How NREL researchers are using gray boxes and jellyfish to advance wave energy

How NREL researchers are using gray boxes and jellyfish to advance wave energy
(photo courtesy Werner Slocum, NREL)

Researchers at the National Renewable Energy Laboratory in the U.S. are employing a unique testing platform that can merge virtual and physical worlds to advance wave energy.

This unassuming platform, a stack of gray metal boxes on a gray concrete floor, can help bridge the chasm between testing technologies and getting wave energy onto the grid, according to Nathan Tom, a mechanical engineer at NREL.

Waves carry enough energy to meet about 34% of the electricity need in the U.S. This resource could pair with other renewable energy sources to power offshore activities — like seafood farming, carbon capture or ocean research — and help decarbonize the power grid. But technology developers must design devices that can generate hearty amounts of energy while surviving ocean conditions — all without draining a developer’s bank.

On one side of that testing chasm is a virtual, numerical world where technology designers can explore generators, play with shape and scale, and even subject their tech to simulated waves. But the real world, the other side of the testing chasm, is often messier than any virtual replica. Ocean trials are risky: If a device does not operate as smoothly as the data said it should or a critical component breaks, developers could waste time and money extracting their defunct design.

This testing platform, called Wave Energy Converter SIMulator (WEC-Sim), blends the freedom of theoretical models with the reality of a physical generator or subsystem.

Tom has used the platform to evaluate a wave energy device under development at NREL. The design, called a variable-geometry wave energy converter, can inflate and deflate to avoid potentially destructive extreme waves. This and other variable-geometry designs could help wave energy technologies generate more energy, survive longer and cost less.

But before developers pivot to such a new design, Tom set out to determine whether variable geometry would be worth the investment, seeking to show close to a 25% increase in efficiency over existing technology. For that exploration, Tom added a layer of complexity to the team’s testing platform. Now, they can feed live data from hardware, like a generator, into WEC-Sim, an open-source code developed by NREL and Sandia National Laboratories.

“So if your motor stopped working, the simulation should respond in real time,” Tom said. “With that, we can get a precise look at exactly how the hardware responds to the simulation and how to improve the design to produce more energy.”

Wave energy developers can use other tools, like NREL’s wave tank or motion platform, to bridge the virtual-physical gap. And while wave tanks can provide valuable data on how a device responds to actual water, most can only fit scaled-down versions of a prototype, meaning developers must still make assumptions about how those data translate to a full-scale device. And the motion platform cannot feed data back into theoretical models that learn, adapt and spit out more accurate feedback.

The team’s testing platform can do both. Developers can simulate a scaled-up version of their design to evaluate larger-scale or even full-scale generators used in smaller wave energy devices — like those designed to power offshore seafood farms, ocean observation sensor, or ocean water desalination.

Ben McGilton, a research engineer at NREL, brought electricity to the project. He used the platform’s data to figure out how to build a more efficient version of the variable-geometry wave energy device — which he said resembles a jellyfish. The testing platform pinpointed potential flaws in the jellyfish design. Now, Tom can hone the design before it undergoes further testing.

In addition, an NREL team working on a wave-powered desalination device plans to assess how the generator reacts if the device charges a battery.

In time, Tom hopes to pair the platform with machine learning algorithms to help identify optimal ways to control how much energy a generator produces as waves swell from small to big to extreme. All this precise data and control could accelerate technology development and help get wave energy technologies out in the water.

This project was funded, in part, by the U.S. Department of Energy’s Water Power Technologies Office.

Originally published by Elizabeth Ingram in Hydro Review.