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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗
DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Chemically Tunable Aspect Ratio Control and Laser Refrigeration of Hexagonal Sodium Yttrium Fluoride Upconverting Materials

Hexagonal sodium yttrium fluoride with Na 3x Y 2-x F 6 stoichiometry (β-NaYF) is a promising material for luminescence upconversion applications due to the narrow crystal field splitting of the Yb(III) ion’s lower 2 F 7/2 manifold. However, growing single crystals of β-NaYF remains an outstanding challenge due to thermal expansion stresses that cause cracking during melt growth. Here, we demonstrate a novel hydrothermal synthesis of β-NaYF with the ability to tune the aspect ratio from microplatelets to microrods with aspect ratios that match computationally predicted cavity (Mie) resonances. These crystals have a root-mean-square roughness below 1 nm after calcination, which makes them ideal for optical cavities. The β-NaYF microcrystals are doped with 10% Yb(III) cations and are used to build optomechanical laser-refrigeration devices consisting of a hexagonal β-NaYF crystal located at the end of a cantilever. Laser refrigeration of these devices by >12.5 °C is observed using calibrated measurements of both the cantilever’s fundamental eigenfrequency and a Boltzmann fit to crystal field luminescence from the Yb(III) ions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Creation of a prototype biomimetic fish to better understand impact trauma caused by hydropower turbine blade strikes

Biomimetic model organisms could be useful surrogates for live animals in many applications if the models have sufficient biofidelity. One such application is for use in field and laboratory tests of fish mortality associated with passage through hydropower turbines. Laboratory trials suggest that blade strikes are especially injurious and often causes mortality when fish are struck by thinner blades moving at higher velocities. Dose-response relationships have been created from these data, but the exact relationship between fish mortality and the actual forces enacted on fish during simulated blade strike testing remains unknown. Here, we describe the methods used to create a prototype biomimetic model fish composed of ballistic gelatin and covered with a surrogate skin to better approximate the biomechanical properties of a fish body. Frozen fish were scanned with high-fidelity laser scanners, and a 3D-printed, reusable mold was created from which to cast our gelatin model. Computed tomography scan data, imaged directly or taken from online data repositories, were also successfully used to create CAD models for use in additive manufacturing of molds. One 3-axis accelerometer was embedded into the gelatin to compare accelerometer data to dose-response data from previous laboratory research on live fish. The resulting model ( i.e. , Gelfish) had a statistically indistinguishable tissue durometer to that of real fish tissue and preliminary blade strike impact testing suggested its overall flexibility was similar to that of live fish. Gelfish was designed with biofidelity as its guiding principle and our results suggest initial experimentation was successful. Future research will include replication of initial Gelfish test results, quantitative measurement of model flexibility relative to real fish, and inclusion of surrogate skeletal structures to enhance biofidelity. Use of more sophisticated sensors would also better quantify the physical forces of blade strike impact and help determine how said forces correlate with rates of mortality observed during tests on live fish.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessment of critical flaw sizes and crack driving forces during additive manufacturing of metallic materials

Additive manufacturing (AM) of complex engineering components is often plagued by a high susceptibility to cracking, particularly in high-strength metallic materials. While alloy design efforts have made progress in mitigating solidification defects, there remains a need for mechanistic guidelines to predict susceptibility to solid-state cracking. To address this gap, driving forces for the growth of melt pool cracks are calculated across a wide range of alloys using an efficient computational framework. Calculations are coupled with rapid single track laser experiments to elucidate trends in cracking from laser melting. The analyses conducted here highlight the important role of material properties in susceptibility to cracking, notably fracture toughness and elastic modulus. An important finding is that residual stresses that are limited in magnitude to the yield stress of the material are likely insufficient to drive cracking during cooling. Furthermore, the implications of these results are discussed in the context of alloy design for AM and residual stress accumulation during AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying Equiaxed vs Epitaxial Solidification in Laser Melting of CMSX-4 Single Crystal Superalloy

The competition between epitaxial vs. equiaxed solidification has been investigated in CMSX-4 single crystal superalloy during laser melting as practiced in additive manufacturing. Single-track laser scans were performed on a powder-free surface of directionally solidified CMSX-4 alloy with several combinations of laser power and scanning velocity. Electron backscattered diffraction (EBSD) mapping facilitated identification of new orientations, i.e., “stray grains” that nucleated within the fusion zone along with their area fraction and spatial distribution. Using high-fidelity computational fluid dynamics simulations, both the temperature and fluid velocity fields within the melt pool were estimated. This information was combined with a nucleation model to determine locations where nucleation has the highest probability to occur in melt pools. In conformance with general experience in metals additive manufacturing, the as-solidified microstructure of the laser-melted tracks is dominated by epitaxial grain growth; nevertheless, stray grains were evident in elongated melt pools. It was found that, though a higher laser scanning velocity and lower power are generally helpful in the reduction of stray grains, the combination of a stable keyhole and minimal fluid velocity further mitigates stray grains in laser single tracks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Real-time tracking of protein unfolding with time-resolved x-ray solution scattering

The correct folding of proteins is of paramount importance for their function, and protein misfolding is believed to be the primary cause of a wide range of diseases. Protein folding has been investigated with time-averaged methods and time-resolved spectroscopy, but observing the structural dynamics of the unfolding process in real-time is challenging. Here, we demonstrate an approach to directly reveal the structural changes in the unfolding reaction. We use nano- to millisecond time-resolved x-ray solution scattering to probe the unfolding of apomyoglobin. The unfolding reaction was triggered using a temperature jump, which was induced by a nanosecond laser pulse. We demonstrate a new strategy to interpret time-resolved x-ray solution scattering data, which evaluates ensembles of structures obtained from molecular dynamics simulations. We find that apomyoglobin passes three states when unfolding, which we characterize as native, molten globule, and unfolded. The molten globule dominates the population under the conditions investigated herein, whereas native and unfolded structures primarily contribute before the laser jump and 30 μs after it, respectively. The molten globule retains much of the native structure but shows a dynamic pattern of inter-residue contacts. Our study demonstrates a new strategy to directly observe structural changes over the cause of the unfolding reaction, providing time- and spatially resolved atomic details of the folding mechanism of globular proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dataset for "A primer on forest structure measurement with lidar for ecologists"

This repository includes data and code accompanying the case study included in the manuscript "A primer on forest structure measurement with lidar for ecologists" (submitted to Ecosphere). We compiled lidar datasets from multiple platforms in a common area to: 1. Demonstrate how differences in sensor characteristics influence density and resolution of lidar data. 2. Provide open-source, co-located datasets for users to further inspect differences in lidar data. 3. Provide example code to perform basic lidar analysis. This case study is meant to allow readers to get hands-on experience with real-world data from different platforms. This case study is not meant to be a rigorous comparison of derived ecological metrics among all sensors; such comparisons can be found throughout other publications referenced throughout the main manuscript. Code includes basic functions in R commonly used to visualize and manipulate lidar data accessible with a normal laptop computer; more sophisticated algorithms for advanced users are also referenced throughout the main manuscript. Terrestrial laser scanning (TLS), mobile laser scanning (MLS), UAS laser scanning (ULS), airborne laser scanning (ALS), and spaceborne laser scanning (SLS) data were collected within the Smithsonian Environmental Research Center (SERC) forest dynamics plot in Maryland, USA. TLS, MLS, and ALS data were collected within 1 month of the 2021 growing season; ULS data were collected in November 2020 (“leaf-off” data) and July 2022 (“leaf-on” data).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fixed-target serial crystallography at the Structural Biology Center

Serial synchrotron crystallography enables the study of protein structures under physiological temperature and reduced radiation damage by collection of data from thousands of crystals. The Structural Biology Center at Sector 19 of the Advanced Photon Source has implemented a fixed-target approach with a new 3D-printed mesh-holder optimized for sample handling. The holder immobilizes a crystal suspension or droplet emulsion on a nylon mesh, trapping and sealing a near-monolayer of crystals in its mother liquor between two thin Mylar films. Data can be rapidly collected in scan mode and analyzed in near real-time using piezoelectric linear stages assembled in an XYZ arrangement, controlled with a graphical user interface and analyzed using a high-performance computing pipeline. Here, the system was applied to two β-lactamases: a class D serine β-lactamase from Chitinophaga pinensis DSM 2588 and L1 metallo-β-lactamase from Stenotrophomonas maltophilia K279a.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Modeling laser-driven ion acceleration with deep learning

Developments in machine learning promise to ameliorate some of the challenges of modeling complex physical systems through neural-network-based surrogate models. High-intensity, short-pulse lasers can be used to accelerate ions to mega-electronvolt energies, but to model such interactions requires computationally expensive techniques such as particle-in-cell simulations. Multilayer neural networks allow one to take a relatively sparse ensemble of simulations and generate a surrogate model that can be used to rapidly search the parameter space of interest. In this study, we created an ensemble of over 1,000 simulations modeling laser-driven ion acceleration and developed a surrogate to study the resulting parameter space. A neural-network-based approach allows for rapid feature discovery not possible for traditional parameter scans given the computational cost. A notable observation made during this study was the dependence of ion energy on the pre-plasma gradient length scale. While this methodology harbors great promise for ion acceleration, it has ready application to all topics in which large-scale parameter scans are restricted by significant computational cost or relatively large, but sparse, domains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Prediction of microstructure formation in laser powder bed fusion process.

The datasets are results analyzing the predicted microstructures in a single track during the laser powder bed fusion additive manufacturing process. They are the outputs by running the opensource code, muMatScale (The code can be cited at: Yuan, Lang, Fattebert, Jean-Luc, and Sabau, Adrian. (2023, August 03). muMatScale. [Computer software]. https://github.com/lang-yuan/muMatScale. https://doi.org/10.11578/dc.20240112.2.) For each set of data, it contains the time-dependent information of Temperature, Fraction Solid, Grain ID, Grain Angle ( crystallographic orientations by Euler angles), and solute Composition. The dataset can be visualized by Paraview (https://www.paraview.org/ ). The 6 datasets are: 1. Baseline_base1_n4e14_dt20: baseline case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 2. Nuc_n4e15_dt20: case with bulk nucleation density of 4E+15/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 3. Nuc_n4e15_dt50: case with bulk nucleation density of 4E+15/m^3, undercooling of 50K, substrate nucleation density of 1.5E+15/m^3 4. Nuc_n6e15_dt20: case with bulk nucleation density of 4E+16/m^3, undercooling of 20K, substrate nucleation density of 1.5E+15/m^3 5. Base4_n4e14_dt20: case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 6.0E+15/m^3 6. Base16_n4e14_dt20: case with bulk nucleation density of 4E+14/m^3, undercooling of 20K, substrate nucleation density of 2.4E+16/m^3

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multiscale Approach to Fast ModSim for Laser Processing of Metals for Future Nuclear Deterrence Environments

Predicting performance of parts produced using laser-metal processing remains an out- standing challenge. While many computational models exist, they are generally too computationally expensive to simulate the build of an engineering-scale part. This work develops a reduced order thermal model of a laser-metal system using analytical Green's function solutions to the linear heat equation, representing a step towards achieving a full part performance prediction in an "overnight" time frame. The developed model is able to calculate a thermal history for an example problem 72 times faster than a traditional FEM method. The model parameters are calibrated using a non-linear solution and microstructures and residual stresses calculated and compared to a non-linear case. The calibrated model shows promising agreement with a non-linear solution.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Compensating for Sintering Distortion in Additively Manufactured Shaped Charge Liners using Physics-Informed Machine Learning

Copper is a challenging material to process using laser-based additive manufacturing due to its high reflectivity and high thermal conductivity. Sintering-based processes can produce solid copper parts without the processing challenges and defects associated with laser melting; however, sintering can also cause distortion in copper parts, especially those with thin walls. In this study, we use physics-informed Gaussian process regression to predict and compensate for sintering distortion in thin-walled copper parts produced using a Markforged Metal X bound powder extrusion (BPE) additive manufacturing system. Through experimental characterization and computational simulation of copper’s viscoelastic sintering behavior, we can predict sintering deformation. We can then manufacture, simulate, and test parts with various compensation scaling factors to inform Gaussian process regression and predict a compensated as-printed (pre-sintered) part geometry that produces the desired final (post-sintered) part.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid Design and Manufacturing of High-Performance Materials for Turbine Blades

This research demonstrated the concept of carbide precipitation-strengthened refractory high entropy alloys (RHEA). The advantage of a precipitation strengthened alloy is all phases are in thermodynamic equilibrium promoting microstructural stability, and consequently retention of properties at elevated temperatures. Additionally, as with any precipitation strengthened – or age hardened - alloy, components can be heat-treated after manufacturing to manipulate the microstructure and optimize properties for performance. This is an advantage of the precipitation strengthened alloys over composites and ceramics materials, where the microstructure and properties are essentially fixed upon the initial materials manufacturing stage. High throughput (HT), multi-scale computer modeling was used to identifying novel RHEA compositions with desired characteristics needed for precipitation strengthening. Designs were validated by producing small ingots via are melting. The results showed that carbides precipitated and consequently, the strength of the alloys (measured in compression) increased after heat-treatment, which is the desired effect. The project also demonstrated the feasibility of producing articles from RHEA by additive manufacturing (AM). Electron beam melting (EBM) and laser direct energy deposition (L-DED) additive manufacturing methods, were explored with various processing parameters were interrogated for both methods. Sound (crack-free and dense) precipitation strengthened RHEA samples were produced via EDM AM, demonstrating the feasibility of the concept.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Power Density Distributions in Electron Beams

Measuring and controlling the power density distribution of electron beams used for welding is critical for producing repeatable welds and for transferring welding parameters between different machines. On any given machine, the power density distribution is controlled by defocusing the beam relative to its sharpest focused condition. However, measuring the power density distribution can be difficult due to the intense nature of welding beams and is further complicated by imperfect electron optics that can distort the beam, making it difficult to quantify. The enhanced modified Faraday cup (EMFC) diagnostic method was used here for beam analysis that utilizes computed tomography to reconstruct the beam’s power density distribution. These results were compared to the International Standards Organization (ISO) method for characterizing laser beams using a second-moment D4σ calculation. For ideal Gaussian-shaped beams, both methods would give the same result. However, for imperfect beams, the calculated D4σ diameter was shown to be about 25% larger relative to the FWe2 diameter measured by the EMFC due to the heavier weighting of data in the tails of the beam by D4σ. Finally, although both methods produce repeatable welds, it is important to understand the differences in the reported beam diameters, divergence angles, and beam parameter products when transferring parameters between machines.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fluid Dynamics Effects on Microstructure Prediction in Single-Laser Tracks for Additive Manufacturing of IN625

Single-track laser fusion were simulated using a heat-transfer-solidification-only (HTS) model and its extension with fluid dynamics (HTS_FD) model using a parallel open-source code, which included laminar fluid dynamics, flat-free surface of the molten alloy, heat transfer, phase-change, evaporation, and surface tension phenomena. The results illustrate that the fluid dynamics affects the solidification and ensuing microstructure. For the HTS_FD simulations, thermal gradient, G was found to exhibit a maximum at the extremity of the solidified pool ( i.e. , at the free surface), while for HTS simulations, G exhibited a maximum around the entire edge of the solidified pool. HTS_FD simulations predicted a wider range of cooling rates than the HTS simulations, exhibited an increased spread in the solidification speed, V variation within the melt-pool with respect to the HTS model results. Primary dendrite arm spacing (PDAS) were evaluated based on power law correlations and marginal stability theory models using the ( G , V ) from HTS and HTS_FD simulations to quantify the effect of the fluid dynamics on the microstructure. At low-laser powers and low-scan speeds, the PDAS obtained with the fluid dynamics model (HTS_FD) was larger by more than 30 pct with respect to the PDAS calculated with the simple HTS model. A new PDAS correlation, i.e. , \( \lambda_{1} \left[ {\mu {\text{m}}} \right] = 832\;G\left[ {\text{K/m}} \right]^{ - 0.5} V\left[ {\text{m/s}} \right]^{ - 0.25} \) , which uses the ( G , V ) results from the HTS_FD model was developed and validated against experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Status and Ambitions of the US Heavy Element Program

The aim of this whitepaper is to highlight the current capabilities and priorities of the US Heavy Element community and to provide the framework for a coordinated advancement of nuclear science from these studies. This is an organized effort to reflect on what has been achieved in the field given the recommendations and initiatives of the 2015 NSAC Long Range Plan, and on what can be realized in the next decade given current and possibly expanded investments. Current investments have positioned the US community to be among the world leaders in studies of the nuclear and chemical properties of the heaviest elements. These include studies of reaction mechanisms, moving us ever closer to the “island of stability”, in spectroscopy, allowing us to better understand nuclear structure at these extreme proton numbers, in chemical behavior, looking to determine how these elements should be placed on the Periodic Table, in performing the first measurements where isotopes are directly identified by their mass numbers, and in laying the foundation for a potential US-led new element discovery experiment. At present, the highest priority of the US Heavy Element community is to capitalize on the current investments by supporting the operations of US facilities at optimal values. These facilities include the Argonne Tandem Linac Accelerator System at Argonne National Laboratory and other Department of Energy facilities such as the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory, which has a dedicated superheavy element program, as well as university laboratories, including Texas A&M University. The High Flux Isotope Reactor at Oak Ridge National Laboratory is crucial to providing the radioactive isotopes required for heavy element science targets. This facility should be supported to provide the actinide materials that are essential for US-based science. Production of stable, rare isotopes for beam material, including 48 Ca, 50 Ti, 54 Cr and 58 Fe, at the Stable Isotope Production and Research Center is critical to continued research in heavy element science and should be a priority. The continued development of targets for heavy element science and retaining US-based expertise is critical for the heavy element community. This is an area that is currently under pressure. For example, the target laboratory at Argonne National Laboratory serves a broad community and is currently under threat due to loss of critical personnel. The skills needed to make targets for nuclear science and develop new targetry methods need to be supported long term at Argonne, Oak Ridge and Lawrence Livermore National Laboratories as well as maintaining the programs at Oregon State University and San José State University as vital pipelines for training students. Advances in theory are the foundation to understand how nuclei behave and to predict those behaviors in new circumstances. Progress in these studies will necessitate continued and new investment and access to high-performance computing. The future health of the heavy element field is dependent on the continuous support of talented early-career professionals at all levels. It is critical that opportunities continue to be created for the next generation to become established in heavy element research so that we can ensure the field is attracting and retaining the best minds for continued success. It is also clear that to ensure diversity of ideas, perspectives and techniques, we need to recruit diverse personnel that are trained at the best facilities. The heavy element community is in support of continued investment to programs with initiatives in diversity, equity, and inclusion. Looking to the next decade of research, support needs to maintain and grow US leadership in heavy element science. Specifically, new investments in state-of-the-art instrumentation will be essential to scientific development of the field and in expanding scientific knowledge. Advances in the next generation of electron cyclotron resonance ion sources, multi-reflection time-of-flight devices, laser spectroscopy, trapping methods and next generation alpha and gamma spectroscopy systems should be prioritized.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗