a wing with an 8 percent thickness/chord ratio. The X-1-2 (serial number 46-063) had a 10 percent ratio wing. The X-1 was powered by an XLR-11 rocket engine, which had four chambers and burned liquid oxygen (LOX) and a mixture of alcohol and water. In 1945, rockets were viewed with suspicion by some engineers. Both NACA and Navy preferred a jet-powered research aircraft, rather than one using a rocket, as the Army Air Forces had selected.

The X-1-1 was delivered by Bell in December 1945. At the same time, the Army Air Forces asked that NACA personnel oversee the instrumentation and data analysis of the X-1 flights. As a result, a NACA team was incorporated into the program.

The first glide flight of the X-1 occurred on Jan. 19, 1946, at Pinecastle Field, Fla., flown by Bell test pilot Jack Woolams because Muroc Army Air Field had been flooded. The X-1-1 was air launched from a B-29. Woolams made a total of 10 glide flights to test the X-1’s low speed handling before it was returned to Bell Aircraft in Buffalo, N.Y., in March 1946 for installation of the rocket engine, and modifications to prepare it for powered flight tests. The aircraft was delivered to Muroc in October 1946.

The first group of NACA engineers arrived at Muroc Field (now Edwards Air Force Base, Calif.), in September 1946 in preparation for the initial flights of the X-1-2.

Bell test pilot Chalmers “Slick” Goodlin made the first glide flight in the X-1-2 on Oct. 11, 1946. After a total of four glide flights, he made the first powered flight on Dec. 9, reaching a speed of Mach 0.79. By June 1947, Bell had proven the airworthiness of both X-1s up to speeds of Mach 0.8. The contract freed the company from responsibility above this speed.

On June 30, 1947, Army Air Forces and NACA representatives agreed on a two-phase flight program. The Army Air Forces would use the X-1-1, with its thinner wing, to conduct an accelerated program to reach Mach 1.1 as quickly as possible. NACA would provide support, such as technical advice and data analysis. NACA would then undertake a slower-paced, more detailed series of research flights at transonic (near the speed of sound) speeds, using the X-1-2 and its thicker wing.

Capt. Charles E. “Chuck” Yeager was selected as the pilot for flights to Mach 1.

He made his first glide flights on Aug. 6, 7 and 8, 1947. Yeager undertook his first powered flight in the X-1-1 on Aug. 29, reaching Mach 0.85.

Over the next six weeks, Yeager came closer to Mach 1, reaching Mach 0.997 on Oct. 10. For the NACA engineers, used to a more cautious step-by-step approach, Yeager and the Air Force seemed to be acting in haste. Still, on Oct. 14, Yeager reached a speed of Mach 1.06 at 43,000 feet, becoming the first man to fly supersonic. Air Force officials designated the flight and all data as Top Secret two hours later.

However, the story of Yeager’s Oct. 14 flight was leaked to a reporter from the magazine Aviation Week, and the Los Angeles Times featured the story as headline news in their Dec. 22 issue. The magazine story was released on Dec. 20. The Air Force threatened legal action against the journalists who revealed the story, but none ever occurred. The news of a straight-wing supersonic aircraft surprised many American experts who, like their German counterparts during World War II, believed that a swept-wing design was necessary to break the sound barrier. On June 10, 1948, Air Force Secretary Stuart Symington announced that the sound barrier had been repeatedly broken by two experimental airplanes.

NACA now began flying the X-1-2 on research missions.

On Oct. 21, 1947, NACA pilot Herbert H. Hoover made a glide flight. Hoover followed this mission on Dec. 16 with a powered flight to Mach 0.84. In January 1948, a second NACA research pilot, Howard C. Lilly, joined the program. The initial NACA flights in the aircraft sought data on turns and pull ups, side slips, and elevator effectiveness at subsonic speeds. It was not until March 4, 1948, that Hoover reached Mach 1.029. Hoover became the second man to reach Mach 1, on the first NACA and the first civilian supersonic flight. Lilly flew at Mach 1.1 on March 31.

Robert A. Champine replaced Hoover and Lilly on the X-1 program in November 1948, undertaking studies of wing pressure distribution, stability and control, and stabilizer effectiveness. John H. Griffith continued these research efforts when he replaced Champine on the X-1 program. Griffith flew the X-1-2 through October 1950, when he left NACA for a job as a company test pilot.

A. Scott Crossfield joined the research efforts in April 1951, followed by Joe Walker in August.

The research usefulness of the first generation X-1 aircraft was nearing an end.

The second generation X-1 aircraft, then under development, would be able to reach twice the speed of sound.

After Yeager’s Mach 1 flight, the X-1-1 had been used by the Air Force to acquire data on stability and control, wing and tail loading, high-altitude flight, and pilot familiarization. After a final flight by Yeager on May 12, 1950, the X-1-1 was retired and given to the Smithsonian Institution. The X-1-2 continued flying, but technical problems brought its work to a close. The X-1-1 and X-1-2 both used a fuel system pressurized with nitrogen. The X-1-2s nitrogen tanks were nearing the end of their fatigue life, risking a possible explosion. Consequently, NACA officials grounded the X-1-2, which later returned in a much-modified state as the X-1E.

The X-1-3 (serial number 46-064) represented the final example of first generation X-1 series. The X-1-3 was externally identical to the other two aircraft. The fuel system in the X-1-3 did not rely on nitrogen pressure, however, but rather on a turbopump. This eliminated the need for the heavy nitrogen tanks, and resulted in a calculated maximum speed of Mach 2.4, a full Mach number higher than the X-1-1 or X-1-2 could reach. Funding cuts and turbopump development problems, however, delayed the aircraft a full three years. The Air Force had also contracted with Bell Aircraft to develop the second generation X-1A, X-1B and X-1D. Interest in the X-1-3 faded, and the Air Force canceled it. NACA, wanting its own Mach 2 aircraft to experiment with, picked up the Air Force’s canceled X-1-3.

The X-1-3 was delivered to Edwards in April 1951. Bell test pilot Joseph Cannon successfully made a glide flight in the aircraft on July 20. On Nov. 9, 1951, a captive flight was made by the X-1-3 aboard the B-50 launch aircraft. This was to be a rehearsal for the first powered flight, as well as a test of the jettisoning system. Engineers canceled the jettisoning tests, however, when nitrogen pressure fell. The B-50, with the fully fueled X-1-3 still attached, landed back at Edwards safely, and preparations began to jettison the LOX. As Cannon pressurized the LOX tank, however, a dull thud was heard, followed by a hiss. A small cloud of white vapor escaped from the X-1-3’s center section. Then, a violent explosion occurred, with yellow flames and black smoke engulfing both the X-1-3 and the B-50. Cannon escaped from the X-1-3, but spent nearly a year in the hospital recovering from severe burns on his legs, arms, and body. The fire and subsequent explosions destroyed both the X-1-3 and B-50.

Variants Later variants of the X-1 were built to test different aspects of supersonic flight. One of these, the X-1A, with Yeager at the controls, inadvertently demonstrated a very dangerous characteristic of fast (Mach 2 plus) supersonic flight: inertia coupling. Only Yeager’s skills as an aviator prevented disaster. Later Mel Apt would lose his life testing the Bell X-2 under similar circumstances.

X-1A — Bell Model 58A Ordered by the Air Force on April 2, 1948, the X-1A (serial number 48-1384) was intended to investigate aerodynamic phenomena at speeds greater than Mach 2 and altitudes greater than 90,000 feet, specifically emphasizing dynamic stability and air loads. Longer and heavier than the original X-1, with a stepped canopy for better vision, the X-1A was powered by the same Reaction Motors XLR-11 rocket engine. The aircraft first flew, unpowered, on Feb. 14, 1953, at Edwards, with the first powered flight on Feb. 21. Both flights were piloted by Bell test pilot Jean “Skip” Ziegler.

After NACA started its high-speed testing with the Douglas Skyrocket, culminating in Scott Crossfield achieving Mach 2.005 on Nov. 20, 1953, the Air Force started a series of tests with the X-1A, which the test pilot of the series, Yeager, named “Operation NACA Weep”. These culminated on Dec. 12, 1953, when Yeager achieved an altitude of 74,700 feet and a new airspeed record of Mach 2.44. Unlike Crossfield in the Skyrocket, Yeager achieved that in level flight. Soon afterwards, the aircraft spun out of control, due to the then not yet understood phenomenon of inertia coupling. The X-1A dropped from maximum altitude to 25,000 feet, exposing the pilot to accelerations of as much as 8g, during which Yeager broke the canopy with his helmet before regaining control.

On May 28, 1954, Maj. Arthur W. Murray piloted the X-1A to a new record of 90,440 feet.

The aircraft was transferred to NACA during September 1954, and subsequently modified. The X-1A was lost on Aug. 8, 1955, when, while being prepared for launch from the airborne RB-50 mothership, an explosion ruptured the plane’s liquid oxygen tank. With the help of crewmembers on the RB-50, test pilot Joseph A. Walker successfully extricated himself from the plane, which was then jettisoned. Exploding on impact with the desert floor, the X-1A became the first of many early X-Planes that would be lost to explosions.

X-1B — Bell Model 58B The X-1B (serial 48-1385) was equipped with aerodynamic heating instrumentation for thermal research (more than 300 thermal probes were installed on its surface). It was similar to the X-1A except for having a slightly different wing.

The X-1B was used for high-speed research by the U.S. Air Force starting in October 1954, prior to being transferred to NACA during January 1955. NACA continued to fly the aircraft until January 1958, when cracks in the fuel tanks forced its grounding. The X-1B completed a total of 27 flights. A notable achievement was the installation of a system of small reaction rockets used for directional control, making the X-1B the first aircraft to fly with this sophisticated control system, later used in the North American X-15. The X-1B is now at the National Museum of the United States Air Force, where it is displayed in the Museum’s Maj. Gen. Albert Boyd and Maj. Gen. Fred Ascani Research and Development Gallery.

X-1C — Bell Model 58C The X-1C (serial 48-1387) was intended to test armaments and munitions in the high transonic and supersonic flight regimes. It was canceled while still in the mockup stage, as the development of transonic and supersonic-capable aircraft like the North American F-86 Sabre and the North American F-100 Super Sabre eliminated the need for a dedicated experimental test vehicle.

X-1D — (Bell Model 58D) The X-1D (serial 48-1386) was the first of the second generation of supersonic rocket planes. Flown from an EB-50A (s/n #46-006), it was to be used for heat transfer research. The X-1D was equipped with a new low-pressure fuel system and a slightly increased fuel capacity. There were also some minor changes of the avionics suite.

On July 24, 1951, with Bell test pilot Jean “Skip” Ziegler at the controls, the X-1D was launched over Rogers Dry Lake, on what was to become the only successful flight of its career. The unpowered glide was completed after a nine-minute descent, but upon landing, the nose landing gear failed and the aircraft slid ungracefully to a stop. Repairs took several weeks to complete and a second flight was scheduled for mid-August. On Aug. 22, 1951, the X-1D was lost in a fuel explosion during preparations for the first powered flight. The aircraft was destroyed upon impact after it was jettisoned from its EB-50A mothership.

X-1E — Bell Model 44 The X-1E was the result of a reconstruction of the X-1-2 (serial 46-063), in order to pursue the goals originally set for the X-1D and X-1-3 (serial 46-064), both lost in explosions during 1951.

The cause of the mysterious explosions was finally traced to the use of Ulmer leather gaskets impregnated with tricresyl phosphate (TCP), a leather treatment, which was used in the liquid oxygen plumbing. TCP becomes unstable and explosive in the presence of pure oxygen and mechanical shock. This mistake cost two lives, caused injuries and lost several aircraft.

The changes included: • A turbopump fuel feed system, which eliminated the high-pressure nitrogen fuel system used in ‘062 and ‘063. Concerns about metal fatigue in the nitrogen fuel system resulted in the grounding of the X-1-2 after its 54th flight in its original configuration. • A re-profiled super-thin wing, based on the X-3 Stiletto wing profile, enabling the X-1E to reach Mach 2. • A ‘knife-edge’ windscreen replaced the original greenhouse glazing. An upward-opening canopy replaced the fuselage side hatch and allowed the inclusion of an ejection seat. • The addition of 200 pressure ports for aerodynamic data, and 343 strain gauges to measure structural loads and aerodynamic heating along the wing and fuselage.

The X-1E first flew on Dec. 15, 1955, a glide-flight controlled by Air Force test pilot Joe Walker. Walker left the X-1E program during 1958, after 21 flights, attaining a maximum speed of Mach 2.21. NACA research pilot John B. McKay took his place during September 1958, completing five flights in pursuit of Mach 3 before the X-1E was permanently grounded after its 26th flight, during November 1958, due to the discovery of structural cracks in the fuel tank wall.

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On October 14, 1947, the Bell X-1 became the first airplane to fly faster than the speed of sound. Piloted by U.S. Air Force Capt. Charles E. "Chuck" Yeager, the X-1 reached a speed of 1,127 kilometers (700 miles) per hour, Mach 1.06, at an altitude of 13,000 meters (43,000 feet). Yeager named the airplane "Glamorous Glennis" in tribute to his wife.

Air-launched at an altitude of 7,000 meters (23,000 feet) from the bomb bay of a Boeing B-29, the X-1 used its rocket engine to climb to its test altitude. It flew a total of 78 times, and on March 26, 1948, with Yeager at the controls, it attained a speed of 1,540 kilometers (957 miles) per hour, Mach 1.45, at an altitude of 21,900 meters (71,900 feet). This was the highest velocity and altitude reached by a manned airplane up to that time.

On October 14, 1947, flying the Bell XS-1 #1, Capt. Charles 'Chuck’ Yeager, USAF, became the first pilot to fly faster than sound. The XS-1, later designated X-l, reached Mach 1.06, 700 mph, at an altitude of 43,000 feet, over the Mojave Desert near Muroc Dry Lake, California. The flight demonstrated that aircraft could be designed to fly faster than sound, and the concept of a ‘sound barrier" crumbled into myth.

The XS-1 was developed as part of a cooperative program initiated in 1944 by the National Advisory Committee for Aeronautics (NACA) and the U.S. Army Air Forces (later the U.S. Air Force) to develop special manned transonic and supersonic research aircraft. On March 16, 1945, the Army Air Technical Service Command awarded the Bell Aircraft Corporation of Buffalo, New York, a contract to develop three transonic and supersonic research aircraft under project designation MX-653. The Army assigned the designation XS-1 for Experimental Sonic-i. Bell Aircraft built three rocket-powered XS-1 aircraft.

The National Air and Space Museum now owns the XS-1 #1, serial 46-062, named Glamorous Glennis by Captain Yeager in honor of his wife. The XS-1 #2 (46-063) was flight-tested by NACA and later was modified as the X-1 "Mach 24" research airplane. (The X-1 E is currently on exhibit outside the NASA Flight Research Center, Edwards, California.) The X-1 #3 (46-064) had a turbopump-driven, low-pressure fuel feed system. This aircraft, known popularly as the X-1-3 Queenie, was lost in a 1951 explosion on the ground that injured its pilot. Three additional X-1 aircraft, the X-1A, X-1B, and X-1D, were constructed and test-flown. Two of these. the X-1A and X-1D, were also lost, as a result of propulsion system explosions.

The two XS-1 aircraft were constructed from high-strength aluminum, with propellant tanks fabricated from steel. The first two XS-1 aircraft did not utilize turbopumps for fuel feed to the rocket engine, relying instead on direct nitrogen pressurization of the fuel-feed system. The smooth contours of the XS-1, patterned on the lines of a .50-caliber machine gun bullet, masked an extremely crowded fuselage containing two propellant tanks, twelve nitrogen spheres for fuel and cabin pressurization, the pilot’s pressurized cockpit, three pressure regulators, a retractable landing gear, the wing carry-through structure, a Reaction Motors, Inc., 6.000-pound-thrust rocket engine, and more than five hundred pounds of special flight-test instrumentation.

Though originally designed for conventional ground takeoffs, all X-1 aircraft were air-launched from Boeing B-29 or B-50 Superfortress aircraft. The performance penalties and safety hazards associated with operating rocket-propelled aircraft from the ground caused mission planners to resort to air-launching instead. Nevertheless, on January 5,1949, the X-1 #1 Glamorous Glennis successfully completed a ground takeoff from Muroc Dry Lake, piloted by Chuck Yeager. The maximum speed attained by the X-1 #1 was Mach 1.45 at 40,130 feet, approximately 957 mph, during a flight by Yeager on March 26, 1948. On August 8,1949, Maj. Frank K. Everest, Jr., USAF, reached an altitude of 71,902 feet, the highest flight made by the little rocket airplane. It continued flight test operations until mid-1950, by which time it had completed a total of nineteen contractor demonstration flights and fifty-nine Air Force test flights.

On August 26, 1950, Air Force Chief of Staff Gen. Hoyt Vandenberg presented the X-1 #1 to Alexander Wetmore, then Secretary of the Smithsonian Institution. The X-1, General Vandenberg stated, "marked the end of the first great period of the air age, and the beginning of the second. In a few moments the subsonic period became history and the supersonic period was born." Earlier, Bell Aircraft President Lawrence D. Bell, NACA scientist John Stack, and Air Force test pilot Chuck Yeager had received the 1947 Robert J. Collier Trophy for their roles in first exceeding the speed of sound and opening the pathway to practical supersonic flight.

This object is not on display at the National Air and Space Museum. It is either on loan or in storage.

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Bell x-1: america’s experimental rocket-powered aircraft that first broke the sound barrier.

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The Bell X-1 was a rocket-powered aircraft designed by Bell for a supersonic joint research project between the National Advisory Committee for Aeronautics (NACA) and the US Army Air Forces (USAAF), later the US Air Force. Initially conceived during the Second World War and built in 1945, the X-1 became the first aircraft to exceed the speed of sound in level flight.

Design and development of the Bell X-1

In 1942, the British Ministry of Aviation began working on a secret project with Miles to produce the world’s first aircraft capable of exceeding the speed of sound. The turbojet-powered M.52 could hit 1,000 MPH and reach an altitude of 36,000 feet in only one minute and 30 seconds.

In 1944, the Ministry of Aviation signed an agreement with the United States to share its research and data. Later that same year, engineers from Bell Aircraft visited Miles and were shown drawings and research pertaining to the development of the M.52. By this time, the American company had already begun designing a rocket-powered aircraft.

The initial specifications Bell set out were for an aircraft that could fly at 800 MPH at 35,000 feet for a sustained period of two to five minutes. On March 16, 1945, the USAAF’s Flight Test Division and the NACA contracted the company to develop three XS-1 aircraft – later the X-1.

Bell X-1 specs

The Bell X-1 had an overall bullet-shaped fuselage with short, stubby wings. The design was optimized for high-speed flight, with low drag and a powerful engine. The aircraft was primarily made of aluminum alloy and fitted with a pressurized cockpit. As its sole purpose was speed, the pilot had a diminished view out of a significantly sloped windshield and didn’t have an ejection seat .

Short straight wings were used, instead of swept ones, due to a lack of knowledge of their efficiency at high speeds. Issues with the aircraft’s compressibility arose in 1947, and it was decided a variable-incidence tailplane would be added. This was similar to the variable-incidence wing seen on the Vought F-8 Crusader .

The X-1 was powered by a single Reaction Motors XLR11-RM-3 four-chamber liquid-fueled rocket engine, producing 6,000 pounds of thrust . Engineers at Bell had considered using turbojet engines, but they couldn’t reach the speed required at high altitudes, and an aircraft that used both turbojet and rocket engines would be far too large and overly complex.

The engine ran on ethyl alcohol diluted with water, with a liquid oxygen oxidizer. The chambers could each be turned on and off individually, allowing the X-1 to change its thrust in 1,500-pound increments. The fuel and oxygen tanks were pressurized with nitrogen, reducing flight time by about one and a half minutes and increasing landing weight by 2,000 pounds.

The X-1 would only take off from a runway once, due to the desire in its design leading to future fleet aircraft with the ability to reach similar high speeds. It was decided that it would be easier to deploy the X-1 via a Boeing B-29 Superfortress .

Putting the aircraft to the test

The Bell X-1 first took to the skies on January 19, 1946. With chief test pilot Jack Woolams at the controls, it completed a glide flight at Pinecastle Army Airfield, Florida. Another test involved the X-1 being dropped from a B-29 Superfortress at 29,000 feet.

Four additional glide tests were conducted at Muroc Army Air Field , California, with the first powered test taking place on December 9, 1946 by Chalmers “Slick” Goodlin. During the flight, only two engine chambers were ignited – however, the X-1 accelerated so fast that one was shut down. At 35,000 feet, it reached a speed of Mach 0.795. The engine was then turned off, and the aircraft descended to 15,000 feet, where all four chambers were tested.

On May 22, 1947, Alvin “Tex” Johnston, Bell’s chief test pilot and program supervisor, completed another test flight. During this, the X-1 reached Mach 0.72, and Johnston concluded that the aircraft was ready to be tested for supersonic flights. Bell wanted to be sure before advancing the program and another three flights were performed.

The USAAF was unhappy with Bell’s trepidation and wanted the program to advance at a greater pace. The contract with Bell was subsequently terminated, with the Flight Test Division taking over the work on June 24, 1947.

Breaking the sound barrier

The first supersonic flight of the Bell X-1 took place on October 14, 1947, conducted by Charles “Chuck” Yeager over the Mojave Desert. The aircraft, nicknamed Glamorous Glennis for his wife, was dropped from a B-29 and hit Mach 1.06 (700 MPH). After the engine burned out, the X-1 glided until it landed on the dry lakebed.

The supersonic flight was top secret. That being said, it leaked to Aviation Week and was headline news for the Los Angeles Times in December 1947. The US Air Force threatened legal action against the news agencies and the individual journalists involved, but this never occurred. On June 10, 1948, Air Force Secretary William Stuart Symington III publicly announced that the sound barrier had been broken by an experimental aircraft.

Chuck Yeager took to the skies again in Glamorous Glennis on January 5, 1949. This flight was significant, for it became the first and only time the X-1 took off from a runway, making it the first conventional takeoff of a supersonic aircraft. Once lifted off of the ground, it displayed an impressive climb, reaching 23,000 feet in a mere 90 seconds.

Bell X-1’s legacy

The Bell X-1 set a standard for all X-craft projects that followed, including four variants of the original aircraft. It also created invaluable research that set in motion American fighter design, with effects throughout the latter half of the 20th century.

More from us: Henschel Hs 129: The Luftwaffe’s Soaring Tank Buster Armed With a 75 mm Cannon

The X-1 was further cemented into the public consciousness with the release of the 1983 film, The Right Stuff , based on the 1979 book of the same name. After being transited to Washington, DC via a B-29 in 1950, the X-1 sees visitors as part of the Milestones of Flight exhibition in the Smithsonian National Air and Space Museum.

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Experimental Design in Chemistry: a Tutorial

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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Bell X-1 , U.S. rocket-powered supersonic research airplane built by Bell Aircraft Corporation , the first aircraft to exceed the speed of sound in level flight. On October 14, 1947, an X-1 launched from the bomb bay of a B-29 bomber and piloted by U.S. Air Force Captain Chuck Yeager over the Mojave Desert of California broke the sound barrier of 1,066 km (662 miles) per hour at an altitude of 13,000 metres (43,000 feet) and attained a top speed of 1,126 km (700 miles) per hour, or Mach 1.06.

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Experimental Design: Types, Examples & Methods

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Introduction to Research Design and Statistics

Research designs, pre-experimental designs, true experimental designs, quasi-experimental designs.

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Bell Aircraft built three of the original X-1s, plus an X-1A and X-1B, an X-1D. There was also an X-1E rebuilt from the X-1 #2. They flew a total of 214 flights between 1946-1958. This was a joint program among the NACA, the Air Force, and Bell Aircraft. The bullet-shaped, rocket-powered aircraft became the first airplane to break the sound barrier on Oct. 14, 1947. Flight research by the NACA continued through such advanced models as the X-1B and X-1E, providing a wealth of information for use in correlating from the X-1 #2 wind-tunnel data with actual flight data and for designing later high-performance aircraft.

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Closed-loop identification of enzyme kinetics applying model-based design of experiments †.

* Corresponding authors

a Institute of Technical Biocatalysis, Hamburg University of Technology, Denickestr. 15, D-21073 Hamburg, Germany E-mail: [email protected]

b Institute of Process Systems Engineering, Hamburg University of Technology, Schwarzenberg-Campus 4, D-21073 Hamburg, Germany

Accurate kinetic models for enzyme catalysed reactions are integral to process development and optimisation. However, the collection of useful kinetic data is heavily dependent on the experimental design and execution. In order to reduce the limitations associated with traditional statistical design and manual experiments, this study introduces an integrated, automated approach to identifying kinetic models based on model-based optimal experimental design. The immobilised formate dehydrogenase of Candida boidinii catalyses the enzymatic reduction of NAD + to NADH and is used as a model system. Continuous collection of UV/Vis data under steady-state conditions is employed to determine the kinetic parameters in a packed bed reactor. Automation of the experimental work was utilised in Python to compensate for the need for more time-consuming data collection. A completely automated closed-loop system was created and appropriate kinetic models for anticipating process dynamics were identified. The automated platform was able to identify the correct kinetic model out of eight candidate models with only 15 experiments. Further extension of the design space improved model discrimination and led to a properly parameterized kinetic model with sufficeintly high parameter precision for the conditions under examination.

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Closed-loop identification of enzyme kinetics applying model-based design of experiments

L. Hennecke, L. Schaare, M. Skiborowski and A. Liese, React. Chem. Eng. , 2024, Advance Article , DOI: 10.1039/D4RE00127C

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August 27, 2024

Keto diet enhances experimental cancer therapy in mice

At a glance.

• Researchers showed how a ketogenic diet can enhance the effects of an experimental anti-cancer drug and starve pancreatic tumors in mice.
• The findings suggest that diet might be paired with drugs to block the growth of certain types of cancerous tumors.

Cancer cells need fuel to survive and thrive. The energy they need usually comes from glucose in the blood. Some studies have shown that intermittent fasting or a ketogenic diet—high in fat and low in carbohydrates—can help to protect against cancer. These cause the body to break down fat to form molecules called ketones, which can serve as the body’s main energy source while glucose is scarce. Fasting and ketogenic diets likely work by limiting the amount of glucose available to feed cancer cells. But some cancers, such as pancreatic cancer, can also use ketones as an energy source.

A research team led by Dr. Davide Ruggero of the University of California, San Francisco, set out to better understand the underlying gene activities and metabolic pathways affected by diet and fasting. They hope to use this knowledge to enhance cancer therapies. The team focused on a protein called eIF4E (eukaryotic translation initiation factor), which is often hijacked by cancer cells. Results appeared in Nature on August 14, 2024.

The researchers found that chemical tags called phosphates are added to eIF4E as mice transition from fed to fasting. Further analyses showed that this phosphorylated eIF4E (P-eIF4E) plays an important role in coordinating the activity of genes involved in processing fats for energy during fasting. When mice were placed on a ketogenic diet instead of fasting, the P-eIF4E protein similarly triggered a shift to using fat for energy.

The scientists next asked how fasting activated eIF4E. They found that free fatty acids, the small molecules released by fat shortly after fasting begins, activated a chain of events leading to eIF4E phosphorylation. This suggests that free fatty acids have a dual role, serving both as an energy source and as signaling molecules that boost fat-based energy production during fasting.

To assess the relevance of these findings to cancers that can thrive on fat, the researchers combined a ketogenic diet with an experimental anti-cancer drug that blocks P-eIF4E. The drug is called eFT508 (or tomivosertib). They found that giving eFT508 alone did not slow the growth of pancreatic tumors in mice, likely because the tumors could survive with energy from carbohydrates. But when mice were given the drug while on a ketogenic diet, the cancer cells no longer had ready access to glucose or fat for energy. The cells then starved, and growth declined.

"Our findings open a point of vulnerability that we can treat with a clinical inhibitor that we already know is safe in humans,” Ruggero says. “We now have firm evidence of one way in which diet might be used alongside pre-existing cancer therapies to precisely eliminate a cancer.”

—by Vicki Contie

Related Links

• Research in Context: Obesity and Metabolic Health
• An mRNA Vaccine To Treat Pancreatic Cancer
• Keto Molecule Offers Clue for Preventing Colorectal Cancer
• Low-Fat Diet Compared to Low-Carb Diet
• Nutrition in Cancer Care
• Pancreatic Cancer

References:  Remodelling of the translatome controls diet and its impact on tumorigenesis. Yang H, Zingaro VA, Lincoff J, Tom H, Oikawa S, Oses-Prieto JA, Edmondson Q, Seiple I, Shah H, Kajimura S, Burlingame AL, Grabe M, Ruggero D. Nature . 2024 Aug 14. doi: 10.1038/s41586-024-07781-7. Online ahead of print. PMID: 39143206.

Funding:  NIH’s National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Cancer Institute (NCI), and National Institute of General Medical Sciences (NIGMS); American Heart Association; American Cancer Society; Howard Hughes Medical Institute.

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Experimental 'public kitchen' welcomes cooks and innovators in Upham's Corner

What if there was such a thing as a public kitchen like there is a public library? That is the question the activist design studio DS4SI decided to test out in its Public Kitchen in an 800 foot temporary storefront in Dorchester.

Down the street from the Boston Public Library Upham’s Corner branch, DS4SI’s experimental space is dominated by an herb tower and a front window with turquoise writing that says Public Kitchen in Cape Verdean Creole, Spanish and Vietnamese.

It is a fully functional, fully stocked kitchen, but it is also a convening space for an intergenerational community.

DS4SI — which stands for “The Design Studio for Social Intervention” — calls on folks of all ages and experiences to come and take part in sharing knowledge and to use the kitchen the same way they would engage with a library.

“To see that collaboration in this neutral space that isn’t someone’s kitchen, or isn’t super heavily facilitated but is really open and we’re all invited to step in as experts in the kitchen has been really beautiful,” says Nohemi Rodriguez, the project lead at the Public Kitchen.

The kitchen is stocked with food basics, spices and pots and pans. Whether it’s a hot sauce-making class, a canning workshop or just dropping by to try someone’s homemade guacamole, it’s all free to the public and people are welcome to invite newcomers in.

Sam Tanyos, of Dorchester, came to cook there on a recent Wednesday evening, and said it is unusual to find public spaces that really encourage people to interact. “I think of libraries where everyone is quietly reading, [or] bars, where you have to spend a lot of money, usually on alcohol and people aren’t necessarily at a bar like welcoming you to join their table,” he said.

Lori Lobenstine, co-founder of DS4SI, considers the space a work of “productive fiction,” meaning “when you hold a space as if it really exists.” It invites people to collectively think out of the box and imagine what infrastructure could improve their community and then tinker with the idea and make it happen.

Lobenstine said Upham’s Corner is known for being an intergenerational area with people of many cultural backgrounds. “It is really important that we’re not at the Seaport,” which she said is pricey and it appeals to a young wealthy class.

The Public Kitchen was originally prototyped in the same space a decade ago by co-founders Kenneth Bailey and Lobenstine. Over time, they’ve executed the same model in different spaces. In 2022, a version of this pop-up was held at Mary Hannon Park in Upham’s Corner for 10 days.

Experimentation and iteration are at the heart of this Public Kitchen. It once was intended to be a short-term pop-up but it is now in its longest-running stint: It opened in July, and will continue to operate Wednesdays, Fridays and Saturdays from 3:00 p.m. to 7:00 p.m. until Oct. 12.

“We think it needs that duration to really become a part of the neighborhood and people’s ideas about everyday life,” Lobenstine said.

The Public Kitchen is a collaboration of various organizers, chefs and panelists. Local chefs volunteer to lead cooking demonstrations, The Food Project donates fresh produce, and local poets lead poetry slams there. The operation is funded by national and local grants including the Mayor’s Office of Arts and Culture and the Barr Foundation.

The kitchen is part of an ongoing project by DS4SI to collect community ideas for new uses of neighborhood spaces. On Sept. 17, the group will convene its next cohort to come up with new ideas for transforming Boston neighborhoods.

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Computer Science > Artificial Intelligence

Title: automated design of agentic systems.

Abstract: Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries. Through extensive experiments across multiple domains including coding, science, and math, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.

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