Engineers at West Virginia University are propelling exploration forward by creating control software for a group of aerial robots (aerobots) that will survey the atmosphere of Venus, the second planet from the sun. 

According to researchers, Venus went through a climate change process that transformed it from an Earth-like environment to an inhospitable world. Studying Venus can help model the evolution of climate on Earth and serve as a reference for what can happen in the future.

Guilherme Pereira and Yu Gu, associate professors in the Department of Mechanical and Aerospace Engineering, tasked with developing the software for the aerobots, which are balloon-based robotic vehicles, hope to play a pivotal role in these discoveries. Their study is supported by a $100,000 NASA Established Program to Stimulate Competitive Research.

“The main goal of the project is to propose a software solution that will allow hybrid aerobots to explore the atmosphere of Venus,” Pereira said. “Although hybrid vehicles were proposed before this project, we are not aware of any software has been created.”

One aerobot concept is the Venus Atmosphere Maneuverable Platform, which is a hybrid airship that uses both buoyancy and aerodynamic lift to control its altitude. The benefit of a hybrid aerobot is its ability to, during the day, behave like a plane, collecting and using energy from the sun to drive its motors, and, during the night, float like a balloon to save energy.

The buoyancy of the vehicle would prevent it from going below 50 km – or 31 miles, below the surface of Venus where the temperature is very high and would damage the vehicle, according to Pereira.

“One of the ideas of our project is to extend the battery life of the vehicle by planning energy-efficient paths, thus allowing it to fly during the night as well,” Pereira said.

The aerobot lifespan at cruise altitude is several months to a year.

Pereira and Gu said their software will have three main goals. The first is to create a motion planner for the vehicles, so they can be commanded to go from their current position to a goal position specified by NASA’s science team using minimum energy and leveraging the winds in the planet. A motion planner is a software that will run on the aerobot’s computer.

“The motion planner will be created by understanding the dynamics of the aerobot, the properties of its solar panels and batteries, and the properties of Venus's atmosphere,” Pereira said. “With the dynamics of the vehicle, the planner will only consider movements that are feasible given certain inputs to the aircraft, such as thrust coming from the propellers or deflections of the control surfaces.”

Pereira said that understanding the solar panels and batteries is important to account for how much charge the vehicle has to power its systems and what its recharging rate is according to the solar intensity.

“The understanding of the atmosphere provides the robots quantities like wind direction and magnitude, pressure, temperature, and solar intensity,” Pereira said.

With these models, the motion planner will calculate the best route for the aerobot.

“We are trying to come up with an optimal energy strategy,” Pereira said. “This is important since the vehicle will be orbiting the atmosphere of Venus in around four days. It will be exposed to long periods without the light on the dark side of the planet and it needs to have enough energy to survive these periods.”

According to Pereira, the motion planner will have access to the position of the aerobot in Venus's atmosphere and the desired goal location. It will also have access to information about the atmosphere in between these two positions.

“Starting from the initial position, the planner will simulate different movements the aerobot could make and associate costs for each of them depending on the quantities mentioned before,” Pereira said.

For example, if the wind is blowing in the same direction as the movement of the aerobot, that would be less costly than moving against the wind direction.

“After that, the motion planner will keep propagating the movements of the aerobot with a smaller cost, creating a tree of possibilities until we reach our destination,” Pereira said.

The second goal of this project is to localize the aerobot vehicles in the atmosphere using information from other vehicles and maps of the planet. There is currently no GPS in Venus, so localization is difficult.

This localization approach will allow several robots to be less lost as a group when they are exploring Venus.

Gu and Pereira plan on using different types of maps for localization.

“We are evaluating the possibility of using maps created before the mission, most likely a Venus topographic map to help the robots to localize themselves,” Gu said.

The third goal for this project is to coordinate the vehicles so that they have improved localization and a better estimation of atmospheric conditions.

The spatial distribution of the aerobots in the atmosphere may allow each aerobot to have a better knowledge of the 3D wind field if each vehicle shares the wind flow in its neighborhood, according to Pereira.

Pereira and Gu’s research will be based on wind models of Venus created by NASA. The researchers also propose that the aerobots carry wind sensors that can be used to estimate the local wind.

“The importance of the wind flow is related to the fact that it can be exploited to take the aerobot to desired locations,” Pereira said. “Just as with sprinters in the Olympics when they get better marks if they are experiencing tail-wind. If the wind is directed towards the goal of the aircraft, the aerobot movement will be aided by the wind and, by consequence, the path will be more energetically efficient.”

To test this, Pereira and Gu plan to develop a Venus atmosphere simulator, where they will evaluate the aerobots’ functionality.

“Several exploratory missions to Venus collected data of wind, temperature, pressure, and air density,” Pereira said. “This information was then used to create a simulator where, given the latitude, longitude, and altitude of the vehicle, we compute all the forces acting on the vehicle.”

Joining Pereira and Gu on the project are Bernardo Martinez Rocamora Jr. and Chizhao Yang, doctoral students in aerospace and mechanical engineering, and Anna Puigvert I Juan, master’s student in mechanical engineering.

A mathematical model developed by Texas A&M researchers can predict temperatures within mosquito breeding grounds, which can be used to estimate populations and track vector-borne diseases. 

With an impressive capability of drinking up to three times their body weight in a single blood meal, mosquitoes are formidable parasites. But to reach adulthood, mosquitoes need to be raised in environments where the temperatures are conducive to their breeding, growth, and development.

In a new study in the journal Scientific Reports, Texas A&M University researchers have developed a mathematical model based on machine learning to precisely predict the local or microclimatic temperature within the breeding grounds of the Aedes albopictus mosquitoes, carriers of the chikungunya and dengue viruses. Their algorithm also reveals that even in winter, the temperature may be warm enough in certain breeding grounds to allow mosquitoes to grow and thrive.

“Our goal is to develop accurate and automated mathematical models for estimating microclimatic temperature, which can greatly facilitate a quick assessment of mosquito populations and consequently, vector-borne disease transmission,” said Madhav Erraguntla, associate professor of practice in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering.

Responsible for around a million deaths globally, mosquitoes continue to wreak havoc to public health in many parts of the world. In addition to  water, temperature plays a critical role at different stages in mosquitoes’ life cycle. Furthermore, The mosquitoes’ development, reproduction and survival can be mathematically modeled on the basis of temperature.

Past studies have largely relied on ambient temperature, or general air temperature, to make predictions about mosquito populations. However, these calculations have not been precise since ambient temperatures can deviate from those within mosquito breeding grounds. Recognizing this shortcoming, scientists rely on sensors, called data loggers, to continually keep track of the temperature, light intensity and humidity within breeding grounds. Despite their advantages, these sensors are inconvenient due to their cost and long-term use.

“People have realized that the microclimatic conditions are important, but right now data loggers are the only way to keep track of temperature,” Erraguntla said. “We wanted to address this gap by automating the process of estimating microclimatic temperatures so that we can model the life cycle of mosquitoes accurately.”

For their experiments, the researchers placed sensors in common mosquito breeding grounds around Houston, including storm drains, shaded areas and inside water meters. In addition, they obtained information on ambient temperatures from the National Oceanic and Atmospheric Administration repository. With this data as training input to a machine learning algorithm, the computer model could predict the microclimatic temperatures for a variety of ambient temperatures and breeding grounds within 1.5 degrees centigrade. Further, the model now could even forecast microclimatic temperatures for any ambient temperature, precluding the need for sensors.

Next, they fed the values of the microclimatic temperatures to another mathematical model, called the population dynamic model, that tracks the life cycle of the mosquitoes. Based on the microclimatic temperature and other parameters, the population dynamic model could estimate the populations at different stages in the lifecycle including eggs, larvae, pupae, and adult Aedes albopictus mosquitoes.

The model also revealed that the insulated conditions of the storm drains could result in the survival of 84% of juveniles and eggs and 96% of adults during the winter months, a time of the year when mosquitoes are assumed to be dormant.

Although their climatic temperature prediction model has a high degree of accuracy, the researchers noted that additional research is needed to affirm if their model is applicable to places outside of Texas.

“Our work automates the prediction of microclimatic conditions, bypassing an otherwise expensive and time-consuming process of placing the sensors in different breeding spots, collecting the sensor data and analyzing it,” Erraguntla said. “From a public health context, this work will help epidemiologists better track mosquito-borne disease transmission and surges in mosquito abundances.”

WildfireDB contains over 17 million data points that capture how fires have spread in the contiguous United States over the last decade 

A team at UC Riverside led by computer science assistant professor Ahmed Eldawy is collaborating with researchers at Stanford University and Vanderbilt University to develop a dataset that uses data science to study the spread of wildfires. The dataset can be used to simulate the spread of wildfires to help firefighters plan emergency responses and conduct evacuation. It can also help simulate how fires might spread soon under the effects of deforestation and climate change, and aid risk assessment and planning of new infrastructure development.

The open-source dataset, named WildfireDB, contains over 17 million data points that capture how fires have spread in the contiguous United States over the last decade. The dataset can be used to train machine learning models to predict the spread of wildfires.

“One of the biggest challenges is to have a detailed and curated dataset that can be used by machine learning algorithms,” said Eldawy. “WildfireDB is the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography.”

First responders depend on understanding and predicting how a wildfire spreads to save lives and property and to stop the fire from spreading. They need to figure out the best way to allocate limited resources across large areas. Traditionally, fire spread is modeled by tools that use physics-based modeling. This method could be improved with the addition of more variables, but until now, there was no comprehensive, open-source data source that combines fire occurrences with geospatial features such as mountains, rivers, towns, fuel levels, vegetation, and weather.

Eldawy, along with UCR doctoral student Samriddhi Singla and undergraduate researcher Vinayak Gajjewar, utilized a novel system called Raptor, which was developed at UCR to process high-resolution satellite data such as vegetation and weather. Using Raptor, they combined historical wildfires with other geospatial features, such as weather, topography, and vegetation, to build a dataset at a scale that included most of the United States.

WildfireDB has mapped historical fire data in the contiguous United States between 2012 to 2017 with spatial and temporal resolutions that allow researchers to home in on the daily behavior of fire in regions as small as 375-meter square polygons. Each fire occurrence includes the type of vegetation, fuel type, and topography. The dataset does not include Alaska or Hawaii.

To use the dataset, researchers or firefighters can select information relevant to their situation from WildfireDB and train machine learning models that can model the spread of wildfires. These trained models can then be used by firefighters or researchers to predict the spread of wildfires in real-time. 

“Predicting the spread of wildfire in real-time will allow firefighters to allocate resources accordingly and minimize loss of life and property,” said Singla, the paper’s first author. 

The paper, “WildfireDB: an open-source dataset connecting wildfire spread with relevant determinants,” will be presented at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks and is available here. A visualization of the dataset is available here. Eldawy, Singla, and Gajjewar were joined in the research by Ayan Mukhopadhyay, Michael Wilbur, and Abhishek Dubey at Vanderbilt University; and Tina Diao, Mykel Kochenderfer, and Ross Shachter at Stanford University.

Exoplanets are experiencing a stratospheric rise. In the three decades since the first confirmed planet orbiting another star, scientists have cataloged more than 4,000 of them. As the list grows, so too does the desire to find Earth-like exoplanets — and to determine whether they could be life-sustaining oases like our own globe. 

The coming decades should see the launch of new missions that can gather ever-larger amounts of data about exoplanets. Anticipating these future endeavors, a team at the University of Washington and the University of Bern has computationally simulated more than 200,000 hypothetical Earth-like worlds — planets that have the same size, mass, atmospheric composition, and geography as modern Earth — all in orbit of stars like our sun. Their goal was to model what types of environments astronomers can expect to find on real Earth-like exoplanets.

As they report in a paper accepted to the Planetary Science Journal and submitted Dec. 6 to the preprint site arXiv, on these simulated exoplanets, one common feature of present-day Earth was often lacking: partial ice coverage.

“We essentially simulated Earth’s climate on worlds around different types of stars, and we find that in 90% of cases with liquid water on the surface, there are no ice sheets, like polar caps,” said co-author Rory Barnes, a UW professor of astronomy and scientist with the UW’s Virtual Planetary Laboratory. “When ice is present, we see that ice belts — permanent ice along the equator — are actually more likely than ice caps.”

The findings shed light on the complex interplay between liquid water and ice on Earth-like worlds, according to lead author Caitlyn Wilhelm, who led the study as an undergraduate student in the UW Department of Astronomy. 

“Looking at ice coverage on an Earth-like planet can tell you a lot about whether it’s habitable,” said Wilhelm, who is now a research scientist with the Virtual Planetary Laboratory. “We wanted to understand all the parameters — the shape of the orbit, the axial tilt, the type of star — that affect whether you have ice on the surface, and if so, where.”

The team used a 1-D energy balance model, which computationally imitates the energy flow between a planet’s equator and poles, to simulate the climates on thousands of hypothetical exoplanets in various orbital configurations around F-, G- or K-type stars. These classes of stars, which include our G-type sun, are promising candidates for hosting life-friendly worlds in their habitable zones, also known as the “Goldilocks” zone. F-type stars are a bit hotter and larger than our sun; K-type stars are slightly cooler and smaller.

In their simulations, the orbits of the exoplanets ranged from circular to pronounced oval. The team also considered axial tilts ranging from 0 to 90 degrees. Earth’s axial tilt is a moderate 23.5 degrees. A planet with a 90-degree tilt would “sit on its side” and experience extreme seasonal variations in climate, much like the planet Uranus.

According to the simulations, which encompassed a 1-million-year timespan on each world, Earth-like worlds showed climates ranging from planet-wide “snowball” climates — with ice present at all latitudes — to a steaming “moist greenhouse,” which is probably similar to Venus’ climate before a runaway greenhouse effect made its surface hot enough to melt lead. But even though most environments in the simulations fell somewhere between those extremes, partial surface ice was present on only about 10% of hypothetical, habitable exoplanets.

The model included natural variations over time in each world’s axial tilt and orbit, which in part explains the general lack of ice on habitable exoplanets, according to co-author Russell Deitrick, a postdoctoral scientist at the University of Bern and researcher with the Virtual Planetary Laboratory. 

“Orbits and axial tilts are always changing,” said Deitrick. “On Earth, these variations are called Milankovitch cycles and are very small in amplitude. But for exoplanets, these changes can be quite large, which can eliminate ice or trigger ‘snowball’ states.”

When partial ice was present, its distribution varied by a star. Around F-type stars, polar ice caps — like what Earth sports currently — were found about three times more often than ice belts, whereas ice belts occurred twice as often as caps for planets around G- and K-type stars. Ice belts were also more common on worlds with extreme axial tilts, likely because seasonal extremes keep the polar climates more volatile than equatorial regions, according to Wilhelm.

The team’s findings of ice on these simulated Earth-like worlds should help in the search for potentially habitable worlds by showing astronomers what they can expect to find, especially regarding ice distribution and the types of climates.

“Surface ice is very reflective, and can shape how an exoplanet ‘looks’ through our instruments,” said Wilhelm. “Whether or not ice is present can also shape how a climate will change over the long term, whether it goes to an extreme — like a ‘snowball Earth’ or a runaway greenhouse — or something more moderate.”

Ice alone, or its absence, does not determine habitability, though.

“Habitability encompasses a lot of moving parts, not just the presence or absence of ice,” said Wilhelm.

Life on Earth has survived snowball periods, as well as hundreds of millions of ice-free years, according to Barnes.

“Our own planet has seen some of these extremes in its own history,” said Barnes. “We hope this study lays the groundwork for upcoming missions to look for habitable signatures in exoplanet atmospheres — and to even image exoplanets directly — by showing what’s possible, what’s common, and what’s rare.”

Rachel Mellman, a recent UW graduate in astronomy, is a co-author of the paper. The research was funded by NASA through grants to the Virtual Planetary Laboratory.