A Nuclear Engine for Future Mars Missions

NASA and the Defense Advanced Research Projects Agency (DARPA) announced Tuesday a collaboration to demonstrate a nuclear thermal rocket engine in space, an enabling capability for NASA crewed missions to MarsNASA and DARPA will partner on the Demonstration Rocket for Agile Cislunar Operations, or DRACO, program.

“NASA will work with our long-term partner, DARPA, to develop and demonstrate advanced nuclear thermal propulsion technology as soon as 2027. With the help of this new technology, astronauts could journey to and from deep space faster than ever – a major capability to prepare for crewed missions to Mars,” said NASA Administrator Bill Nelson.

Using a nuclear thermal rocket allows for faster transit time, reducing risk for astronauts. Reducing transit time is a key component for human missions to Mars, as longer trips require more supplies and more robust systems. Maturing faster, more efficient transportation technology will help NASA meet its Moon to Mars Objectives.

Other benefits to space travel include increased science payload capacity and higher power for instrumentation and communication. In a nuclear thermal rocket engine, a fission reactor is used to generate extremely high temperatures. The engine transfers the heat produced by the reactor to a liquid propellant, which is expanded and exhausted through a nozzle to propel the spacecraftNuclear thermal rockets can be three or more times more efficient than conventional chemical propulsion.

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Solar Power Station in Space

The UK government is reportedly considering a £16 billion proposal to build a solar power station in space. Space-based solar power is one of the technologies to feature in the government’s Net Zero Innovation Portfolio. It has been identified as a potential solution, alongside others, to enable the UK to achieve net zero by 2050. But how would a  in space work? What are the advantages and drawbacks to this technology?

Space-based solar power involves collecting solar energy in space and transferring it to Earth. While the idea itself is not new, recent technological advances have made this prospect more achievable.

The space-based  involves a solar power satellite—an enormous spacecraft equipped with . These panels generate electricity, which is then wirelessly transmitted to Earth through high-frequency radio waves. A ground antenna, called a rectenna, is used to convert the radio waves into electricity, which is then delivered to the .

A space-based solar power station in orbit is illuminated by the Sun 24 hours a day and could therefore generate electricity continuously. This represents an advantage over terrestrial solar power systems (systems on Earth), which can produce electricity only during the day and depend on the weather.

With  projected to increase by nearly 50% by 2050, space-based solar power could be key to helping meet the growing demand on the world’s energy sector and tackling global temperature rise.

Source: https://phys.org/

How to Predict Stress at Atomic Scale

The amount of stress a material can withstand before it cracks is critical information when designing aircraft, spacecraft, and other structures. Aerospace engineers at the University of Illinois Urbana-Champaign used machine learning for the first time to predict stress in copper at the atomic scale.

According to Huck Beng Chew and his doctoral student Yue Cui, materials, such as copper, are very different at these very small scales.

Left: Machine learning based on artificial neural networks as constitutive laws for atomic stress predictions. Right: Quantifying the local stress state of grain boundaries from atomic coordinate information

Metals are typically polycrystalline in that they contain many grains,” Chew said. “Each grain is a single crystal structure where all the atoms are arranged neatly and very orderly.  But the atomic structure of the boundary where these grains meet can be very complex and tend to have very high stresses.”

These grain boundary stresses are responsible for the fracture and fatigue properties of the metal, but until now, such detailed atomic-scale stress measurements were confined to molecular dynamics simulation models. Using data-driven approaches based on machine learning enables the study to quantify, for the first time, the grain boundary stresses in actual metal specimens imaged by electron microscopy.

“We used molecular dynamics simulations of copper grain boundaries to train our machine learning algorithm to recognize the arrangements of the atoms along the boundaries and identify patterns in the stress distributions within different grain boundary structures,” Cui said. Eventually, the algorithm was able to predict very accurately the grain boundary stresses from both simulation and experimental image data with atomic-level resolution.

We tested the accuracy of the machine learning algorithm with lots of different grain boundary structures until we were confident that the approach was reliable,” Cui explained. The task was more challenging than they imagined, and they had to include physics-based constraints in their algorithms to achieve accurate predictions with limited training data.

When you train the machine learning algorithm on specific grain boundaries, you will get extremely high accuracy in the stress predictions of these same boundaries,” Chew said, “but the more important question is, can the algorithm then predict the stress state of a new boundary that it has never seen before?” For Chew , the answer is yes, and very well in fact.

Source: https://aerospace.illinois.edu/

How To Levitate Objects With Light

Researchers at Caltech have designed a way to levitate and propel objects using only light, by creating specific nanoscale patterning on the objects’ surfaces. Though still theoretical, the work is a step toward developing a spacecraft that could reach the nearest planet outside of our solar system in 20 years, powered and accelerated only by light. The research was done in the laboratory of Harry Atwater, Howard Hughes Professor of Applied Physics and Materials Science in Caltech’s Division of Engineering and Applied Science.

Decades ago, the development of so-called optical tweezers enabled scientists to move and manipulate tiny objects, like nanoparticles, using the radiative pressure from a sharply focused beam of laser light. This work formed the basis for the 2018 Nobel Prize in Physics. However, optical tweezers are only able to manipulate very small objects and only at very short distances. Ognjen Ilic, postdoctoral scholar and the study’s first author, gives an analogy: “One can levitate a ping pong ball using a steady stream of air from a hair dryer. But it wouldn’t work if the ping pong ball were too big, or if it were too far away from the hair dryer, and so on.”

With this new research, objects of many different shapes and sizes—from micrometers to meters—could be manipulated with a light beam. The key is to create specific nanoscale patterns on an object’s surface. This patterning interacts with light in such a way that the object can right itself when perturbed, creating a restoring torque to keep it in the light beam. Thus, rather than requiring highly focused laser beams, the objects’ patterning is designed to “encode” their own stability. The light source can also be millions of miles away.

“We have come up with a method that could levitate macroscopic objects,” says Atwater, who is also the director of the Joint Center for Artificial Photosynthesis. “There is an audaciously interesting application to use this technique as a means for propulsion of a new generation of spacecraft. We’re a long way from actually doing that, but we are in the process of testing out the principles.”

In theory, this spacecraft could be patterned with nanoscale structures and accelerated by an Earth-based laser light. Without needing to carry fuel, the spacecraft could reach very high, even relativistic speeds and possibly travel to other stars.

Atwater also envisions that the technology could be used here on Earth to enable rapid manufacturing of ever-smaller objects, like circuit boards.

A paper describing the research appears online in the journal Nature Photonics.

Source: https://www.caltech.edu/