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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Track reconstruction as a service for collider physics

Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms have been adapted to run on massively parallel coprocessors, such as graphics processing units (GPUs), to reduce processing time. Nevertheless, challenges remain in fully harnessing the computational capacity of coprocessors in a scalable and non-disruptive manner. This paper proposes an inference-as-a-service approach for particle tracking in high energy physics experiments. To evaluate the efficacy of this approach, two distinct tracking algorithms are tested: Patatrack, a rule-based algorithm, and Exa.TrkX, a machine learning-based algorithm. The as-a-service implementations show enhanced GPU utilization and can process requests from multiple CPU cores concurrently without increasing per-request latency. The impact of data transfer is minimal and insignificant compared to running on local coprocessors. This approach greatly improves the computational efficiency of charged particle tracking, providing a solution to the computing challenges anticipated in the High-Luminosity LHC era.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

ML–Enabled FPGA Framework for Fast Quantum State Discrimination in Mid-Circuit Measurement Regimes

Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times. To overcome this bottleneck, we present an in-situ machine learning (ML) inference engine implemented on an FPGA for real-time quantum state discrimination. Our design performs inference directly on digitized readout signals with 40 ns latency, supports both qubit and qutrit readout, and enables conditional operations without host-side intervention. This capability is critical for MCM and for feedback-driven protocols such as quantum error correction. We validate the system on superconducting transmon hardware, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. We further demonstrate conditional qutrit logic driven by FPGA-resident classification, highlighting the potential of low-latency ML-on-FPGA control for NISQ applications and scalable fault-tolerant quantum computing.

Vora, Neel [Lawrence Berkeley National Laboratory ↗
DOE OSTI2025

ForceFinder

SAND2025-11750O ForceFinder extends the Structural Dynamics Python Libraries (SDynPy) with comprehensive tools for inverse source estimation (ISE) tasks via frequency response function (FRF) matrix inversion. The software is designed for transfer path analysis and multiple-input/multiple-output (MIMO) vibration control problems. It allows users to estimate sources through various algorithms, from the basic Moore-Penrose pseudo-inverse to statistical learning methods such as Tikhonov regularization via an L-curve and elastic net regularization via an information criterion. ForceFinder uses an object-oriented framework, where all components of the ISE problem—such as FRFs, responses, and transformations—are stored in a "SourcePathReceiver" object. This software can be applied to any noise and vibration problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Carter, Steven [Sandia National Lab. (SNL-CA), Liv↗
NASA NTRS2018

Commissioning of NASA's 3rd Generation Tracking and Data Relay Satellites (TDRS KLM)

In the summer of 2017, the third and final spacecraft of the 3rd generation of the Tracking and Data Relay Satellites (TDRS) launched aboard an Atlas V rocket from Complex 41 on the Eastern Test Range. Finishing final testing and integration in the first quarter of 2018, the TDRS-M communication and navigation satellite completes a constellation that began service in the early 1980s. The 3rd generation of spacecraft, TDRS-K, L, and M, not only provided beneficial systems engineering lessons in handling anomalous Radio Frequency and Doppler interference as well as integrating new spacecraft into an aging ground support infrastructure, but also supplies NASA with a valuable test bed for new operational concepts and technologies useful in defining the future architecture of the NASA Space Network. This paper presents an overview of the TDRS-K, L, and M missions, including transfer orbit, Level 5 bus and payload testing, and finally NASA-led Level 6 testing, which includes active TDRS System (TDRSS) users. Highlights include relevant testing results, commissioning challenges, and lessons learned. The final discussion includes a brief overview of future NASA communication and navigation technologies and network architectures.

Data Relay↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS1995

Capsule Aerothermodynamics. CFD Prediction Methodology; Database for CFD Validation; Capsule Flight Programs: AFE and Apollo Lessons Learned

Features of chemically reacting separated hypersonic flows are identified and issues concerning their analysis and simulation are discussed. Emphasis is placed on flows of high temperature dissociating and ionizing air and current methods for studying and characterizing these flows, including separation, are reviewed. The aeroassist orbital transfer vehicle and its flight trajectory are used for illustration. Thermochemical nonequilibrium phenomena are emphasized and extension of continuum analysis to the high altitude slip-flow regime is considered.

Deiwert, George S.↗
NASA NTRS2005

Next Generation Launch Technology Program Lessons Learned

In November 2002, NASA revised its Integrated Space Transportation Plan (ISTP) to evolve the Space Launch Initiative (SLI) to serve as a theme for two emerging programs. The first of these, the Orbital Space Plane (OSP), was intended to provide crew-escape and crew-transfer functions for the ISS. The second, the NGLT Program, developed technologies needed for safe, routine space access for scientific exploration, commerce, and national defense. The NGLT Program was comprised of 12 projects, ranging from fundamental high-temperature materials research to full-scale engine system developments (turbine and rocket) to scramjet flight test. The Program included technology advancement activities with a broad range of objectives, ultimate applications/timeframes, and technology maturity levels. An over-arching Systems Engineering and Analysis (SE&A) approach was employed to focus technology advancements according to a common set of requirements. Investments were categorized into three segments of technology maturation: propulsion technologies, launch systems technologies, and SE&A.

Cook, Stephen↗
DOE OSTI2025

Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island

Background: Urban heat island (UHI), where built areas are warmer compared to non-urban regions, increases human related diseases and mortality. A key challenge in UHI analysis is the designation of sites as urban or suburban/rural; however, the growing complexity of green spaces in urban areas and the predominance of the transportation sector in nonurban areas creates a dilemma for distinct delineation. Objectives: This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. Methods: Atmospheric gases and aerosols were used as direct tracers of urbanization for UHI analysis. Inorganic gases and particulate matter were monitored in two sites in a southeastern US city with varying degrees of urbanization. VOCs were analyzed using a proton transfer reaction time-of-flight mass spectrometer. Results: The more-urbanized site exhibited warmer night conditions and elevated total oxidant levels, leading to the formation of nanometer-sized particles. Machine learning analysis revealed similar atmospheric pollutant profiles for both sites, suggesting comparable sources and variability. Biogenic VOCs were enhanced at the less-urbanized site; however, levels of anthropogenic aromatic VOCs were comparable for both sites. A comprehensive mass spectra analysis revealed distinct molecular backbones per site that further affirmed the applicability of VOCs as indicators of urbanization. Conclusion: This study concludes that VOCs provide more direct and accurate information than typical inorganic gases and PM parameters for characterizing the degree of urbanization. Further exploration of VOCs can enhance our understanding of UHI dynamics and its interaction with vegetation in urban green spaces.

Air quality sensor↗
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS1995

The AAMP5/AAMP-FV project

This presentation describes a project, formal verification of the microcode in the AAMP5 microprocessor, conducted to explore how formal techniques for specification and verification could be introduced into an industrial process. Sponsored by the Systems Validation Branch of NASA Langley and by Collins Commercial Avionics, a division of Rockwell International, it was conducted by Collins and the SRI International Computer Science Laboratory. The project consisted of specifying in the PVS language developed by SRI a portion of a Rockwell proprietary microprocessor, the AAMP5, at both the instruction set and register-transfer levels and using the PVS theorem prover to prove the microcode correct for a representative subset of instructions. While this presentation includes a brief technical overview, its emphasis is on the lessons learned in using PVS for an example of this size and the implications for using formal methods in an industrial setting. The central result of this project was to demonstrate the feasibility of formally specifying a commercial microprocessor and the use of mechanical proofs of correctness to verify microcode. This is particularly significant since the AAMP5 was not designed for formal verification, but to provide a more than three fold performance improvement, by pipelining instruction execution, while remaining object code compatible with the earlier AAMP2. As a consequence, the AAMP5 is one of the most complex microprocessors to which formal methods have been applied. Another key result was the discovery of both actual and seeded errors. Two actual microcode errors were discovered and corrected during development of the formal specification, illustrating the value of simply creating a precise specification. Two seeded errors were systematically uncovered while doing correctness proofs. One of these was an actual error that had been discovered after first fabrication but left in the microcode provided to SRI. The other error was designed to be unlikely to be detected by walkthroughs, testing, or simulation. Several other results emerged during the project, including the ease with which practicing engineers became comfortable with PVS, the need for libraries of general purpose theories, the usefulness of formal specification in revealing errors, the natural fit between formal specification and inspections, the difficulty of selecting the best style of specification for a new problem domain, the high level of assurance provided by proofs of correctness, and the need to engineer proof strategies for reuse.

Miller, Steven P.↗
NASA NTRS1998

VentureStar(trademark) Reaping the Benefits of the X-33 Program

Major X-33 flight hardware has been delivered, and assembly of the vehicle is well underway in anticipation of its flight test program commencing in the summer of 1999. Attention has now turned to the operational VentureStar(trademark), the first single-stage-to-orbit (SSTO) reusable launch vehicle. Activities are grouped under two broad categories: (1) vehicle development and (2) market/business planning, each of which is discussed. The mission concept is presented for direct payload delivery to the International Space Station and to low Earth orbit, as well as payload delivery with an upper stage to Geosynchronous Transfer Orbit (GTO) and other high energy orbits. System requirements include flight segment and ground segment. Vehicle system sizing and design status is provided including the application of X-33 traceability and lessons learned. Technology applications to the VentureStar(trademark) are described including the structure, propellant tanks, thermal protection system, aerodynamics, subsystems, payload bay and propulsion. Developing a market driven low cost launch services system for the 21 st Century requires traditional and non-traditional ways of being able to forecast the evolution of the potential market. The challenge is balancing both the technical and financial assumptions of the market. This involves the need to provide a capability to meet market segments that in some cases are very speculative, while at the same time providing the financial community with a credible revenue stream.

Sumrall, J.↗
DOE OSTI2023

One System, Many Models: Designing a Surrogate Model for Sulfur Thermal Energy Storage: Preprint

Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.

computational fluid dynamics↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
NASA NTRS2014

Fundamentals of Cryogenics

Analysis of the extreme conditions that are encountered in cryogenic systems requires the most effort out of analysts and engineers. Due to the costs and complexity associated with the extremely cold temperatures involved, testing is sometimes minimized and extra analysis is often relied upon. This short course is designed as an introduction to cryogenic engineering and analysis, and it is intended to introduce the basic concepts related to cryogenic analysis and testing as well as help the analyst understand the impacts of various requests on a test facility. Discussion will revolve around operational functions often found in cryogenic systems, hardware for both tests and facilities, and what design or modelling tools are available for performing the analysis. Emphasis will be placed on what scenarios to use what hardware or the analysis tools to get the desired results. The class will provide a review of first principles, engineering practices, and those relations directly applicable to this subject including such topics as cryogenic fluids, thermodynamics and heat transfer, material properties at low temperature, insulation, cryogenic equipment, instrumentation, refrigeration, testing of cryogenic systems, cryogenics safety and typical thermal and fluid analysis used by the engineer. The class will provide references for further learning on various topics in cryogenics for those who want to dive deeper into the subject or have encountered specific problems.

Testing↗
DOE OSTI2023

Data-Centric AI and the Open Energy Data Initiative (OEDI)

This presentation emphasizes the critical importance of data-centric AI. The limitations of model-centric AI when dealing with poor or insufficient data are highlighted, and it is illustrated how training models on inaccurate or noisy data leads to suboptimal results. This talk advocates for a hybrid approach that combines a focus on data quality and model parameters to achieve optimal results. The Open Energy Data Initiative (OEDI) is introduced as a valuable resource for obtaining high-quality energy-related datasets, hosting nearly 2,000 publicly accessible datasets, including 99 solar-related datasets, totaling over 2.7 petabytes of data. OEDI's data lakes enable users to query and work with data without extensive transfers. In conclusion, the significance of data-centric AI and adherence to data curation best practices is emphasized, positioning OEDI as a prime source of high-quality data for AI and machine learning in the renewable energy sector.

AI↗
DOE OSTI2020

ServiceX A Distributed, Caching, Columnar Data Delivery Service

We will describe a component of the Intelligent Data Delivery Service being developed in collaboration with IRIS-HEP and the LHC experiments. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analysis. This work is motivated by the data engineering challenges posed by HL-LHC data volumes and the increasing popularity of python and Spark-based analysis workflows.ServiceX gives analyzers the ability to query events by dataset metadata. It uses containerized transformations to extract just the data required for the analysis. This operation is colocated with the data to avoid transferring unnecessary branches over the WAN. Simple filtering operations are supported to further reduce the amount of data transferred.Transformed events are cached in a columnar datastore to accelerate delivery of subsequent similar requests. ServiceX will learn commonly related columns and automatically include them in the transformation to increase the potential for cache hits by other users.Selected events are streamed to the analysis system using an efficient wire protocol that can be readily consumed by a variety of computational frameworks. This reduces time-to-insight for physics analysis by delegating to ServiceX the complexity of event selection, slimming, reformatting, and streaming.

Galewsky, B.↗