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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Computer-aided-engineering system for modeling and analysis of ECLSS integration testing

The accurate modeling and analysis of two-phase fluid networks found in environmental control and life support systems is presently undertaken by computer-aided engineering (CAE) techniques whose generalized fluid dynamics package can solve arbitrary flow networks. The CAE system for integrated test bed modeling and analysis will also furnish interfaces and subsystem/test-article mathematical models. Three-dimensional diagrams of the test bed are generated by the system after performing the requisite simulation and analysis.

Sepahban, Sonbol↗
NASA NTRS1990

The environment power system analysis tool development program

The Environment Power System Analysis Tool (EPSAT) is being developed to provide space power system design engineers with an analysis tool for determining system performance of power systems in both naturally occurring and self-induced environments. The program is producing an easy to use computer aided engineering (CAE) tool general enough to provide a vehicle for technology transfer from space scientists and engineers to power system design engineers. The results of the project after two years of a three year development program are given. The EPSAT approach separates the CAE tool into three distinct functional units: a modern user interface to present information, a data dictionary interpreter to coordinate analysis; and a data base for storing system designs and results of analysis.

Jongeward, Gary A.↗
NASA NTRS2003

National Combustion Code Validated Against Lean Direct Injection Flow Field Data

Most combustion processes have, in some way or another, a recirculating flow field. This recirculation stabilizes the reaction zone, or flame, but an unnecessarily large recirculation zone can result in high nitrogen oxide (NOx) values for combustion systems. The size of this recirculation zone is crucial to the performance of state-of-the-art, low-emissions hardware. If this is a large-scale combustion process, the flow field will probably be turbulent and, therefore, three-dimensional. This research dealt primarily with flow fields resulting from lean direct injection (LDI) concepts, as described in Research & Technology 2001. LDI is a concept that depends heavily on the design of the swirler. The LDI concept has the potential to reduce NOx values from 50 to 70 percent of current values, with good flame stability characteristics. It is cost effective and (hopefully) beneficial to do most of the design work for an LDI swirler using computer-aided design (CAD) and computer-aided engineering (CAE) tools. Computational fluid dynamics (CFD) codes are CAE tools that can calculate three-dimensional flows in complex geometries. However, CFD codes are only beginning to correctly calculate the flow fields for complex devices, and the related combustion models usually remove a large portion of the flow physics.

Iannetti, Anthony C.↗
NASA NTRS1979

Engine Design

Shown in static view and in operation on a test stand, the engine pictured is an advanced aircraft propulsion system being developed by Teledyne CAE, Toledo, Ohio, manufacturer of turbofans, turbojets and turbomachinery. In the design and analysis phases of this and other engines, the company makes extensive use of COSMIC's NASTRAN computer program. NASTRAN serves as an analytical tool for design of many engine components, such as those glowing in the test stand photo. Teledyne CAE also uses two other COSMIC programs in studies of engine airflow. Use of the three computer programs affords significant annual savings.

Source record↗
DOE OSTI2024

BIM Interoperability Tool for Improving IFC-based Model Data Exchange Between Architectural Design and Structural Analysis for Linear Piping Components

There is a growing need in the AEC industry for the digitalization of model-based data exchange in BIM workflows. However, users continue to face difficulties exchanging data between BIM-based computer-aided design (CAD) and computer-aided engineering (CAE) software, even with the open, non-proprietary data exchange format called Industry Foundation Classes (IFC). Proposed solutions in academic research focus primarily on building systems, with comparatively little attention to piping models. Therefore, this paper introduces an interoperability tool for enabling piping model data exchange between architectural and structural analysis domains. The tool is tested using a piping model created in Autodesk Revit and shows marked improvement for IFC-based CAD-to-CAE interoperability over existing practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

U-splines: Splines over unstructured meshes

U-splines are a novel approach to the construction of a spline basis for representing smooth objects in Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE). A spline is a piecewise-defined function that satisfies continuity constraints between adjacent cells in a mesh. U-splines differ from existing spline constructions, such as Non-Uniform Rational B-splines (NURBS), subdivision surfaces, T-splines, and hierarchical B-splines, in that they can accommodate local variation in cell size, polynomial degree, and smoothness simultaneously over more varied mesh configurations. Mixed cell types (e.g., triangle and quadrilateral cells in the same mesh) and T-junctions are also supported, although the continuity of interfaces with triangle and tetrahedral cells is limited in the present work. The U-spline algorithm introduces a new technique for using local null space solutions to construct basis functions for the global spline null space problem. The U-spline construction is presented for curves, surfaces, and volumes with higher dimensional generalizations possible. Lastly, a set of requirements are given to ensure that the U-spline basis is positive, forms a partition of unity, is complete, and is locally linearly independent.

42 ENGINEERING↗
DOE OSTI2024

A Deep Learning Approach for Detection and Localization of Leaf Anomalies

The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.

Calabro', Davide↗
DOE OSTI2023

GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

42 ENGINEERING↗
DOE OSTI2021

Assessment of Prognostic Value of High-Sensitivity Cardiac Troponin T for Early Prediction of Chemoradiation Therapy-Induced Cardiotoxicity in Patients with Non-Small Cell Lung Cancer: A Secondary Analysis of a Prospective Randomized Trial

Cardiotoxicities induced by cancer therapy can negatively affect quality of life and survival. We investigated whether high-sensitivity cardiac troponin T (hs-cTnT) levels could serve as biomarker for early detection of cardiac adverse events (CAEs) after chemoradiation therapy (CRT) for non-small cell lung cancer (NSCLC).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. Moreover, the construction of our operator admits an efficient implementation which we detail and construct. As an experimental validation of our operator, we consider the task of compressing partial differential equation (PDE) simulation data from fixed meshes. Here, we show that QuadConv can match the performance of standard discrete convolutions on uniform grid data by comparing a QuadConv autoencoder (QCAE) to a standard convolutional autoencoder (CAE). Further, we show that the QCAE can maintain this accuracy even on non-uniform data. In both cases, QuadConv also outperforms alternative unstructured convolution methods such as graph convolution.

Compression↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2022

Carbon acquisition ecological strategies to connect soil microbial biodiversity and carbon cycling

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. Further, we describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Full-wave modeling of RF waves in fusion plasmas with finite element method: Progress in past decades and its future role

This paper reviews the progress in computing radio frequency (RF) wave fields in fusion plasmas, specifically focusing on simulations utilizing the finite element method (FEM) over the past few decades. Computing RF wave fields in fusion plasmas presents unique challenges due to large simulation domains, complex antenna structures, non-local dielectric properties, and wide ranges of spatial scales. It highlights key developments and outlines future directions, primarily addressing waves in the ion cyclotron (IC) to lower hybrid (LH) frequency range. We begin with briefly revisiting earlier developments before the widespread availability of modern computer-aided engineering (CAE) software based on FEM. This historical perspective illuminates early progress and the physics difficulties that motivated ongoing work within the community. Modern wave simulations for RF antennas based on FEM are characterized by the use of detailed 3D antenna model geometry generated from engineering CAD software and localized wave dielectric model. Significant advancements have also been made in improving physics models to include phenomena such as RF sheath rectification and wave scattering. FEM-based RF simulations have also been applied to compute wave propagation in the core region, where the inclusion of non-local dielectric response is crucial. This is a challenging goal, and several promising approaches have been proposed in this area. Additionally, RF simulation development initiatives based on open-source libraries have gained popularity, demonstrating scalability and flexibility in extending physics models. This paper will discuss the advantages and disadvantages of using such a publicly available FEM library.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Thermal Stability of Silica for Application in Thermal Energy Storage

Thermal energy storage (TES) systems have enabled concentrating solar power (CSP) to remain competitive in the modern energy mix by providing economical load shifting grid services and firming up intermittent solar resource. Free from siting constraints, TES also shows promise as an economical alternative to traditional pumped-storage hydropower (PSH) and compressed air energy storage (CAES). As potential thermal energy storage media, some solid particles demonstrate stability over wide temperature ranges which allows for increased sensible energy storage density and is essential in achieving low-cost storage. Silica sand, in the form of a-quartz, is one such candidate. This work presents a brief review of relevant silica thermophysical properties and further investigates the thermal stability of silica particles as a candidate TES media by subjecting them to two different thermal tests: (1) a 500-hour thermal treatment at 1200 degrees C under varied atmospheres; and (2) cycling 25, 50, and 100 times between 300 degrees C and 1200 degrees C. For both tests, particle stability is examined by means of pre- and post-treatment Mie scattering. An additional XRD analysis is conducted for the 500-hour treatment in air. Results indicate limited changes in both particle distribution and crystallographic structure which is promising for the application as solid particle media for thermal energy storage.

concentrating solar power↗
DOE OSTI2024

Eigenmode twist by energy flux during fast-ion driven instabilities

Twisting of the spatial structure of eigenmodes by energy fluxes generated due to the spatial channeling (SC)—the fast-ion energy and momentum transfer across the magnetic field by destabilized modes—is considered. It is revealed that there is a correlation between the direction of the radial wave energy flux, orientation of the mode twist (MT), and the direction of mode rotation. The mode twist parameter is introduced and relations connecting it with the energy flux are established. It is shown the energy flux transforms zeros of the radial profile of the mode amplitude into minima (i.e., zeros disappear). It is found that the radial group velocity and phase velocity of reversed shear Alfvén eigenmodes (RSAEs) have opposite directions. These findings can be used for diagnostics of the energy fluxes during fast-ion driven instabilities and understanding whether the SC degrades or improves plasma performance. Specific calculations are carried out for fast magnetoacoustic modes, FMMs (known also as compressional Alfvén eigenmodes, CAEs), and Alfvén eigenmodes, AEs. A DIII-D experiment where RSAEs were observed is considered. It is concluded that an outward SC took place in this experiment. Peculiarities of various modes are discussed, which may explain why twisting of AEs, but not of FMMs, was observed experimentally in the DIII-D and NSTX tokamaks.

Kolesnichenko, Ya. I. (ORCID:0000000323219681)↗
DOE OSTI2022

Application of simulations and theory of sub-cyclotron frequency modes to DIII-D

Theoretical and numerical study of Alfvén eigenmodes (AEs) in the sub-cyclotron frequency range explains the observed frequency scaling with beam parameters in experiments across different devices. Global Alfvén (GAEs) and compressional Alfvén eigenmodes (CAEs) are frequently excited during neutral beam injection (NBI) in the National Spherical Torus Experiment (NSTX/NSTX-U), as well as other beam-heated devices such as MAST and DIII-D. These modes are driven unstable through the Doppler shifted cyclotron resonance with the NBI ions and can be excited in ITER due to super-Alfvénic velocities and anisotropy of the fast ion distribution. Numerical models and theory for sub-cyclotron frequency modes, previously developed and used to study the excitation of GAEs/CAEs in the NSTX(-U), have been successfully applied to explain the DIII-D observations. Simulations for DIII-D demonstrate that the modes with ω/ω ci ≈ 0.6, previously mis-identified as compressional Alfvén eigenmodes, have shear polarization and should be identified as the GAEs. Simulation results match the observed frequencies and estimated toroidal mode numbers in DIII-D experiments. Good agreement is found between scaling of the experimentally observed GAE frequencies with NBI parameters for NSTX, NSTX-U and DIII-D and scaling predicted by the theory and simulations. Here, the DIII-D simulation results also demonstrate that GAEs can be excited for larger aspect-ratio devices and smaller (sub- Alfvénic) beam injection velocities, therefore they can be unstable in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Overview of fast particle experiments in the first MAST Upgrade experimental campaigns

Abstract MAST-U is equipped with on-axis and off-axis neutral beam injectors (NBI), and these external sources of super-Alfvénic deuterium fast-ions provide opportunities for studying a wide range of phenomena relevant to the physics of alpha-particles in burning plasmas. The MeV range D-D fusion product ions are also produced but are not confined. Simulations with the ASCOT code show that up to 20% of fast ions produced by NBI can be lost due to charge exchange (CX) with edge neutrals. Dedicated experiments employing low field side (LFS) gas fuelling show a significant drop in the measured neutron fluxes resulting from beam-plasma reactions, providing additional evidence of CX-induced fast-ion losses, similar to the ASCOT findings. Clear evidence of fast-ion redistribution and loss due to sawteeth (ST), fishbones (FB), long-lived modes (LLM), Toroidal Alfvén Eigenmodes (TAE), Edge Localised Modes (ELM) and neoclassical tearing modes (NTM) has been found in measurements with a Neutron Camera (NCU), a scintillator-based Fast-Ion Loss Detector (FILD), a Solid-State Neutral Particle Analyser (SSNPA) and a Fast-Ion Deuterium- α (FIDA) spectrometer. Unprecedented FILD measurements in the range of 1–2 MHz indicate that fast-ion losses can be also induced by the beam ion cyclotron resonance interaction with compressional or global Alfvén eigenmodes (CAEs or GAEs). These results show the wide variety of scenarios and the unique conditions in which fast ions can be studied in MAST-U, under conditions that are relevant for future devices like STEP or ITER.

Physics↗
DOE OSTI2021

Progress and prospects of thermo-mechanical energy storage – A critical review

The share of electricity generated by intermittent renewable energy sources is increasing (now at 26% of global electricity generation) and the requirements of affordable, reliable and secure energy supply designate grid-scale storage as an imperative component of most energy transition pathways. The most widely deployed bulk energy storage solution is pumped-hydro energy storage (PHES), however, this technology is geographically constrained. Alternatively, flow batteries are location independent and have higher energy densities than PHES, but remain associated with high costs and low lifetimes, which highlights the importance of developing and utilizing additional larger-scale, longer-duration and long-lifetime energy storage alternatives. Here, we review a class of promising bulk energy storage technologies based on thermo-mechanical principles, which includes: compressed-air energy storage (CAES), liquid-air energy storage (LAES) and pumped-thermal electricity storage (PTES). The thermodynamic principles upon which these thermo-mechanical energy storage (TMES) technologies are based are discussed and a synopsis of recent progress in their development is presented, assessing their ability to provide reliable and cost-effective solutions. The current performance and future prospects of TMES systems are examined within a unified framework and a thermoeconomic analysis is conducted to explore their competitiveness relative to each other as well as when compared to PHES and flow battery systems. This includes carefully selected thermodynamic and economic methodologies for estimating the component costs of each configuration in order to provide a detailed and fair comparison at various system sizes. The analysis reveals that the technical and economic characteristics of TMES systems are such that, especially at higher discharge power ratings and longer discharge durations, they can offer promising performance (round-trip efficiencies higher than 60%) along with high lifetimes (> 30 years), low specific costs (often below 100 $/kWh), low ecological footprints and unique sector-coupling features compared to other storage options. TMES systems have significant potential for further progress and the thermoeconomic comparisons in this paper can be used as a benchmark for their future evolution.

25 ENERGY STORAGE↗