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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Multiscale Modeling of Carbon Fiber Reinforced Polymer (CFRP) for Integrated Computational Materials Engineering Process

In this work, a multiscale modeling framework for CFRP is introduced to study hierarchical structure of CFRP. Four distinct scales are defined: nanoscale, microscale, mesoscale, and macroscale. Information at lower scales can be passed to higher scale, which is beneficial for studying effect of constituents on macroscale part’s mechanical property. This bottom-up modeling approach enables better understanding of CFRP from finest details. Current study focuses on microscale and mesoscale. Representative volume element is used at microscale and mesoscale to model material’s properties. At microscale, unidirection CFRP (UD) RVE is used to study properties of UD. The UD RVE can be modeled with different volumetric fraction to encounter non-uniform fiber distribution in CFRP part. Such consideration is important in modeling uncertainties at microscale level. Currently, we identified volumetric fraction as the only uncertainty parameters in UD RVE. To measure effective material properties of UD RVE, periodic boundary conditions (PBC) are applied to UD RVE to ensure convergence of obtained properties. Properties of UD is directly used at mesoscale woven RVE modeling, where each yarn is assumed to have same properties as UD. Within woven RVE, there can be many potential uncertainties parameters to consider for a physical modeling of CFRP. Currently, we will consider fiber misalignment within yarn and angle between wrap and weft yarns. PBC is applied to woven RVE to calculate its effective material properties. The effect of uncertainties are investigated quantitatively by Gaussian process. Preliminary results of UD and Woven study are analyzed for efficacy of the RVE modeling. This work is considered as the foundation for future multiscale modeling framework development for ICME project.

Composite, ICME, Multiscale Modeling↗
DOE OSTI2016

Resonance Parameter Adjustment Based on Integral Experiments

Our project seeks to allow coupling of differential and integral data evaluation in a continuous-energy framework and to use the generalized linear least-squares (GLLS) methodology in the TSURFER module of the SCALE code package to update the parameters of a resolved resonance region evaluation. We recognize that the GLLS methodology in TSURFER is identical to the mathematical description of a Bayesian update in SAMMY, the SAMINT code was created to use the mathematical machinery of SAMMY to update resolved resonance parameters based on integral data. Traditionally, SAMMY used differential experimental data to adjust nuclear data parameters. Integral experimental data, such as in the International Criticality Safety Benchmark Experiments Project, remain a tool for validation of completed nuclear data evaluations. SAMINT extracts information from integral benchmarks to aid the nuclear data evaluation process. Later, integral data can be used to resolve any remaining ambiguity between differential data sets, highlight troublesome energy regions, determine key nuclear data parameters for integral benchmark calculations, and improve the nuclear data covariance matrix evaluation. Moreover, SAMINT is not intended to bias nuclear data toward specific integral experiments but should be used to supplement the evaluation of differential experimental data. Using GLLS ensures proper weight is given to the differential data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Integrating Smart Plug and Process Load Controls into Energy Management Information System Platforms: A Landscaping Study

Plug and process loads (PPLs) already represent a significant fraction of total energy use in commercial buildings and their contribution is expected to increase in the future. Although plug-in devices are typically not monitored or controlled, new wireless technologies are being developed that can provide these functions. These 'smart' outlets collect data from connected devices and transmit it to cloud services for energy measurement, tracking, and control purposes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Potential Integration Between Residual Biogenic Process Resources and Green Hydrogen [Slides]

This presentation highlights previously published analysis on the use of off-gases from biomass pyrolysis processes towards energy utilities. The potential for use of pyrolysis off-gases in existing steam reformers for green hydrogen production is assessed. Impacts on various operations within a steam reforming system (with natural gas as the primary base feed) from the introduction of pyrolysis off-gases in various proportions are quantified to assess the feasibility of reusing existing industrial facilities with renewably sourced gases.

09 BIOMASS FUELS↗
DOE OSTI2016

Injection molding of high precision optics for LED applications made of liquid silicone rubber

Light Emitting Diodes (LED) conquer the growing global market of lighting technologies. Due to their advantages, they are increasingly used in consumer products, in lighting applications in the home and in the mobility sector as well as in industrial applications. Particularly, with regard to the increasing use of high-power LED (HP-LED) the materials in the surrounding area of the light emitting semiconductor chip are of utmost importance. While the materials behind the semiconductor chip are optimized for maximum heat dissipation, the materials currently used for the encapsulation of the semiconductor chip (primary optics) and the secondary optics encounter their limits due to the high temperatures. In addition certain amounts of blue UV radiation degrade the currently used materials such as epoxy resins or polyurethanes for primary optics. In the context of an ongoing joint research project with various partners from the industry, an innovative manufacturing method for high precision optics for LED applications made of liquid silicone rubber (LSR) is analyzed at the Institut of Plastics Processing (IKV), Aachen. The aim of this project is to utilize the material-specific advantages of high transparent LSR, especially the excellent high temperature resistance and the great freedom in design. Therefore, a high integrated injection molding process is developed. For the production of combined LED primary and secondary optics a LED board is placed in an injection mold and overmolded with LSR. Due to the integrated process and the reduction of subcomponents like the secondary optics the economics of the production process can be improved significantly. Furthermore combined LED optics offer an improved effectiveness, because there are no losses of the light power at the transition of the primary and secondary optics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

INTEGRATION OF COST MODELS AND PROCESS SIMULATION TOOLS FOR OPTIMUM COMPOSITE MANUFACTURING PROCESS

Manufacturing cost of resin transfer molded composite parts is significantly influenced by the cycle time, which is strongly related to the time for both filling and curing of the resin in the mold. The time for filling can be optimized by various injection strategies, and by suitably reducing the length of the resin flow distance during the injection. The curing time can be reduced by the usage of faster curing resins, but it requires a high pressure injection equipment, which is capital intensive. Predictive manufacturing simulation tools that are being developed recently for composite materials are able to provide various scenarios of processing conditions virtually well in advance of manufacturing the parts. In the present study, we integrate the cost models with process simulation tools to study the influence of various parameters such as injection strategies, injection pressure, compression control to minimize high pressure injection, resin curing rate, and demold time on the manufacturing cost as affected by the annual part volume. A representative automotive component was selected for the study and the results are presented in this paper

42 ENGINEERING↗
DOE OSTI2016

Bioelectrochemical Integration of Waste Heat Recovery, Waste-to- Energy Conversion, and Waste-to-Chemical Conversion with Industrial Gas and Chemical Manufacturing Processes

Many U.S. manufacturing facilities generate unrecovered, low-grade waste heat, and also generate or are located near organic-content waste effluents. Bioelectrochemical systems, such as microbial fuel cells and microbial electrolysis cells, provide a means to convert organic-content effluents into electric power and useful chemical products. A novel biochemical electrical system for industrial manufacturing processes uniquely integrates both waste heat recovery and waste effluent conversion, thereby significantly reducing manufacturing energy requirements. This project will enable the further development of this technology so that it can be applied across a wide variety of US manufacturing segments, including the chemical, food, pharmaceutical, refinery, and pulp and paper industries. It is conservatively estimated that adoption of this technology could provide nearly 40 TBtu/yr of energy, or more than 1% of the U.S. total industrial electricity use, while reducing CO 2 emissions by more than 6 million tons per year. Commercialization of this technology will make a significant contribution to DOE’s Industrial Technology Program goals for doubling energy efficiency and providing a more robust and competitive domestic manufacturing base.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗
DOE OSTI2018

Demonstration of Chemical Decladding and Disposition Options for Non-Al Clad Fuels in H-Canyon

Aqueous disposition of non-Al clad fuels was studied in order to determine if H-Canyon would be a viable disposition option for this type of L-Basin fuel. A review of the L-Basin fuel types and its condition was done suggesting two major cladding types, Zircaloy and 304L stainless steel needed to be studied. Dissolution flowsheets options were reviewed. Sulfex and Zirflex processes were chosen as best for H-Canyon integration. Both process flowsheets were demonstrated on a lab scale to determine decladding rate, and off-gas and waste production. The resulting data suggested decladding in H-Canyon was an option but dissolver corrosion as well as off-gas and waste generation needed to be addressed. Small scale dissolution in a hot cell of smaller damaged fuel is a viable option with the flowsheets tested.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Modular Biomass Gasification for Co-Production of Hydrogen and Power

The overall goal of this project was to demonstrate a novel process-intensified and modular Combined Hydrogen, Heat and Power (CH 2 P) production technology, targeting commercial scales of 5-to-10-megawatt electric (MWe) equivalent of hydrogen (H 2 ). The process technology integrates a novel adaptation of the steam-iron process to produce high purity H 2 from gasification of biomass and biomass blends with a compression-ready carbon dioxide (CO 2 ) stream, enabling significant progress towards achieving $1 per kilogram (kg) cost for clean H 2 .

08 HYDROGEN↗
DOE OSTI2021

Rapidly ramp cryogenic air separation unit without loss of O2 product purity—application for low-carbon fossil-fuel plants

The rapid integration of intermittent renewable sources into the electricity grid is driving the need for low-carbon, fossil-fuel power plant capable of rapid ramping. Therefore, a cryogenic air separation unit (ASU) as part of low-carbon, fossil-fuel power plant should be capable of rapid ramping. However, highly integrated and nonlinear processes of the ASU would significantly restrict this rapid ramping. To overcome this fundamental issue, we study the basic dynamic process of a state-of-the-art double-column ASU. A flow-driven dynamic model was established in Aspen Plus Dynamics, assuming perfect flow controls, to capture the basic dynamics of ramping ASU. We find the vapor-liquid counter-current flow structure in the low-pressure column is critical to the air separation when rapidly ramping the ASU. This flow structure is established based on a complicated heat integration process. It is simplified as an apparent counter-current heat transfer process in this work, which greatly reduces the complexity for studying the dynamics of ASU. For keeping this basic flow structure, the heat integration is maintained as its heat duty follows the ASU load, through several basic feed-forward and feed-back controllers on the critical stream flowrates. Moreover, we find a fundamental issue of mismatched dynamics in the low-pressure column, resulting in a significant loss of O2 product purity when rapidly ramping down the ASU and an obvious fluctuation of O2 product purity. Based on these explorations, we propose a basic control method for rapidly ramping the ASU without loss of O2 product purity. Results show the ASU basic dynamic process successfully ramps at a rate up to 10%/min (40-100% load) while maintaining the O2 product purity within 95.2-95.6 mol%.

Cheng, Mao↗
DOE OSTI2023

Oak Ridge National Laboratory Security by Design: A Plant Design and Component-Based Approach to Technical Solutions to Insider and Outsider Threat

Security costs for nuclear reactors have been shown to rise over the lifecycle of a nuclear facility as new threats, security weaknesses, and the generic threat profile of the plant location change over time. This paper conducts a review of the technological gaps that currently exist in Security by Design (SeBD) based on the literature available and attempts to close those gaps through proposing technological constructs and processes that can integrate safety and security in the design process of a plant, with a primary focus on SMR technology. Under the new modular and mass manufacturing regime, where plant designs attempt to reduce costs with potential economies of scale and modular construction, the current SeBD definitions focusing on state level regulators are not considered adequate since the licensing stage is often too late in the design process to integrate the true cost savings of SeBD. Thus, this paper attempts to remedy the current logic in order to allow for continuous implementation of security alongside the development of the safety concept for the plant. If adopted within the design process the establishment of this type of technology will allow for a technological ecosystem to evolve that will imbed security concepts into the physical design of the components of the plant.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered by conceptual and methodological challenges that arise from nature–society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature–society systems.

42 ENGINEERING↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.

54 ENVIRONMENTAL SCIENCES↗