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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 NTRS2015

Generation of Library Models for Verification of Android Applications

Android applications are difficult to verify and test since they have many external dependencies. To overcome this problem, environment generation can be used to create a model of the environment to simulate the behavior of these external dependencies. Creating this environment model manually is a tedious process and although there are many techniques available to generate models, the key lies in identifying how these techniques can be applied to a specific domain. In this paper we discuss two static analysis tools OCSEGen and Modgen and how they can be applied to the Android domain to generate models for specific parts of the environment.

Verification↗
NASA NTRSDate not supplied

Inlet Vortex Generator Design for the X-59 Low Boom Flight Demonstrator

An array of vortex generators was designed for the X-59 Low Boom Flight Demonstrator aircraft inlet. Prior to the a High Speed Inlet Distortion Test, concern regarding potentially high measurements of steady state or dynamic distortion prompted an effort to examine the use of passive flow control to reduce distortion levels. Design of experiments methodology was used to develop two matrices of vortex generators placed inside the inlet serpentine duct and upstream of the inlet on the compression surface, varying on several geometric parameters. Reynolds-Averaged Navier-Stokes simulations were performed using the FUN3D flow solver at Mach 1.45 to analyze the impact of each configuration on total pressure recovery, circumferential distortion, and radial distortion. Results from simulations of the baseline aircraft are compared with data acquired in the NASA Glenn Research Center’s 8- by 6-foot Supersonic Wind Tunnel. A vortex generator configuration with vanes placed upstream of the inlet terminal shock was shown to significantly increase total pressure recovery and decrease circumferential distortion with no penalty to radial distortion levels. Inlet mass flow sweeps at Mach 1.45, 0.6, and 0.3 indicated no detrimental effects from the vanes at off-design conditions.

Vortex generators↗
NASA NTRSDate not supplied

Inlet Vortex Generator Design for the X-59 Low Boom Flight Demonstrator

An array of vortex generators was designed for the X-59 Low Boom Flight Demonstrator aircraft inlet. Prior to the a High Speed Inlet DistortionTest, concern regarding potentially high measurements of steady state or dynamic distortion prompted an effort to examine the use of passive flow control to reduce distortion levels. Design of experiments methodology was used to develop two matrices of vortex generators placed inside the inlet serpentine duct and upstream of the inlet on the compression surface, varying on several geometric parameters. Reynolds- Averaged Navier-Stokes simulations were performed using the FUN3D flow solver at Mach 1.45 to analyze the impact of each configuration on total pressure recovery, circumferential distortion, and radial distortion. Results fromsimulations of the baseline aircraft are compared with data acquired in the NASA Glenn Research Center’s 8- by 6-foot SupersonicWind Tunnel. A vortex generator configuration with vanes placed upstream of the inlet terminal shock was shown to significantly increase total pressure recovery and decrease circumferential distortion with no penalty to radial distortion levels. Inlet mass flow sweeps at Mach 1.45, 0.6, and 0.3 indicated no detrimental effects from the vanes at off-design conditions.

Vortex generators↗
NASA NTRSDate not supplied

Aerodynamic loss generation in a low-pressure turbine stage with high lift blading

The present work aims to investigate the loss generation mechanisms in a highly loaded low-pressure turbine (LPT) stage. The turbine employs high-lift airfoils to reduce the number of blades and therefore the overall engine weight. Large Eddy Simulation (LES) is applied to study the detailed flow fields at two representative Reynolds numbers (150,000 and 40,000). The calculated flow fields are compared with available measured data. LES calculates the measured aerodynamic performance of the current turbine stage well. Both the measurements and the CFD simulations indicate that most of the aerodynamic loss is generated by the development of secondary flows near the rotor hub. An area of low momentum and high aerodynamic loss forms at roughly 25 % span above the hub downstream of the rotor. Formation and migration of a vortex system near the hub is the primary source of the loss generation. The hub wall vortex system in this highly loaded turbine stage is quite different from the horse-shoe vortex system in a typical stationary turbine cascade. Calculated instantaneous and time-averaged flow fields at two Reynolds numbers are examined in detail to study Reynolds number effects on the turbine stage performance. Steady and unsteady flow separation on the rotor suction side is calculated at both high and low Reynolds numbers. At the lower Reynolds number of 40,000, thicker blade boundary layers increase the area of flow separation on the rotor suction side, resulting in a 1.9 % increase in aerodynamic loss compared to the higher Reynolds number case.

low pressure turbine↗
NASA NTRS2023

Loft: An Automated Mesh Generator for Stiffened-Shell Aerospace Vehicles

Loft is an automated mesh generation code designed for aerospace vehicle structures. Based on user input, it can generate meshes for wings, noses, tanks, fuselage sections, thrust structures, etc. As the mesh is generated, each element is assigned properties that mark what part of the vehicle it is associated with. This property assignment is an extremely powerful feature making possible detailed analysis tasks such as load application and sizing. Loft can save its meshes in NASTRAN bulk data deck, EDS’ I-DEAS Universal File format, Abaqus input file format, VRML 2.0 (Virtual Reality Modeling Language) and STL (Stereo Lithography) files. The property assignment scheme was designed to make sizing in Collier Research’s HyperSizer easy. Support for other mesh storage formats can be added as needed.

Structural modeling↗
NASA NTRS2023

Utilizing Code Generation from Models for Electric Aircraft Motor Controller Flight Software

The processes followed to develop and test safety-critical flight software significantly increase development effort and overhead in comparison to both non-safety-critical and non-flight software. Generating code from models can be used to reduce development effort; however, the processes for using code generation to develop flight software are relatively immature. This work presents an approach for developing safety-critical flight software utilizing code generation from models to reduce development time and effort. The presented use-case is of electric aircraft motor control software developed for NASA's X-57 Maxwell aircraft.

flight software↗
NASA NTRSDate not supplied

Technology Demonstrator of an Americium-Fuelled Radioisotope Stirling Generator

Americium-241 is used as the fuel of choice in European Radioisotope Power Systems. The University of Leicester have developed a number of systems using this fuel type, including a 200 Wth heat source. To maximise the electrical output that can be generated from the heat source, the University of Leicester and NASA Glenn Research Center have collaborated on a radioisotope Stirling generator concept, building on the long heritage of dynamic power conversion developments at NASA GRC. Here we present the design of a technology demonstrator of the Americium-fuelled Radioisotope Stirling Generator (Am-RSG).

Americium-241↗
DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

MATBOX, an Open-Source Microstructure Analysis Toolbox for Meshing, Generation, Segmentation, and Characterization of 3D Heterogenous Volumes

Battery performance is strongly correlated with electrode microstructural properties. To account for its impact, lithium-ion battery (LIB) models either abstract the microstructural heterogeneity of composite electrodes using effective macroscopic properties (macro- or meso- scale models) or directly solve the system of equations on the microstructure geometry or mesh (microstructure-scale models). Therefore, to be adequate, both families of models require information from the microstructure geometry, which can be provided by the numerical tool presented in this work. MATBOX is a MATLAB open-source application [1] developed by NREL for performing various microstructure-related tasks including microstructure numerical generation, image filtering and microstructure segmentation, microstructure characterization and correlation, visualization, and microstructure meshing. MATBOX was originally developed for the analysis of LIB electrode microstructures; however, the algorithms provided by the toolbox are widely applicable to other heterogeneous materials. The toolbox provides a user-friendly experience thanks to a Graphical-User Interface, requires no coding by the user, and is well documented. This presentation will illustrate various MATBOX features for the characterization of a LIB electrode, including a fully automated Representative Volume Element (RVE) analysis, the numerical generation of complex 'virtual' microstructure, including dual-layer electrodes and carbon-binder additive phase, and the meshing of a complex NMC/graphite full cell microstructure suitable for 3D finite-element modeling. Other modules (segmentation, visualization, and correlation) will be briefly presented. Thanks to its modular, open-source approach, MATBOX can easily incorporate third-party algorithms to eventually build a standard in the field that will benefit the whole scientific community. Effective diffusion coefficient [2], additive phase numerical generation [3], and meshing [4] third-party algorithms have been already integrated in the toolbox with more to come.

DIRECT ENERGY CONVERSION,MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Maximizing the Benefits of On-Site Renewable Energy Generation Using On-Site Energy Storage

The commercial and industrial building sectors have a major role to play in a U.S. low-carbon future. Building owners can make a difference by deploying on-site renewable generation and storage technologies, which facilitates future-proofing their buildings as well as more effectively meeting the service requirements of the building. This resource provides an overview of common generation, storage, and load management technologies in behind-the-meter systems, and serves as a primer to demonstrate the impact on net building loads required from the grid for different sizes of renewable generation along with on-site electrical and thermal storage. These discussions can help commercial building owners communicate the value of these systems to project teams and develop innovative behind-the-meter solutions to achieve their energy goals.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2023

Nonlinear Dispersion Relation and Out‐of‐Plane Second Harmonic Generation in MoSSe and WSSe Janus Monolayers

Janus transition metal dichalcogenides are an emerging class of atomically thin materials with engineered broken mirror symmetry that gives rise to long‐lived dipolar excitons, Rashba splitting, and topologically protected solitons. They hold great promise as a versatile nonlinear optical platform due to their broadband harmonic generation tunability, ease of integration on photonic structures, and nonlinearities beyond the basal crystal plane. Here, second and third harmonic generation in MoSSe and WSSe Janus monolayers is studied. Polarization‐resolved spectroscopy is used to map the full second‐order susceptibility tensor of MoSSe, including its out‐of‐plane components. In addition, the effective third‐order susceptibility and the second‐order nonlinear dispersion close to exciton resonances for both MoSSe and WSSe are measured at room and cryogenic temperatures. This work sets a bedrock for understanding the nonlinear optical properties of Janus transition metal dichalcogenides and probing their use in the next‐generation on‐chip multifaceted photonic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Abstract A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ionic Thermoelectric Generators in Vertical and Planar Topologies Based on Fluorinated Polymer Hybrid Materials with Ionic Liquids

Abstract Ionic thermoelectrics (TEs), in which voltage generation is based on ion migration, are suitable for applications based on their low cost, high flexibility, high ionic conductivity, and wide range of Seebeck coefficients. This work reports on the development of ionic TE materials based on the poly(vinylidene fluoride‐trifluoroethylene), Poly(VDF‐co‐TrFE), as host polymer blended with different contents of the ionic liquid, IL, 1‐ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide, [EMIM][TFSI]. The morphology, physico‐chemical, thermal, mechanical, and electrical properties of the samples are evaluated together with the TE response. It is demonstrated that the IL acts as a nucleating agent for polymer crystallization. The mechanical properties and ionic conductivity values are dependent on the IL content. A high room temperature ionic conductivity of 0.008 S cm −1 is obtained for the sample with 60 wt% of [EMIM][TFSI] IL. The TE properties depend on both IL content and device topology‐vertical or planar‐the largest generated voltage range being obtained for the planar topology and the sample with 10 wt% of IL content, characterized by a Seebeck coefficient of 1.2 mV K −1 . Based on the obtained maximum power density of 4.9 µW m −2 , these materials are suitable for a new generation of TE devices.

Pereira, Nelson↗
DOE OSTI2021

Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions

Abstract The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen‐4‐2Q‐A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices.

Anand, Abhinav↗
DOE OSTI2022

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Abstract Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Automatic Digitization and Orientation of Scanned Mesh Data for Floor Plan and 3D Model Generation

This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a “pen-and-ink” style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design.

Sharma, Ritesh↗
DOE OSTI2023

Fitting a deep generative hadronization model

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods. However, these approaches will not work with data, where we do not have a matching between observed hadrons and partons. In this paper, we develop a protocol for fitting a deep generative hadronization model in a realistic setting, where we only have access to a set of hadrons in data. Our approach uses a variation of a Generative Adversarial Network with a permutation invariant discriminator. We find that this setup is able to match the hadronization model in Herwig with multiple sets of parameters. This work represents a significant step forward in a longer term program to develop, train, and integrate machine learning-based hadronization models into parton shower Monte Carlo programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗