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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanistic insight into the effects of A-site doping in perovskite nanorods on interfacial Li-ion and Na-ion transport in polymer–perovskite composite electrolytes

Composite polymer electrolytes (CPEs) offer a promising pathway to safer and higher energy density electrochemical energy storage yet optimizing interfacial ion transport remains a critical challenge due to high resistances and a lack of understanding of the complex interfacial electrostatic interactions. This work investigates the mechanistic origins of the interfacial ion transport enhancement in single-ion conducting (SIC) polymer electrolytes filled with A-site disordered perovskite LaxMyTiOz (LMTO) (M = Li, Na, K) nanorods. By integrating Density Functional Theory (DFT) with broadband dielectric spectroscopy (BDS) and pulsed-field-gradient NMR (PFG-NMR), we analyze the ion transport behavior in Li-based and Na-based SIC systems containing compositionally distinct LMTO fillers (LMTO800 and LMTO900). Specifically, we introduce the site-to-site energy difference (ΔE) descriptor, which extends beyond single-site descriptors such as adsorption energy and ion binding energies. We demonstrate that ΔE together with ion binding energies provide a more comprehensive picture of the energy landscape at the polymer–ceramic interface. The combined DFT and experimental results show that A-site doping in the ceramic filler can effectively modulate ΔE and ion binding energies, creating a favorable potential energy landscape that promotes enhanced ion transport along the interface. This comprehensive study illustrates the impact of ceramic compositional tuning on interfacial ion transport and provides materials design rules towards real-world applications.

B shepard, Lauren [Pennsylvania State University]↗
DOE OSTI2020

A Closer Look at Acyl-ACPs in Lipid Metabolism

Acyl lipids are a wide range of compounds that have diverse functions in membrane synthesis, energy storage, and signal transduction. Fatty acids are key building blocks of acyl lipids that are synthesized by the fatty acid synthase complex via sequential condensation of two-carbon units to reach lengths of up to 18 carbons (Li-Beisson et al., 2013). During this process, the elongating acyl chains are attached to Acyl Carrier Proteins (ACPs) through a phosphopantetheine group linked to a Ser residue of ACPs to form acyl-ACPs, which are central components in de novo fatty acid synthesis. However, the roles of acyl-ACPs in lipid metabolism are rarely studied due to a lack of methods to accurately quantify individual acyl-ACPs and their cargoes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events

As the electricity grid is moving towards a 100% Renewable Energy Source Bulk Power Grid, the overall operations of the power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to make sure the grid is operating reliably. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning analysis framework to deconstruct some primary drivers for price formation in modern electricity markets with high renewable energy and the outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data and in this paper it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England.

machine learning (ML), electricity markets, Renewa↗
DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Spin-transparent storage rings for quantum computing

Spin-transparent storage rings, where any spin direction repeats after one full turn, can be used in conjunction with ion traps as a new quantum computing platform [1]. Advantages of spin-transparent rings for quantum computing include: large numbers of stored qubits; long quantum coherence times of up to several hours; long storage lifetimes; and room temperature operation. These exceptional qualities mean rings could provide a scalable way to implement algorithms with deep complexity requiring many quantum operations while simultaneously providing a large number of qubits. This new platform where the qubit has long quantum coherence time can also be used as a quantum sensor or a part of a quantum memory.

Accelerator Physics↗
DOE OSTI2020

ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research

Throughout its long history, the Office of Advanced Scientific Computing Research (ASCR) has built the critical technologies to ensure U.S. leadership in energy science and national security. It has made its parent agency, the Department of Energy (DOE) and its Office of Science, the world’s recognized leader in computational science. ASCR’s stated mission is “to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” To accomplish this goal, ASCR oversees a large complex of computing and networking facilities and is responsible for procuring, deploying and operating high-performance computing (HPC), networking and storage resources; conducting basic research in mathematics and computer science; developing and sustaining a large body of software; and collaborating with other Office of Science programs, academia and industry. ASCR’s computational science leadership has a long history, predating even DOE’s inception. Applied mathematics and advanced computing were both elements of the Manhattan Project’s Theoretical Division. In the 1950s, DOE’s predecessor, the Atomic Energy Commission, created a mathematics program to develop and apply digital computing by supporting researchers at universities and AEC laboratories. Several organizational and name changes later, this program would grow and become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
DOE OSTI2023

How an Autonomous Offshore Power System Can Transform the Ocean Economy - A Hypothetical Case Study Utilizing an Autonomous Offshore Power System in Northern Lights Carbon Capture and Storage Project

An Autonomous Offshore Power System (AOPS) provides in-situ power, energy storage, real-time data and communications support, asset management, and other capabilities at sea. It has applications for all offshore industries: energy, defense and security, aquaculture, science and research, and communications. Furthermore, this paper highlights how an AOPS can reduce cost, complexity, and carbon-intensity for existing offshore operations and enable new capabilities for offshore industry leaders. This new AOPS technology has two primary advantages. First, it unlocks the autonomous, electric future of the ocean economy via ‘local’ power generation and energy storage, in addition to real-time connection to the data cloud. Second, it helps enable a material change in the global energy mix through cost-effective, reliable generation and storage technology for use cases including mobile/static data-gathering and reporting systems, operating equipment, and charging networks for uncrewed surface vessels. AOPS technology will help transform the ocean economy, and thus has implications for offshore industry leaders as they push to reduce costs today and make an autonomous and decarbonized future possible.

16 TIDAL AND WAVE POWER↗
NASA NTRS1981

Solar heated office complex--Greenwood, South Carolina

Report contains thorough docuumentation of project meeting 85 percent of building heat requirements. System uses roof mounted recirculating water solar panels and underground hot water energy storage. Aluminum film reflectors increase total solar flux captured by panels.

Source record↗
NASA NTRS1988

A simplified, general-purpose deep-space ranging correlator design

A much-simplified, yet more general-purpose multi-channel deep-space ranging system correlator design that was used in past JPL spacecraft ranging systems is described. The method applies to detection of both single-component and multiple-component ranging codes, in either sequential (mu) or composite (pi) transmitted forms, and using either pseudonoise or square-wave components. Using this design, the Phobos Probe ranging system correlator computational complexity was reduced by over three orders of magnitude in multiply-and-add circuits and 45,000 bits of accumulator storage.

Tausworthe, R. C.↗
DOE OSTI2021

Autonomous Energy Systems: A Distributed Approach to Control Millions of Energy Devices

Energy systems of all sizes are becoming increasingly complex. The National Renewable Energy Laboratory has developed new controls that will support real-time operations and management of renewables, storage, electric vehicles and loads for grid efficiency and resilience. This fact sheet presents an overview of these autonomous energy systems.

AES↗
DOE OSTI2024

Application Guide for the Cyber-Resilient Design Framework for Hybrid Systems

As the energy landscape evolves, hybrid power plants—integrating multiple renewable energy sources (e.g., solar, wind, and battery storage) with the bulk power electric system—play a crucial role in meeting growing energy demands. However, with a hybrid power plant’s increased number of components, complex network connectivity and digitization of controls, these systems face growing cybersecurity risks. To help mitigate these risks, Idaho National Laboratory (INL) presents this application guide specifically designed for operators and systems engineers to evaluate the cybersecurity resilience of their hybrid power plant’s design.

14 SOLAR ENERGY↗
DOE OSTI2021

Synthesis and Single Crystals of Refractory Oxides of Lanthanides and Thorium

At the completion of this program, we can report that we developed a considerable degree of technical improvements in our ability to perform hydrothermal reactions at high temperatures and pressures. We can now routinely perform reactions at 700-750°C and 200 MPa. Currently we are in the process of exploiting this new technology synthesizing a range of exotic new materials investigating relatively poorly understood materials. Our initial efforts focused on the chemistry of rare earth oxides with tetravalent and pentavalent oxides. We recently published a study of the lanthanides with Nb 5+ and Ta 5+ ions, where we grew oxides such as RENdO 4 and RETaO 4 as high quality single crystals. These compounds were targeted as potential hosts for luminescent and scintillation materials, particularly given that they are among the densest oxide hosts and hence have good potential as absorbers for high energy radiation like X-rays and gamma rays. We also isolated a range of unusual new rare earth tantalates with very complex structures. indicating that the chemistry is very sensitive to conditions. We performed some fairly comprehensive examinations of the solid-state chemistry of rare earth ions with various tetravalent metal ions especially Si 4+ , Ge 4+ , Sn 4+ and Ti 4+ . Given the potential role of rare earth silicates in immobilizing radioactive waste elements in long-term storage, and the similarity of our hydrothermal fluids with known geological conditions, this chemistry continues to be relevant. We prepared an extensive series of new lanthanide germanates (e.g. RE 13 Ge 6 O 31 (OH), BaRE 10 (GeO 4 ) 4 O 8 ). and found that there is there is almost no overlap between the chemistry of the rare earth silicates. Stannic oxide (SnO 2 ) is much more refractory and requires higher temperatures and of mineralizer concentrations. One significant result is the growth of RE 2 Sn 2 O 7 pyrochlore single crystals. These are of interest because the rare earth stannate pyrochlores are known to display a wide range of magnetic frustration such as spin ice behavior. We grew high quality single crystals of rare earth germanate and stannate pyrochlores and this led to a collaboration with Professor Kate Ross at Colorado State. Preliminary measurements, indicate that the crystals contain no detectable defects or site disorder. Initial neutron diffraction on single crystals was performed at Oak Ridge, and more detailed experiments involving the Ross group are underway at both NIST and ORNL. This particular chemistry has turned out to be the most potentially significant work on this project and the collaborative effort with Prof. Ross is the topic of a DoE renewal project on quantum materials. Our initial foray into the hydrothermal chemistry of rare earth titanates has also been very promising and a range of cubic and polar ferroic phases of the light rare earths RE 2 Ti 2 O 7 (RE = La - Pr) in the P2 1 phase. We also discovered an interesting new phase Ce 2 Ti 4 O 11 that can have implications in heavy metal immobilization and storage. along with a series of new rare earth titanates (La 5 Ti 4 O 15 (OH) Sm 3 TiO 5 (OH) 3 and Lu 5 Ti 2 O 11 (OH) with exceptionally complex structures. One interesting sidelight has been high temperature hydrothermal chemistry terbium, including the growth of large crystals of TbO(OH). This is not a new compound but it is the first time it has been grown as large single crystals. The Tb atom density is almost as high as that in Tb 2 O 3 and has a very high Verdet constant (ca. 70), making it a very attractive candidate as a Faraday rotator. Unfortunately it is not in a cubic structure but he material is hard, stable, pure and inexpensive, so should still be an attractive Faraday oscillator. We recently received a patent on this material. We also synthesized K 2 Tb(Ge 2 O 7 ) containing stable octahedral Tb 4+ ions, which appears to be the first example of a well-characterized Tb 4+ complex. Given that Tb 4+ has been proposed as a benign surrogate for more treacherous tetravalent ions such as Cf 4+ and Bk 4+ , we think that Tb 4+ silicates can be a particularly useful study for actinide immobilization and related work. We also began reaction studies with rare earths and both ReO 2 and RuO 2 . These resulted in large single crystals of species like RE 5 Ru 2 O 12 , RE 4 Re 2 O 11 , REReO 4 and RE 2 ReO 5 . Several of these samples have already been sent to ORNL for magnetic and neutron diffraction studies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability

The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A coupled thermo-hydro-mechanical model for simulating leakoff-dominated hydraulic fracturing with application to geologic carbon storage

A potential risk of injecting CO2 into storage reservoirs with marginal permeability (≲ 10 mD (1 mD = 10 –15 m 2 )) is that commercial injection rates could induce fracturing of the reservoir and/or the caprock. Such fracturing is essentially fluid-driven fracturing in the leakoff-dominated regime. Recent studies suggested that fracturing, if contained within the lower portion of the caprock complex, could substantially improve the injectivity without compromising the overall seal integrity. Modeling this phenomenon entails complex coupled interactions among the fluids, the fracture, the reservoir, and the caprock. Here, we develop a simple method to capture all these interplays in high fidelity by sequentially coupling a hydraulic fracturing module with a coupled thermal-hydrological-mechanical (THM) model for nonisothermal multiphase flow. The model was made numerically tractable by taking advantage of self-stabilizing features of leakoff-dominated fracturing. The model is validated against the PKN solution in the leakoff-dominated regime. Moreover, we employ the model to study thermo-poromechanical responses of a fluid-driven fracture in a field-scale carbon storage reservoir that is loosely based on the In Salah project's Krechba reservoir. The model reveals complex yet intriguing behaviors of the reservoir-caprock-fluid system with fracturing induced by cold CO 2 injection. We also study the effects of the in situ stress contrast between the reservoir and caprock and thermal contraction on the vertical containment of the fracture. The proposed model proves effective in simulating practical problems on length and time scales relevant to geological carbon storage.

58 GEOSCIENCES↗
DOE OSTI2021

Workflows Community Summit: Tightening the Integration between Computing Facilities and Scientific Workflows

Scientific workflows are used almost universally across science domains for solving complex and largescale computing and data analysis problems. The importance of workflows is highlighted by the fact that they have underpinned some of the most significant discoveries of the past decades. Many of these workflows have significant computational, storage, and communication demands, and thus must execute on a range of large-scale computer systems, from local clusters to public clouds and upcoming exascale HPC platforms. Managing these executions is often a significant undertaking, requiring a sophisticated and versatile software infrastructure. Historically, infrastructures for workflow execution consisted of complex, integrated systems, developed in-house by workflow practitioners with strong dependencies on a range of legacy technologies—even including sets of ad hoc scripts. Due to the increasing need to support workflows, dedicated workflow systems were developed to provide abstractions for creating, executing, and adapting workflows conveniently and efficiently while ensuring portability. While these efforts are all worthwhile individually, there are now hundreds of independent workflow systems. These workflow systems are created and used by thousands of researchers and developers, leading to a rapidly growing corpus of workflows research publications. The resulting workflow system technology landscape is fragmented, which may present significant barriers for future workflow users due to many seemingly comparable, yet usually mutually incompatible, systems that exist. In order to tackle some of the challenges described above, the DOE-funded ExaWorks and NSF-funded WorkflowsRI projects have organized in 2021 a series of events entitled the “Workflows Community Summit”. The third edition of the “Workflows Community Summit” explored workflows challenges and opportunities from the perspective of computing centers and facilities. This third summit builds on two prior summits (https://workflowsri.org/summits) that (i) established a high level vision for workflows research; and (ii) explored technical approaches for realizing that vision. The third summit brought together a small group of facilities representatives with the aim to understand how workflows are currently being used at each facility, how facilities would like to interact with workflow developers and users, how workflows fit with facility roadmaps, and what opportunities there are for tighter integration between facilities and workflows. This report documents and organizes the wealth of information provided by the participants before, during, and after the summit.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Close to real life

NASA's Numerical Aerodynamic Simulation (NAS) facility for CFD modeling of highly complex aerodynamic flows employs as its basic hardware two Cray-2s, an ETA-10 Model Q, an Amdahl 5880 mainframe computer that furnishes both support processing and access to 300 Gbytes of disk storage, several minicomputers and superminicomputers, and a Thinking Machines 16,000-device 'connection machine' processor. NAS, which was the first supercomputer facility to standardize operating-system and communication software on all processors, has done important Space Shuttle aerodynamics simulations and will be critical to the configurational refinement of the National Aerospace Plane and its intergrated powerplant, which will involve complex, high temperature reactive gasdynamic computations.

Peterson, Victor L.↗