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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

3-D Modeling off the CFR Engine for the Investigation of Knock on Natural Gas

Engine knock and misfire are barriers to pathways leading to high-efficiency Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG (65< MN<95). In order to realize diesel-like efficiencies, spark-ignited NG engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding combustion-engine interactions important in flame propagation and end-gas autoignition. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior with NG. The CFR engine model is used to investigate fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). Cylinder wall, piston, valves, and port surface temperatures are estimated with a 1-D Three-Pressure Analysis (TPA) model. Model validation was performed by comparing model predictions with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction and BMEP.

Bestel, Diego↗
DOE OSTI2020

MULTI-DIMENSIONAL MODELING OF THE CFR ENGINE FOR THE INVESTIGATION OF SI NATURAL GAS COMBUSTION AND CONTROLLED END-GAS AUTOIGNITION

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emission limits and the wide range of chemical reactivity in pipeline quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its methane number (analogous to the octane number for liquid SI fuels) can span from 65 - 95. In order to realize diesel-like efficiencies, SI natural gas engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding the combustion-engine interactions pertaining to flame propagation and end-gas autoignition (EGAI). However, EGAI, if controlled, provides an opportunity to increase SI natural gas engine efficiency by increasing combustion rate and the total burned fuel, mitigating the effects of the slow flame speeds of natural gas fuels which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, in order to study EGAI phenomenon, the present work highlights multi-dimensional computational fluid dynamics (CFD) models of the Cooperative Fuel Research (CFR) engine. The CFR engine models are used to investigate fuel-engine interactions that lead to EGAI with natural gas, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). A Three-Pressure Analysis, performed with GT-Power, was used to estimate initial and boundary conditions for the three-dimensional CFD model. CONVERGE CFD v2.4 was used for the three-dimensional CFD modeling where the level set G-Equation model and SAGE detailed chemical kinetics solver were used. An assessment of the different modeling approaches is also provided to evaluate their limitations, advantages and disadvantages, and for which situations they are most applicable. Model validation was performed with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction, and IMEP and shows good agreement in Peak Cylinder Pressure (PCP), PCP crank angle, and the location of the 10%, 50%, and 90% mass fraction burned (CA10, CA50, and CA90, respectively). The models can predict the onset crank angle and pressure rise rate for light, medium, and heavy EGAI under a variety of fuel reactivities and engine operating conditions.

Bestel, Diego↗
DOE OSTI2020

A Mechanical Test Frame for Property Evaluations at Cryogenic Temperature

On-board storage of hydrogen fuel has been designated as a limiting factor in the advancement of fuel cell technologies in the automotive industry. The use of cryo-compressed Type III pressure vessels presents one option to overcome this barrier. These multi-material vessels will be expected to perform at high pressure and extreme low temperatures, which creates a complex engineering design challenge. Many materials exhibit highly temperature-dependent properties, which must be considered for the efficient design of cryo-compressed pressure vessels. Complicating this issue, mechanical property data at cryogenic temperatures is sparse. This technical paper provides a description of a test apparatus commissioned specifically to help address this shortcoming. A mechanical test frame was retrofitted with a continuous flow cryostat capable to evaluate various mechanical properties throughout a broad temperature range of 25 °C to -269 °C with a load limit of 10 kN. An investigation of the thermomechanical properties of an epoxy resin was carried out as a demonstration.

Mechanical characterization, cryogenic pressure ve↗
DOE OSTI2024

DAISY Benchmark Performance Data

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]_DAISY_[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. - MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). - Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). - WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Initial Design and Experimental Results of a Novel Near-Isothermal Compressor for Heat Pump Applications

In efforts to increase the efficiency of residential and commercial air conditioners and heat pumps, it is found that the compressor has the highest electrical energy usage of the system. Therefore, it is appropriate to try to increase the efficiency of this component to reduce its energy usage. Another challenge with heat pump design is that some compressor types have drawbacks that make modulation difficult. To answer these challenges, we are developing an isothermal liquid compressor. The compressor uses propylene glycol to compress carbon dioxide. In the compression chamber the propylene glycol can enter either from the bottom to create a liquid piston for compression or it can enter through a spray nozzle at the top of the chamber. In the latter case, heat transfer from the gas to be compressed to the liquid droplets is high. This allows near isothermal operation of the compressor, which increases the efficiency by 17% to 30%, compared to adiabatic compression. In addition, the isothermal liquid compressor enables very efficient and simple part load modulation.Experimental results demonstrating the operation of the liquid compressor are presented. Initial data demonstrated a temperature rise of 7 K at pressure ratios of almost 4. For comparison at the same initial pressure, temperature, and pressure ratio, adiabatic compression would result in a temperature increase of approximately 70 K. Plotting data on a P-h diagram demonstrates that the compression started at superheated state and ended in supercritical state. Testing was later performed with repeated compressions in the superheated region of the P-h diagram at liquid flow rates of 2 x 10-3 m3/min and 2 x 10-3 m3/min to understand the limitations of the prototype for use with an actual heat pump system. This work demonstrated a novel cycle on a T-s diagram. Results from this work will be used to develop a second-generation prototype where more rapid cycling is possible.

Kowalski, Steve↗
DOE OSTI2024

Spike-and-Slab Shrinkage Priors for Structurally Sparse Bayesian Neural Networks

Network complexity and computational efficiency have become increasingly significant aspects of deep learning. Sparse deep learning addresses these challenges by recovering a sparse representation of the underlying target function by reducing heavily overparameterized deep neural networks. Specifically, deep neural architectures compressed via structured sparsity (e.g., node sparsity) provide low-latency inference, higher data throughput, and reduced energy consumption. In this article, we explore two well-established shrinkage techniques, Lasso and Horseshoe, for model compression in Bayesian neural networks (BNNs). To this end, we propose structurally sparse BNNs, which systematically prune excessive nodes with the following: 1) spike-and-slab group Lasso (SS-GL) and 2) SS group Horseshoe (SS-GHS) priors, and develop computationally tractable variational inference, including continuous relaxation of Bernoulli variables. We establish the contraction rates of the variational posterior of our proposed models as a function of the network topology, layerwise node cardinalities, and bounds on the network weights. Furthermore, we empirically demonstrate the competitive performance of our models compared with the baseline models in prediction accuracy, model compression, and inference latency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Numerical Simulation of Thermodynamic Parameters of High-Porosity Copper

We propose that a modified equation of state be used for calculating the thermodynamically equilibrium TEC2 model for improving the reliability of description of thermodynamic parameters of shock loading of pure materials and heterogeneous mixtures of various porosities. For copper, the parameters of a thermally consistent equation of state have been determined. A reliable description of shock-wave loading of copper has been obtained using a small number of fitting parameters determined from the concordance with experimental data. Thermodynamic parameters have been simulated for copper with various porosities; the compression ratio and the temperature along the shock adiabat have been determined, and the value of heat capacity along the normal isobar has been calculated. The results of calculations are compared with available experimental results obtained by different authors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Visualization Quality Assessment

Understanding how inaccuracies in visualizations affect users’ perception and understanding of scientific data is hard. Inaccuracies in visualizations are quite common and could arise from a range of sources such as errors in the original dataset arising from compression artifacts, errors in the capturing device, noise during transmission of the data, effects due to the algorithm being used to convert data to visualization images, images generated from neural networks, and sources we have yet to discover. Many image quality assessment metrics have been developed to quantify image errors. However, these are usually focused on “natural images” rather than visualizations of scientific data. Common image quality assessment metrics (IQAs) include MSE, PSNR, perceptual metrics such SSIM, FSIM as well as perceptual metrics using deep learning approaches. However, a critical part of understanding how errors are perceived by humans, and subsequently developing more accurate quality assessment metrics, is through user evaluation studies. The goal of this software is to develop a visualization quality assessment (VQA) process that will enable the generation of VQAs that can be used to quantify errors in scientific data visualizations. The VQA development process will include software to support user evaluation experimental design, analysis of visualization differences against standard quality metrics, and the ability to develop additional VQA metrics specific to scientific visualization images.

Grosset, Andre↗
DOE OSTI2024

Deep Koopman Neural Network for Analyzing High-Energy-Density Simulations of Electrical Wire Explosions

Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Experimental and Theoretical Investigation of Shock-Induced Reactions in Energetic Materials

In this work, shock-induced reactions in high explosives and their chemical mechanisms were investigated using state-of-the-art experimental and theoretical techniques. Experimentally, ultrafast shock interrogation (USI, an ultrafast interferometry technique) and ultrafast absorption spectroscopy were used to interrogate shock compression and initiation of reaction on the picosecond timescale. The experiments yielded important new data that appear to indicate reaction of high explosives on the timescale of tens of picoseconds in response to shock compression, potentially setting new upper limits on the timescale of reaction. Theoretically, chemical mechanisms of shock-induced reactions were investigated using density functional theory. The calculations generated important insights regarding the ability of several hypothesized mechanisms to account for shock-induced reactions in explosive materials. The results of this work constitute significant advances in our understanding of the fundamental chemical reaction mechanisms that control explosive sensitivity and initiation of detonation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Crystal plasticity finite element simulation of lattice rotation and x-ray diffraction during laser shock compression of tantalum

Here we present a crystal plasticity model tailored for high-pressure, high-strain-rate conditions that uses a multiscale treatment of dislocation-based slip kinetics. We use this model to analyze the pronounced plasticity-induced lattice rotations observed in shock-compressed polycrystalline tantalum via in situ x-ray diffraction. By making direct comparisons between experimentally measured and simulated texture evolution, we can explain how the details of the underlying slip kinetics control the degree of lattice rotation that ensues. Specifically, we show that only the highly nonlinear kinetics caused by dislocation nucleation can explain the magnitude of the rotation observed under shock compression. We demonstrate a good fit between our crystal plasticity model and x-ray diffraction data and exploit the data to quantify the dislocation nucleation rates that are otherwise poorly constrained by experiment in the dynamic compression regime.

36 MATERIALS SCIENCE↗
DOE OSTI2026

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Esaulov, Vladislav [Georgia State University, Atla↗
DOE OSTI2013

Hydration studies of calcium sulfoaluminate cements blended with fly ash

The main objective of this work is to study the hydration and properties of calcium sulfoaluminate cement pastes blended with fly ash (FA) and the corresponding mortars at different hydration ages. Laboratory X-ray powder diffraction, rheological studies, thermal analysis, porosimetry and compressive strength measurements were performed. The analysis of the diffraction data by Rietveld method allowed quantifying crystalline phases and overall amorphous contents. The studied parameters were: i) FA content, 0, 15 and 30 wt.%; and ii) water addition, water-to-CSA mass ratio (w/CSA = 0.50 and 0.65), and water-to-binder mass ratio (w/b = 0.50). Finally, compressive strengths after 6 months of 0 and 15 wt.% FA [w/CSA = 0.50] mortars were similar: 73 ± 2 and 72 ± 3 MPa, respectively. This is justified by the filler effect of the FA as no strong evidences of reactivity of FA with CSA were observed. These results support the partial substitution of CSA cements with FA with the economic and environmental benefits.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Concrete material science: Past, present, and future innovations

Concrete is flying off, but it is simultaneously facing tremendous challenges in terms of environmental impact, financial needs, societal acceptance and image. Based on an historical approach of the science of concrete and reinforced concrete in particular, this paper calls for the exploration of radical changes in three key aspects of concrete use: reinforcement, binder content, and implementation methods. More precisely, it is suggested that, in parallel to the introduction of robotic fabrication methods, digital technologies may be key for the introduction several innovations like (i) rebar-free reinforcement using non-convex granular media; (ii) compression-optimized concrete structures, using topology optimization, architectural geometry, and 3D-printing or origami-patterned formworks; (iii) truly digital concrete through the coupling of massive data collection and deep learning.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A historical analysis of the co-evolution of gasoline octane number and spark-ignition engines

In our work, the authors reviewed engine, vehicle, and fuel data since 1925 to examine the historical and recent coupling of compression ratio and fuel antiknock properties (i.e., octane number) in the U.S. light-duty vehicle market. The analysis identified historical timeframes, trends, and illustrated how three factors: consumer preferences, technical capabilities, and regulatory legislation, affect personal mobility. Data showed that throughout history these three factors have a complex and time sensitive interplay. Long term trends in the data were identified where interaction and evolution between all three factors was observed. Transportation efficiency per unit power (gal/ton-mi/hp) was found to be a good metric to integrate technical, societal, and regulatory effects into the evolutional pathway of personal mobility. From this framework, discussions of future evolutionary changes to personal mobility are also presented.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Crystal plasticity modeling of strain-induced martensitic transformations to predict strain rate and temperature sensitive behavior of 304 L steels: Applications to tension, compression, torsion, and impact

This paper advances crystallographically-based Olson-Cohen (direct γ → α’) and deformation mechanism (indirect γ→ε→α’) phase transformation models for predicting strain-induced austenite to martensite transformation. Here, the advanced transformation models enable predictions of not only strain-path sensitive, but also of strain-rate and temperature sensitive deformation of polycrystalline stainless steels (SSs). The deformation of constituent grains in SSs is modeled as a combination of anisotropic elasticity, crystallographic slip, and phase transformation, while the hardening is based on the evolution of dislocation density and explicit shifts in phase fractions. Such grain-scale deformation is implemented within the meso-scale elasto-plastic self-consistent (EPSC) homogenization model, which is coupled with the implicit finite element (FE) method to provide a constitutive response at each FE integration point for solving boundary value problems at the macro-scale. Parameters pertaining to the hardening and transformation models within FEEPSC are calibrated and validated on a suite of data including flow curves and phase fractions for monotonic compression, tension, and torsion as a function of strain-rate and temperature for wrought and additively manufactured (AM) SS304L. To illustrate the potential and accuracy of the integrated multi-level FE-EPSC simulation framework, geometry, mechanical response, phase fractions, and texture evolution are simulated during gas-gun impact deformation of a cylinder and quasi-static tension of a notched specimen made of AM SS304L. Details of the simulation framework, comparison between experimental and simulation results, and insights from the results are presented and discussed.

304L steels↗
DOE OSTI2025

Interpreting dynamic-compression experiments to uncover the time dependence of freezing: Application to gallium

Using pulsed-power magnetic field sources to compress gallium to gigapascal pressures on nanosecond timescales, we report here experiments on shockless dynamic compression of a liquid metal. Time-resolved velocimetry data reveal signatures of rapid freezing from a metastable liquid state, and we demonstrate that the kinetics of this nonequilibrium solidification can be accurately simulated with a computational modeling framework we have developed in previous studies, where classical nucleation theory is coupled with hydrodynamics. Notably, velocity traces in some of our experiments show evidence of a phase transition, while others do not, even though other types of evidence suggest that solidification may be occurring in all of them. We explain how predictions made by our models regarding the presence or absence of these phase-transition signatures motivated additional experiments that later confirmed the theoretical predictions. Our analysis shows that due to the rapid, quasi-isentropic nature of the loading path, our experiments were able to compress liquid gallium to metastable states that are undercooled below the equilibrium melt temperature by more than 300 K and exhibit pressures that approach five times the equilibrium melt pressure. The understanding gained in this study should form the basis for future dynamic-compression experiments aimed at interrogating melt curves at high pressures.

gallium↗