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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Transition Metal Oxide Powders Made from Flame Spray Pyrolysis for Li-Ion Batteries

In 2012 the U.S. Department of Energy introduced its EV Everywhere Grand Challenge, with a stated goal of enabling “plug-in electric vehicles (PEVs) that are as affordable and convenient for the American family as gasoline-powered vehicles by 2022.” This requires a reduction in battery costs from the current $500/kWh to around $125/kWh. Most approaches have been to eliminate vehicle weight through lightweighting and to improve the energy density of battery materials. But what if we were able to just make the materials cheaper instead? To this end we introduce a green, low-cost method of producing cathode materials using an integrated flame spray pyrolysis process.

25 ENERGY STORAGE↗
DOE OSTI2021

A Novel Hollow Fiber Membrane Reactor for High Purity H 2 Generation from Thermal Catalytic NH 3 Decomposition (Final Report)

Ammonia (NH 3 ), as a promising carbon neutral liquid fuel (CNLF) and an effective H 2 source, can be synthesized from air and water (N 2 extracted from air and H 2 from water) using renewable energy sources. To produce H 2 as an intermediate, it is essential to develop effective and economic NH 3 decomposition technologies. We have been developing an innovative hollow fiber membrane reactor for high purity H 2 generation from thermal catalytic NH 3 decomposition. The objective of the proposed research is to fabricate a compact and robust prototype with high energy efficiency (>80%) to deliver high purity (>99%) H2 at high rate per volume (>0.15 g H 2 /h/cm 3 ) from NH3 decomposition at low temperature (<450 °C) and high conversion (>99%). Our overall technical approach is to effectively combine an active catalyst for NH 3 decomposition with a selective H 2 membrane separation process in a novel, compact modular system to generate high purity H 2 and simultaneously achieve high NH 3 conversion and optimize the entire system energy efficiency. This novel integrated membrane reactor design, together with low-cost, highly active Ru-based catalyst, makes it most appropriate for the project objective. The proposed applied research and development work, therefore, represents an innovative and transformational solution to H 2 generation from thermal catalytic NH 3 decomposition. During the course of our research we i) synthesized and characterized ruthenium(Ru)-based catalysts, evaluated their catalytic performance for NH 3 decomposition and optimized the catalyst composition and performance based on machine learning, ii) prepared SAPO-34, MFI membranes on α-alumina hollow fibers and measured their performance for H 2 /N 2 , H 2 /N 2 /NH 3 separation and evaluated carbon molecular sieve (CMS) and Pd/Ag membranes for H 2 /N 2 , H 2 /N 2 /NH 3 separation at different conditions, iii) designed and evaluated several types of membrane reactors for H 2 production from NH 3 decomposition, and iv) evaluated the H 2 delivery cost base on catalysts development, membrane development and membrane reactor. For 3,1,12 RuYK catalyst, H 2 productivity of 6.35 mmol/min/gcat with NH 3 conversion of 95~98% was achieved at 5 bar and 400 °C; for SAPO-34 membrane, the best membrane showed H 2 permeance of 7.56×10 -7 mol/(m 2 ∙s∙Pa) and H 2 /N 2 selectivity of 23.1 at 20 °C; For CMS and Pd/Ag membranes, both showed superior stability for 71%H 2 /24%N 2 /5%NH 3 mixture separation at 450 °C and 7 bar for over 120 h; for the membrane reactor, high H 2 purity of >99.99% with NH 3 conversion of >99.4% were achieved in Pd/Ag membrane reactor at 450 °C, and the NH 3 concentration was always below 10 ppb in H 2 product. The H 2 generation rate was calculated to be as high as 0.47 g/h/cm 3 and the energy efficiency was calculated to be 88%. The membrane reactor showed superior stability for over 370 h and high H 2 purity, H 2 production rate and NH 3 conversion. Based on calculation, the H 2 delivery cost can be as low as $3.66/kg.

08 HYDROGEN↗
DOE OSTI2024

Photovoltaic and Cost Analysis for Winston-Salem, North Carolina

This study assesses the feasibility of solar installations at various sites in Winston-Salem, focusing on factors such as solar resource availability, electricity costs, and rooftop area for photovoltaic systems. The analysis begins by estimating daily energy requirements based on annual electricity use, followed by adjusting for seasonal and operational fluctuations. Using regional solar data from the National Solar Radiation Database (NSRDB), we calculate average peak sun hours to determine effective system sizing. A parametric approach using the System Advisor Model (SAM) refines this sizing process, incorporating a safety margin of 1.2 to address demand peaks. Each PV system is designed to fit available rooftop space, and a coverage threshold of 70% is identified as optimal for maximizing cost savings and sustainability. This threshold allows installations to meet substantial energy demands, supporting energy resilience and enhancing economic returns.

14 SOLAR ENERGY↗
DOE OSTI2024

Strategy to Develop a Control Scheme for Core Thermal Performance Optimization

This report outlines a three-year research plan for optimizing reactor core thermal performance through use of a Digital Twin (DT) model and a set of neutron detectors to correspondingly adapt the reactor control strategy. It identifies reactor features and achievable neutron measurements that factor into this optimization task. It considers spatial effects that are important and how they can be managed by a real-time algorithm that controls reactivity actuators. The report presents a set of tasks, along with corresponding methods, for accomplishing this objective. The final task in this set is to validate the combined optimization procedure in the Purdue University’s PUR-1 reactor, for which we have an agreement of understanding. Additionally, we describe an alternative approach for overcoming some of the limitations inherent in the above approach, which includes the computational costs of high-fidelity simulations and achievable integration of neutron measurements into the DT. The proposed algorithms lower the technology readiness level (TRL) of the overall approach as some additional analysis and the development of a new neutron detector concept are necessary. In particular, a sensor measuring the gradient of the neutron flux is required, and the prototype is expected to be validated in the PUR-1 reactor. The potential benefits and the opportunity to significantly push forward the state-of-the-art make this approach worthy of further exploration in the upcoming years.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

BETO 2021 Peer Review - Catalytic Upgrading of Biochemical Intermediates (CUBI) 2.3.1.101-104

The Catalytic Upgrading of Biochemical Intermediates (CUBI) project is a multi-lab effort within ChemCatBio (CCB) that is focused on improving the catalytic upgrading of intermediates from biochemical deconstruction and/or biological conversion (i.e. fermentation) to fuels and chemical co-products. This is accomplished by developing catalysts with improved performance and durability, mitigating the impact of biogenically-derived inhibitors (including water), and evaluating intensified processes to reduce separations requirements and improve carbon utilization. Specific catalytic upgrading routes being developed are: 1) fermentation-derived 2,3-butanediol (BDO); 2) fermentation-derived carboxylic acids; and 3) lignocellulosic sugar-derived furfurals. These routes represent key technology pathways being investigated within BETO's biochemical conversion portfolio. By utilizing experimental and characterization capabilities and modeling tools across the 4 CUBI labs and collaborations with CCB Enabling Projects, the CUBI project is advancing catalytic upgrading process performance and robustness. Its impact is already well-established by several high-impact journal articles and patent publications, along with industrial engagement in related, competitively-awarded collaborations. The CUBI project will result in >25% MFSP cost reduction in the catalytic upgrading section of integrated biochemical conversion routes, as quantified by ongoing TEA modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters

This project, ML-ACCEPT, took a significant step toward achieving the first objective of the DIFFERENTIATE program: to help engineers to more rapidly and cost-effectively consider a wider range of more novel concepts before selecting an engineering-optimal architecture for high-fidelity detailed design and evaluation. Compared to existing methods, the ML-ACCEPT project investigated innovative technologies to make the design of power converters more cost-effective and time-efficient by i) integrating recent breakthroughs in ML, power electronics, simulation software, and optimization to research, develop, and developed a suite of ML-enhanced hypothesis generation tools for power converter design; and ii) facilitating the integration of the proposed software tools into existing power-converter design work-flows. Some existing tools for power-converter design have a certain level of intelligence to aid the design process, but to our knowledge none of them has ML-enhanced capability of automating the electrical circuit design process by selecting, configuring, and tailoring the individual components that enable available resources (e.g., high-voltage DC power) to be converted to the desired output (e.g., lower-voltage DC power with a desired voltage ripple), subject to application-specific (e.g., plasma generation and automotive applications) thermal and packaging considerations. The state-of-the-art circuit design of power converters is still heavily reliant on human experts to select the optimal topology and search for design parameters with human’s experience and intuitions, which can be very time-consuming, inefficient, and labor intensive. The team is composed of University of Michigan-Dearborn (UM-Dearborn), Lawrence Livermore National Laboratory (LLNL), Oak Ridge National Laboratory (ORNL), Modelon Inc., and lighthouse customers.

42 ENGINEERING↗
DOE OSTI2023

Enhancing the EVI-X National Framework to Address Emerging Questions on Charging Infrastructure Deployment

As large national investments in EV charging continue to be made, this project seeks to improve the state-of-the-art in EV infrastructure analysis. New and improved quantitative analysis capabilities will position VTO to support EERE Leadership, assist the DOT/DOE Joint Office through timely analysis, and provide a strategy for the deployment of the national charging network. This project supports on-going development of NREL's EVI-X Suite of charging infrastructure analysis tools, including tools supporting large-scale network planning, local site design decisions, and comprehensive financial analysis. EVI-X development complements and leverages other VTO-funded efforts to ensure inputs to EVI-X and output produced by EVI-X are consistent with and well-integrated into the VTO portfolio (e.g., EV adoption scenarios from TEMPO, charging infrastructure design provided to EVs@Scale, etc.).

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

NREL Planning Resources for States

This presentation summarizes some NREL resources and capabilities that states might want to access to help with decarbonization planning. It describes three types of resources: already-available, public datasets describing technology costs and future grid evolution; jurisdiction-specific integration studies that require directly partnering with NREL; and an envisioned new data product comprising state-specific standard scenarios that could be collaboratively funded by the states.

annual technology baseline↗
DOE OSTI2020

Refining the Approach to Process Flowsheet Development and Maturation at Hanford - 20049

Rigorous process flowsheet management strategies form the basis of an enduring approach to refining the River Protection Project Integrated Process Flowsheet. Incorporation of lessons learned, recognizing upstream and downstream impacts and understanding barriers to implementation of process flowsheets from the outset of the decision-making process will help future revisions to the River Protection Project Integrated Process Flowsheet be more robust, technically defensible, cost effective, and time efficient, ultimately leading to an improved environmental stewardship of the Hanford site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

On-PIC Light Source Integration & Micro-dispensing of Solder Paste for Flip Chip Application

To reach the next level benefits of photonic integrated circuits (PICs), the Integrated Photonic Systems Roadmap-International describes the necessity of either heterogeneous or hybrid integration of light sources [1]. A new approach for hybrid integration is Photonic Wire Bonding where 3D nanolithography is used to pattern a polymer waveguide that connects light sources to PIC waveguides. The waveguides resemble electrical wire bonds and essentially do the same as their electrical counterpart for packaging photonic chips together. In order to reap the full benefits of a photonic wire bonds, the light source must be carefully packaged. In this work I seek to expand RIT’s photonic packaging capability by establishing a packaging process to integrate light sources, specifically an Indium Phosphide distributed feedback lasers and a reflective semiconductor optical amplifiers, directly onto a photonic integrated circuits. Another necessary requirement for PIC packaging is densely integrated electrical connectivity. In this thesis I developed a fine pitch flip chip interconnect technique demonstrated using gold stud bumps in conjunction with micro-dispensed solder paste. This work opens the door to high density photonic flip chip applications. The micro-dispensed solder paste dots having diameters 50-125 µm are currently being tested at a pitch of 150 µm . The gold stud bumps are formed with 1mil gold wire creating bumps with a diameter of 40-60 µm depending on specific parameter values. These capabilities will allow RIT to assemble and test novel photonic integrated devices, cutting down on the time and cost associated with third party assembly and tests facilities. Thereby keeping RIT at the forefront of photonic research.

Wongk, Nicole↗
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
DOE OSTI2020

Photonic integration for UV to IR applications

Photonic integration opens the potential to reduce size, power, and cost of applications normally relegated to table- and rack-sized systems. Today, a wide range of precision, high-end, ultra-sensitive, communication and computation, and measurement and scientific applications, including atomic clocks, quantum communications, processing, and high resolution spectroscopy, are ready to make the leap from the lab to the chip. However, many of these applications operate at wavelengths not accessible to the silicon on insulator-based silicon photonics integration platform due to absorption, power handling, unwanted nonlinearities, and other factors. Next generation photonic integration will require ultra-wideband photonic circuit platforms that scale from the ultraviolet to the infrared and that offer a rich set of linear and nonlinear circuit functions as well as low loss and high power handling capabilities. This article provides an assessment of the field in ultra-wideband photonic waveguides to bring power efficient, ultra-high performance systems to the chip-scale and enable compact transformative precision measurement, signal processing, computation, and communication techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On the Development of Compact HTS Coil Modules for Large Bore High Field Superconducting Magnets

Lower cost, high current density superconducting coil modules producing higher magnetic fields and cooled affordably are crucial for obtaining cost-effective, compact commercial fusion reactors. Accessibility to low cost, higher field magnets (>30 T) is also critical for the discovery of new quantum phenomena in materials, cosmic frontier and other topics in basic science research. The Princeton Plasma Physics Laboratory (PPPL) is working with Princeton University to develop unique large bore, compact superconducting magnets to support science experiments including the development of new instrumentation for condensed matter physics and Axion dark matter search in the cosmic frontier. Core elements of these experiments are unique for access to lower cost, simple fabrication of compact superconducting magnets that can be cooled affordably, while integrated with dedicated science instruments. Conductor qualification and coil design concepts are discussed in support of needs for these experiments. PPPL has the unique expertise and experimental facilities to design, construct and test subscale coil modules for these projects. Compact high field coil modules were fabricated and tested to validate coil design concepts and coil performance. Finally, the design and model coil integration challenges are discussed to identify performance risks and demonstrate feasibility for deploying full scale large bore compact superconducting magnets for cost effective operations of multiple laboratory experiments.

axion dark matter↗