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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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DOE OSTI2024

Materials (FR 2023 Annual Progress Report)

This document summarizes the progress of VTO Materials R&D projects supported during the fiscal year 2023. The Propulsion Materials portfolio is closely aligned with other VTO subprograms to identify critical materials needs for next-generation high-efficiency powertrains for both heavy- and light-duty vehicles. The Lightweight Materials portfolio works closely with industry through the U.S. DRIVE Partnership to understand light-duty vehicle structural weight-reduction goals and identify technical challenges that prevent the deployment of lightweight materials.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

High power density compact drive integrated motor for electric transportation

This project, initially part of OPEN 2018, and subsequently the ASCEND effort, targeted demonstration of significant enhancements in internal permanent magnet (IPM) motor torque and power density for current and future ground and air electric transportation applications. These were achieved through: (1) embedded two-phase system thermal management, (2) coupled, multi-scale electrical-electromagnetic-thermal-mechanical co-design and optimization, (3) size and weight reduction of motor and drive electronics through elimination of redundant cooling and coupling hardware, and (4) higher efficiency operation of SiC wide bandgap power electronics packaging through high temperature operation (200 oC). The proposed approach utilizes a single dielectric coolant for closed loop two-phase thermal management, and a combined heat rejection unit for the IPM and drive. Wick assisted liquid delivery for evaporative thermal management is utilized for the motor, and the drive electronics utilize the same coolant in flow boiling within the cold plate structures. Through the use of three-dimensional packaging for SiC, and novel drive topologies with reduced switching losses, significant increases in power density and compactness were targeted.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

High-Speed Layup and Forming of Automotive Composite Components

This Project is focused on the design and manufacture of automotive components that meet functional and environmental requirements of an existing automotive application at a cost of ≤ $\$$11.00 per kilogram weight reduction. This project fosters the development of composite material technologies suitable for high volume automotive processes and run rates as well as industry workforce development with these technologies. Current automotive manufacturing involves utilizing steel or aluminum in sheet form which is rapidly stamped into components at rates up to 3600 per hour. The metallic sheets are available in many different thicknesses, strength levels, and manufacturing rates are reasonable independent of part size. While composite materials are available for use in automotive applications, the material cost, labor to manufacture and the processing of the waste far exceed the cost compared to metallic designs. Typical composite layer by layer layup procedures don’t meet the desired 60 second layup time that current automotive processes require and are also restricted by part size. Due to these factors, composites have not yet made advances into today’s high volume automotive applications. Industry partners DURA, BASF, Ford, and IACMI core innovation partner MSU collaborated to develop a manufacturing process technology that is capable of manufacturing composite blanks at high volume and independent of part size. IACMI core innovation partner Purdue provided FEA analysis and cost modelling. The objective of this project was to demonstrate a composite sheet layup and consolidation process that can be commercialized for high volume requirements, identify potential layup equipment suppliers, and develop a process of 60 second layup, forming, and trimming of a continuous fiber automotive component for the mainstream market.

42 ENGINEERING↗
DOE OSTI2026

Development of in-situ polymerized intrinsically conductive resin and low-cost carbon pigments offering high conductivity for sensing, EMI shielding and lighting protection

Electrically conductive composites are emerging across diverse industries such as electronic, automotive, aerospace, advanced air mobility, biomedical, infrastructure, defense and security offering static charge dissipation, electromagnetic interference shielding, lighting protection, sensing, dicing, corrosion monitoring, etc. Conductivity enhanced composites provide several advantages compared to conventional metals including weight reduction, corrosion resistance, energy efficient processability, tunable properties and multifunctionality. Polymers are typically insulating in nature and require conducting filler for electron transport. However, dispersion and polymer-filler interphases are critical and often disrupt conducting pathways. Besides, conductive fillers such as graphene, carbon nanotube, MXene, silver nanowire, etc. are expensive, limiting their wide adoption in composite industry. On the other hand, a limited number of intrinsically conductive polymers are available among which polyaniline (PANI) has been widely studied due to its high conductivity, thermal and chemical stability. However, PANI is difficult to process and exhibits weak mechanical properties. In brief, there is a significant demand for electrically conductive polymer formulation with cost-effective conducting fillers that offer processability in scale to expand the market of conductivity enhanced materials.

Kumar, Vipin [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2026

MadGraph5_aMC@NLO + Pythia 8.3 event samples for cell resampling studies (13 TeV, Z+jets and top quark pair production)

This record contains two simulated proton–proton collision samples at a center-of-mass energy of √s = 13 TeV, each of 100,000 events, stored as complete truth-level particle records. The samples were produced for a study of optimal-transport-based cell resampling of negatively weighted Monte Carlo events, and are released so that the study can be reproduced and to support further work on negative-weight reduction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Thermal Analysis of Large Area Additive Manufacturing Resistance Heating Composites for Out of Oven/Autoclave Applications

Additive Manufacturing (AM) of carbon fiber (CF) reinforced composite has received growing attention because of the design flexibility, superior mechanical properties, improved thermal properties, and weight reduction. Autoclave tooling was proven to be a successful application for large scale AM technology. The capital cost, and cost associated with heating, and cycle time in a conventional autoclave process is relatively high. Thus, an innovative design of AM mold with an efficient heating scheme is essential. This study represents an innovative method of the resistive heating of composite molds which does not require a room size oven for heating during the curing processing. Therefore, it has the potential to reduce the operating cost drastically. For the design validation and feasibility study, we performed a numerical analysis of the wire embedded and AM mold parts. The goal of this study is to determine and optimize the thermal behavior of the printed mold with embedded wire technology. It is anticipated that the larger distance between the embedded wires along the printing direction (z-direction) increase the cold spot, on the other hand, a close distance of the wire can create the unwanted localize heating, thus melting. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) was used for the simulation purpose. Thermal characterization was set to 100°C to avoid the thermal deformation or bulging on the part surface.

billah, Kazi↗
DOE OSTI2020

Thermal Analysis of Large Area Additive Manufacturing Resistance Heating Composites for Out of Oven/Autoclave Applications

Additive Manufacturing (AM) of carbon fiber (CF) reinforced composite has received growing attention because of the design flexibility, superior mechanical properties, improved thermal properties, and weight reduction. Autoclave tooling was proven to be a successful application for large scale AM technology. The capital cost, and cost associated with heating, and cycle time in a conventional autoclave process is relatively high. Thus, an innovative design of AM mold with an efficient heating scheme is essential. This study represents an innovative method of the resistive heating of composite molds which does not require a room size oven for heating during the curing processing. Therefore, it has the potential to reduce the operating cost drastically. For the design validation and feasibility study, we performed a numerical analysis of the wire embedded and AM mold parts. The goal of this study is to determine and optimize the thermal behavior of the printed mold with embedded wire technology. It is anticipated that the larger distance between the embedded wires along the printing direction (z-direction) increase the cold spot, on the other hand, a close distance of the wire can create the unwanted localize heating, thus melting. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) was used for the simulation purpose. Thermal characterization was set to 100°C to avoid the thermal deformation or bulging on the part surface.

billah, Kazi↗
DOE OSTI2023

An uncertainty-aware strategy for plasma mechanism reduction with directed weighted graphs

In this work, we present a framework for the analysis and reduction of plasma mechanisms by means of weighted directed graphs, in which reactions and species are both treated as nodes. The methodology consists of two distinct analyses. The first, which is qualitative, relies on graph spatializations via force-directed algorithms to discover the predominant global patterns in the chemical model. The second ranks the reactions based on their shortest paths' lengths from/to the species of interest and their relative contributions to the power balance. Further, this quantitative investigation enables a strategy for mechanism reduction that is fully automatized, as it does not require any expert knowledge, highly effective, as it generates reduced mechanisms that are highly accurate while relying on a small number of processes, and easily interpretable, as the algorithm justifies the importance of the retained reactions by outputting their related chemical pathways. Additionally, the work proposes a methodology extension that employs ensembles of graphs to improve the robustness of the reduced mechanism to reaction parameter uncertainties. The approach, here tested for steady-state predictions of a plasma system characterizing negative hydrogen ion sources, is general and can be used in a wide variety of applications outside the particular nuclear fusion context demonstrated in this work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2020

Analysis and Evaluation of Thermally Annealed Pyrolytic Graphite Heat Spreader for Power Modules

Next generation power modules demand increased heat extraction capability along with reduced weight and volume. In this paper, thermally annealed pyrolytic graphite (TPG) is analyzed and compared with conventional materials used in power modules for thermal management. Fundamental properties of TPG are explained and compared with commonly used materials in power module heat spreaders and substrates. The encapsulated TPG based heat spreader is manufactured and compared with bulk copper in simulation and experimental based analysis. The results show that encapsulated TPG based heat spreader achieves more than 50% reduction in thermal resistance along with 48% reduction in weight in the heat spreader layer.

Gurpinar, Emre↗
DOE OSTI2024

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

58 GEOSCIENCES↗
DOE OSTI2022

Machine learning-based optimization of air-cooled heat sinks

Machine learning-based models using Artificial Neural Network (ANN) and greedy search algorithm are used to optimize air-cooled parallel plate-finned heat sinks (PPFHSs) subjected to laminar flow over an extensive range of design parameters. Here, the thermal and hydraulic performances of PPFHSs are represented by heat transfer coefficient (h) and pressure drop (ΔP), respectively. Optimization objectives for PPFHS designs can vary from industry to industry depending on their design priorities. The present study proposes a novel and generalized optimization method that defines practical optimization objectives and provides an accurate optimization process to design effective PPFHSs for a wide range of industrial applications with different design requirements. Three optimization objectives are presented in this study: (i) the largest h PΔ, (ii) the largest h within a specified maximum allowed flow rate, and (iii) the lowest weight that maximizes h for operation within the maximum allowed flow rate. While the shortcoming of the first objective is demonstrated, the other two objectives are found to be suitable for designing effective heat sinks (HSs) across different applications. Results suggest a promising trend from the third objective to develop HSs with ~ 37-68% lower weight, 80-85% reduced ΔP, and negligible penalty in h compared with optimized HSs obtained from the second objective. However, since the third objective leads to HSs with thinner fins, structural analysis should be performed to ensure reliable operation of the HSs.

42 ENGINEERING↗
DOE OSTI2022

Nebulized delivery of a broadly neutralizing SARS-CoV-2 RBD-specific nanobody prevents clinical, virological, and pathological disease in a Syrian hamster model of COVID-19

There remains an unmet need for globally deployable, low-cost therapeutics for the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Previously, we reported on the isolation and in vitro characterization of a potent single-domain nanobody, NIH-CoVnb-112, specific for the receptor-binding domain (RBD) of SARS-CoV-2. Here, we report on the molecular basis for the observed broad in vitro neutralization capability of NIH-CoVnb-112 against variant SARS-CoV-2 pseudoviruses. The structure of NIH-CoVnb-112 bound to SARS-CoV-2 RBD reveals a large contact surface area overlapping the angiotensin converting enzyme 2 (ACE2) binding site, which is largely unencumbered by the common RBD mutations. In an in vivo pilot study, we demonstrate effective reductions in weight loss, viral burden, and lung pathology in a Syrian hamster model of COVID-19 following nebulized delivery of NIH-CoVnb-112. These findings support the further development of NIH-CoVnb-112 as a potential adjunct preventative therapeutic for the treatment of SARS-CoV-2 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

THERMAL ANALYSIS AND DESIGN OF SELF-HEATING MOLDS USING LARGE-SCALE ADDITIVE MANUFACTURING FOR OUT-OF-AUTOCLAVE APPLICATIONS

Autoclave processing is a commonly used state-of-the-art fiber-reinforced composite manufacturing technology, albeit high capital cost, long cycle times and high energy consumption. Alternatively, out-of-autoclave processing reduces the initial and operating costs while producing composite structures with similar quality as that of autoclave parts. Additive Manufacturing (AM) the scaled-up molds for out-of-autoclave process using carbon fiber (CF) reinforced composite offers design flexibility, enhanced mechanical, and thermal properties in addition to reduction in weight and cost. However, heating of these molds using an oven is still expensive and necessitates an energy-efficient heating process. In this study, resistive heating through heating elements embedded within fiber reinforced composite molds is used as an efficient heating mechanism. The goal is to design wire embeddings and determine the optimal heat flux density to achieve a target uniform temperature of 1000C across the mold surface. To this end, numerical analyses were performed to evaluate the temperature distribution across the composite mold surface for a given wire placement and mold configuration. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) were used in the thermal analysis. Time taken to reach the steady state temperature was also estimated. Design guidelines for wire embeddings were included to enable efficient manufacturing of fiber-reinforced composites through out-of-autoclave molds.

Pokkalla, Deepak↗
DOE OSTI2020

Design and Development of a Robotic Exoskeleton Device for use in Glove Boxes - 20374

Glovebox operators in nuclear facilities often operate under radioactive environment and typically perform monotonous, dull and repetitive work. This can result in muscle fatigue leading to increased levels of accidents. Current research work aims in assisting the glovebox operators by designing a novel upper limb exoskeleton device to reduce injuries and increase worker efficiency. Almost none of the commercially available hand and upper body exoskeletons are directly suitable for use in gloveboxes. This explains the novelty of the present work in developing a wearable powered robotic exoskeleton device. The device consists of electronics, motors and spring systems mounted on adjustable plates and is supported by a stationary frame behind the worker. The device is intended to be portable and adjustable according to the operator height. The elbow has a flexion movement assisted with motor control by way of a Bowden cable system. The system is floated on a spring and pulley combination that allows for extra safety and smoothness of operation. The end points are fixed near the pivot points to help minimize unwanted movement/torque. The extension movement of the upper and lower arm are assisted by gravity and a counter spring. The shoulder and upper arm movement are held in a neutral position with a spring system for counter balance allowing small movements within the glovebox. This initial concept considers only the vertical movement of the arm. The abduction and adduction movement will not be assisted or impeded by the over shoulder plate or at the forearm plate allowing for full range of motion. Several glovebox aspects such as the level of task assistance, ergonomic design, minimum hindrance in dexterity/operator movements and tear resistance for hazardous safety reasons will be considered in the design of the exoskeleton. Computer models will be simulated to test the load levels and stress conditions on the exoskeleton to enhance the designs. The exoskeleton focusses on major muscular involvement reduction through weight offset and active assistance with minimal parts and hence the device is derived with several joint links and contains both passive and active joints. The paper details the various stages in the design development and prototype testing of the exoskeleton device. Upon successful prototype device development, human testing will be conducted in a replica of the SRS-235F hot cell facility constructed at FIU-ARC. The final stage of the project will be potential testing/deployment of the exoskeleton device in one of the glovebox facilities in Savannah River Nuclear Waste site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗