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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Conversational Grid Storage: Bridging Rucio and LLMs with Model Context Protocol

Experiments at Fermilab use Rucio to handle datasets that can be up to exabyte scale. However, navigating through Rucio’s syntax-heavy Command Line Interface (CLI) is a major workflow obstruction for researchers who just want to check quotas, track data identifiers (DIDs), or locate data sets. This project introduces a natural language interface. By building a containerized Model Context Protocol (MCP) server, an AI agent is created that translates plain English queries into data operations.

Akella, Kashyap [Fermilab; Illinois U., Urbana (ma↗
DOE OSTI2021

Massively parallel hierarchical control system and method

An electronic control system is disclosed for controlling individually controllable elements of an external component. In one embodiment the system may include a state translator subsystem for receiving a state command from an external subsystem. The state translator subsystem may have at least one module for processing the state command and generating operational commands for controlling the elements to achieve a desired state or condition. A programmable calibration command translation layer (PCCTL) subsystem may be included which receives and uses the operational commands to generate granular level commands for controlling the elements. A feedback control layer subsystem may be included which applies the granular level commands to the elements, and further modifies the granular level commands as needed to control the elements in closed loop fashion.

Panas, Robert Matthew↗
DOE OSTI2024

System Modeling and Robust Stability Region Analysis for Multi-inverters Based on VSG

As the number of inverter-interfaced distributed generators (IIDGs) increases, the decrease in the inertia of the microgrid affects the stability of microgrid voltage and current. The virtual synchronous generator (VSG) control method simulates the output characteristics of the synchronous machine to improve the inertia and damping of the microgrid. However, low-frequency oscillations can occur in systems with multi-VSGs connected in parallel, primarily when operating in island mode. Therefore, this article develops the small-signal state-space model for multi-inverters based on VSG and analyses the robust stability region (RSR) of the control parameters. Firstly, an extensible small-signal state-space model is established and simplified in the dq axis, which is a necessary preprocessing for the subsequent stability analysis. Secondly, the guardian map method based on bialternate product operation is constructed, and the RSR is established through translation mapping and rotation mapping. Therefore, the solution of RSR is transformed into the problem of identifying the Hurwitz matrix. Finally, the simulations and experiments verify that the calculated region can ensure stable operation during startup and meet the requirement to suppress low-frequency oscillations. In conclusion, the method of solving RSR proposed in this paper provides a basis for selecting controller parameters in practical engineering.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Persistent Acoustic Sensing for Monitoring A Reactor Facility - Oral Presentation

Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios (MINOS) project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at a large research reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operation. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper will present recent research advances in this area including studies to explore the transferability of learned parameters from one smartphone to another, the detectability of events from multiple sensors at different locations simultaneously, and the feasibility of porting analytical tools directly to the smartphones to allow edge computing with optimized configurations.

42 ENGINEERING↗
DOE OSTI2021

Persistent Acoustic Sensing for Monitoring A Reactor Facility

Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at the High Flux Isotope Reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operations. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper presents recent research in this area including the use of low-frequency acoustics to tip-off transfer operations and cue a more comprehensive analysis by other sensor networks.

42 ENGINEERING↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2022

Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration

According to the U.S. Department of Energy Combined Heat and Power Installation Database, approximately 80 GW of electrical power is produced in the United States using combined heat and power (CHP) systems. Studies have also shown that a potential market for CHP applications is larger than the existing market. Since fuel consumption is the most significant part of the operating cost for a CHP plant, higher efficiency translates to increased power output for the same quantity of fuel.

CHHP, LHV, CHP↗
DOE OSTI2023

Ultra-Efficient CHP with High Power/Heat Ratio Using a Novel Argon Power Cycle

According to the U.S. Department of Energy Combined Heat and Power Installation Database, approximately 80 GW of electrical power is produced in the United States using combined heat and power (CHP) systems. Studies have also shown that a potential market for CHP applications is larger than the existing market. Since fuel consumption is the most significant part of the operating cost for a CHP plant, higher efficiency translates to increased power output for the same quantity of fuel.

APC, CHP, RICE↗
DOE OSTI2026

Guideline for Characterizing and Evaluating a Candidate Project Site for Solar Thermal Applications

This document presents a structured procedure for characterizing and evaluating candidate project sites for concentrating solar power (CSP) and solar heat for industrial processes (SHIP) applications. The objective is to provide project developers, researchers, and other stakeholders with a consistent, technology-agnostic framework for early-stage site assessment, enabling informed decision-making prior to significant investment in project development. Site selection is a critical factor in project success or failure for both CSP and SHIP projects. Key factors such as solar resource availability, land characteristics, environmental and regulatory constraints, infrastructure availability, and community context are determined by the choice of project site and can materially impact project performance, cost, schedule, and overall viability. This procedure is designed to systematically evaluate these factors, identify potential fatal flaws, and prioritize the most favorable candidate sites for further development. The process begins with rapid screening-level evaluation, using publicly available data to assess solar resource, land availability and suitability, zoning and land-use compatibility, and exclusion zones such as protected lands or sensitive habitats. Sites that meet the minimum screening criteria advance to a more detailed characterization. Subsequent sections of this report provide guidance for a next-level assessment of the most important technical and environmental parameters, including: 1) Solar resource quality, variability, and uncertainty using multiyear datasets and, where appropriate, on-site measurement campaigns; 2) Meteorological conditions such as wind, temperature, extreme weather events, and soiling impacts; 3) Land characteristics including slope, shading, and geotechnical conditions; and 4) Environmental and regulatory considerations, including permitting processes, endangered species, cultural resources, and visual impacts. The procedure also addresses infrastructure and integration considerations, including: 1) Grid interconnection requirements for CSP power generation projects; 2) Electrical and operational integration for SHIP facilities; 3) Water availability, quality, and permitting constraints, which are particularly critical for CSP in arid regions; and 4) Site access, construction logistics, and availability of workforce and supporting services. Recognizing the importance of social and economic context, the procedure includes evaluation of community engagement factors, such as stakeholder sentiment, proximity to sensitive visual receptors, workforce development opportunities, and local economic incentives. The outputs of these assessments are synthesized in a cost and risk evaluation, translating site characteristics into expected impacts on capital cost, operating cost, schedule, and technical risk. This is complemented by screening-level performance modeling, including 8760 simulations and long-term projections, to quantify expected energy or thermal output, assess variability thereof, and support comparison between candidate sites. Finally, the procedure provides high-level guidance on a structured go/no-go decision framework, categorizing sites based on identified risks and constraints, and outlining a clear path forward to feasibility studies and front-end engineering design for viable projects. By standardizing the site characterization process across both CSP and SHIP applications, this guideline aims to: 1) Improve consistency and transparency in early-stage project evaluation; 2) Reduce development risk and avoid investment in nonviable project sites; 3) Support collaboration between developers, researchers, and public agencies; and 4) Accelerate successful deployment of concentrating solar technologies for both power generation and industrial process heat.

14 SOLAR ENERGY↗
DOE OSTI2022

Methods for Translating ReEDS Solutions to Production Cost Modeling Tools

Capacity expansion modeling tools are increasingly being utilized to investigate a wide range of potential future scenarios, particularly with high penetrations of variable and energy-constrained resources that may be operated differently than current dispatch paradigms. While capacity expansion models are particularly adept at making investment decisions for future years, they must make compromises in operational aspects to maintain computational tractability. However, it is of high interest to determine if such future systems would be able to maintain reliability during a variety of grid conditions, and to identify any potential operational challenges for these systems at finer temporal resolution than is typically captured in CEMs. As such, there is value in having an automated tool that can convert many CEM investment pathways into inputs for a more detailed production cost model, in this case the PLEXOS commercial software package. This paper describes the methodology used to make that translation for the NREL-developed ReEDS model, making use of two internally developed tools - PIDG and beetle - along with a set of processing scripts. We describe the current assumptions, data sets, and important operational characteristics of these translations along with an example of the extended analyses that may be done through this connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗
DOE OSTI2021

Asymptotic reversibility of thermal operations for interacting quantum spin systems via generalized quantum Stein’s lemma

Abstract For quantum spin systems in any spatial dimension with a local, translation-invariant Hamiltonian, we prove that asymptotic state convertibility from a quantum state to another one by a thermodynamically feasible class of quantum dynamics, called thermal operations, is completely characterized by the Kullback–Leibler (KL) divergence rate, if the state is translation-invariant and spatially ergodic. Our proof consists of two parts and is phrased in terms of a branch of the quantum information theory called the resource theory. First, we prove that any states, for which the min and max Rényi divergences collapse approximately to a single value, can be approximately reversibly converted into one another by thermal operations with the aid of a small source of quantum coherence. Second, we prove that these divergences collapse asymptotically to the KL divergence rate for any translation-invariant ergodic state. We show this via a generalization of the quantum Stein’s lemma for quantum hypothesis testing beyond independent and identically distributed situations. Our result implies that the KL divergence rate serves as a thermodynamic potential that provides a complete characterization of thermodynamic convertibility of ergodic states of quantum many-body systems in the thermodynamic limit, including out-of-equilibrium and fully quantum situations.

Physics↗
DOE OSTI2026

Cybersecurity Workforce Training for SMR Integration into Distribution Grids: A Competency Framework and Containerized Hands-On Lab for the SMR/DER/Microgrid Boundary

Small modular reactors (SMRs) and microreactors are entering the U.S. distribution grid as synchronous generation on feeders designed for loads and inverter-based distributed energy resources (DERs). No existing cybersecurity training program addresses this intersection of nuclear operations, DER management, and operational technology security. As subcontractor to Iowa State University on the CyDERMS Center, Argonne analyzed the relevant standards and training landscape, translated the resulting gaps into a twelve-objective competency framework across distribution-operator and graduate-analyst role tracks, and built a containerized training lab using a ∼400-bus composite grid model behind a realistically simulated Modbus TCP SCADA stack. The analysis isolates the balance-of-plant / energy-management-system (BOP/EMS) boundary as the critical jurisdictional seam where, as of March 2026, neither NRC nor NERC CIP cleanly claims cybersecurity responsibility for distribution-connected SMRs. The framework maps each objective across NIST CSF 2.0, ISA/IEC 62443, NIST NICE Task–Knowledge–Skill statements, and NRC RG 5.71 awareness-and-training controls. The training lab implements operator-recognition assessment scenarios spanning grid-side disturbances and telemetry-layer anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Niobium-tin as a transformative technology for low-beta linacs

Niobium-tin has been identified as the most promising next-generation superconducting material for accelerator cavities. This is due to the higher critical temperature (Tc = 18 K) of Nb3Sn compared to niobium (TC = 9.2 K), which leads to greatly reduced RF losses in the cavity during 4.5 K operation. This allows two important changes during cavity and cryomodule design. First, the higher Tc leads to negligible BCS losses when operated at 4.5 K, which allows for a higher frequency to be used, translating to significantly smaller cavities and cryomodules. Second, the reduced dissipated power lowers the required cryogenic cooling capacity, meaning that cavities can feasibly be operated on 5-10 W cryocoolers instead of a centralized helium refrigeration plant. These plants and distribution systems are costly and complex, requiring skilled technicians for operation and maintenance. These fundamental changes present an opportunity for a paradigm shift in how low-beta linacs are designed and operated. Fabrication challenges and first coated cavity test results are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Nb3Sn Superconducting Cavity Developments for Heavy-ion Beams

Nb3Sn has been identified as the most promising next-generation superconducting material for accelerator cavities. This is due to the higher critical temperature (Tc = 18 K) of Nb3Sn compared to niobium (Tc = 9.2 K), which leads to greatly reduced RF losses in the cavity during 4.5 K operation. This allows two important changes during cavity and cryomodule design. First, the higher Tc leads to negligible BCS losses when operated at 4.5 K, which allows for a higher frequency to be used, translating to significantly smaller cavities and cryomodules. Second, the reduced dissipated power lowers the required cryogenic cooling capacity, meaning that cavities can feasibly be operated on 5-10 W cryocoolers instead of a centralized helium refrigeration plant. These plants and distribution systems are costly and complex, requiring skilled technicians for operation and maintenance. These fundamental changes present an opportunity for a paradigm shift in how low-beta linacs are designed and operated. Fabrication and testing results of first prototypes are discussed.

Petersen, T. [Argonne]↗
DOE OSTI2021

Measurements of dynamic surface changes by digital holography for in situ plasma erosion applications

There are currently few viable diagnostic techniques for in situ measurement of plasma facing component erosion. Digital holography is intended to fill this gap. Progress on the development of single and dual CO 2 laser digital holography diagnostics for in situ plasma facing component erosion is discussed. The dual laser mode’s synthetic wavelength allows the measurable range to be expanded by a factor of ~400 compared to single laser digital holography. This allows the diagnostic to measure surface height changes of up to 4.5 μm in single laser mode and up to 2 mm in dual laser mode. Results include ex situ measurements of plasma eroded targets and also dynamic measurements of nm and μm scale motion of a target mounted on a precision translation stage. Dynamic measurements have successfully been made with the system operating in both single and dual laser modes, from ~50 nm to ~4 μm in single laser mode and up to ~400 μm in dual laser mode (limited only by the stage speed and camera acquisition duration). These results demonstrate the feasibility of using digital holography to characterize plasma facing component erosion dynamically, i.e., during plasma exposure. Results of proof-of-principle in situ digital holographic measurements of targets exposed to an electrothermal arc plasma source are presented.

Smith, Cary↗