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Computational Science News

Dec. 22, 2020

Top 20 NREL Stories of 2020

NREL researchers and staff reached countless goals and achieved numerous successes in science, partnerships, and technology commercialization in 2020, from breaking world records to launching new initiatives. Here are just a few of the highlights.

Nov. 5, 2020

WESyS: the Open-Source Waste-to-Energy System Simulation Model

WESyS simulates the possible evolution of the U.S. waste-to-energy industry based on industrial, technological, and policy-related factors.

Oct. 12, 2020

NREL Computational Science Center Welcomes Leading Princeton Researcher Dr. Michael Mueller

NREL and Princeton University recently formalized the first joint appointment under a new master agreement between the two institutions, under which NREL's experts in computational science and combustion simulation will work closely with Dr. Michael Mueller from Princeton.

Oct. 2, 2020

Updated OpenOA Software Improves Energy Production Predictions To Reduce Investment Risk

A new version of OpenOA has been released, which includes features designed to help wind plant operators identify and analyze the different factors that drive wind plant performance.

Aug. 24, 2020

From Streams to Rivers: NREL Upgrades Data Exchange for High-Use, Real-Time Applications

A recent upgrade by NREL's Data Analysis and Visualization Group (DAV) has brought some order to laboratory data flow with a platform that makes data streaming and access much easier for ESIF researchers and partners.

Aug. 7, 2020

Two-for-One Energy from Photons, Now Better than Ever

NREL researchers have found a promising group of materials for tomorrow's super-efficient solar cells, demonstrating how a carefully designed molecule can efficiently split the energy imparted by one photon into two excited states and keep them separated for several microseconds.

July 7, 2020

News Release: Machine Learning Approach Produces 50X Higher-Resolution Climate Data

Researchers at NREL have developed a novel machine learning approach to quickly enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times—an enhancement that has never been achieved before with climate data.


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