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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

XANES reflects coordination change and underlying surface disorder of zinc adsorbed to silica

In this work, zinc K -edge X-ray absorption near-edge structure (XANES) spectroscopy of Zn adsorbed to silica and Zn-bearing minerals, salts and solutions was conducted to explore how XANES spectra reflect coordination environment and disorder in the surface to which a metal ion is sorbed. Specifically, XANES spectra for five distinct Zn adsorption complexes (Zn ads ) on quartz and amorphous silica [SiO 2(am) ] are presented from the Zn–water–silica surface system: outer-sphere octahedral Zn ads on quartz, inner-sphere octahedral Zn ads on quartz, inner-sphere tetrahedral Zn ads on quartz, inner-sphere octahedral Zn ads on SiO 2(am) and inner-sphere tetrahedral Zn ads on SiO 2(am) . XANES spectral analysis of these complexes on quartz versus SiO 2(am) reveals that normalized peak absorbance and K -edge energy position generally decrease with increasing surface disorder and decreasing Zn–O coordination. On quartz, the absorption-edge energy of Zn ads ranges from 9663.0 to 9664.1 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On SiO 2(am) , the absorption-edge energy of Zn ads ranges from 9662.3 to 9663.4 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On both silica substrates, octahedral Zn ads presents a single K -edge peak feature, whereas tetrahedral Zn ads presents two absorbance features. The energy space between the two absorbance peak features of the XANES K -edge of tetrahedral Zn ads is 2.4 eV for Zn on quartz and 3.2 eV for Zn on SiO 2(am) . Linear combination fitting of samples with a mixture of Zn ads complex types demonstrates that the XANES spectra of octahedral and tetrahedral Zn ads on silica are distinct enough for quantitative identification. These results suggest caution when deciphering Zn speciation in natural samples via linear combination approaches using a single Zn ads standard to represent sorption on a particular mineral surface. Correlation between XANES spectral features and prior extended X-ray absorption fine structure (EXAFS) derived coordination environments for these Zn ads on silica samples provides insight into Zn speciation in natural systems with XANES compatible Zn concentrations too low for EXAFS analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Column separation of tetravalent cerium fission products from trivalent rare earth radio-isotopes

Rapid and efficient isolation of individual rare earth element (REE) radioisotopes from complex mixtures is necessary to support the fields of nuclear forensics, medical isotope production and nuclear physics measurements. The separations must be robust and generate sufficiently high purity samples for subsequent radiological analysis. Current methodologies utilize a laborious two step Ce(IV)-iodate precipitation followed by Ce(IV)-nitrate di-(2-ethylhexyl)phosphoric acid/n-heptane extraction. This work reports an alternative method to isolate Ce fission product isotopes equilibrated with milligrams of cold carrier, from the remaining REE’s using a Ce(IV)/LN-resin (Eichrom Technologies) based separation. Optimization of Ce(IV) loading, REE(III) elution volume and subsequent Ce(III) stripping step utilized 166m Ho tracer. Mixed fission product experiments provided a further test of this LN-resin method, with analysis of purified 141/144 Ce, 147 Nd and 91 Y. Overall, while decontamination factors were lower for the LN-resin method, (vs. the combined precipitation/extraction) the simplicity of the technique and the satisfactory fission product analysis results point to the efficacy of this method.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Nondestructive equipment study

Identification of existing nondestructive Evaluation (NDE) methods that could be used in a low Earth orbit environment; evaluation of each method with respect to the set of criteria called out in the statement of work; selection of the most promising NDE methods for further evaluation; use of selected NDE methods to test samples of pressure vessel materials in a vacuum; pressure testing of a complex monolythic pressure vessel with known flaws using acoustic emissions in a vacuum; and recommendations for further studies based on analysis and testing are covered.

Source record↗
DOE OSTI2019

Handling and Treatment of Sodium-Potassium Alloy (NaK) after the Loss of Key Personnel - 19002

In September 2010, during cleanup and removal efforts associated with a glovebox containing a sodium-potassium alloy (NaK) cooling system, a NaK reaction event occurred. Near the end of the evolution, which was at a Materials and Fuels Complex (MFC) research facility at the Idaho National Laboratory, a 2 to 3-ounce sample of NaK was being transferred from a metal can to a polyethylene bottle so it could be sent to the disposal outlet for verification analysis. During the transfer, a reaction of the NaK with ambient air was observed in both the metal can and sample container. The potential for a reaction was identified during the planning of this activity and mitigating steps were included in the procedures. In response, the two containers were flooded with argon, closed, and observed overnight. No additional reaction was noted. The sample container and metal can were packaged into a 10-gallon drum and transferred to a permitted storage location pending disposal. While the other containers generated from the deactivation and decommissioning activity were sent off-site for disposal, the 10-gal drum remained due to concerns by a staff specialist that the material could contain potassium superoxide (KO{sub 2}) as a result of the reaction event. The intent at that time was to treat the NaK in a Resource Conservation and Recovery Act (RCRA)-permitted treatment facility at MFC, instead of sending it off-site for disposal. The container was labeled, inspected, and managed as a hazardous waste. Due to the complexities associated with safely handling and treating NaK and KO{sub 2}, the container remained in storage. Treatment and disposal of the NaK and KO{sub 2} container was revisited in 2016, when researchers and chemists were consulted regarding the potential for superoxide formation and the best means to safely store and treat the container. The facility came to realize that due to the retirements of key personnel, it needed to provide current employees operational experience with the handling and treatment of NaK. An outside company was identified and contracted to provide training and consulting services. Several Battelle Energy Alliance, Inc. (BEA) personnel then attended an alkali metals safety training class. The training course provided participants with classroom and hands-on instruction associated with: - Hazards associated with handling and using sodium and NaK; - Proper personal protection equipment, spill control techniques, and first aid; - Common design approaches for buildings, systems, and equipment used to contain sodium and NaK; - What to expect in the event of a water reaction and how to respond; - How to properly prepare for a fire and contain/extinguish small metal fires; - A hands-on demonstration of NaK reaction and controls. Following the training, a procedure was developed to open the container, and inspect and treat the NaK. In June 2017, facility operations personnel successfully completed the inspection and treatment of the NaK drum, eliminating the legacy liability. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2022

Machine learning approaches to streamline and enhance the analysis of multiscale imaging data for bioaerosol and soil particles

Bioaerosol and soil particles are ubiquitous in the environment. They are multicomponent and complex in nature displaying mixed inorganic and organic components. The way components are mixed in a bioaerosol sample is referred to as its mixing state. Soil particles are also a mixture of inorganic (mineral) and organic (soil organic matter) components. Bioaerosol particles contribute to a major fraction of coarse mode atmospheric particles, especially in the tropical areas, contributing up to 80 % of the particle mass concentration. The mixing state of particles is crucial to evaluate because it impacts several important environmental processes such as warm and cold cloud formation and radiation budget. Mixing states in aerosols are accompanied by chemical reactions across solid-liquid-gas interfaces. In this study, we utilized elemental compositions and microcopy images of thousands of atmospheric particles acquired by computer-controlled scanning electron microscope equipped with an energy-dispersive x-ray spectrometer to compute the mixing state of atmospheric particles. A 2D convolutional neural network (CNN), also known as convnet, was used to model the relationship between low resolution imaging data and higher resolution spectroscopy data, with the former as training input and the latter as target output. Two types of CNNs were implemented and tested; a basic CNN and an Inception-v3 network. For binary classification, the basic CNN achieved an accuracy of 84.29 % across all atom types, and the Inception-v3-like network achieved an accuracy 85.51 %. This study demonstrates the applicability of deep learning to handle large amounts of imaging/chemical spectroscopy data efficiently and evaluate particle mixing state from a range of environmental samples.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

New Tools for Automating Arcjet Sample Recession Tracking and Analysis

Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.

Ablation↗
NASA NTRS2013

The Regolith Biters: A Divide-And-Conquer Architecture for Sample-Return Missions

The collective interaction of simple systems can be leveraged to attain complex goals. Based on this principle, we envision space system architectures where the core functional components are decoupled, autonomous, and cooperative. We aim to pursue this vision in the context of small-body sample-return missions. After all, no experimental study sheds more light into our understanding of the origin and evolution of the Solar System than the analysis of samples from asteroids and comets. We also believe that their study is important from a strategic perspective: meteorite impacts pose a direct and credible threat to life on Earth, and the development of contingency small-body deflection missions presupposes some knowledge of the target body. The current architectural paradigm for sample-return missions is centered around a design where spacecraft and sampling device are merged into a single, complex system. We argue that this monolithic approach couples the navigation and sample-collection problems, making both more difficult. In contrast, we propose a decoupled system based on the coordinated interaction between a spacecraft and a collective of small, simple devices - the Regolith Biters (RBs). A spacecraft carrying a number of RBs would travel to the vicinity of a small body. From a favorable vantage point, and while remaining within a safe distance in a non-colliding trajectory, it would release the RBs towards the target body. Upon encountering the body, they would bite the regolith (thus retaining a sample), and eject back to orbit. The spacecraft, being endowed with appropriate navigation and tracking capabilities, would rendezvous with and collect those RBs within its reach, and bring them back to Earth. Separating the navigation and sampling concerns removes the need for proximity operations with the small body-the stage in current architectures that carries the most challenges and risks. Eliminating the need for proximity operations brings back to the discussion the exploration of exciting prospects, like highly active comets, fast-rotating bodies, and binary systems. Distributing the sampling problem among a collective of agents provides the opportunity to sample multiple regions in a single mission. It also provides robustness to various environmental conditions, and may enable the distributed, in situ characterization of the body. In the search for reliability, current architectures rely on complexity: an elaborate system should succeed at once. We rely on numbers: a given agent may fail at any stage, but success is attained by the collective.

Asteroids↗
DOE OSTI2024

High throughput automated characterization of enamel microstructure using synchrotron tomography and optical flow imaging

The remarkable damage-tolerance of enamel has been attributed to its hierarchical microstructure and the organized bands of decussated rods. A thorough characterization of the microscale rod evolution within the enamel is needed to elucidate this complex structure. While prior efforts in this area have made use of single particle tracking to track a single rod evolution to various degrees of success, such a process can be both computationally and labor intensive, limited to the evolution path of a single rod, and is therefore prone to error from potentially tracking outliers. Particle image velocimetry (PIV) is a well-established algorithm to derive field information from image sequences for processes that are time-dependent, such as fluid flows and structural deformation. In this work, we demonstrate the use of PIV in extracting the full-field microstructural distribution of rods within the enamel. Further, enamel samples from a wild African lion were analyzed using high-energy synchrotron X-ray micro-tomography. Results from the PIV analysis provide sufficient full-field information to reconstruct the growth of individual rods that can potentially enable rapid analysis of complex microstructures from high resolution synchrotron datasets. Such information can serve as a template for designing damage-tolerant bioinspired structures for advanced manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimizing Sub-Sampled STEM Imaging for Beam Sensitive Materials and Dynamic Processes

The goal of this research is to quantify how sub-sampling and inpainting (broadly classified as methods in Artificial Intelligence) can be used to obtain the highest resolution images under any experimental conditions, and to use this to reproducibly control dynamic in-situ processes in gases and liquids on the nanoscale. As atomic diffusion under non-extreme conditions is typically in the sub-ms time domain, the ability to use this sub-sampling approach to reduce the effect of the electron beam and to speed-up the imaging process represents the optimum combination of spatial and temporal resolution achievable in a conventional microscope. This LDRD funding demonstrated the feasibility of imaging dynamics in complex energy systems using this methodology by establishing the optimum experimental sampling conditions (optimizing the number of pixels, speed, contrast and resolution) for each experiment. For in-situ liquid cell experiments this analysis shows that simply by distributing the same dose and dose rate maximally in space and time, the kinetics for radiolysis products can be modified significantly. The final stage of this work, i.e. the experimental demonstration of its use for nucleation and growth, was severely curtailed by issues with COVID19. However, all the experiments are designed and currently underway, with the goal to complete the final demonstration and publication as soon as safety concerns for laboratory access are met.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Protein Conformational States—A First Principles Bayesian Method

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Symbolic calculations in a finite dynamic element analysis

A second order problem is outlined for which MACSYMA was used only for checking purpose. A fourth order problem is briefly described for which a symbolic system is necessary. At the end, some sample output is displayed to indicate the complexity of the computational problem.

Gupta, K. K.↗
NASA NTRS1990

Neural net diagnostics for VLSI test

This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

Lin, T.↗
NASA NTRS2016

Advanced Laser Architecture for Two-Step Laser Tandem Mass Spectrometer

Future astrobiology missions will focus on planets with significant astrochemical or potential astrobiological features, such as small, primitive bodies and the icy moons of the outer planets that may host diverse organic compounds. These missions require advanced instrument techniques to fully and unambiguously characterize the composition of surface and dust materials. Laser desorptionionization mass spectrometry (LDMS) is an emerging instrument technology for in situ mass analysis of non-volatile sample composition. A recent Goddard LDMS advancement is the two-step laser tandem mass spectrometer (L2MS) instrument to address the need for future flight instrumentation to deconvolve complex organic signatures. The L2MS prototype uses a resonance enhanced multi-photon laser ionization mechanism to selectively detect aromatic species from a more complex sample. By neglecting the aliphatic and inorganic mineral signatures in the two-step mass spectrum, the L2MS approach can provide both mass assignments and clues to structural information for an in situ investigation of non-volatile sample composition. In this paper we will describe our development effort on a new laser architecture that is based on the previously flown Lunar Orbiter Laser Altimeter (LOLA) laser transmitter for the L2MS instrument. The laser provides two discrete midinfrared wavelengths (2.8 m and 3.4 m) using monolithic optical parametric oscillators and ultraviolet (UV) wavelength (266 nm) on a single laser bench with a straightforward development path toward flight readiness.

Laser↗
DOE OSTI2019

A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters

First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution for thirty-one parameters focusing on the H 2 O 2 and HO 2 reactions resulting from conditioning on ninety-one experiments. Established literature values are used for the remaining parameters in the mechanism as well as other thermodynamic and transport data needed to specify fluid properties. The samples are computed using an affine invariant sampler starting with broad, noninformative priors. Autocorrelation analysis shows that O(1M) samples are sufficient to obtain a reasonable sampling of the posterior. The resulting distribution identifies strong positive and negative correlations and several non-Gaussian characteristics. Using samples drawn from the posterior, we investigate the impact of parameter uncertainty on the prediction of two more complex flames: a 2D premixed flame kernel and the ignition of a hydrogen jet issuing into a heated chamber. The former represents a combustion regime similar to the target experiments used to calibrate the mechanism and the latter represents a different combustion regime. For the premixed flame, the net amount of product after a given time interval has a standard deviation of less than 2% whereas the standard deviation of the ignition time for the jet is more than 10%. The samples used for these studies are posted online. These results indicate the degree to which parameters consistent with the target experiments constrain predicted behavior in different combustion regimes. Finally, this process provides a framework for both identifying reactions for further study from candidate mechanisms as well as combining uncertainty quantification and propagation to, ultimately, tie uncertainty in laboratory flame experiments to uncertainty in end-use numerical predictions of more complicated scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Metal (hydr)oxide surface precipitates and their effects on potassium sorption

Surface precipitation has been shown to occur on rapid time scales in clay and metal oxide mineral systems. The formation of surface precipitates is hypothesized to present new potential sorption sites for potassium (K), where K can become incorporated into newly formed interlayer spaces (e.g., between tetrahedral–octahedral–tetrahedral stacked sheets). The objective of this study is to determine the effects of newly formed mineral surface precipitates on K sorption. Potassium adsorption experiments were conducted by utilizing Al 2 O 3 and SiO 2 sorbents in the presence of various cations (magnesium, zinc, and nickel) that helped to catalyze the formation of surface precipitates. Dissolved concentrations of elements were monitored via inductively coupled plasma optical emission spectrometry (ICP-OES). Solids were characterized via X-ray diffraction (XRD), and K surface complexation was analyzed via X-ray absorption near edge structure (XANES) spectroscopy. X-ray diffraction analysis indicated bayerite, layered double hydroxides (LDH), and silicated LDH were formed as reaction products, thus creating new surface sites for potential K adsorption. The presence of Si increased K adsorption perhaps due to its role in the formation of LDH surface precipitates. When the differences between observed and theoretical surface area normalized K sorption densities were averaged, a 31% increase in K adsorption was observed in the presence of Si. XANES analysis indicated that the binding mechanism of K to Si is different than that of K to Al, perhaps due to the presence of inner-sphere complexation of K to Al-oxide. Samples reacted for one month versus one week yielded more intense XANES post-edge peaks which indicated that the K sorption complex changes over time. Altogether, our findings provide novel insights into the mechanisms of K fixation in soil and has high implication in providing improved K fertilizer recommendation to growers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Bayesian approach to time-domain photonic Doppler velocimetry analysis

Photonic Doppler velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, the short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration, and separation of the window function. Here, in this study, we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace, without using the spectrogram for analysis. This is clearly a difficult inference problem due to the highly periodic nature of the data, but we find that with carefully chosen prior distributions for the model parameters, we can accurately recover the injected velocity from synthetic data. We validate this method using PDV data collected at the STAR two-stage light gas gun at Sandia National Laboratories, recovering shock-front velocities in quartz that are consistent with those inferred using the STFT-based approach and are interpolated across regions of low signal-to-noise data. Although this method does not rely on the same user choices as the STFT, we caution that it can be prone to misspecification if the chosen model is not sufficient to capture the velocity behavior. Analysis using posterior predictive checks can be used to establish whether a better model is required, although more complex models come with additional computational cost, often taking more than several hours to converge when sampling the Bayesian posterior. We, therefore, recommend it be viewed as a complementary method to that of the STFT-based approach.

Allison, James R. [First Light Fusion Ltd., Yarnto↗