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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Investigation of Effects of Material Architecture on the Elastic Response of a Woven Ceramic Matrix Composite

To develop methods for quantifying the effects of the microstructural variations of woven ceramic matrix composites on the effective properties and response of the material, a research program has been undertaken which is described in this paper. In order to characterize and quantify the variations in the microstructure of a five harness satin weave, CVI SiC/SiC, composite material, specimens were serially sectioned and polished to capture images that detailed the fiber tows, matrix, and porosity. Open source quantitative image analysis tools were then used to isolate the constituents and collect relevant statistics such as within ply tow spacing. This information was then used to build two dimensional finite element models that approximated the observed section geometry. With the aid of geometrical models generated by the microstructural characterization process, finite element models were generated and analyses were performed to quantify the effects of the microstructure and its variation on the effective stiffness and areas of stress concentration of the material. The results indicated that the geometry and distribution of the porosity appear to have significant effects on the through-thickness modulus. Similarly, stress concentrations on the outer surface of the composite appear to correlate to regions where the transverse tows are separated by a critical amount.

Goldberg, Robert K.↗
NASA NTRSDate not supplied

NASA Earth: Synthetic Spectranomics - Deep Learning of Surface 3-D Geometry, Chemistry, and Hyperspectra to Inform Next-generation Land Models

Machine and deep learning (ML/DL) have transformed our approach to Earth observation and system modeling (EOSM), unifying both in view of ML/DL models as a form of data assimilation (DA). Trained on diverse Earth observation records, detailed physical models, or hybrids of both in physics-informed machine learning, ML/DL may improve upon existing Earth system model (ESM) formulations while creating entirely new classes of models. One important application of deep learning is observation synthesis, allowing ESM developers to prepare for the increased spatial, temporal, and spectral/polar resolution of proposed future observing systems. This may involve the retrospective application of learning algorithms to existing observational records, with or without physical radiative transfer models, to co-inform mission planning and ESM development while providing a degree of data continuity for new missions.

Adam Erickson↗
NASA NTRS2023

The Regional Aerosol Model Intercomparison Project (RAMIP)

Changes in anthropogenic aerosol emissions have strongly contributed to global and regional trends in temperature, precipitation, and other climate characteristics and have been one of the dominant drivers of decadal trends in Asian and African precipitation. These and other influences on regional climate from changes in aerosol emissions are expected to continue and potentially strengthen in the coming decades. However, a combination of large uncertainties in emission pathways, radiative forcing, and the dynamical response to forcing makes anthropogenic aerosol a key factor in the spread of near-term climate projections, particularly on regional scales, and therefore an important one to constrain. For example, in terms of future emission pathways, the uncertainty in future global aerosol and precursor gas emissions by 2050 is as large as the total increase in emissions since 1850. In terms of aerosol effective radiative forcing, which remains the largest source of uncertainty in future climate change projections, CMIP6 models span a factor of 5, from −0.3 to −1.5 W m−2. Both of these sources of uncertainty are exacerbated on regional scales. The Regional Aerosol Model Intercomparison Project (RAMIP) will deliver experiments designed to quantify the role of regional aerosol emissions changes in near-term projections. This is unlike any prior MIP, where the focus has been on changes in global emissions and/or very idealised aerosol experiments. Perturbing regional emissions makes RAMIP novel from a scientific standpoint and links the intended analyses more directly to mitigation and adaptation policy issues. From a science perspective, there is limited information on how realistic regional aerosol emissions impact local as well as remote climate conditions. Here, RAMIP will enable an evaluation of the full range of potential influences of realistic and regionally varied aerosol emission changes on near-future climate. From the policy perspective, RAMIP addresses the burning question of how local and remote decisions affecting emissions of aerosols influence climate change in any given region. Here, RAMIP will provide the information needed to make direct links between regional climate policies and regional climate change. RAMIP experiments are designed to explore sensitivities to aerosol type and location and provide improved constraints on uncertainties driven by aerosol radiative forcing and the dynamical response to aerosol changes. The core experiments will assess the effects of differences in future global and regional (Africa and the Middle East, East Asia, North America and Europe, and South Asia) aerosol emission trajectories through 2051, while optional experiments will test the nonlinear effects of varying emission locations and aerosol types along this future trajectory. All experiments are based on the shared socioeconomic pathways and are intended to be performed with 6th Climate Model Intercomparison Project (CMIP6) generation models, initialised from the CMIP6 historical experiments, to facilitate comparisons with existing projections. Requested outputs will enable the analysis of the role of aerosol in near-future changes in, for example, temperature and precipitation means and extremes, storms, and air quality.

anthropogenic aerosol emissions↗
DOE OSTI2024

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational autoencoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.

42 ENGINEERING↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
NASA NTRS2011

Dynamic Gate Product and Artifact Generation from System Models

Model Based Systems Engineering (MBSE) is gaining acceptance as a way to formalize systems engineering practice through the use of models. The traditional method of producing and managing a plethora of disjointed documents and presentations ("Power-Point Engineering") has proven both costly and limiting as a means to manage the complex and sophisticated specifications of modern space systems. We have developed a tool and method to produce sophisticated artifacts as views and by-products of integrated models, allowing us to minimize the practice of "Power-Point Engineering" from model-based projects and demonstrate the ability of MBSE to work within and supersede traditional engineering practices. This paper describes how we have created and successfully used model-based document generation techniques to extract paper artifacts from complex SysML and UML models in support of successful project reviews. Use of formal SysML and UML models for architecture and system design enables production of review documents, textual artifacts, and analyses that are consistent with one-another and require virtually no labor-intensive maintenance across small-scale design changes and multiple authors. This effort thus enables approaches that focus more on rigorous engineering work and less on "PowerPoint engineering" and production of paper-based documents or their "office-productivity" file equivalents.

engineering paradigm↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1972

Automated procedures for sizing aerospace vehicle structures /SAVES/

Results from a continuing effort to develop automated methods for structural design are described. A system of computer programs presently under development called SAVES is intended to automate the preliminary structural design of a complete aerospace vehicle. Each step in the automated design process of the SAVES system of programs is discussed, with emphasis placed on use of automated routines for generation of finite-element models. The versatility of these routines is demonstrated by structural models generated for a space shuttle orbiter, an advanced technology transport,n hydrogen fueled Mach 3 transport. Illustrative numerical results are presented for the Mach 3 transport wing.

Giles, G. L.↗
NASA NTRS1983

Modular design attitude control system

Application of modular control techniques to the attitude control of a prototype flexible spacecraft and a prototype flexible space platform was further developed by determining numerical values for the physical parameters of a four body approximation of the MSFC/hybrid deployable truss incorporated in the space platform model, generating sensitivity coefficients for the model of the flexible spacecraft, evaluating the changes in the digital computer simulation of the flexible spacecraft resulting from the addition of another rigid body to the model and comparing attitude control effectiveness with actuators on more than one rigid body of the model with that for the case in which the actuators were restricted to one body.

Chichester, F. D.↗
NASA NTRSDate not supplied

Enhancements, Verification, and VMS Integration of VTOL Concept Vehicle Simulation Models

Advanced Rotorcraft Technology (ART) and the NASA Ames Aeromechanics branch have jointly developed FLIGHTLAB simulation models for Advanced Air Mobility (AAM) VTOL concept vehicles. The overarching purpose of the simulation model development is to establish a set of well defined reference vehicles for FLIGHTLAB users and the rotorcraft community. The ongoing research effort and enhancement of these AAM simulation models to fulfill the role of quality reference vehicles is this paper’s focus. The content of this paper expands on the established characteristics of these AAM models in three primary areas. First, enhancement of the lift+cruise and tiltwing models with elastic airframe properties is discussed. The process of setting up the elastic airframe model in FLIGHTLAB, as well as the impacts on flight characteristics are explained. The introduction of the elastic airframe modeling allows these models to be used in flight dynamics, loads, and vibration analysis of the configuration designs. Next, linear model generation from the enhanced simulation model is covered. Confirming the validity of the linearized models is of importance, as these linear models are utilized for flight control design and tuning for these experimental configurations. For the final focus, the progress towards implementation of these models into the NASA Ames Vertical Motion Simulator (VMS) is described. This task seeks to demonstrate the procedures of integrating a FLIGHTLAB flight simulation in the VMS environment, test fully integrated simulation with communication between flight dynamics, control, and propulsion models, and explore the essential aspects of simulation model integration in a full flight simulator environment. This includes I/O definition, initialization, trim, flying, etc. By expanding the capabilities of the AAM simulation models, they continue to develop as valuable and approachable modeling references.

VMS↗
NASA NTRS2006

International Space Station Centrifuge Rotor Models A Comparison of the Euler-Lagrange and the Bond Graph Modeling Approach

The assembly and operation of the International Space Station (ISS) require extensive testing and engineering analysis to verify that the Space Station system of systems would work together without any adverse interactions. Since the dynamic behavior of an entire Space Station cannot be tested on earth, math models of the Space Station structures and mechanical systems have to be built and integrated in computer simulations and analysis tools to analyze and predict what will happen in space. The ISS Centrifuge Rotor (CR) is one of many mechanical systems that need to be modeled and analyzed to verify the ISS integrated system performance on-orbit. This study investigates using Bond Graph modeling techniques as quick and simplified ways to generate models of the ISS Centrifuge Rotor. This paper outlines the steps used to generate simple and more complex models of the CR using Bond Graph Computer Aided Modeling Program with Graphical Input (CAMP-G). Comparisons of the Bond Graph CR models with those derived from Euler-Lagrange equations in MATLAB and those developed using multibody dynamic simulation at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) are presented to demonstrate the usefulness of the Bond Graph modeling approach for aeronautics and space applications.

Nguyen, Louis H.↗
NASA NTRS1998

Geometry Modeling and Grid Generation for Design and Optimization

Geometry modeling and grid generation (GMGG) have played and will continue to play an important role in computational aerosciences. During the past two decades, tremendous progress has occurred in GMGG; however, GMGG is still the biggest bottleneck to routine applications for complicated Computational Fluid Dynamics (CFD) and Computational Structures Mechanics (CSM) models for analysis, design, and optimization. We are still far from incorporating GMGG tools in a design and optimization environment for complicated configurations. It is still a challenging task to parameterize an existing model in today's Computer-Aided Design (CAD) systems, and the models created are not always good enough for automatic grid generation tools. Designers may believe their models are complete and accurate, but unseen imperfections (e.g., gaps, unwanted wiggles, free edges, slivers, and transition cracks) often cause problems in gridding for CSM and CFD. Despite many advances in grid generation, the process is still the most labor-intensive and time-consuming part of the computational aerosciences for analysis, design, and optimization. In an ideal design environment, a design engineer would use a parametric model to evaluate alternative designs effortlessly and optimize an existing design for a new set of design objectives and constraints. For this ideal environment to be realized, the GMGG tools must have the following characteristics: (1) be automated, (2) provide consistent geometry across all disciplines, (3) be parametric, and (4) provide sensitivity derivatives. This paper will review the status of GMGG for analysis, design, and optimization processes, and it will focus on some emerging ideas that will advance the GMGG toward the ideal design environment.

Samareh, Jamshid A.↗
DOE OSTI2026

AI-assisted rapid crystal structure generation towards a target local environment

In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.

Ridwan, Osman Goni [University of North Carolina a↗
NASA NTRS1979

Low-frequency broadband noise generated by a model rotor

Low-frequency broadband noise generated by model rotors is attributed to the interaction of ingested turbulence with the rotor blades. The influence of free-stream turbulence in the low-frequency broadband noise radiation from model rotors has been experimentally investigated. The turbulence was generated in the M.I.T. anechoic wind tunnel facility with the aid of bipolar grids of various sizes. The spectra and the intensity of the low-frequency broadband noise have been studied as a function of parameters which characterize the turbulence and of helicopter performance parameters. The location of the peak intensity was observed to be strongly dependent on the rotor-tip velocity and on the longitudinal integral scale of turbulence. The size scale of turbulence had negligible effect on the intensity of low-frequency broadband noise. The experimental data show good agreement with an ad hoc model based on unsteady aerodynamics.

Aravamudan, K. S.↗
DOE OSTI2025

Symplectic machine learning model for fast simulation of space-charge effects

Symplectic simulation of space-charge effects is crucial for the design and operation of high-intensity particle accelerators. Traditional methods for simulating these effects are often computationally expensive, resulting in significant overhead. In this work, we introduce a generative model based on a U-Net architecture within a generative adversarial network framework to efficiently simulate space-charge effects. The model is trained to predict the transverse multiparticle space-charge Hamiltonian, which can be physically computed using a gridless spectral method. The one-step symplectic transverse transfer map for the particles is then obtained by differentiating the predicted Hamiltonian. Benchmarking results demonstrate that this generative model achieves an order of magnitude higher computational efficiency compared to the spectral method, providing a highly efficient alternative for simulating space-charge effects with a large number of particles. By maintaining symplecticity, the model effectively preserves the phase-space structure and mitigates nonphysical errors in long-term simulations. This model has been integrated into jutrack, a novel autodifferentiable accelerator modeling code developed in the julia programming language.

Beam code development & simulation techniques↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗