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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Continuous Liquid–Liquid Extraction and in-Situ Membrane Separation of Miscible Liquid Mixtures

Separation operations are critical across a wide variety of manufacturing industries and account for about one quarter of all in-plant energy consumption in the United States. Conventional liquid-liquid separation operations require either thermal or chemical treatment, both of which have a large environmental impact and carbon footprint. Consequently, there is a great need to develop sustainable, clean methodologies for separation of miscible liquid mixtures. The greatest opportunities to achieve this lie in replacing high-energy separation operations (e.g., distillation) with low-energy alternatives such as liquid-liquid extraction. One of the primary design challenges in liquid-liquid extraction is to maximize the interfacial area between two immiscible (e.g., polar and non-polar) liquids for efficient mass transfer. However, this often involves energy-intensive methods including ultrasonication, pumping the feed and the extractant through packed columns with high tortuosity, or using a supercritical fluid as an extractant. Emulsifying the feed and the extractant, especially with a surfactant, offers a large interfacial area, but subsequent separation of emulsions can be energy-intensive and expensive. Thus, emulsions are typically avoided in conventional extraction operations. Herein, we discuss a novel, easily scalable, platform separation methodology termed CLEANS (Continuous Liquid-liquid Extraction And iN-situ membrane Separation). CLEANS integrates emulsion-enhanced extraction with continuous, gravity-driven, membrane-based separation of emulsions into a single unit operation. Furthermore, our results demonstrate that the addition of a surfactant and emulsification significantly enhance extraction (by > 250% in certain cases), even for systems where the best extractants for miscible liquid mixtures are known. Utilizing the CLEANS methodology, we demonstrate continuous separation of a wide range of miscible liquid mixtures, including soluble organic molecules from oils, alcohols from esters, and even azeotropes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Data for 3-Hydroxypropionic Acid Recovery from Fermentation Broth through Novel Downstream Processing: Technoeconomic Analysis

This study develops and validates a simplified, fully solvent-free downstream processing (DSP) strategy for high-purity recovery of 3-hydroxypropionic acid (3-HP) from real fermentation broth containing 62.3 g/L of 3-HP. Optimized activated carbon treatment achieved 98% color removal, while Amberlite IRA-67 was operated at pH 4.5 and 30 °C to minimize product loss. This is the first integrated demonstration of a fully solvent-free DSP enabling recovery of bio-based 3-HP as both a solid sodium salt and a concentrated aqueous solution, supported by techno-economic analysis. At lab scale, the process achieved 77.3% recovery of sodium 3-HP with 83.2% (w/w) purity and produced a 30% (w/v) aqueous solution. Techno-economic analysis yielded minimum selling prices of $0.551/kg for the solution and $0.892/kg for the salt, both below target thresholds for cost-competitive bio-acrylic acid production. Overall, these results demonstrate an efficient, scalable, and economically viable industrial pathway for 3-HP recovery.

Bioproducts↗
DOE OSTI2025

Axisymmetric gyrokinetic simulation of ASDEX-Upgrade scrape-off layer using a conservative implicit BGK collision operator

Collisions play an important role in turbulence and transport of fusion plasmas. For kinetic simulations, as the collisionality increases in the domain of interest, the size of the time step to resolve the collisional physics can become overly restrictive in an explicit time integration scheme, leading to high computational cost. With the aim of overcoming such restriction, we have implemented an implicit Bhatnagar–Gross–Krook (BGK) collision operator for use in the discontinuous Galerkin full-f gyrokinetic solver within the Gkeyll framework, which, when combined with Gkeyll's traditional explicit time integrator for collisionless advection, can significantly increase the time step in gyrokinetic simulations of highly collisional regimes. To ensure conservation of density, momentum, and energy, we utilize an iterative scheme to correct the discretized approximation to the equilibrium Maxwellian distribution to which the BGK collision operator relaxes. We have further generalized the BGK infrastructure, both the implicit scheme and the correction routine, to handle cross-species collisions. This improved implicit and conservative BGK operator is benchmarked against the more accurate but more computationally expensive Lenard–Bernstein–Dougherty (LBD) operator, which has been utilized in prior studies with Gkeyll. The implicit BGK operator enables 2D axisymmetric simulations of the ASDEX-Upgrade scrape-off layer to run 56 times faster to completion than the simulations with the LBD operator, because the BGK operator is more robust and converges at a lower resolution than is required by the LBD operator. Additionally, in this more collisional limit, we demonstrate that the results of our simulations utilizing the implicit BGK operator agreed well with simulations utilizing the more computationally expensive LBD operator.

Gyrokinetic simulations↗
DOE OSTI2020

CORC $^{\circledR}$ cable terminations with integrated Hall arrays for quench detection

ReBCO superconducting cables have the potential to enable compact thermonuclear fusion reactors that operate at magnetic fields exceeding 20 T and allow operation at temperatures far exceeding the boiling point of liquid helium, potentially allowing for demountable magnets. Normal zone detection remains a challenge, and while novel quench detection techniques are an active area of research, few are non-invasive, provide real-time quench detection, and have been demonstrated with current ramp rates relevant for fusion reactors. To address this problem, a CORC$^{\circledR}$ cable termination is developed with integrated Hall sensors to monitor current redistribution as a proxy for quench detection. The methodology exploits the current sharing and layered topology in CORC$^{\circledR}$ cables, and allows quench detection using a localized sensor instead of co-wound voltage wires or optical fibers. Furthermore, experiments are presented where current redistribution is measured from induced quenches, and in a 0.2 meter CORC$^{\circledR}$ sample it is found that the Hall sensors detect normal zone transitions with a similar magnitude and temporal resolution as voltage measurements. To emulate the conditions of dynamic poloidal and central solenoidal fields, experiments are repeated with ramp rates up to 10 kA s -1 that demonstrate the potential to detect normal zone development over a range of experimental parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2026

Asi Nuclear Energy Sensors Data Portal Chatbot And Data Structuring Tool

The Idaho National Laboratory (INL) is advancing the development of an AI-powered chatbot and data structuring tool specifically designed to accelerate data mining processes for sensor-related information and seamlessly integrate the results into the ASI Sensors Data Portal (https://nes.energy.gov/). By doing so, the software aims to enhance the accessibility, usability, and organization of sensor data for nuclear energy applications. The software initial phase focuses on retrieving comprehensive datasets, prioritizing the past five years of publicly available information from the Office of Scientific and Technical Information (OSTI). These datasets will be meticulously processed to ensure compatibility, employing cleaning and preprocessing steps to eliminate irrelevant, incomplete, or corrupted information, thus establishing a robust foundation for subsequent AI use. The data will serve as the backbone for training an AI model and chatbot, which will act as an interactive tool enabling users to ask complex, context-specific questions and receive accurate, validated answers derived from constrained literature. In parallel, the project incorporates a data structuring process supported by AI to organize sensor information from multiple sources into a standardized format. This structured data will include detailed sensor specifications, such as measurement range, applications, accuracy, and operating conditions, generated and documented with AI. These specifications will be systematically integrated into the sensor portal. To maintain the highest levels of accuracy and relevance, all AI-generated outputs will be reviewed and validated by subject matter experts (SMEs), with additional fields or parameters added as needed. Future stages of the project aim to expand the dataset beyond OSTI to include other sources and potentially incorporate unclassified controlled information (UCI) with restricted access protocols to address security and confidentiality requirements.

Mapes, NormanJ. [Idaho National Laboratory (INL), ↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2026

Integrated Simulation of Weld Residual Stress Evolution and Crack Propagation Using XFEM

Nuclear power plant components operate in environments that promote multiple degradation mecha- nisms, several of which involve crack initiation and growth. An ongoing effort in the U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program is developing a general capability within the Multiphysics Object Oriented Simulation Environment (MOOSE) framework for simulating three-dimensional crack growth under a range of driving conditions, including fatigue, stress corrosion cracking (SCC), brittle fracture, and stress-relaxation cracking. This report demonstrates an end-to-end workflow that uses this capability to model weld-residual-stress-driven SCC in the J-groove weld of a pressurized-water reactor control rod drive mechanism penetration in the vessel head. The workflow consists of a thermomechanical welding simulation with temperature-dependent plasticity, followed by cooldown to ambient conditions, and a restart of the simulation using the MOOSE extended finite element method (XFEM) module to propagate a three-dimensional crack through the residual stress field. New welding capabilities were developed to properly initialize newly activated elements in the weld region, and robustness improvements were made to the mesh-based algorithm for defining cutting planes in the 3D XFEM algorithm, allowing it to handle complex crack fronts and stress fields. Together these advances allowed the simulated SCC crack to grow from an initial elliptical flaw in the weld, across the weld, through the tube wall, and almost to the triple point (where the weld, tube, and reactor pressure vessel head intersect) over roughly 36 years of simulated service. These results demonstrate a workflow that can be extended to fully three-dimensional welding simulations and more complex crack interaction problems.

42 - ENGINEERING↗
DOE OSTI2024

Modeling and Validation of a Residential Multi-Functional Variable Refrigerant Flow Heat Pump System with Heat Recovery

To bridge the existing gap in modeling the variable refrigerant flow heat pump systems with heat recovery (VRFHR), we developed a suite of dynamic VRFHR system models in Modelica. These models are specifically tailored for residential multi-functional VRFHR (MF-VRFHR) applications, including space conditioning and domestic hot water (DHW) heating, utilizing both the TIL library for HVAC equipment and the Buildings library for thermal load calculations. The development comprises essential component models, including the newly developed heat recovery unit (HRU), along with system models that integrate the heat pump system and building envelope. These system models accommodate various operational modes such as heating-only, cooling-only, and heating-recovery (including heating-dominant and cooling-dominant) modes. Furthermore, we propose an efficient optimization-based model calibration method that identifies critical model parameters while utilizing a small amount of data obtained from either real systems or manufacturer's specifications. We demonstrate the effectiveness of these models and the proposed calibration method for a MF-VRFHR system installed in Richland, WA. The developed models are calibrated and validated using data collected under different operational modes during both heating and cooling seasons. The results show that the models capture the system dynamics and achieve high accuracy, with the coefficient of the variation of the root-mean-square-error less than 15% for variables such as outdoor unit power consumption, compressor speed, space temperature and DHW temperature. The validated models serve as a reliable representation of the MF-VRFHR system, facilitating the development and validation of optimized controls needed to realize the full benefits of integrated heat pump systems. Future research will utilize these models to develop advanced controls and optimize system performance for improved energy efficiency and demand flexibility.

Modeling, Variable refrigerant flow (VRF) systems,↗
DOE OSTI2021

High-order algorithmic developments and optimizations for large-scale GPU-accelerated simulations (Milestone CEED-MS36)

The goal of this milestone was to improve the high-order software ecosystem for CEED-enabled ECP applications by making progress on efficient matrix-free kernels targeting forthcoming ECP architectures. These kernels included matrix-free preconditioning and the development of new set of CEED solver bake-off problems. As part of this milestone, we also released the next version of the CEED software stack, CEED-4.0, reported on results from several application collaborations, and documented the efforts of porting to AMD GPUs for Frontier and other modern architectures, such as Fugaku. The specific tasks addressed in this milestone were: (1) Port and run CEED benchmarks/miniapps on Frontier EA systems; (2) Demonstrate performant libCEED integration in MFEM, Nek and applications; (3) Matrix-free preconditioning of high-order operators; (4) Benchmark problems for fast high-order solvers on GPU platforms; and (5) Public release of CEED-4.0. The artifacts delivered include the next version of the CEED software stack, CEED-4.0, the next libCEED release, libCEED-0.8, and a number of developments integrated within applications to improve their GPU and CPU performance and capabilities. See the CEED website, https://ceed.exascaleproject.org and the CEED GitHub organization, https://github.com/ceed for more details.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Data Science Model for Detecting Intelligent Malware

This study focused on developing a robust artificial intelligence (AI) model capable of detecting and characterizing advanced malware in Internet of Things (IoT) devices using network data. By analyzing network traffic with various machine learning (ML) models, our AI model can identify and characterize malicious activities to significantly improve malware detection accuracy and reliability as compared to traditional methods. The developed AI/ML model was trained using network data from IoT devices, leveraging classifiers such as Random Forest, Gradient Boosting, AdaBoost, and others to optimize detection performance. This project demonstrates a scalable framework for real-time malware detection and characterization in IoT networks, capable of identifying infected devices and facilitating the necessary steps to remove or isolate them, thereby preventing further infections. Although digital twin (DT) integration is not yet implemented in the current model, it represents a promising future enhancement. By creating a virtual replica of physical IoT devices, DT technology would allow for real-time monitoring and analysis without directly accessing operational technology, thus reducing the risk of compromising or reducing the performance of actual devices. This integration would further enhance the security of IoT ecosystems, combining AI technology to better flag and detect indications of malware-infected devices within a nuclear system environment.

42 ENGINEERING↗
DOE OSTI2021

Integrating Transactive Energy into Reliability Evaluation for a Self-healing Distribution System with Microgrid

Non-utility owned distributed energy resources (DERs) are mostly untapped currently, but they can provide many grid services such as voltage regulation and service restoration, if properly controlled, and can improve the distribution systems reliability when coordinated with utility-owned assets such as self-healing control and microgrids. This paper integrates transactive energy control into the distribution system reliability evaluation to quantitatively assess the impact of non-utility owned DERs on reliability improvement. Here, a transactive reactive power control strategy is designed to incentivize the DERs to provide reactive power support for improving voltage profiles thus enabling additional customer load restoration during an outage. Also, an operational sequence to coordinate the non-utility owned DERs with the utility owned self-healing control and utility owned microgrids is designed and integrated into the service restoration process with the operational constraints guaranteed by checking the three-phase unbalanced power flow for post-fault network reconfiguration. The reliability indices are then calculated through a Monte Carlo simulation. The transactive reactive power control strategy is tested on a four-feeder distribution system operated by Duke Energy in the U.S. Results demonstrate that the non-utility owned DERs with the transactive control improve the reliability of both the system and critical loads by more than 30%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Electric Power Grid and Natural Gas Network Operations and Coordination

In this report, we present findings from three studies related to the coordination of natural gas and electricity system operations. We first propose and demonstrate a modeling platform for examining the interdependence of natural gas and electricity networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We use this platform to analyze the value of day-ahead coordination of power and natural gas network operations and to show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and variable renewable energy sources. In the second study, we utilize our modeling platform to consider the U.S. Federal Energy Regulatory Commission (FERC) Order 809, issued in 2015 to improve day-ahead and intraday coordination of power and gas systems. Finally, in the third study we expand our modeling platform to focus on market-based coordination of electricity and natural gas system operations for a real system, namely a subset of the power and gas networks in the Front Range region of Colorado. We use real system data to evaluate the benefits of coordination operations under different conditions, including different levels of renewable penetration and the use of time-variant, shaped flow nominations. Our results indicate that coordination at various timescales can contribute to a reduction in curtailed gas in high-stress periods (such as those with large ramps in gas offtakes) and a reduction in energy consumption of gas compressor stations. We find that intraday coordination can reduce total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. We observe these benefits for the test system as well as in the Colorado case study, where we find that coordination and shaped flows may provide additional value for systems with high penetration of variable renewable energy. Together, these three studies demonstrate a pathway for integrated gas and electricity grid modeling and for studying the benefits of coordinated operations of these increasingly interdependent energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Integrated Transportation-Energy Systems Modeling

Transportation is currently the least-diversified energy demand sector, with over 90% of global transportation energy use coming from petroleum product. After over a century of petroleum dominance, however, many leading experts anticipate major electrification trends that could disrupt the transportation energy demand landscape. These changes in electricity demand complement profound changes happening within electric power supply systems, including integration of variable renewables, distributed generation and storage, and greater participation in power system planning and operations from traditionally passive consumers. This broader context underscores the importance of understanding how transportation electrification will impact electricity demand, including changes in the load shapes that characterize the system and the opportunity to leverage flexible EV charging to more cost-effectively balance demand and supply. This talk provides an overview of recent findings on infrastructure requirements to support EV adoption, integration challenges and the impact of EV on power systems, and opportunities to leverage flexible (or smart) EV charging to support power system planning and operations.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Performance Characteristics of a Rotating Detonation Combustor Exiting Into a Pressurized Plenum to Simulate Gas Turbine Inlet

Abstract The present study aims to experimentally characterize the performance of a rotating detonation combustion (RDC) system integrated with a pressurized downstream plenum to simulate the high-pressure inlet conditions of power-generating gas turbines. A thorough understanding of the operational behavior including wave mode behavior, static pressure profile along the combustor length, and dynamic features of pressure fluctuations is crucial for successful integration of RDC with the turbine. In this study, two RDC configurations are investigated, RDC with a constant area annulus and RDC with a converging nozzle. In both cases, the RDC flow exited into a plenum chamber kept at pressures varying from 155 kPa to 330 kPa. RDC was operated on methane and oxygen-enriched air to represent reactants used in land-based power generation. Experiments were conducted for the two RDCs configurations operated at three reactant mass flow rates (0.23, 0.32, and 0.46 kg/s). The RDC performance is characterized by time-averaged static pressure measurements, and wave velocity determined by ionization probes. In addition, dynamic pressure measurements were recorded both inside and near the exit of RDC channel to investigate wave interactions between RDC and downstream plenum. Results show that the RDC with the converging nozzle achieved superior performance while minimizing detrimental interactions with the reflected shock and/or acoustic waves from the downstream plenum.

Engineering↗
DOE OSTI2025

Cost-optimized energy storage operation for a grid-connected solar PV system at community and individual scales

This study provides a comparative analysis of grid-connected PV-integrated battery storage at individual and community scales. The paper addresses the challenge of managing energy demand-generation mismatch by using a battery energy storage optimization algorithm, which minimizes operational costs while accounting for battery degradation. Also, this work introduces a broader evaluation basis that includes seasonal variability, grid exchange smoothness, and scalability across different battery capacities. Results show that community-scale storage more effectively dampens grid exchange power fluctuations and reduces system costs, particularly with moderate price differences between electricity buying and selling prices and low battery capacities. The paper also analyzes the impacts of static control versus cost-optimized battery system management. Here, it is shown that the gap in system costs between the cost-optimized and static control scenarios widens as the price difference increases.

25 ENERGY STORAGE↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗