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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Continuous Data Acquisition for Liquid Argon Time Projection Chamber Neutrino Detectors Using FPGA-Based Real-Time Compression Algorithms

Liquid argon time projection chambers (LArTPCs) have been proposed as neutrino detectors that combine both large sizes to maximize the number of neutrino interactions and detailed recording of the interaction. The readout of thousands of channels at MHz sampling rates produce images of the neutrino-nucleus interaction with millimeter-scale resolution, enabling the identification of the resulting particles and offering multiple handles to measure their energies at the expense of large data rates. Continuous acquisition of the LArTPC data is required to enable the use of high-latency triggers for capturing non-accelerator-beam events. We describe the case of the continuous readout of the MicroBooNE LArTPC, that grants the possibility of acquiring the neutrino burst from a supernova using the SNEWS (Supernova Early Warning System) alert as delayed trigger. The continuous data acquisition is accomplished by using real-time compression algorithms (zero suppression and Huffman compression) implemented in an FPGA in the readout electronics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Quantifying neural information content: a case study of the impact of hippocampal adult neurogenesis

Neural networks have an intrinsic information content encapsulated by neural firing behaviors and constituted by their neural encoding. Shannon entropy is a fundamental method to quantize the amount of information in a variety of sources such as communication channels. Many approaches have been devised to apply this sort of information measure to neurons with mixed success. However, doing so typically requires knowledge of the firing behavior probability distributions for the neurons of interest (whether modeled or recordings), and furthermore often is only applicable for single neurons and not ensembles. We have observed that conventional compression methods may help overcome some of the limiting factors of standard techniques and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Refractive index of lithium fluoride at high dynamic stresses

Alkali halides are prototypical ionic solids whose refractive index is a fundamental property related to their lattice structure and ionic polarizability. In particular, lithium fluoride (LiF) has the largest band gap of any known transparent material and maintains transparency at high pressures, making it well suited for use as an optical window in dynamic compression experiments. While empirical fits to the measured density dependence of the refractive index have been provided, a model that is valid over the entire pressure range of experiments and based on a polarization-based theoretical description is lacking. We present a refractive index model for dynamically compressed LiF based on the Lorentz-Lorenz equation, where the molecular polarizability is determined using a single oscillator model with a strain polarizability parameter Λ=0.73. Here, we show that our modeling approach provides an excellent match to the LiF refractive index data for both shock and ramp compression experiments to 900 GPa (density compression greater than threefold). Additionally, updated fits are provided to determine the refractive index correction for LiF windows used in dynamic compression experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

3D Gaussian Splatting for Volume Compression

This codebase uses machine learning to train a collection of 3D Gaussian distributions to approximate scientific volume data. Because this collection uses less memory than the original dataset, it can be used as a compressed model of the original data for applications such as visualization.

Dyken, Landon↗
DOE OSTI2026

SPRUCE Whole Ecosystem Warming (WEW) Environmental Data and Water Table Summaries, Marcell Experimental Forest, Minnesota, 2015-2024

This data set contains observations of photosynthetically active radiation (PAR), precipitation, soil temperature, soil volumetric water content, air temperature, relative humidity, and normalized water table depth that are summarized on a daily, weekly, monthly, and annual basis for each of the SPRUCE plots. Observations span 2015-2024. This dataset draws on several datasets (Hanson et al. 2016; Hanson et al. 2020; and Warren, unpublished data) and compiles these environmental observations into useful formats for data analysis. These environmental metrics can be used to understand the environmental conditions inside SPRUCE environmental chambers throughout the durations of the experiment and can be paired with other data for modeling and analysis. R code used to generate these files is provided as part of the data package. This dataset contains four data files in comma separate (.csv) format and a compressed folder (*.zip) containing three R (*.r) scripts. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. User note: Users must cite the original dataset/s along with this dataset when publishing any analyses using this dataset. Details on the dataset used to compile each variable are available in the header row of the files and in the user guide.

air temperature↗
DOE OSTI2021

A Rapid Compression Machine Study of 2-Phenylethanol Autoignition at Low-To-Intermediate Temperatures

To meet the increasing anti-knocking quality demand of boosted spark-ignition engines, fuel additives are considered an effective approach to tailor fuel properties for satisfying the performance requirements. Thus, screening/developing bio-derived fuel additives that are best-suited for advanced spark-ignition engines has become a significant task. 2-Phenylethanol (2-PE) is an attractive candidate that features high research octane number, high octane sensitivity, low vapor pressure, and high energy density. Recognizing that the low temperature autoignition chemistry of 2-PE is not well understood and the need for fundamental experimental data at engine-relevant conditions, rapid compression machine (RCM) experiments are therefore conducted herein to measure ignition delay times (IDTs) of 2-PE in air over a wide range of conditions to fill this fundamental void. These newly acquired IDT data at low-to-intermediated temperatures, equivalence ratios of 0.35–1.5, and compressed pressures of 10–40 bar are then used to validate the 2-PE model developed by Shankar et al. (2017). It is found that this literature model greatly overpredicts the current RCM data. The comparison of experimental and simulated results also provides insights into 2-PE autoignition behaviors at varying conditions. Further chemical kinetic analyses demonstrate that the absence of the O2-addition pathway of β-R. radical in the 2-PE model of Shankar et al. (2017) could account for the model discrepancies observed at low-to-intermediated temperatures.

Fang, Ruozhou (ORCID:0000000348315100)↗
DOE OSTI2022

CEAZ: Accelerating Parallel I/O Via Hardware-Algorithm Co-Designed Adaptive Lossy Compression

As supercomputers continue to grow to exa-scale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also help us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ’s dual-quantization algorithm to our hardware use case. (4) We evaluate CEAC on five real-world datasets with both a single FPGA board and 256 nodes from Bridges2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2× of throughput and 9.6× of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 32.7× and 31.4×, respectively.

Zhang, Chengming↗
DOE OSTI2020

Computationally efficient evaluation of optimum homogeneous charge compression ignition operating range with accelerated multizone engine cycle simulation

The very intensive calculations necessary to define a performance map requiring evaluation of over a hundred individual operating points can be efficiently conducted with accelerated multizone for engine cycle simulation, leading to a definition of regions of acceptable and optimum homogeneous charge compression ignition operation. Accelerated multizone for engine cycle simulation has the virtue of enabling accurate evaluation of many operating conditions based on thermal stratification data from a single fluid mechanics run at motored conditions. This is possible because thermal stratification is more sensitive to engine geometry than to operating conditions. In this article, accuracy of accelerated multizone for engine cycle simulation is demonstrated by comparison with experimental data for iso-octane homogeneous charge compression ignition operation over a broad range of lean equivalence ratios (0.14–0.28). The validated accelerated multizone for engine cycle simulation model is then applied to generating a performance map for an engine controlled by appropriately adjusting equivalence ratio and internal exhaust gas recirculation. Regions of acceptable and optimum combustion are identified. It is finally demonstrated that while indicated mean effective pressure remains low for optimum homogeneous charge compression ignition operation (1–4 bar), this is sufficient for a large fraction of typical driving in light-duty vehicles. Much driving including idle can therefore be done in homogeneous charge compression ignition mode at high efficiency and low (essentially zero) NOx and particulate matter emissions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Benchmarking the Suitability of Novec$^{\mathrm{TM}}$ 4710 for Application in Flux Compression Generators

Here, an experimental study evaluated the feasibility of replacing traditional insulating gases such as SF 6 with C 4 F 7 N (3M TM , Novec 4710) in flux compression generator (FCG) applications. Currently available data indicate that Novec 4710 could offer certain performance benefits over SF 6 . However, the available literature is focused on low frequency (50–60 Hz) and dc at static pressures. To evaluate the performance of Novec 4710 under the pulsed dynamic pressure and temperature conditions found in an FCG, we report a performance comparison between three sets of identical FCGs using air, SF 6 , and Novec 4710 as the insulating gas. The generators used in this study had a single stage, directly seeded design with an armature diameter of 25 mm and a stator diameter of 46 mm. To highlight the performance of the different gases rather than any wire insulation, the stator was constructed with uninsulated wire. Furthermore, the generators were seeded aggressively, making the performance difference between the different gases more apparent. The performance was monitored with a pair of differential Rogowski coils that captured the generators’ di / dt while also using high-speed videography to capture possible gaseous breakdown signatures. The data gathered during this study indicate that Novec 4710 performs at least as well as SF 6 in FCG applications, if not significantly better.

42 ENGINEERING↗
DOE OSTI2021

Lightweight LSTM for CAN Signal Decoding

This paper describes an approach to identify undecoded Controller Area Network (CAN) data from one vehicle, based on the data similarity to previously decoded CAN data from another vehicle. Modern vehicles communicate data and signals from on-board sensors and controllers through the CAN bus. Networked sensors contain information such as wheel speeds, fuel gauges, turn signals, and radar signals. In the effort to use this information and make cars safer through human-in-the-loop CPS, signals on the CAN bus such as wheel speed and radar can be used to support the driver. However, data from the CAN bus are encoded and in some cases compressed, and different car manufacturers use different encoding schemes to represent data on the CAN bus. With hundreds of messages and thousands of possible encoding schemes to consider, it is laborious to identify the unique bits and encoding schemes that represent signals on each vehicle. In this study, we propose a method for training a Long Short-Term Memory (LSTM) neural network on known radar signals from one vehicle manufacturer, a Toyota, and successfully apply the network to identify the encoding for radar signals on a different vehicle, a Honda. By augmenting the training dataset with varied encoding bit boundaries, a small and lightweight LSTM network can learn to recognize radar data across different encoding schemes. The results are an improvement on exhaustive-search algorithms and other methods previously used in the search for such signals.

Ngo, Paul↗
DOE OSTI2019

Assessing the Quality of Molecular Simulations for Vapor–Liquid Equilibria: An Analysis of the TraPPE Database

As molecular modeling and simulation techniques become increasingly important sources of thermophysical property and phase equilibrium data, the ability to assess the robustness of that data becomes more critical. Recently, the use of the compressibility factor (Z) has been suggested as a metric for testing the quality of simulation data for vapor–liquid equilibria (VLE). Int his paper, we analyze predicted VLE data from the transferable potentials for phase equilibria (TraPPE) database and show that, apart from data entry or typographical errors, Z will always be well-behaved in Gibbs ensemble Monte Carlo (GEMC) simulations even when the simulations are not sufficiently equilibrated. However, this is not true for grand canonical Monte Carlo simulations. When the pressure is calculated from the internal forces, then pressure and density are strongly correlated for the vapor phase and, for GEMC simulations, it is recommended to treat Z as an instantaneous mechanical property. From analysis of the TraPPE VLE data, we propose a complementary metric based on the predicted vapor pressures at three neighboring temperatures and their deviation from a local Clausius–Clapeyron fit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
DOE OSTI2024

Measurements of K-edge and L-edge extended x-ray absorption fine structure at the national ignition facility (invited)

High-energy-density laser facilities and advances in dynamic compression techniques have expanded access to material states in the Terapascal regime relevant to inertial confinement fusion, planetary science, and geophysics. However, experimentally determining the material temperature in these extreme conditions has remained a difficult challenge. Extended X-ray Absorption Fine Structure (EXAFS), referring to the modulations in x-ray absorption above an absorption edge from photoelectrons’ interactions with neighboring atoms, has proven to be a versatile and robust technique for probing material temperature and density for mid-to-high Z elements under dynamic compression. The current platform at the National Ignition Facility has developed six configurations for EXAFS measurements between 7 and 18 keV for different absorption edges (Fe K, Co K, Cu K, Ta L3, Pb L3, and Zr K) using a curved-crystal spectrometer and a bright, continuum foil x-ray source. Here, in this work, we describe the platform geometry, x-ray source performance, spectrometer resolution and throughput, design considerations, and data in ambient and dynamic-compression conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lost Circulation Materials in Geothermal Drilling: Thermal Degradation, Viscosity, Compression, and Flow Loop Tests

This dataset provides a set of experimental data on the performance of lost circulation materials (LCMs) used in geothermal drilling operations. It includes results from four distinct tests: thermal degradation, viscosity measurements, compression tests, and high-temperature flow loop experiments. The materials tested, such as cedar fiber, cotton seed hulls, magma fiber, microcellulose, and sawdust, were analyzed under a range of conditions to assess their effectiveness in mitigating fluid losses in geothermal wells. Data on thermal degradation explores mass loss, water loss, and gas release for each material when subjected to temperatures between 90C and 250C. Viscosity measurements capture the behavior of LCM mixtures under different temperatures, while compression tests investigate the structural integrity of both undegraded and thermally degraded materials. The high-temperature flow loop experiments simulate geothermal well conditions, analyzing how different materials and mixtures affect fluid flow and sealing performance in a fractured reservoir environment. In addition to the experimental data, the dataset includes photos documenting the materials, schematics of test setups, and several published manuscripts that provide detailed insights into the experimental methods and findings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Energy balance and Alfvén Mach numbers in compressible magnetohydrodynamic turbulence with a large-scale magnetic field

Energy equipartition is a powerful theoretical tool for understanding astrophysical plasmas. It is invoked, for example, to measure magnetic fields in the interstellar medium (ISM), as evidence for small-scale turbulent dynamo action, and, in general, to estimate the energy budget of star-forming molecular clouds. Here, in this study, we motivate and explore the role of the volume-averaged root-mean-squared (rms) magnetic coupling term between the turbulent, δB , and large-scale, B 0 , fields, $\langle$(δB·B 0 ) 2 $\rangle$$_{V}^{1/2}$. By considering the second moments of the energy balance equations we show that the rms coupling term is in energy equipartition with the volume-averaged turbulent kinetic energy for turbulence with a sub-Alfvénic large-scale field. Under the assumption of exact energy equipartition between these terms, we derive relations for the magnetic and coupling term fluctuations, which provide excellent, parameter-free agreement with time-averaged data from 280 numerical simulations of compressible magnetohydrodynamic (MHD) turbulence. Furthermore, we explore the relation between the turbulent mean field and total Alfvén Mach numbers, and demonstrate that sub-Alfvénic turbulence can only be developed through a strong, large-scale magnetic field, which supports an extremely super-Alfvénic turbulent magnetic field. This means that the magnetic field fluctuations are significantly subdominant to the velocity fluctuations in the sub-Alfvénic large-scale field regime. Throughout our study, we broadly discuss the implications for observations of magnetic fields and understanding the dynamics in the magnetized ISM.

79 ASTRONOMY AND ASTROPHYSICS↗