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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Rapid Procedure Development Approach for Multi-stage Validation of Novel Nuclear Power Plant Systems

The integration of renewable energy sources into the modern grid introduces variability in generation, challenging nuclear reactors to remain economically viable amid low wholesale electricity prices. To address this, Idaho National Laboratory?s Flexible Plant Operation and Generation program explores alternative revenue streams, including commoditizing excess thermal energy. A Thermal Power Dispatch (TPD) system enables steam extraction from the secondary loop for industrial uses like hydrogen production via high-temperature steam electrolysis.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Integrating agrivoltaics into smallholder farming systems to enhance food production and irrigation efficiency under climate stress

Smallholder agricultural growers and entrepreneurs (SAGE) have a crucial role in sustaining food security; their cultivation accounts for roughly 30% of global crop production and contributes substantially to the diversity and nutritional value of food systems. Despite their awareness of and flexibility to adapt to productivity challenges, SAGE communities remain highly vulnerable to heat and drought stress, which threatens both livelihoods and local food availability. While intensification and adaptive farm practices offer partial resilience, their capacity to buffer sudden climatic extremes remains limited. Agrivoltaics, the dual use of land for solar electricity generation and crop cultivation, has emerged as a promising strategy to enhance agricultural productivity and its resilience to climate extremes by altering microclimatic conditions. Although proven effective in hot and arid regions, the benefits of agrivoltaics for temperate climates and small-scale farms remain minimally explored. To address this gap, we evaluated the performance of a small-scale agrivoltaics system in a temperate climate for high-value crops, including leafy vegetables and garlic. We investigate whether the system can (i) protect crops during extreme heat events and (ii) enhance productivity and reduce irrigation requirements during hot and dry periods unsuitable for conventional production. Our findings provide evidence that agrivoltaics is a climate-resilient farming strategy under current and projected climate scenarios, capable of improving yields (by +43% to +127% for the leafy vegetables grown) and reducing water consumption, while creating complementary economic opportunities through decentralized energy generation systems. This work supports the integration of agrivoltaics into small-scale agricultural systems as an innovative pathway to strengthen food security, bolster farmer livelihoods, and enable multiple co-benefits from broader solar energy adoption.

14 SOLAR ENERGY↗
DOE OSTI2025

Interleaved Cuk Converter Wave Energy System With Advanced Control and Grid Support Functions for Seamless Integration

This paper presents an innovative wave energy conversion system that integrates an interleaved Cuk converter with advanced nonlinear control for seamless grid integration. The system efficiently extracts power on the DC side using the interleaved Cuk converter, while an inverter manages power transfer to the grid or load on the AC side. A nonlinear control architecture, based on the Lyapunov energy function, ensures stable and optimal operation under varying wave conditions, effectively addressing the challenges of variability and unpredictability inherent in wave energy. The proposed system also incorporates features to enhance power quality and minimize losses, making it a robust solution for renewable energy integration. The system's effectiveness in harnessing wave energy and achieving smooth grid integration is validated through comprehensive computer simulations in MATLAB/Simulink, with case study results demonstrating its capabilities.

HYDRO ENERGY,TIDAL AND WAVE POWER↗
DOE OSTI2026

Preliminary Introduction of the IOTA Bake System

The Integrable Optics Test Accelerator (IOTA) ring is vital to the advancement of accelerator sciences, and a large part of its attractiveness to accelerator physicists is its modularity and the versatility that this function provides. Up until this point, the FAST accelerator has provided electron beam for the studies in IOTA. With the soon to be commissioned IOTA Proton Injector in lieu, the requirement for better vacuum to support future proton studies in the ring have arrived. Our solution is the IOTA Bake System which has the goal of facilitating this requirement.

Thompson, T. K. [Fermilab]↗
DOE OSTI2026

First results from a high-frame-rate, multi-GHz ionizing particle detection system geared toward accelerator diagnostic applications

An integrated detection system, consists of a thin diamond sensor incorporated into a compact signal loop, coupled in turn to an application-specific integrated circuit readout chip, has been tested for the first time, using ps-duration electron pulses from the Next Linear Collider Test Accelerator at the SLAC National Accelerator Laboratory. For readout frame rates as high as 120 kHz, the detection system is shown to achieve a linear dynamic range of 10 3 and a bandwidth of 4–5 GHz, with no ringing seen on the tail of the output pulse. These results represent the best-performing multi-GHz ionizing particle detection system achieved to date.

Ferezghi, Mohammadreza Mohseni [University of Cali↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Cyber-Physical System: Design for Sustainability and Resilience

When considering the design tools needed in the transition from numeric models to pilot plant, cyber-physical systems (CPS) come to the forefront as a method to model complex integrated energy systems. CPS approach has proven to be valuable to identify opportunities for economically viable early adoption of integrated energy technologies. This tutorial will introduce the concepts and the roles of CPS in co-design to minimize risks for pilot plant and technology deployment. This tutorial will also layout basic requirements for the CPS development, which requires a highly interdisciplinary effort with expertise in sensors, hardware testing, real-time modeling, controls, and system integration.

Harun, Nor Farida↗
DOE OSTI2025

A Data-Driven Framework for Predicting the Sorting and Screening Performance of an Integrated Biomass Feedstock Preprocessing System

The characteristics of mechanically sorted and screened lignocellulosic biomass, such as the mass contents of corn stover anatomical fractions (leaves, husks, stalks, cobs, etc.), can be used to calculate the intermediate feedstock quality attributes “yield” and “purity” that indicate the conversion efficiency of biocrude. No prior study has investigated the correlations from the characteristics of raw biomass and preprocessing unit operation parameters to those intermediate feedstock quality attributes. This work presents a data-driven framework for assessing and predicting the intermediate feedstock quality attributes in an integrated biomass feedstock preprocessing system. Our study used corn stover as a typical type of herbaceous biomass because of its abundance in the U.S. It began with data acquisition of moisture content, particle size distribution, and anatomical fractions of the materials after each unit operation in the system. The objective of this preprocessing system is to minimize husks and leaves and maximizing cobs and stalks by mechanically separating the materials into three streams via disc screen and air separator. Prototype neural network models were then developed to evaluate the feasibility of predicting process outcomes based on measurable parameters. It is found that incorporating physical constraints into these prediction models significantly enhances the accuracy of the predicted yield and purity against the ground truth data. The experimental data and model predictions indicate that decreasing throughput increases purity, while higher throughput results in lower purity. Finally, an optimization problem was introduced to search optimal combinations of feed material properties and preprocessing unit operation parameters, as the intermediate feedstock quality attributes – yield and purity, appeared to be competing factors. The study also suggests the continual need to improve the data-driven framework’s predictability by incorporating more accurate physical models to describe the dynamics in the preprocessing units such as the air separator.

09 - BIOMASS FUELS↗
DOE OSTI2024

Hydrogen Production, Grid Integration, and Scaling for the Future

The project will explore near and long-term visions towards the commercialization of grid integrated electrolysis systems to inform deployment across the planning, procurement, and operation stages of hydrogen production on the grid. It will leverage NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios, also creating a digital twin for emulation in the Advanced Research on Integrated Energy Systems (ARIES) virtual environment and performing hardware-in-the-loop (HIL) testing of pilot scale, decentralized, and centralized hydrogen systems.

electrolysis systems↗
DOE OSTI2025

Feynman path integrals for discrete-variable systems: Walks on Hamiltonian graphs

We propose a natural, parameter-free, discrete-variable formulation of Feynman path integrals. We show that for discrete-variable quantum systems, Feynman path integrals take the form of walks on the graph whose weighted adjacency matrix is the Hamiltonian. By working out expressions for the partition function and transition amplitudes of discretized versions of continuous-variable quantum systems, and then taking the continuum limit, we explicitly recover Feynman's continuous-variable path integrals. We also discuss the implications of our result.

Feynman diagrams↗
DOE OSTI2025

Real-time Simulation Model of a Vanadium Redox Flow Battery Energy Storage System with Power Electronics Integration

This paper presents a real-time (RT) model of a 20 kW multi-stack vanadium redox flow battery (VRFB) system including power electronics converters in a single real-time framework. Implemented on a Typhoon HIL 604 device, this model is designed to simulate the operation of the flow battery with system-level controls. To provide a realistic overview of a VRFB system, the model incorporates a closed-loop control system for managing the speeds of two centrifugal pumps and an electro-thermal model of the battery stacks, enabling realtime temperature estimation. Furthermore, a two-stage power electronics topology and associated control solution are presented, operating with distinct strategies for interfacing the battery model with the main grid and a local load. Results from simulations demonstrate that the VRFB model is well suited for RT environment, and system can manage power during both charging and discharging cycles, while a SCADA monitor is used to display critical variables for safe, efficient, and reliable operation of the VRFB systems.

Rezende Da Costa Reis Kimpara, Renata [ORNL] (ORCI↗
DOE OSTI2025

Cyber-Physical Reformer Development at NETL

Integrated energy systems are considered one of promising technologies to provide efficient, reliable and resilient power generation. The U.S. Department of Energy, National Energy Technology Laboratory is building an automated reformer-solid oxide fuel cell-gas turbine integrated energy system using a cyber-physical systems (CPS) approach, which exploits the advantage of both numerical model and physical system, as well as gapping the inaccessible technologies. Both the fuel reformer and the fuel cell stack are designed to be CPS components, of which the hardware representations are physically integrated with the gas turbine. A compact design is used with the consideration of future commercialization by placing all components inside of a pressure chamber, which is pressurized by the compressor discharge. (Presented virtually at the MILLENNIUM CLEAN and SUSTAINABLE POWER Workshop 2025)

cyber-physical↗
DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonexistence of integrable nonlinear magnetic fields with invariants quadratic in momenta

Nonlinear, completely integrable Hamiltonian systems that serve as blueprints for novel particle accelerators at the intensity frontier are promising avenues for research, as Fermilab’s Integrable Optics Test Accelerator (IOTA) example clearly illustrates. Here, we show that only very limited generalizations are possible when no approximations in the underlying Hamiltonian or Maxwell equations are allowed, as was the case for IOTA. Specifically, no such systems exist with invariants quadratic in the momenta, precluding straightforward generalization of the Courant-Snyder theory of linear integrable systems in beam physics. We also conjecture that no such systems exist with invariants of higher degree in the momenta. This leaves solenoidal magnetic fields, including their nonlinear fringe fields, as the only completely integrable static magnetic fields, albeit with invariants that are linear in the momenta. The difficulties come from enforcing Maxwell equations; without constraints, we show that there are many solutions. In particular, we discover a previously unknown large family of integrable Hamiltonians.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluate data lake design for the accelerator control system

Increasing precision in automation for modern particle accelerators not only creates a requirement to gather data from all devices but also demands scalable and high-performance data infrastructure with the capability of handling vast incoming device data. A well architected data lake is suitable for such a system which integrates real-time data acquisition, transient data caching, and long-term storage. This paper evaluates data lake architecture for an Accelerator Control System (ACS), focusing on two critical components of a data lake, data cache and long-term storage.

Jaikar, Amol [Fermilab]↗
DOE OSTI2024

Integration of computer vision system to track the alignment SRF cavities into the test cryostat for PIP-II at Fermilab

PIP-II cryomodules use a computer vision system (H-BCAMs system) to monitor the alignment of SRF cavities and focusing lenses during assembly, testing, and operation. This contribution details the integration of the H-BCAMs into the Spoke Test Cryostat (STC) at Fermilab, which is utilized for cold testing SRF cavities prior to their integration into the string assembly. Thermal and structural finite element analyses were employed to estimate the cavities’ deformations, to be validated during cold testing in the STC using H-BCAMs. Notably, this marks the first instance of H-BCAMs integration into a cryostat and operation within a cryogenic environment.

43 PARTICLE ACCELERATORS↗