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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Efficient techno-economic optimization of integrated energy systems using Bayesian optimization
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Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators
Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.
Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System
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Negative Imaginary Control Using Hybrid Integrator-Gain Systems: Application to MEMS Nanopositioner
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Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids
Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.
Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches
Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.
Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes
The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.
High-Resolution 3D Acoustic Borehole Integrity Monitoring System [Slides]
Abstract not provided.
Research and Development of a Ventilation-Integrated Comfort System
From an indoor air quality perspective, the best residential ventilation strategies include filtering outdoor air and distributing that air to all occupied parts of a home. From an energy standpoint, it's desirable that energy be transferred from the exhaust air to the incoming outdoor air to limit heating and cooling impacts. Heat or energy recovery ventilation systems (HRVs or ERVs) can provide these functions, but researchers have seen many poor installations related to design, installation, and operation and maintenance. More robust ventilation systems may involve an ERV with a dedicated duct distribution system and controls. Such a duct system can be costly to install, and many builders reduce these costs by connecting an ERV to a central heating and cooling duct system. While this can sometimes be done effectively, researchers have seen consistent challenges with low, inconsistent, or imbalanced flow rates; high electricity consumption; and - of greatest concern - outdoor air short-circuiting or not being delivered to occupied spaces at all. Most ERVs are designed to operate with their own duct system; they are not designed as an add-on to much larger HVAC systems. With support from the US DOE Building Technologies Office, Steven Winter Associates, Inc. (SWA) partnered with Mitsubishi Electric Trane US, CORE Energy Recovery Solutions, and Therma-Stor LLC to design and test VICS prototypes. A conceptual diagram is shown in Figure 1, and the latest prototype (shown in Figure 2) was manufactured by Therma-Stor and tested in SWA's facility in Norwalk, CT. The ERV heat exchanger was provided by CORE, and the VICS was installed in conjunction with a 1-ton, inverter heat pump provided by Mitsubishi.
Integrated Mechanical System Pods (CRADA Final Report)
LBNL provided input to setting the requirements related to indoor air quality and contributed to the development of metrics and test plans to evaluate prototypes of the mechanical pods, which were developed by other members of the project team. The testing of prototypes was conducted by and at Syracuse University, another project partner. LBNL also collaborated with RMI to develop a basic plan for data collection when the pods are deployed for field testing in Phase 2.
Multiperiod Generalized Disjunctive Programming Optimization in IDAES: Simultaneous Operation and Design of an Integrated Energy System.
Abstract not provided.
Beyond Price-Taker: Multiscale Optimization of a Wind-Battery Integrated Energy System within the Wholesale Electricity Market
This work presents the optimization of a wind-battery IES using the multiscale optimization framework proposed in our previous work to quantify errors from the price-taker assumption. The framework, built over Prescient (an open-source package for solving production cost models), is applied to the RTS-GMLC dataset, an open-source dataset that is representative of the southwest U.S. wholesale electricity market. The framework provides detailed bidding, market clearing, and control processes of an IES, and it can quantify how the IES interacts with the market. In this work, we use the retrofit of a wind farm with a battery storage system as an example to show the difference in the market outcomes and revenues obtained from both price-taker and multiscale optimization approaches. Our work goes beyond price-taker and deep dives into quantifying IES-market interaction in optimizing IES. This framework enables users to explore how different design and operation decisions of energy systems interact with the market and provides a more accurate evaluation than the price-taker assumption.
Highly Integrated Power Systems for Transition to a Sustainable Energy Sector
Brief introduction slides for a panel discussion at the SUstainable PolyEnergy generation and HaRvesting Conference and Low Emission Advanced Power (LEAP) Workshop held 2023 September 6 - 8.
Advanced Research on Integrated Energy Systems (ARIES) Cyber Range Overview and Threat-to-Consequence Demonstration
This presentation was presented at the Aggregation and Grid Security Workshop - held on June 17-18, 2025, at NREL in Golden, Colorado. The goal of the two-day workshop was to address the critical cybersecurity challenges for the future electric grid. The threat-to-consequence demonstration showcases NREL's capability to model, simulate, test, and evaluate cyberattacks targeting energy systems that coincide with natural hazards, as well as the ramifications for the energy grid as a whole.
Multiperiod Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models
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Adapting HPC Systems for Integrated Research Infrastructure
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Energy efficient integrated photonic systems based on inverse design
The energy footprint of modern information processing and communications systems is immense and utilizes a significant fraction of total global energy usage. Data centers alone consume over 70 billion kilowatt-hours per year. Much of this energy usage is intrinsic to the use of electronic wiring, making optical-based technologies a necessary and promising route to mitigating energy consumption in short- to medium-distance communication links. In this project, we developed and implemented a framework for the design of optical components, based on machine learning, which enables optical components relevant to optical information processing to be realized at their physical performance limits.