From the layers of Earth beneath our feet to the shifting clouds over our heads, there is much we do not know about how our planet works.
For the rest of the year, researchers in Pacific Northwest National Laboratory's Atmospheric Sciences & Global Change Division will help two small businesses develop new technologies that could help unravel these mysteries—a mini camera for the clouds and a big data system for environmental sensing.
Among a large field of projects recently selected for Phase I funding through the U.S. Department of Energy (DOE) Small Business Innovation Research (SBIR) program, the teams aim to prove their prototypes are feasible and have enough market potential to compete for Phase II funding next year.
Mini Camera for the Clouds
For Physical Optics Corp., located in Torrance, California, PNNL is defining the requirements for a new type of camera that can take 3-D images of the tiniest components within clouds—water droplets, ice crystals, and aerosols (small particles, like dust or soot). Current technologies only provide flat 2-D images of size and shape. Without the additional variable of depth, scientists have only a limited view of the cloud structure.
These details are important because they affect the life cycle of the cloud—how it evolves, spreads, or dissipates. They create the conditions that hold moisture and increase the cloud's longevity, or form ice crystals that fall as rain, snow, or hail, depleting the cloud. Combined with other meteorological variables, these conditions determine how much energy stays in the upper atmosphere versus what passes down to Earth.
The kicker for the new camera is that it has to be small enough and light enough to be carried by a drone. Referred to as unmanned aerial systems, or UAS, these vehicles are gaining increasing use in science applications. Because of their slow airspeed, they can gather more detailed data than traditional research aircraft.
But the additional data also present a computational challenge. And with atmospheric research extending to the Arctic, the tools of the trade need to be rugged enough to withstand frigid temperatures.
"We're optimizing the instrument parameters, data requirements, and computation constraints for the Arctic UAS application," said Gourihar Kulkarni, an atmospheric scientist and the PNNL project lead. "This region is the canary in the coal mine when it comes to Earth's climate, so if we can get better data about atmospheric processes there, we can improve climate models across the board."
PNNL will send the requirements to Physical Optics Corp., which will modify its existing instrument and software—not used for research applications before. It will send its new design to PNNL for testing in a cloud chamber, and PNNL will send its results back for analysis and design modifications.
By the end of Phase I, funded at $225,000, the team should have a final prototype design, a niche market identified, and a plan for Phase II development and field testing.
Big Data for Environmental Sensing
Following the migration of molecules among rock, soil, and plants is staggering in its scope and complexity. Understanding the interactions between the good and the bad—nutrients and contaminants—requires monitoring, data management, and analysis on a massive scale.
Building on its expertise in subsurface science, PNNL will help Innovative Wireless Technologies (IWT) Inc., located in Lynchburg, Virginia, enhance its Integrated Environmental Quality Sensing (IEQS) system for improved hydrological, microbiological, and geochemical monitoring and modeling. The improvements will allow researchers to collect a broad range of in situ sensor data, dynamically control sensor operation, and manage and visualize the data in real time. Ultimately, stakeholders could use this enhanced system for real-time decision making.
To get there, IWT plans to add three new open-source components to its IEQS system:
- a non-relational database, which scales well for large volumes of complex data;
- visualization tools for data display; and
- a workflow management interface for seamless integration of data management, cross-domain modeling, data analysis, and collaborative research.
The company will start by surveying preferred open-source tools in each category and identifying the pros and cons for each tool.
"Because of our experience and expertise supporting DOE missions in these areas, they asked us to serve in a consultant mode," said Xingyuan Chen, a research scientist and PNNL lead for the project. "We'll review IWT's evaluations and selections for database and visualization tools, survey the DOE research community, then let IWT know which tools are suitable for DOE missions or if we see any red flags."
The team's goal for the enhanced system is to reduce bottlenecks and increase efficiency by enabling an iterative cycle between predictive model development and model-driven experimentation to advance understanding of complex subsurface systems and watershed systems. This can improve predictions of how natural and managed systems respond to perturbations and support cleanup of contaminated sites.
By the end of Phase I, funded at $150,000, IWT will integrate and demonstrate the selected graphic-based database and visualization tools in its existing data management platform as proof-of-concept and prepare a final report.
The Phase I projects end in December 2018. The teams can then apply for Phase II. At this stage, DOE winnows down the field to those with the most commercial potential. Those teams then have two years to advance their prototypes to market readiness and finish commercialization plans.