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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies

Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

42 ENGINEERING↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2014

Distributed Prognostics based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

centrifugal pump↗
NASA NTRS1998

Modeling, Simulation and Analysis of Public Key Infrastructure

Security is an essential part of network communication. The advances in cryptography have provided solutions to many of the network security requirements. Public Key Infrastructure (PKI) is the foundation of the cryptography applications. The main objective of this research is to design a model to simulate a reliable, scalable, manageable, and high-performance public key infrastructure. We build a model to simulate the NASA public key infrastructure by using SimProcess and MatLab Software. The simulation is from top level all the way down to the computation needed for encryption, decryption, digital signature, and secure web server. The application of secure web server could be utilized in wireless communications. The results of the simulation are analyzed and confirmed by using queueing theory.

Liu, Yuan-Kwei↗
NASA NTRSDate not supplied

Validation of Loci-Stream for Autogenous Pressurization of Cryogenic Propellant Tank

Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.

cryogenic fluid management↗
NASA NTRSDate not supplied

Validation of Loci-Stream for Autogenous Pressurization of Cryogenic Propellant Tank

Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.

CFM↗
NASA NTRSDate not supplied

Validation of Two Phase Flow Modeling Techniques in Loci-Stream using Axial Jet Pressure Control and Autogenous Pressurization Experiment

Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.

cryogenic fluid management↗
NASA NTRS2002

A quantitative risk model for early lifecycle decision making

Decisions made in the earliest phases of system development have the most leverage to influence the success of the entire development effort, and yet must be made when information is incomplete and uncertain. We have developed a scalable cost-benefit model to support this critical phase of early-lifecycle decision-making.

requirements optimization summarization tradeoffs↗
DOE OSTI2026

Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems

Here, we present a perspective on recent progress in machine-learning (ML) force-field approaches for large-scale Landau–Lifshitz–Gilbert (LLG) simulations of metallic spin systems. Building on a generalization of the Behler–Parrinello (BP) architecture originally developed for quantum molecular dynamics, we develop scalable and transferable ML models that faithfully capture the complex, environment-dependent electron-mediated exchange fields characteristic of itinerant magnets. A central ingredient of this framework is the implementation of symmetry-aware magnetic descriptors based on group-theoretical bispectrum formalisms. Leveraging these ML force fields, LLG simulations faithfully reproduce hallmark non-collinear magnetic orders—such as the 120° and tetrahedral states—on the triangular lattice, and successfully capture the complex spin textures emerging in the mixed-phase states of a square-lattice double-exchange model under thermal quench. We further discuss a generalized potential theory that extends the BP formalism to incorporate both conservative and nonconservative electronic torques, thereby enabling ML models to learn nonequilibrium exchange fields from computationally demanding microscopic approaches such as nonequilibrium Green’s-function techniques. This extension yields quantitatively accurate predictions of voltage-driven domain-wall motion and establishes a foundation for quantum-accurate, multiscale modeling of nonequilibrium spin dynamics and spintronic functionalities.

Descriptors↗
DOE OSTI2025

Multiphysics simulation of recent experiments on alkali‐silica reaction expansion in reinforced concrete members

Alkali‐silica reaction (ASR) is an important degradation process that causes volumetric expansion and damage in concrete, and is affected significantly by the local temperature, moisture and stress conditions that often vary across the regions of a structure. Numerical simulation is essential to predict the progression and effects of ASR on the performance of structures. Because of the interactions between thermal and moisture transport and mechanical deformation, it is important for numerical models to represent all these physical phenomena and the coupling between them. Simulations of ASR in reinforced concrete (RC) structures are further complicated by the need to capture interactions between concrete and embedded reinforcing bars. Here, this paper describes the implementation of a scalable, coupled‐physics ASR model for simulating RC structures and assesses the ability of that model to predict ASR‐induced expansion in recent laboratory tests on RC block and beam specimens. These laboratory tests and the simulation approach were selected because of their applicability to RC structural‐scale simulations. This validation study helps builds confidence the ability of this approach to model ASR expansion in large, complex RC structures, which is a current high‐priority need.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model

Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Space radiation absorbed dose distribution in a human phantom

The radiation risk to astronauts has always been based on measurements using passive thermoluminescent dosimeters (TLDs). The skin dose is converted to dose equivalent using an average radiation quality factor based on model calculations. The radiological risk estimates, however, are based on organ and tissue doses. This paper describes results from the first space flight (STS-91, 51.65 degrees inclination and approximately 380 km altitude) of a fully instrumented Alderson Rando phantom torso (with head) to relate the skin dose to organ doses. Spatial distributions of absorbed dose in 34 1-inch-thick sections measured using TLDs are described. There is about a 30% change in dose as one moves from the front to the back of the phantom body. Small active dosimeters were developed specifically to provide time-resolved measurements of absorbed dose rates and quality factors at five organ locations (brain, thyroid, heart/lung, stomach and colon) inside the phantom. Using these dosimeters, it was possible to separate the trapped-proton and the galactic cosmic radiation components of the doses. A tissue-equivalent proportional counter (TEPC) and a charged-particle directional spectrometer (CPDS) were flown next to the phantom torso to provide data on the incident internal radiation environment. Accurate models of the shielding distributions at the site of the TEPC, the CPDS and a scalable Computerized Anatomical Male (CAM) model of the phantom torso were developed. These measurements provided a comprehensive data set to map the dose distribution inside a human phantom, and to assess the accuracy and validity of radiation transport models throughout the human body. The results show that for the conditions in the International Space Station (ISS) orbit during periods near the solar minimum, the ratio of the blood-forming organ dose rate to the skin absorbed dose rate is about 80%, and the ratio of the dose equivalents is almost one. The results show that the GCR model dose-rate predictions are 20% lower than the observations. Assuming that the trapped-belt models lead to a correct orbit-averaged energy spectrum, the measurements of dose rates inside the phantom cannot be fully understood. Passive measurements using 6Li- and 7Li-based detectors on the astronauts and inside the brain and thyroid of the phantom show the presence of a significant contribution due to thermal neutrons, an area requiring additional study.

STS-91 Shuttle Project↗
DOE OSTI2025

Rippled metamaterials with scale-dependent tailorable elasticity

Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.

Applied Physical Sciences↗
DOE OSTI2026

Carbon dioxide pipeline network transportation cost model: evaluating economic and geographic factors for efficient carbon capture, storage, and utilization

This study presents a comprehensive pipeline network modeling framework to estimate the CO 2 delivery cost for CO 2 utilization and geologic CO 2 storage across the United States. We developed a Python-based CO 2 pipeline transportation cost model leveraging Argonne National Laboratory’s pipeline engineering expertise and detailed natural gas transmission pipeline cost data across U.S. regions. Using existing road corridors as practical routing guides, the model designs pipeline networks that aggregate CO 2 from one or multiple sources and deliver it to selected destinations. It then minimizes the total transportation cost by optimizing pipeline diameters and incorporating booster pumps. A key contribution is the incorporation of up-to-date, region-specific cost factors with itemized components for materials, labor, miscellaneous construction expenses, and right-of-way acquisition. Results emphasize that regional variation and economies of scale associated with CO 2 pipeline costs are significant and should be explicitly accounted for in screening and planning studies. By combining realistic routing constraints with regionalized cost inputs, the model provides transparent design methodology and location-specific insights into source–destination delivery costs, including the effects of routing complexity along existing road networks. We demonstrate the model with two illustrative case studies – one for CO 2 storage and one for CO 2 utilization – in which the model designs pipeline networks spanning hundreds of miles across the states, collecting CO 2 from multiple sources and delivering it to designated endpoints while minimizing levelized cost of delivery via diameter and compression optimization. The model offers a practical, scalable approach for alternative design option screening and early-stage CO 2 transportation planning.

CCS↗
NASA NTRS2013

Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study

One of the current challenges in tropical cyclone (TC) research is how to improve our understanding of TC interannual variability and the impact of climate change on TCs. Recent advances in global modeling, visualization, and supercomputing technologies at NASA show potential for such studies. In this article, the authors discuss recent scalability improvement to the multiscale modeling framework (MMF) that makes it feasible to perform long-term TC-resolving simulations. The MMF consists of the finite-volume general circulation model (fvGCM), supplemented by a copy of the Goddard cumulus ensemble model (GCE) at each of the fvGCM grid points, giving 13,104 GCE copies. The original fvGCM implementation has a 1D data decomposition; the revised MMF implementation retains the 1D decomposition for most of the code, but uses a 2D decomposition for the massive copies of GCEs. Because the vast majority of computation time in the MMF is spent computing the GCEs, this approach can achieve excellent speedup without incurring the cost of modifying the entire code. Intelligent process mapping allows differing numbers of processes to be assigned to each domain for load balancing. The revised parallel implementation shows highly promising scalability, obtaining a nearly 80-fold speedup by increasing the number of cores from 30 to 3,335.

tropical cyclone interannual variability↗
DOE OSTI2026

GP Cosmology Surrogate v1.0

GP Cosmology Surrogate is a Python library for building and training a generalized multi-output Gaussian process (GP) framework of @takhtaganov2021cosmic. In this approach, the surrogate is constructed sequentially, guided by a Bayesian optimization acquisition function that targets reduction of emulation error in the regions most consistent with the observational data. This adaptive design concentrates computational resources where they have the greatest impact on inference accuracy. The library supports efficient training for separable GP kernels, which allows the use of Kronecker algebra to handle high-dimensional input spaces and large numbers of correlated outputs. This makes it well suited for applications such as modeling cosmological power spectra, large-scale physical simulations, and multi-output hyperparameter tuning. By combining scalable multi-output GP modeling with data-driven adaptive sampling, GPsurrogate enables parameter inference and optimization with substantially fewer simulations than conventional space-filling designs.

Lukic, Zarija [Lawrence Berkeley National Laborato↗