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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Multiscale Progressive Failure Analysis of 3D Woven Composites

Application of three-dimensional (3D) woven composites is growing as an alternative to the use of ply-based composite materials. However, the design, analysis, modeling, and optimization of these materials is more challenging due to their complex and inherently multiscale geometries. Herein, a multiscale modeling procedure, based on efficient, semi-analytical micromechanical theories rather than the traditional finite element approach, is presented and applied to a 3D woven carbon–epoxy composite. A crack-band progressive damage model was employed for the matrix constituent to capture the globally observed nonlinear response. Realistic microstructural dimensions and tow-fiber volume fractions were determined from detailed X-ray computed tomography (CT) and scanning electron microscopy data. Pre-existing binder-tow disbonds and weft-tow waviness, observed in X-ray CT scans of the composite, were also included in the model. The results were compared with experimental data for the in-plane tensile and shear behavior of the composite. The tensile predictions exhibited good correlations with the test data. While the model was able to capture the less brittle nature of the in-plane shear response, quantitative measures were underpredicted to some degree.

3D woven composites↗
NASA NTRSDate not supplied

Joint Communication Resource Allocation and Velocity Selection in Urban Air Mobility via Multi-agent Reinforcement Learning

With traffic congestion problems becoming more severe in urban areas, the National Aeronautics and Space Administration promotes the Urban Air Mobility (UAM) concept, which envisages a safe and efficient air transportation system. However, the increased communication demands in UAM can exacerbate the spectrum scarcity. Therefore, a new communication resource allocation solution is necessary. In this paper, we focus on uplink UAM communications, where multiple aerial vehicles (AV) perform cargo/passenger delivery tasks. With predefined flight paths, AVs make decisions on communication resource allocation and velocity selection to complete their missions under safety constraints. Accordingly, we formulate a joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. We first model the optimization problem as a Markov game and propose a multi-agent reinforcement learning based solution. Simulation results corroborate the effectiveness of the proposed solution.

Ruixuan Han↗
DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

Giridhar, Nishant↗
DOE OSTI2024

Optimization of Solid Oxide Electrolysis Cell Systems Accounting for Long-Term Performance and Health Degradation

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

Giridhar, Nishant↗
DOE OSTI2025

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Projects in Germany and the Netherlands—Review and Lessons Learned

Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2025

Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models

We develop a new interatomic force field for Silicene, a 2D material with a buckled hexagonal lattice structure with high polymorphism. We introduce new parameterizations of a Tersoff model using a hierarchical multi-reward reinforcement learning (RL) methodology coupled with a continuous Monte Carlo Tree Search optimization. Our model significantly outperforms existing methods by enhancing the accuracy of predictions for the structural and thermodynamic properties of seven silicene polymorphs-including structure, energy, equation of state, elasticity, and phonon dispersion-when compared to established models. We further make a comprehensive comparison of the various models in predicting the mechanical and thermal properties of silicene. We trace the origin of the improved performance to the description of the angular dependence in the bond-order term, suggesting that modifying the angular terms in short-range models is essential to capture the structural diversity in low dimensional systems.

2D materials↗
DOE OSTI2021

Characterizing the Roles of Biogeochemical Cycling and Ocean Circulation in Regulating Marine Copper Distributions

Copper (Cu) is a key micronutrient for marine phytoplankton. Its oceanic biogeochemical cycle has elicited considerable attention due to dissolved Cu exhibiting a unique linear profile with depth. Several processes have been proposed for explaining this behavior. Here, in this study, we characterize the relationships between the observed Cu, PO 4 3– , and Si on a global scale. We find that the depth profiles of Cu resemble those of Si more than of PO 4 3– in the global ocean. To understand their relationships, we couple the biogeochemical and internal circulation processes in a model, fitting optimal Cu:PO 4 3– uptake ratios and remineralization length-scales to replicate the marine Cu distributions. The modeling results suggest that Cu uptake needs in the Southern Ocean are substantially higher than those in other oceanic regions. In addition, our modeling results indicate a deep Cu remineralization in the global ocean. We offer an alternative mechanism that relies on biogeochemical cycling and internal circulation to produce the linear depth profiles of dissolved Cu. Our results suggest that diatoms are likely the major phytoplankton dominating oceanic Cu cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data driven learning of robust nonlocal models

Nonlocal models use integral operators that embed length-scales in their definition. However, the integrands in these operators are difficult to define from the data that are typically available for a given physical system, such as laboratory mechanical property tests. In contrast, molecular dynamics (MD) does not require these integrands, but it suffers from computational limitations in the length and time scales it can address. To combine the strengths of both methods and to obtain a coarse-grained, homogenized continuum model that efficiently and accurately captures materials' behavior, we propose a learning framework to extract, from MD data, an optimal nonlocal model as a surrogate for MD displacements. Our framework guarantees that the resulting model is mathematically well-posed, physically consistent, and that it generalizes well to settings that are different from the ones used during training. The efficacy of this approach is demonstrated with several numerical tests for single layer graphene both in the case of perfect crystal and in the presence of thermal noise.

36 MATERIALS SCIENCE↗
NASA NTRS1980

A pilot modeling technique for handling-qualities research

A brief survey of the more dominant analysis techniques used in closed-loop handling-qualities research is presented. These techniques are shown to rely on so-called classical and modern analytical models of the human pilot which have their foundation in the analysis and design principles of feedback control. The optimal control model of the human pilot is discussed in some detail and a novel approach to the a priori selection of pertinent model parameters is discussed. Frequency domain and tracking performance data from 10 pilot-in-the-loop simulation experiments involving 3 different tasks are used to demonstrate the parameter selection technique. Finally, the utility of this modeling approach in handling-qualities research is discussed.

Hess, R. A.↗
NASA NTRS2006

Mixed Modeling of a SAW Delay Line Using VHDL-AMS

To aid in the development of SAW sensors for aerospace applications we have created a model of a SAW Delay line using VHDL. The model implements the Impulse Response method to calculate the frequency response, impedance, and insertion loss. The model includes optimization for the number of finger pairs in the IDTs and for the aperture height. This paper presents the model and the results from the model for a SAW delay line design.

Wilson, William C.↗
DOE OSTI2026

Agent-based Modeling for the Multi-objective Optimization of Energy Production Pathways: Integrated Techno-Economics and Life Cycle Assessment

This project developed an integrated modeling framework to evaluate how purpose-grown bioenergy feedstocks could support large-scale sustainable aviation fuel production in the United States. Led by Colorado State University with project partners, the work combined geographically resolved analysis, techno-economic assessment, life cycle assessment, multi-objective optimization, and agent-based modeling to examine feedstock performance, deployment potential, land-use implications, and adoption dynamics. The overall goal was to identify practical and sustainable pathways for producing aviation fuel from feedstocks such as energy crops, woody crops, oilseeds, and algae while helping inform policy, investment, and research needs tied to national SAF goals. In essence, the project provides a systems-level assessment of the opportunities and constraints associated with scaling bioenergy-based aviation fuels in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1981

A model for the submarine depthkeeping team

The most difficult task the depthkeeping team must face occurs during periscope-depth operations during which they may be required to maintain a submarine several hundred feet long within a foot of ordered depth and within one-half degree of ordered pitch. The difficulty is compounded by the facts that wave generated forces are extremely high, depth and pitch signals are very noisy and submarine speed is such that overall dynamics are slow. A mathematical simulation of the depthkeeping team based on the optimal control models is described. A solution of the optimal team control problem with an output control restriction (limited display to each controller) is presented.

Ware, J. R.↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this poster we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design across all periods is ensured using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occur-ring formation water from the oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this paper we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design is ensured across all periods using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2026

Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Stochastic Derivative-Free Optimization

Bi-fidelity stochastic optimization has gained increasing attention as an efficient approach to reduce computational costs by leveraging a low-fidelity (LF) model to optimize an expensive high-fidelity (HF) objective. In this paper, we propose ASTRO-BFDF, an adaptive sampling trust-region method specifically designed for unconstrained bi-fidelity stochastic derivative-free optimization problems. In ASTRO-BFDF, the LF function serves two purposes: (i) to identify better iterates for the HF function when the optimization process indicates a high correlation between them and (ii) to reduce the variance of the HF function estimates using bi-fidelity Monte Carlo (BFMC). The algorithm dynamically determines sample sizes while adaptively choosing between crude Monte Carlo and BFMC to balance the trade-off between optimization and sampling errors. We prove that the iterates generated by ASTRO-BFDF converge to a first-order stationary point almost surely. Additionally, we demonstrate the effectiveness of the proposed algorithm through numerical experiments on synthetic benchmarks and simulation optimization problems involving discrete event systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Design and evaluation of flight directors for V/STOL aircraft

A brief review of model-based techniques for the design of aircraft flight directors is undertaken. An analytical director design technique which utilizes an optimal control model of the human pilot is then discussed in more detail. The analytical and experimental results of three specific director design studies are discussed, all involving control of a light utility helicopter. Finally, a general design methodology is discussed which can aid in the specification of pilot-centered display requirements.

Hess, R. A.↗