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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 379 records · Page 21

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NASA NTRS2012

Generation After Next Propulsor Research: Robust Design for Embedded Engine Systems

The National Aeronautics and Space Administration, United Technologies Research Center and Virginia Polytechnic and State University have contracted to pursue multi-disciplinary research into boundary layer ingesting (BLI) propulsors for generation after next environmentally responsible subsonic fixed wing aircraft. This Robust Design for Embedded Engine Systems project first conducted a high-level vehicle system study based on a large commercial transport class hybrid wing body aircraft, which determined that a 3 to 5 percent reduction in fuel burn could be achieved over a 7,500 nanometer mission. Both pylon-mounted baseline and BLI propulsion systems were based on a low-pressure-ratio fan (1.35) in an ultra-high-bypass ratio engine (16), consistent with the next generation of advanced commercial turbofans. An optimized, coupled BLI inlet and fan system was subsequently designed to achieve performance targets identified in the system study. The resulting system possesses an inlet with total pressure losses less than 0.5%, and a fan stage with an efficiency debit of less than 1.5 percent relative to the pylon-mounted, clean-inflow baseline. The subject research project has identified tools and methodologies necessary for the design of next-generation, highly-airframe-integrated propulsion systems. These tools will be validated in future large-scale testing of the BLI inlet / fan system in NASA's 8 foot x 6 foot transonic wind tunnel. In addition, fan unsteady response to screen-generated total pressure distortion is being characterized experimentally in a JT15D engine test rig. These data will document engine sensitivities to distortion magnitude and spatial distribution, providing early insight into key physical processes that will control BLI propulsor design.

Arend, David J.↗

NASA NTRSDate not supplied

Nuclear Electric Propulsion (NEP) Developments for Human Mars Missions

Nuclear Electric Propulsion (NEP) has the potential to enable human missions to Mars and other ambitious missions throughout the solar system. This paper will discuss ongoing NEP research and development efforts and challenges to developing NEP systems for these missions. The baseline NEP vehicle design is comprised of multiple subsystems operating in concert. The major subsystems include a nuclear reactor, a dynamic power conversion system to generate electrical power, heat rejection/radiators, a power management and distribution system to deliver power to the electric propulsion (EP) system and to the crewed transit habitat, and the EP system. An additional chemical propulsion system is also included to execute higher-thrust maneuvers in the Earth and Martian gravity wells. Specific issues that will be addressed include mission trajectory design and optimization, concurrent subsystem technology advancement plans and maturation efforts, and systems-level modeling of the entire vehicle to permit the fast exploration of trade-offs that can occur with changes in operating conditions or through the use of different technologies.

Kurt A Polzin↗

NASA NTRS2022

Electrodeionization Salt Removal from Water

This project developed a method to selectively extract sodium (Na) from complex, mission-relevant wastewaters using electricity as a driving force. The team investigated capacitive deionization (CDI), which has been used for general desalination but not for selective removal of Na. Three tasks supported this overall aim: designing and building a reactor and electrodes, engineering absorption and desorption parameters, and investigating varying water compositions and sources. Potential benefits of this approach include integrating into existing Environmental Control & Life Support Systems (ECLSS) treatment trains, reducing salinity for hydroponic installations, and producing food preservatives. Significant accomplishments, including building new reactors and characterizing their optimal operations and applications, advance the frontier of in-situ resource utilization for planetary habitation.

Luke Roberson↗

NASA NTRS2004

Advanced Adiabatic Demagnetization Refrigerators for Continuous Cooling

The research at Houston was focused on optimizing the design of superconducting magnets for advanced adiabatic demagnetization refrigerators (ADRs), assessing the feasibility of using high temperature superconducting (HTS) magnets in ADRs in the future, and developing techniques to deposit HTS thin and thick films on high strength, low thermal conductivity substrates for HTS magnet leads. Several approaches have been tested for the suggested superconducting magnets.

Chu, Paul C. W.↗

NASA NTRSDate not supplied

Advanced Parametric Lobe Mixer Concepts for Internally Mixed Nozzles

A continuation of efforts to optimize a dual-stream lobe mixer for supersonic commercial flight are detailed, adding scarfing and scalloping features to a baseline lobe mixer previously optimized within a baseline parametric space. These "advanced features" are parameterized by four quantities: scarfing angle 𝜁, scallop height ℎ 𝑠 , scallop depth 𝑑 𝑠 , and scallop conic focal parameter 𝜌 𝑠 . Two baseline mixers, one parallel and one vortical, form the basis for an eighteen-case study to investigate the effects of each parameter in single-axis fashion. Aerodynamic performance is evaluated through velocity coefficient 𝐶 𝑉 . Moderate performance increases are seen with increasing scallop height and depth on the parallel mixer, and with increasing scarfing angle on the vortical mixer. Flowfield analysis is presented through visualization of axial and cross-stream vorticity development through the duct, as well as density variation. A correlation between axial vorticity and density variation decay was found, with earlier introduction of axial vorticity found to result in improved aerodynamic performance and potential noise suppression.

Lobe Mixer↗

NASA NTRS2014

National Space Biomedical Research Institute (NSBRI) JSC Summer Projects

This project optimized the calorie content in a breakfast meal replacement bar for the Advanced Food Technology group. Use of multivariable optimization yielded the highest weight savings possible while simultaneously matching NASA Human Standards nutritional guidelines. The scope of this research included the study of shelf-life indicators such as water activity, moisture content, and texture analysis. Key metrics indicate higher protein content, higher caloric density, and greater mass savings as a result of the reformulation process. The optimization performed for this study demonstrated wide application to other food bars in the Advanced Food Technology portfolio. Recommendations for future work include shelf life studies on bar hardening and overall acceptability data over increased time frames and temperature fluctuation scenarios.

Dowdy, Forrest Ryan↗

NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗

DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Development and Evaluation of a Novel Fuel Injector Design Method using Hybrid-Additive Manufacturing (Final Report)

The widespread application of metal additive manufacturing (AM) technologies has enabled exploration of complex design spaces to achieve optimally performing components. Current optimization techniques make use of several advanced methods to provide designs that are superior to existing versions. However, they seldom discuss the manufacturability of the optimal designs. The objective of this project was to develop a design optimization tool that simultaneously optimizes fuel injector hardware and the combustor flow field with optimization functions and constraints that consider both combustor performance and manufacturability using advanced AM methods and post-processing. In this way, the resultant hardware design is inherently imbued with our most advanced knowledge of combustion physics and AM methods from its conception.

36 MATERIALS SCIENCE↗

NASA NTRS1996

Improved NASA-ANOPP Noise Prediction Computer Code for Advanced Subsonic Propulsion Systems

Recent experience using ANOPP to predict turbofan engine flyover noise suggests that it over-predicts overall EPNL by a significant amount. An improvement in this prediction method is desired for system optimization and assessment studies of advanced UHB engines. An assessment of the ANOPP fan inlet, fan exhaust, jet, combustor, and turbine noise prediction methods is made using static engine component noise data from the CF6-8OC2, E(3), and QCSEE turbofan engines. It is shown that the ANOPP prediction results are generally higher than the measured GE data, and that the inlet noise prediction method (Heidmann method) is the most significant source of this overprediction. Fan noise spectral comparisons show that improvements to the fan tone, broadband, and combination tone noise models are required to yield results that more closely simulate the GE data. Suggested changes that yield improved fan noise predictions but preserve the Heidmann model structure are identified and described. These changes are based on the sets of engine data mentioned, as well as some CFM56 engine data that was used to expand the combination tone noise database. It should be noted that the recommended changes are based on an analysis of engines that are limited to single stage fans with design tip relative Mach numbers greater than one.

Kontos, K. B.↗

DOE OSTI2024

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Particles comprised of a thermal neutron absorbing material (HfAl3) are dispersed in a metal matrix material with high thermal conductivity (aluminum) to conduct the heat generated by neutron capture away from the fuel and materials. This metal matrix composite is very promising for use as a conduction-cooled neutron absorber and has the potential to be useful as a shroud or heat sink for testing advanced fast reactor fuels and materials in an existing thermal reactor. To design and optimize the absorber block system for advanced reactor designs, fundamental understanding of the irradiation effect on material properties is necessary. This dataset contains TEM characterization results of neutron irradiated HfAl3-Al composite specimens. It is focused on the irradiation induced defects (dislocation lines and loops) characterization using the on-zone axis bright field STEM technique. This data was collected using a FEI Tecnai G2 F30 S/TEM at the Microscopy and Characterization Suite (MaCS), Center for Advanced Energy Studies (CAES).

Guillen, Donna↗

DOE OSTI2024

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Guillen, Donna↗

NASA NTRS1989

Robust perturbation guidance for the advanced launch system

Real-time optimal trajectory generation for constructing guidance laws seems feasible. However, these deterministic guidance schemes may be sensitive to system and environmental uncertainties. To overcome this deficiency, it is suggested that the accessory problem about an optimal path, which generates a quadratic cost criterion in the perturbed states and control away from the optimal trajectory, be used as the cost criterion. A modified quadratic cost is minimized with respect to the perturbed control but maximized with respect to additive uncertainties in the linearized dynamics and in the linearized measurements. In this way, a robust neighboring optimum scheme is constructed. A perturbed guidance scheme is stated. To illustrate the theory, the second stage of a launch vehicle is considered. An optimal path is generated, and about this path a robust neighboring optimum guidance law is constructed and tested.

Speyer, Jason L.↗

NASA NTRS1992

Observations on computational methodologies for use in large-scale, gradient-based, multidisciplinary design incorporating advanced CFD codes

How a combination of various computational methodologies could reduce the enormous computational costs envisioned in using advanced CFD codes in gradient based optimized multidisciplinary design (MdD) procedures is briefly outlined. Implications of these MdD requirements upon advanced CFD codes are somewhat different than those imposed by a single discipline design. A means for satisfying these MdD requirements for gradient information is presented which appear to permit: (1) some leeway in the CFD solution algorithms which can be used; (2) an extension to 3-D problems; and (3) straightforward use of other computational methodologies. Many of these observations have previously been discussed as possibilities for doing parts of the problem more efficiently; the contribution here is observing how they fit together in a mutually beneficial way.

Newman, P. A.↗

NASA NTRS2004

Microstructural Optimization of High Temperature SiC/SiC Composites

Advanced materials and process approaches are described that have allowed NASA to develop five state-of-the-art silicon carbide (SiC) fiber-reinforced hybrid-SiC matrix composite systems that can operate under mechanical loading and oxidizing conditions for hundreds of hours at upper use temperatures from 1200 to 1450 C. Key data are presented that demonstrate the ability of these materials to meet the first-level property requirements for hot-section components in advanced gas turbine engines.

DiCarlo, James A.↗

DOE OSTI2024

3D printed optimized electrodes for electrochemical flow reactors

Recent advances in 3D printing have enabled the manufacture of porous electrodes which cannot be machined using traditional methods. With micron-scale precision, the pore structure of an electrode can now be designed for optimal energy efficiency, and a 3D printed electrode is not limited to a single uniform porosity. As these electrodes scale in size, however, the total number of possible pore designs can be intractable; choosing an appropriate pore distribution manually can be a complex task. To address this challenge, we adopt an inverse design approach. Using physics-based models, the electrode structure is optimized to minimize power losses in a flow reactor. The computer-generated structure is then printed and benchmarked against homogeneous porosity electrodes. We show how an optimized electrode decreases the power requirements by 16% compared to the best-case homogeneous porosity. Future work could apply this approach to flow batteries, electrolyzers, and fuel cells to accelerate their design and implementation.

25 ENERGY STORAGE↗

NASA NTRS1992

Application of Taguchi methods to dual mixture ratio propulsion system optimization for SSTO vehicles

The application of advanced technologies to future launch vehicle designs would allow the introduction of a rocket-powered, single-stage-to-orbit (SSTO) launch system early in the next century. For a selected SSTO concept, a dual mixture ratio, staged combustion cycle engine that employs a number of innovative technologies was selected as the baseline propulsion system. A series of parametric trade studies are presented to optimize both a dual mixture ratio engine and a single mixture ratio engine of similar design and technology level. The effect of varying lift-off thrust-to-weight ratio, engine mode transition Mach number, mixture ratios, area ratios, and chamber pressure values on overall vehicle weight is examined. The sensitivity of the advanced SSTO vehicle to variations in each of these parameters is presented, taking into account the interaction of each of the parameters with each other. This parametric optimization and sensitivity study employs a Taguchi design method. The Taguchi method is an efficient approach for determining near-optimum design parameters using orthogonal matrices from design of experiments (DOE) theory. Using orthogonal matrices significantly reduces the number of experimental configurations to be studied. The effectiveness and limitations of the Taguchi method for propulsion/vehicle optimization studies as compared to traditional single-variable parametric trade studies is also discussed.

Stanley, Douglas O.↗

DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗