On a sunny day, the structure can passively pump out steam hot enough to sterilize medical equipment, as well as to use in cooking and cleaning. The steam may also supply heat to industrial processes, or it could be collected and condensed to produce desalinated, distilled drinking water.

The researchers previously developed a sponge-like structure that floated in a container of water and turned the water it absorbed into steam. But a big concern is that contaminants in the water caused the structure to degrade over time. The new device is designed to be suspended over the water, to avoid any possible contamination.

The suspended device is about the size and thickness of a small digital tablet or e-reader, and is structured like a sandwich: The top layer is made from a material that efficiently absorbs the sun’s heat, while the bottom layer efficiently emits that heat to the water below. Once the water reaches the boiling point (100 C), it releases steam that rises back up into the device, where it is funneled through the middle layer — a foam-like material that further heats the steam above the boiling point, before it’s pumped out through a single tube.

“It’s a completely passive system — you just leave it outside to absorb sunlight,” says Thomas Cooper, assistant professor of mechanical engineering at York University, who led the work as a postdoc at MIT. “You could scale this up to something that could be used in remote climates to generate enough drinking water for a family, or sterilize equipment for one operating room.”

The team’s results are detailed in a paper to be published in Nature Communications. The study includes researchers from the lab of Gang Chen, the Carl Richard Soderberg Professor of Power Engineering at MIT.

A clever combination

In 2014, Chen’s group reported the first demonstration of a simple, solar-driven steam generator, in the form of a graphite-covered carbon foam that floats on water. This structure absorbs and localizes the sun’s heat to the water’s surface (the heat would otherwise penetrate down through the water). Since then, his group and others have looked to improve the efficiency of the design with materials of varying solar-absorbing properties. But almost every device has been designed to float directly on water, and they have all run into the problem of contamination, as their surfaces come into contact with salt and other impurities in water.

The team decided to design a device that instead is suspended above water. The device is structured to absorb short-wavelength solar energy, which in turn heats up the device, causing it to reradiate this heat, in the form of longer-wavelength infrared radiation, to the water below. Interestingly, the researchers note that infrared wavelengths are more readily absorbed by water, versus solar wavelengths, which would simply pass right through.

For the device’s top layer, they chose a metal ceramic composite that is a highly efficient solar absorber. They coated the structure’s bottom layer with a material that easily and efficiently emits infared heat. Between these two materials, they sandwiched a layer of reticulated carbon foam — essentially, a sponge-like material studded with winding tunnels and pores, which retains the sun’s incoming heat and can further heat up the steam rising back up through the foam. The researchers also attached a small outlet tube to one end of the foam, through which all the steam can exit and be easily collected.

Finally, they placed the device over a basin of water and surrounded the entire setup with a polymer enclosure to prevent heat from escaping.

“It’s this clever engineering of different materials and how they’re arranged that allows us to achieve reasonably high efficiencies with this noncontact arrangement,” Cooper says.

Full steam ahead

The researchers first tested the structure by running experiments in the lab, using a solar simulator that mimics the characteristics of natural sunlight at varying, controlled intensities. They found that the structure was able to heat a small basin of water to the boiling point and produce superheated steam, at 122 C, under conditions that simulated the sunlight produced on a clear, sunny day. When the researchers increased this solar intensity by 1.7 times, they found the device produced even hotter steam, at 144 C.

On Oct. 21, 2017, they tested the device on the roof of MIT’s Building 1, under ambient conditions. The day was clear and bright, and to increase the sun’s intensity further, the researchers constructed a simple solar concentrator — a curved mirror that helps to collect and redirect more sunlight onto the device, thus raising the incoming solar flux, similar to the way a magnifying glass can be used to concentrate a sun’s beam to heat up a patch of pavement.

With this added shielding, the structure produced steam in excess of 146 C over the course of 3.5 hours. In subsequent experiments, the team was able to produce steam from sea water, without contaminating the surface of the device with salt crystals. In another set of experiments, they were also able to collect and condense the steam in a flask to produce pure, distilled water.

Chen says that, in addition to overcoming the challenges of contamination, the device’s design enables steam to be collected at a single point, in a concentrated stream, whereas previous designs produced more dilute spray.

“This design really solves the fouling problem and the steam collection problem,” Chen says. “Now we’re looking to make this more efficient and improve the system. There are different opportunities, and we’re looking at what are the best options to pursue.”

The long-term goal of the overall field of research is to design intelligent machines capable of changing their behavior to enhance human trust in them. The new models were developed in research led by assistant professor Neera Jain and associate professor Tahira Reid, in Purdue University’s School of Mechanical Engineering.

“Intelligent machines, and more broadly, intelligent systems are becoming increasingly common in the everyday lives of humans,” Jain said. “As humans are increasingly required to interact with intelligent systems, trust becomes an important factor for synergistic interactions.”

For example, aircraft pilots and industrial workers routinely interact with automated systems. Humans will sometimes override these intelligent machines unnecessarily if they think the system is faltering.

“It is well established that human trust is central to successful interactions between humans and machines,” Reid said.

The researchers have developed two types of “classifier-based empirical trust sensor models,” a step toward improving trust between humans and intelligent machines.

A YouTube video is available at https://www.youtube.com/watch?v=Mucl6pAgEQg.

The work aligns with Purdue’s Giant Leaps celebration, acknowledging the university’s global advancements made in AI, algorithms and automation as part of Purdue’s 150th anniversary. This is one of the four themes of the yearlong celebration’s Ideas Festival, designed to showcase Purdue as an intellectual center solving real-world issues.

The models use two techniques that provide data to gauge trust: electroencephalography and galvanic skin response. The first records brainwave patterns, and the second monitors changes in the electrical characteristics of the skin, providing psychophysiological “feature sets” correlated with trust.

Forty-five human subjects donned wireless EEG headsets and wore a device on one hand to measure galvanic skin response.

One of the new models, a “general trust sensor model,” uses the same set of psychophysiological features for all 45 participants. The other model is customized for each human subject, resulting in improved mean accuracy but at the expense of an increase in training time. The two models had a mean accuracy of 71.22 percent, and 78.55 percent, respectively.

It is the first time EEG measurements have been used to gauge trust in real time, or without delay.

“We are using these data in a very new way,” Jain said. “We are looking at it in sort of a continuous stream as opposed to looking at brain waves after a specific trigger or event.”

Findings are detailed in a research paper appearing in a special issue of the Association for Computing Machinery’s Transactions on Interactive Intelligent Systems. The journal’s special issue is titled “Trust and Influence in Intelligent Human-Machine Interaction.” The paper was authored by mechanical engineering graduate student Kumar Akash; former graduate student Wan-Lin Hu, who is now a postdoctoral research associate at Stanford University; Jain and Reid.

“We are interested in using feedback-control principles to design machines that are capable of responding to changes in human trust level in real time to build and manage trust in the human-machine relationship,” Jain said. “In order to do this, we require a sensor for estimating human trust level, again in real-time. The results presented in this paper show that psychophysiological measurements could be used to do this.”

The issue of human trust in machines is important for the efficient operation of “human-agent collectives.”

“The future will be built around human-agent collectives that will require efficient and successful coordination and collaboration between humans and machines,” Jain said. “Say there is a swarm of robots assisting a rescue team during a natural disaster. In our work we are dealing with just one human and one machine, but ultimately we hope to scale up to teams of humans and machines.”

Algorithms have been introduced to automate various processes.

“But we still have humans there who monitor what’s going on,” Jain said. “There is usually an override feature, where if they think something isn’t right they can take back control.”

Sometimes this action isn’t warranted.

“You have situations in which humans may not understand what is happening so they don’t trust the system to do the right thing,” Reid said. “So they take back control even when they really shouldn’t.”

In some cases, for example in the case of pilots overriding the autopilot, taking back control might actually hinder safe operation of the aircraft, causing accidents.

“A first step toward designing intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real time,” Jain said.

To validate their method, 581 online participants were asked to operate a driving simulation in which a computer identified road obstacles. In some scenarios, the computer correctly identified obstacles 100 percent of the time, whereas in other scenarios the computer incorrectly identified the obstacles 50 percent of the time.

“So, in some cases it would tell you there is an obstacle, so you hit the brakes and avoid an accident, but in other cases it would incorrectly tell you an obstacle exists when there was none, so you hit the breaks for no reason,” Reid said.

The testing allowed the researchers to identify psychophysiological features that are correlated to human trust in intelligent systems, and to build a trust sensor model accordingly. “We hypothesized that the trust level would be high in reliable trials and be low in faulty trials, and we validated this hypothesis using responses collected from 581 online participants,” she said.

The results validated that the method effectively induced trust and distrust in the intelligent machine.

“In order to estimate trust in real time, we require the ability to continuously extract and evaluate key psychophysiological measurements,” Jain said. “This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor.”

The EEG headset records signals over nine channels, each channel picking up different parts of the brain.

“Everyone’s brainwaves are different, so you need to make sure you are building a classifier that works for all humans.”

For autonomous systems, human trust can be classified into three categories: dispositional, situational, and learned.

Dispositional trust refers to the component of trust that is dependent on demographics such as gender and culture, which carry potential biases.

“We know there are probably nuanced differences that should be taken into consideration,” Reid said. “Women trust differently than men, for example, and trust also may be affected by differences in age and nationality.”

Situational trust may be affected by a task’s level of risk or difficulty, while learned is based on the human’s past experience with autonomous systems.

The models they developed are called classification algorithms.

“The idea is to be able to use these models to classify when someone is likely feeling trusting versus likely feeling distrusting,” she said.

Jain and Reid have also investigated dispositional trust to account for gender and cultural differences, as well as dynamic models able to predict how trust will change in the future based on the data.

Put simply, shear flow occurs when two fluids — where fluids are a liquid, a gas or a plasma (the amorphous superhot gas that makes up stars like our sun or that occurs in a fusion device) — pass by one another such as when wind flows over a lake or hot gas jets from a galaxy. The turbulent chaos that occurs as a result of the interacting fluids can be exceedingly difficult to recreate in the numerical models scientists use to describe and understand a wide range of phenomena.

Shear, for instance, is a confounding factor for critical applied problems such as predicting the diffusion of smoke from massive wildfires. Smoke from fires such as those that recently occurred in California can be widely dispersed thousands of miles from the source and contribute to problems of air quality.

“These models are really helpful in understanding systems where the flow is fast,” says Adrian Fraser, a University of Wisconsin-Madison graduate student in physics and the lead author of a study published Monday, Dec. 10, in the journal Physics of Plasmas.

But even using the world’s most powerful supercomputers in a show of brute force, certain phenomena are too complex and dynamic to be reliably recreated in silico.

Scientists have tried to get around the problem by simplifying and parsing their models to look at elements of a system in the hope they can be reassembled to account for the whole. But in doing so, Fraser notes, researchers may have overlooked a common collective effect that not only has an influence on the dynamics of a system, but, according to the new research, seems to be a convenient handle for greatly simplifying the digital recreation of phenomena such as the spread of heat and chemicals in a system — problems that now overwhelm even the most powerful supercomputers.

Using those state-of-the-art supercomputers, Fraser’s team, including UW-Madison physics professors Paul Terry and Ellen Zweibel along with MJ Pueschel of the University of Texas, looked at how turbulence plays out over long periods of time when its motions include a component that normally dies away very quickly. Looking at the system in detail, the researchers observed that this seemingly transient component is amplified over time and exerts greater influence than was known.

“This is the one collective motion that had been assumed not to matter in these systems. We showed that it does matter,” says Fraser. “And by noting that, we were able to dramatically improve existing models for how shear-flow turbulence changes in different systems.”

Most previous studies focused on representing motions with components that do not die away because they are instead directly driven by the shear.

Measuring how heat or dye diffuses in a stationary fluid is straightforward, Fraser explains, but “if the fluid is turbulent it is really difficult to figure out how the dye or heat diffuses from one part of the fluid to another part because of all the complicated motions that occur in turbulence.”

By representing the system with both growing and decaying motions, it is easier to see the whole picture and greatly simplify the system for modeling.

“The end result is a simple model that predicts results that are very consistent with the massive simulations we performed,” says Fraser, noting that previously intractable problems for designing fusion experiments, improving weather models, and understanding astrophysical phenomena such as star formation will be more easily addressed without the need for expensive supercomputers.

Vyacheslav Lukin, program director for Plasma Physics and Accelerator Science at the National Science Foundation, says the new study will help the research community continue to resolve complex plasma physics problems. “Further progress in accurately modeling large-scale plasma systems critically depends on our ability to combine analytical methods with high fidelity direct numerical simulations, and these new results should help us to make another step in that direction.”

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Materials provided by University of Wisconsin-Madison. Original written by Terry Devitt. Note: Content may be edited for style and length.