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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2025

High-Resolution Simulations of Geological CO 2 Injection: Application to the SPE11 Benchmark

Geological carbon sequestration (GCS) will play a critical role in decarbonization and in facilitating the transition to clean energy systems. Because CO 2 is highly mobile, ensuring its safe and permanent injection into subsurface geological formations involves monitoring over larger spatial domains and longer time periods than is typical for hydrocarbon reservoirs. This can benefit from simulation tools capable of modeling key CO 2 trapping mechanisms, particularly those optimized for speed and scalability on high-performance computing systems. Using isothermal versions of the SPE11B and SPE11C benchmark cases, we conduct a mesh refinement study simulating CO 2 injection into kilometer-scale rock formations at centimeter resolution with the GEOS open-source simulation framework. We focus on how mesh refinement improves the accuracy of convective mixing in both 2D and 3D simulations. The computational costs associated with achieving a converged solution highlight the need for predictive upscaling techniques. A systematic performance scaling analysis—including both central processing unit (CPU) and graphics processing unit (GPU) architectures—complements the “Results” section.

Geosciences↗
NASA NTRSDate not supplied

Traffic Flow Analysis for Package Delivery Drones using a Queueing Model

A key component of the small unmanned aircraft systems traffic management ecosystem is the design of scalable algorithms for strategic deconfliction of drones prior to takeoff. In this work, we focus on efficient flow management of drones on a network of intersecting edges subject to two kinds of spacing constraints: 1) between any two adjacent vehicles on an edge and 2) between any two vehicles on two different edges arriving one after the other at an intersection. The spacing is designed to enable non-intersection of operational volumes corresponding to two different vehicles thereby properly separating the vehicles inside each volume. For simplicity, we assume a constant ground speed for the drones and fixed dimensions for the operational volume blocks. The deconfliction is managed by adjusting the takeoff time of the drones, thereby regulating their arrival time at various crossing waypoints in the network. This framework allows us to study the maximum flow (throughput) of vehicles on a network of edges connecting depots to drop off sites subject to the temporal spacing constraints. The departure scheduling of individual drones results in a combinatorial optimization problem. To alleviate this, we solve a max-flow formulation and use queueing theory to simplify the analysis and provide upper bounds to the underlying optimization problem for individual drone departure scheduling. Our results indicate that throughput drops rapidly after the density of drones in the network passes the max-flow limits.

Alexey A Munishkin↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2025

Scaled-up fabrication of durable and porous adsorbent-coated minichannels on aluminum for CO 2 separation

A method was developed to fabricate zeolite 13X adsorbent-coated minichannels on aluminum for CO₂ adsorption applications in this study. It emphasizes the innovative use of aluminum as a substrate, which offers airtight assembly, paving the way for highly efficient adsorption systems. An optimized coating process was developed using a slurry of Zeolite 13X, yeast, sugar, and xanthan gum, resulting in durable and highly porous layers that enhance CO₂ capture performance. A PETG peeler was designed to remove the top layer of the yeast-engineered adsorbent coatings, revealing a super porous and foamy structure. The teeth of the peeler were designed and fabricated for high repeatability and rapid prototyping. Breakthrough experiments were conducted on the scaled-up adsorbent bed using gas mixtures of 80% CO₂, 20% N₂, and 20% CO₂, 80% N₂ to represent different industrial scenarios. The performance of the bed was evaluated at flow rates of 160 and 190 cm³ min -1 using a Raman Laser Gas Analyzer (RLGA), demonstrating stable adsorption without degradation across multiple cycles. Computational modeling of integral transport phenomena under the chosen experimental conditions was pursued using gPROMS ProcessBuilder™, and the modeling results were compared with those from the tests for adsorption time, which resulted in an error margin of 2% to 9% for the breakthrough time, confirming the easy reproducibility of the design through modeling. This research advances CO₂ capture technologies by providing an effective and scalable solution for producing aluminum-based adsorbent coated beds, supporting industrial carbon capture efforts.

CO2 capture↗
DOE OSTI2026

Quantum utility in simulating the real-time dynamics of the Fermi–Hubbard model using superconducting quantum computers

The Fermi–Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi–Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an optimized second-order Trotterization for the time evolution in the Fermi–Hubbard model, specifically tailored for the limited qubit connectivity of quantum architectures, such as IBM's platforms. Notably, both Trotterization approaches are scalable and maintain a constant circuit depth at each Trotter step, regardless of the qubit count, enabling us to precisely investigate the relaxation dynamics in the Fermi–Hubbard model by measuring the expectation value of the Néel observable (staggered magnetization) for time-evolved quantum states. Lastly, our successful measurement of expectation values in such large-scale quantum many-body systems, especially at longer time scales with larger entanglement, highlights the quantum utility of superconducting quantum platforms over conventional classical approximation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Aligned carbon nanotubes for 1,000x improvement in computing (CRADA Final Report)

As part of the Cyclotron Road program, the Aligned Carbon team worked to improve and bring to market their technology to manufacture carbon nanotubes (CNTs) for integrated circuits. There are multiple applications which can benefit from the use of CNT based transistors in integrated circuits, including high performance computing, RF data transmission, and biosensing. Each of these applications faces a problem of poor starting CNT material to come to market. Aligned Carbon offers material with the highest ceiling of performance based around a fully aligned, uniform, on-wafer growth of CNTs and our proprietary purification process to remove metallic CNTs leaving only the semi-conducting ones behind. Further, Aligned Carbon has been guided by scalable manufacturing processes that will fit seamlessly into the integrated circuit foundry model of chip fabrication.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Parallel Navier-Stokes computations on shared and distributed memory architectures

We study a high order finite difference scheme to solve the time accurate flow field of a jet using the compressible Navier-Stokes equations. As part of our ongoing efforts, we have implemented our numerical model on three parallel computing platforms to study the computational, communication, and scalability characteristics. The platforms chosen for this study are a cluster of workstations connected through fast networks (the LACE experimental testbed at NASA Lewis), a shared memory multiprocessor (the Cray YMP), and a distributed memory multiprocessor (the IBM SPI). Our focus in this study is on the LACE testbed. We present some results for the Cray YMP and the IBM SP1 mainly for comparison purposes. On the LACE testbed, we study: (1) the communication characteristics of Ethernet, FDDI, and the ALLNODE networks and (2) the overheads induced by the PVM message passing library used for parallelizing the application. We demonstrate that clustering of workstations is effective and has the potential to be computationally competitive with supercomputers at a fraction of the cost.

Hayder, M. Ehtesham↗
NASA NTRSDate not supplied

MBSE Execution of Scalable Autonomous Operations for a High Density Vertiplex

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) and NASA SE processes were executed via MBSE using MagicDraw. Since the adoption of MBSE and utilization of MagicDraw, the systems engineering team has made tremendous strides in each pillar of MBSE including, requirements, behavior, and structure. Scalable Autonomous Operations (SAO) was a stage in the development of the High Density Vertiplex focusing on the autonomous terminal operations of a vertiport with sUAS aircraft. MBSE served the systems engineering team to document and verify the physical architecture and capture a logical architecture of SAO for distribution to the AAM community. This paper will detail methodologies that were created to successfully execute NASA SE processes via MBSE in the SAO stage as well as highlight challenges and lessons learned.

Demetrios Katsaduros↗
NASA NTRSDate not supplied

Sub-kW Class Hall-Effect Thruster Power Processing Unit for Wide Output Range Applications

The National Aeronautics and Space Administration (NASA) Small Spacecraft Electric Propulsion (SSEP) project is maturing high-propellant throughput sub-kilowatt Hall-effect thruster technologies to enable small spacecraft deep space science and exploration missions with high delta-v requirements. In support of this effort, development of a power processing unit (PPU) capable of providing discharge power of up to 1 kW continues to be pursued at the NASA Glenn Research Center (GRC). Previous reported work included a successful integrated test of a scalable, modular breadboard discharge power supply with the NASA-H64M laboratory model Hall-effect thruster and presentation of notional designs for the various auxiliary power supplies needed for thruster operation. Since that time, auxiliary power supply designs have been completed and fabricated, with the cathode heater and keeper power supplies being successfully tested with a hollow cathode assembly (HCA) in the NASA GRC Vacuum Facility 56 (VF-56). The desire for a lower mass, higher efficiency, and more versatile PPU to maximize performance of power and mass-limited small spacecraft has led to the exploration of a discharge power supply based on a series-parallel (LCC) resonant topology. This topology has enabled the discharge power supply to operate over a wider output range at switching frequencies 4-5 times higher than previous design iterations. Simulation models of the topology have been developed and a breadboard of the topology has been fabricated and evaluated on both resistive loads and an integrated Hall thruster test. This paper will present collected performance and integrated test data from both the fabricated auxiliary and resonant discharge power supplies. Advantages of the resonant converter architecture over more traditional pulse-width modulated (PWM) techniques in Hall-effect thruster discharge power supply applications will also be described.

electric propulsion↗
NASA NTRSDate not supplied

Sub-kW Class Hall-Effect Thruster Power Processing Unit for Wide Output Range Applications

The National Aeronautics and Space Administration (NASA) Small Spacecraft Electric Propulsion (SSEP) project is maturing high-propellant throughput sub-kilowatt Hall-effect thruster technologies to enable small spacecraft deep space science and exploration missions with high delta-v requirements. In support of this effort, development of a power processing unit (PPU) capable of providing discharge power of up to 1 kW continues to be pursued at the NASA Glenn Research Center (GRC). Previous reported work included a successful integrated test of a scalable, modular breadboard discharge power supply with the NASA-H64M laboratory model Hall-effect thruster and presentation of notional designs for the various auxiliary power supplies needed for thruster operation. Since that time, auxiliary power supply designs have been completed and fabricated, with the cathode heater and keeper power supplies being successfully tested with a hollow cathode assembly (HCA) in the NASA GRC Vacuum Facility 56 (VF-56). The desire for a lower mass, higher efficiency, and more versatile PPU to maximize performance of power and mass-limited small spacecraft has led to the exploration of a discharge power supply based on a series-parallel (LCC) resonant topology. This topology has enabled the discharge power supply to operate over a wider output range at switching frequencies 4-5 times higher than previous design iterations. Simulation models of the topology have been developed and a breadboard of the topology has been fabricated and evaluated on both resistive loads and an integrated Hall thruster test. This paper will present collected performance and integrated test data from both the fabricated auxiliary and resonant discharge power supplies. Advantages of the resonant converter architecture over more traditional pulse-width modulated (PWM) techniques in Hall-effect thruster discharge power supply applications will also be described.

electric propulsion↗
DOE OSTI2025

Dynamical Sketching for Enhanced Communication Efficiency in Federated Learning

Federated learning (FL) has revolutionized distributed machine learning by enabling collaborative model training without sharing local data. However, communication efficiency and privacy guarantees remain significant challenges. This paper introduces a dynamic sketching mechanism in FL, optimizing the trade-off between communication efficiency and model accuracy. By dynamically selecting the sketch matrix size, our approach adapts to the evolving characteristics of the data and the model, ensuring optimal performance across diverse scenarios. We leverage Bayesian optimization to systematically tune the sketch parameters, achieving an effective balance between resource efficiency and model performance. Experimental results on the MNIST dataset using a convolutional neural network (CNN) architecture validate the proposed method's efficiency and scalability. Our dynamic sketching approach significantly outperforms fixed-size sketching techniques, achieving higher compression ratios (up to 62x) and providing better privacy guarantees while maintaining high model accuracy. These findings highlight the robustness and versatility of our approach and make it a valuable solution for privacy-preserving, communication-efficient federated learning.

Afrose, Sharmin [ORNL]↗
NASA NTRS2019

Computational Performance of Progressive Damage Analysis of Composite Laminates using Abaqus/Explicit with 16 to 512 CPU Cores

The computational scaling performance of progressive damage analysis using Abaqus/ Explicit is evaluated and quantified using from 16 to 512 CPU cores. Several analyses were conducted on varying numbers of cores to determine the scalability of the code on five NASA high performance computing systems. Two finite element models representative of typical models used for progressive damage analysis of composite laminates were used. The results indicate a 10 to 15 times speed up scaling from 24 to 512 cores. The run times were modestly reduced with newer generations of CPU hardware. If the number of degrees of freedom is held constant with respect to the number of cores, the model size can be increased by a factor of 20, scaling from 16 to 512 cores, with the same run time. An empirical expression was derived relating run time, the number of cores, and the number of degrees of freedom. Analysis cost was examined in terms of software tokens and hardware utilization. Using additional cores reduces token usage since the computational performance increases more rapidly than the token requirement with increasing number of cores. The in- crease in hardware cost with increasing cores was found to be modest. Overall the results show relatively good scalability of the Abaqus/Explicit code on up to 512 cores.

Bergan, A. C.↗
NASA NTRS2004

SPIN or LURCH : a Comparative Assessment of Model Checking and Stochastic Search for Temporal Properties in Procedural Code

The difficulty of how to test large systems, such as the one on board a NASA robotic remote explorer (RRE) vehicle, is fundamentally a search issue: the global state space representing all possible has yet to be solved, even after many decades of work. Randomized algorithms have been known to outperform their deterministic counterparts for search problems representing a wide range of applications. In the case study presented here, the LURCH randomized algorithm proved to be adequate to the task of testing a NASA RRE vehicle. LURCH found all the errors found by an earlier analysis of a more complete method (SPIN). Our empirical results are that LURCH can scale to much larger models than standard model checkers like SMV and SPIN. Further, the LURCH analysis was simpler than the SPIN analysis. The simplicity and scalability of LURCH are two compelling reasons for experimenting further with this tool.

verification↗
DOE OSTI2025

Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and Its Scalability for High-Density 3-D Ferroelectric Capacitor Memory

In this work, we present a comprehensive experimental and modeling study on the scaling of vertical 2T-nC ferroelectric random access memory (FeRAM) hybrid cells, comprising n metal-ferroelectric–metal (MFM) capacitors, to demonstrate a high-performance and high-density 3-D capacitor memory. Our contributions include: 1) successful process integration of vertical 2T-3C FeRAM cells by stacking MFM structures on top of Si CMOS transistors; 2) experimental validation of memory cell functionality, confirming the feasibility of the vertical 2T-nC FeRAM architecture; 3) an analysis of scaling effects on parasitic capacitance in densely integrated 3-D arrays, using 3-D technology computer-aided design (TCAD) simulations; 4) exploration of aggressive stacking of write bitlines (WBLs) to enhance memory density, where ferroelectric linear capacitance ( C FE ) enables self-boosted inhibition under the V W /2 scheme, but renders the V W /3 scheme ineffective due to intolerable write disturbances; and 5) assessment of horizontal scaling, revealing significant increases in read disturbances caused by interplane capacitance between adjacent WBLs ( C Z ). This work represents an early exploration into the potential of 2T-nC FeRAM as a scalable and efficient 3-D memory solution.

42 ENGINEERING↗
NASA NTRS2017

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni↗
DOE OSTI2025

Enhancing Grid Resilience with HIVE: Decentralized V2G Coordination for Black Starts

This paper proposes the HIVE (Harmonized Integration of Vehicle Energy for Grid Support) model, a novel game-theoretic framework for decentralized coordination of electrified vehicles to enable black start and load restoration during grid outages. In the absence of a central controller, HIVE employs a cooperative game to model vehicle interactions, allowing autonomous decision-making while admitting to a Nash equilibrium for grid restoration. The framework addresses the heterogeneity of vehicles and their operational constraints, selecting a lead vehicle for grid-forming and coordinating grid-following vehicles to support prioritized loads. Applied to a hospital blackout scenario, HIVE demonstrates robust performance in forming an islanded microgrid and sustaining critical loads under varying vehicle availability, state of charge, and power constraints. Simulation results highlight the model’s effectiveness in ensuring decentralized coordination of energy allocation and prioritizing loads, offering a scalable solution for resilient grid operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2024

First Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and its Scalability for High-Density 3D Ferroelectric Capacitor Memory

In this article, we perform a comprehensive experimental and modeling study into the scaling of vertical 2T-nC ferroelectric random-access memory (FeRAM) hybrid cell to demonstrate a high performance and high-density 3D capacitor memory. We demonstrate: i) first time successful integration of the vertical 2T-3C FeRAM cell by stacking the vertical metal-ferroelectricmetal (MFM) stack on top of Si CMOS transistors; ii) successful experimental operation of the memory cell, including the quasi-nondestructive read out (QNRO) of the polarization without write back after 106 read cycles; iii) the write bit line (WBL) heavily screens the coupling between neighboring strings, making it a minor concern; V ) aggressive stacking of the WBLs, i.e., number of MFMs in a string, could facilitate the self-boosting during write operation due to ferroelectric linear capacitance (CFE), which allows self-boosted inhibition for Vw/2 scheme and worsens the Vw/3 scheme as disturb increases to intolerable 2Vw/3; v) aggressive horizontal scaling significantly increases the read disturb to cells on neighboring planes due to capacitance between two WBLs (Cz).

42 ENGINEERING↗