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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Compositional heterogeneity in Cs y FA 1- y Pb(Br x I 1- x ) 3 perovskite films and its impact on phase behavior

Hybrid organic inorganic lead halide perovskite semiconductors of the form Cs y FA 1-y Pb(Br x I 1-x ) 3 are promising candidate materials for high-efficiency photovoltaics. Notably, cation and anion substitution can be used to tune the band gaps to optimize performance and improve stability. However, multi-component materials can be prone to compositional and structural inhomogeneity and the extent, length scale and impact of this heterogeneity on lead halide perovskite properties are not well understood. Here we use synchrotron X-ray diffraction to probe the evolution of crystal structure across the tetragonal-to-cubic phase transition for a series of Cs y FA 1-y Pb(Br x I 1-x ) 3 thin films with x = 0.05 to 0.3 and y = 0.17 to 0.40. We find that the transition occurs across a broad temperature range of approximately 40 °C, much larger than for pure compounds such as MAPbI 3 and MAPbBr 3 . Finally, we hypothesize that this smearing of the phase transition is due to compositional inhomogeneities that give rise to a distribution of local transition temperatures and we estimate the composition varies by about 10% to 15% with likely greater heterogeneity for the halide anion than the cation. This approach of mapping the transition is a simple and effective method of assessing heterogeneity, enabling evaluation of its impact.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Measurement of 2D density profiles using a second-harmonic, dispersion interferometer

A second-harmonic, dispersion interferometer is used to image large-area (≃5 cm 2 ) plasma-jet and gas-jet density profiles. Achromatic telescopes magnify the diameters of the primary-laser beam (1064 nm) and its second-harmonic (532 nm) before probing the sample and de-magnify the beam diameters after the sample, where the primary beam transfers its phase change to a second, second-harmonic beam, allowing the sample’s dispersive-phase change to be measured between two, orthogonally polarized second harmonic beams. The telescopes produce an azimuthally symmetric, dispersive-phase shift in the sample + background phase-change image and in the background phase-change image, which is removed by digital subtraction. The interferometer’s performance was verified using standard-optical components as dispersive elements (BK7 lenses and wedge plates), resolving a minimum, phase-change sensitivity of ΔΦ min ≳ 15 mrad and spatial resolution of Δx res ≃ 100 μm. The phase change produced by unknown-density objects (a pulsed-plasma-jet and a pulsed-gas-jet) was measured, and their data were used to recover the original, 2D density profiles using an inverse Abel transform: peak-number density, N gas ≃ 6 × 10 20 cm –3 and N e ≃ 5 × 10 16 cm –3 ; line-integrated density, ∫N gas dl ~ 2 × 10 19 cm –2 and ∫N e dl ~ 1 × 10 16 cm –2 . The techniques and methods developed here are scalable to even larger probe-beam diameters and frame-capture rates, leading to a diagnostic capability that is well-suited for applications involving the real-time measurement of density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A customizable data management framework for high-repetition-rate high-energy-density science

The high-energy-density (HED) physics community is moving toward a new paradigm of high-repetition-rate (HRR) operation. To fully leverage the scientific power of HRR HED facilities, all of the components of each subsystem (laser, targetry, and performance diagnostics) must be connected and synchronized in a reliable and robust manner while the data acquired are tagged and archived in real time. To this end, GA has begun developing a generalized NoSQL-database framework, the MongoDB repository for information and archiving. An organizational strategy has been developed that shifts HED data organization from a shot-based to a diagnostic-based approach in order to increase archival and retrieval efficiency that lends itself to optimization applications. This work is a first step in pushing HRR HED science toward data management solutions that emphasize machine actionability and aim to stimulate community engagement to define data standards in HED science.

Instruments & Instrumentation↗
DOE OSTI2024

Ex situ calibration of the scattered-light time-history diagnostic on the National Ignition Facility

The scattered-light time-history diagnostic (SLTD) suite measures time-resolved scattered light in three wavelength bands: stimulated Brillouin scattering (350–352 nm), stimulated Raman scattering (430–760 nm), and plasma emission at half the laser frequency (695–735 nm), at 15 locations around the National Ignition Facility (NIF) target chamber. The SLTD, along with the full-aperture backscatter station (FABS), collects scattered light from direct- and indirect-drive inertial confinement fusion experiments. The SLTD calibration was revisited after a discrepancy between FABS and SLTD measurements was observed on NIF polar direct-drive experiments. An integrated calibration of the SLTD was performed for the first time, and individual components were also calibrated for the wavelengths of 351, 527, and 532 nm. The optical transmission of the instrument was measured to be (1.12 ± 0.04) × 10 –7 and (1.96 ± 0.11) × 10 –7 for the wavelengths of 351 and 532 nm, respectively. The revised calibration at 351 nm brings the SLTD measured scattered energy in agreement with the FABS measured scattered energy after additionally accounting for the degradation of an optical element in FABS. Furthermore, this decreased the inferred absorption by 7% for a representative experiment. However, discrepancies remain between FABS and SLTD measurements in the SRS band (532 nm).

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Functionally graded magnetic materials: a perspective to advance charged particle optics through compositional engineering

Additive manufacturing has ushered in a new paradigm of bottom-up materials-by-design of spatially non-uniform materials. Functionally graded materials have locally tailored compositions to provide optimized global properties and performance. In this letter, we propose an opportunity for the application of graded magnetic materials as lens elements for charged particle optics. A Hiperco50/Hymu80 (FeCo-2 V/Fe-80Ni-5Mo) graded magnetic alloy was successfully additively manufactured via Laser Directed Energy Deposition with spatially varying magnetic properties. The compositional gradient is then applied using computational simulations to demonstrate how a tailored material can enhance the magnetic performance of a critical, image-forming component of a transmission electron microscope.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2023

Multiphysics Modeling of Hydrokinetic Turbine Energy Conversion System

Hydrokinetic turbines (HKTs) hold great promise as a renewable energy source, but high maintenance costs and limited energy output hinder their widespread adoption. The lack of comprehensive research on HKT drivetrain designs creates a knowledge gap in enhancing generation efficiency and cost reduction. A model of the HKT system with a focus on electric drivetrains and power converters is required to address this knowledge gap. This paper first introduces a MATLAB-averaged model integrating electrical-mechanical-thermal domains and aging behaviors within multi-time frames. Then a PLECS model, which incorporates maximum power point tracking and dq reference framed control for AC-DC-AC power converters, is enriched by a dynamic thermal model to predict fast transients accurately. These models optimize the design at the component level, resulting in improved integrated system performance. Furthermore, the validation of the models is carried out through hardware experiments for the averaged model and hardware-in-the-loop testing for the dynamic model.

ADVANCED PROPULSION SYSTEMS,HYDRO ENERGY↗
DOE OSTI2025

Probabilistic Resilience-Oriented Assessment Approach for Transmission Networks Under Wildfires

The rising threat of wildfires poses significant challenges to power transmission networks, particularly in areas prone to such disasters. Traditional approaches for wildfire risk assessment neglect some potential wildfire scenarios. Here, this paper introduces a probabilistic resilience-oriented assessment approach for power transmission networks to address this gap. Initially, a probabilistic wildfire model is developed to capture uncertainties in ignition, intensity, and fire spread. Next, a spatiotemporal fragility model is constructed to assess the impact of wildfires on transmission corridors, incorporating Thermal Aging (TA) and Dynamic Thermal Rate (DTR) change. Finally, a comprehensive resilience metric is defined to evaluate system performance, leveraging the fragility model to determine component and system-level resilience. The approach employs a combinatorial enumeration method to generate potential wildfire scenarios, enhanced by an impact-increment-based state enumeration (IISE) method for computational efficiency. The proposed method provides critical insights for identifying system vulnerabilities and developing robust strategies to protect transmission networks from wildfires. The efficacy of this approach is validated through extensive scenarios of the RTS-GMLC system across Southern California, Nevada and Arizona.

Vahedi, Soroush [Univ. of Connecticut, Storrs, CT ↗
DOE OSTI2022

LIQUID AIR COMBINED CYCLE

Hybrid integration of thermal energy storage with gas turbines can provide compact, cost-effective, long-duration energy storage while reducing the fuel consumption of dispatchable resources needed for the reliability of renewable dominant electric grids. The Liquid Air Combined Cycle™ (LACC) is a hybrid energy storage system using cryogenic liquid air as an energy storage medium and gas turbine exhaust heat to extract stored energy. The storage tank is charged using liquefaction processes employing electric motor-driven compressors to pressurize the air, heat exchangers to reject heat of compression, and expanders to reduce the temperature and liquefy the air. Proven cryogenic refrigeration processes can be selected based on capital cost (per kg/s of liquid air produced), efficiency (kJ per kg of air produced), and operating factors including startup speed and load following capability. This paper presents results of studies undertaken for the U.S. Department of Energy to evaluate cost and performance tradeoffs for charge and discharge cycle components, optimize charge and discharge cycles, and assess the techno-economic potential of LACC technology.

Conlon, William↗
DOE OSTI2024

Matrix-Free High-Performance Saddle-Point Solvers for High-Order Problems in \(\boldsymbol{H}(\operatorname{\textbf{div}})\)

Here, this work describes the development of matrix-free GPU-accelerated solvers for high-order finite element problems in H(div). The solvers are applicable to grad-div and Darcy problems in saddle-point formulation, and have applications in radiation diffusion and porous media flow problems, among others. Using the interpolation–histopolation basis, efficient matrix-free preconditioners can be constructed for the (1, 1)-block and Schur complement of the block system. With these approximations, block-preconditioned MINRES converges in a number of iterations that is independent of the mesh size and polynomial degree. The approximate Schur complement takes the form of an M-matrix graph Laplacian and therefore can be well-preconditioned by highly scalable algebraic multigrid methods. High-performance GPU-accelerated algorithms for all components of the solution algorithm are developed, discussed, and benchmarked. Numerical results are presented on a number of challenging test cases, including the “crooked pipe” grad-div problem, the SPE10 reservoir modeling benchmark problem, and a nonlinear radiation diffusion test case.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multiverse: Bayesian model selection for neural networks

Multiverse is a code repository for a set of tools for Bayesian model selection for neural networks. The goal of the tools is to provide capabilities for selecting among prior and model specifications for Bayesian neural networks. Multiverse will include tools for creating Bayesian neural networks, evaluating the Bayesian model evidence, and performing inference in Bayesian neural networks. These components are written in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning and signal processing.

Klein, Natalie↗
DOE OSTI2024

Successful cleanroom installation of PIP-II SSR2 coupler using robotic arm

To minimize the contamination of SRF cavities, remote installation techniques are needed during the installation of components. Recent work at Fermilab has been performed to begin the process of developing techniques for assembling cavities using robotics. Multiple alignment methods were prototyped including alignment and computer vision methods. Using a remotely controlled robotic arm, the alignment and installation of couplers have been successfully performed on prototype PIP-II SSR2 cavities in a cleanroom. The installation process will be shown to show to demonstrate the potential of future installations on other cavities and cavity ancillaries.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Quantitative Performance Assessment of Proxy Apps and Parents (Report for ECP Proxy App Project Milestone ADCD-504-28)

The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications and their role in the overall ECP ecosystem. Our FY22 March/April milestone (ADCD- 504-28) proposed to: Assess the fidelity of proxy applications compared to their respective parents in terms of kernel and I/O behavior, and predictability. Similarity techniques will be applied for quantitative comparison of proxy/parent kernel behavior. MACSio evaluation will continue and support for OpenPMD backends will be explored. The execution time predictability of proxy apps with respect to their parents will be explored through a carefully designed scaling study and code comparisons. Note that in this FY, we also have quantitative assessment milestones that are due in September and are, therefore, not included in the description above or in this report. Another report on these deliverables will be generated and submitted upon completion of these milestones. To satisfy this milestone, the following specific tasks were completed: Study the ability of MACSio to represent I/O workloads of adaptive mesh codes. Re-define the performance counter groups for contemporary Intel and IBM platforms to better match specific hardware components and to better align across platforms (make cross-platform comparison more accurate). Perform cosine similarity study based on the new performance counter groups on the Intel and IBM P9 platforms. Perform detailed analysis of performance counter data to accurately average and align the data to maintain phases across all executions and develop methods to reduce the set of collected performance counters used in cosine similarity analysis. Apply a quantitative similarity comparison between proxy and parent CPU kernels. Perform scaling studies to understand the accuracy of predictability of the parent performance using its respective proxy application. This report presents highlights of these efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Recycling and Reuse of Tungsten-Rhenium Refractory Alloy Powder for Additive Manufacturing (Quarterly Progress Report - 01 April 2023-30 June 2023)

Savannah River National Laboratory in partnership with 6K Inc is working on demonstrating laser and electron-beam powder bed additive manufacturing processes of W-24Re alloy and developing powder reconditioning (deoxidation) technology via plasma spheroidization and investigating the properties and performance of additively manufactured W-24Re components produced using both virgin and recycled powders.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Report Outlining Computed Tomography Strategy and Microscopy Approach to Qualifying AM 316 Materials

This report is part of work package CR-22OR0406012, Automated, High-Throughput Materials Characterization Techniques , under the Advanced Materials and Manufacturing Technologies program (AMMT). The project’s primary objective is to leverage our AI-based rapid and high-throughput automated characterization framework to qualify additively manufactured 316 materials comprehensively, focusing on optimizing the additive manufacturing process and evaluating the performance of 3D-printed stainless steel components. This report outlines our strategy for leveraging the automated characterization process for qualifying 316H materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

High Voltage High Power Traction Inverter Thermal Management

This project develops thermal management solutions for various 530-kW inverter topologies being developed by Oak Ridge National Laboratory (ORNL). The thermal management solutions will provide high thermal performance and be designed to keep inverter components (e.g., power modules, capacitors) within allowable operating temperatures to help meet the project objectives. Steady-state and transient thermal models of the inverter and cooling system will be created and used to predict component temperatures under various drive cycle conditions.

33 ADVANCED PROPULSION SYSTEMS↗