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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Shifts in the swine nasal microbiota following Bordetella bronchiseptica challenge in a longitudinal study

Bordetella bronchiseptica is a widespread, highly infectious bacterial pathogen that causes respiratory disease in swine and increases the severity of respiratory infections caused by other viral or bacterial pathogens. However, the impact of B. bronchiseptica infection on the swine respiratory microbiota has not been thoroughly investigated. Here, we aim to assess the influence of B. bronchiseptica infection on the community structure and abundance of members of the swine nasal microbiota. To do so, the nasal microbiota of a non-infected control group and a group infected with B. bronchiseptica (BB group) were characterized prior to B. bronchiseptica strain KM22 challenge (day 0) and on selected days in the weeks following B. bronchiseptica challenge (days 1, 3, 7, 10, 14, 21, 36, and 42). Bordetella bronchiseptica was cultured from nasal samples of the BB group to assess nasal colonization. The results showed that B. bronchiseptica colonization did not persistently affect the nasal bacterial diversity of either of the treatment groups (alpha diversity). However, the bacterial community structures (beta diversity) of the two treatment groups significantly diverged on day 7 when peak colonization levels of B. bronchiseptica were detected. This divergence continued through the last sampling time point. In addition, Pasteurella, Pasteurellaceae (unclassified), Mycoplasma, Actinobacillus, Streptococcus, Escherichia-Shigella, and Prevotellaceae (unclassified) showed increased abundances in the BB group relative to the control group at various time points. This study revealed that B. bronchiseptica colonization can disturb the upper respiratory tract microbiota, and further research is warranted to assess how these disturbances can impact susceptibility to secondary infections by other respiratory pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2024

Overview of Quantum Sensing Materials and Techniques for Energy Sector Applications

The energy sector has become increasingly dependent upon highly sensitive sensing devices for a wide range of applications. Variables such as temperature, pH, electromagnetic fields, ions, and pressure must be measured with high precision, often in harsh conditions (e.g. highly corrosive environments due to high temperature, pressure and humidity). These sensors are deployed in infrastructure such as transformers, pipelines, mines, nuclear power plants, and other areas to ensure safe operating conditions and uninterrupted, optimized service. Moreover, new opportunities for sensors have emerged due to the expansion of smart grids/meters, driverless vehicles, and the discovery of new oil, gas, and critical mineral deposits. The continued maturation of quantum sensing technologies offers exciting opportunities for quantum-enhanced measurements within the energy sector that may provide significant improvements in sensitivity beyond the classical limit. Here, an overview of established and emerging quantum materials for sensing applications will be provided, from trapped ions to color centers in diamond and silicon carbide. Associated quantum sensing methods for these materials will be discussed, with an emphasis on platforms that are nearing commercial viability. Specific application opportunities within the energy sector for quantum sensors will then be analyzed, including oil/gas discovery, greenhouse gas emission monitoring, pH and ion sensing, current measurement in electric vehicle batteries, and quantum-enhanced spectroscopy. Remaining barriers, such as quantum sensor platform miniaturization and ruggedization, will also be analyzed. A specific project at the National Energy Technology Laboratory involving the functionalization of qubits using metal-organic frameworks for enhanced quantum sensing will also be highlighted. Here, nitrogen vacancy centers (NV) in nanodiamonds, a commercially available qubit with long coherence times and utilizable quantum properties at room temperature, are encapsulated in a controlled way using the metal-organic framework ZIF-8. The ZIF-8 provides a well-defined, porous scaffold that can be controlled to promote the selective uptake of specific target analytes, such as gas molecules or ions. The quantum sensing performance of this composite material is either preserved or enhanced following ZIF-8 encapsulation; the optically detected magnetic resonance response of the NV nanodiamonds with and without the ZIF-8 coating are identical, while the ZIF-8 coating increases the longitudinal spin relaxation lifetime (T1) of the NV centers, an important parameter for spin relaxometry-based quantum sensing experiments. Taken together, these results demonstrate the importance of qubit functionalization as a crucial step for rationally designing high performance quantum sensors.

Crawford, Scott↗
DOE OSTI2021

Toward white light emission from plasmonic-luminescent hybrid nanostructures

We study the light emission of plasmonic-luminescent hybrid nanostructures consisting of Ag nanoparticles (NPs) embedded in europium oxide (EuO X ). The Ag NPs present a bidimensional organization in the nanostructures and they optically behave as oblate spheroids. The photoluminescence (PL) spectral response of the nanostructures evolves from a narrow red emission characteristic of Eu 3+ ions in absence of Ag NPs to a broad blue-green emission band associated with Eu 2+ ions when the layer of Ag NPs is present. This behavior is not related to a change in the Eu 2+ /Eu 3+ ratio, which is verified by compositional analysis. Instead, a detailed investigation of the PL emission of the nanostructures suggests that the coupling of the Ag NPs to the Eu 2+ ions present in the EuO X layer, which manifests itself in an efficient sensitization of these ions, enhances their broad visible emission. In particular, the longitudinal mode of the Ag NPs surface plasmon is considered to be responsible for the efficient energy transfer for the non-normal incidence excitation PL configuration used. Finally, the use of a capping amorphous Al 2 O 3 layer allows improving the robustness of hybrid nanostructures and further enhances their PL emission. These findings provide a new path to actively control the selective excitation of Eu 2+ and Eu 3+ ions via a controlled coupling with the surface plasmon resonance modes of the Ag NPs and points to these nanostructures as promising building blocks for the development of integrable white light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Start-to-End Simulation of the Drive-Beam Longitudinal Dynamics for Beam-Driven Wakefield Acceleration

Collinear beam-driven wakefield acceleration (WFA) relies on shaped driver beam to provide higher accelerating gradient at a smaller cost and physical footprint. This acceleration scheme is currently envisioned to accelerate electron beams capable of driving free-electron laser *. Start-to-end simulation of drive-bunch beam dynamics is crucial for the evaluation of the design of accelerators built upon WFA. We report the start-to-end longitudinal beam dynamics simulations of an accelerator beamline capable of producing high charge drive beam. The generated wakefield when it passes through a corrugated waveguide results in a transformer ratio of 5. This paper especially discusses the challenges and criteria associated with the generation of temporally-shaped driver beam, including the beam formation in the photoinjector, and the influence of energy chirp control on beam transport stability.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Contextual modeling and Bayesian Optimization for Improved Injection at the Fermilab Booster

The Fermilab accelerator complex delivers high-intensity proton beams to serve the lab’s neutrino, muon, and fixed-target programs. A normal-conducting Linac accelerates H− beam to 400 MeV and injects into the Booster rapid cycling synchrotron via charge exchange, which accelerates protons to 8 GeV. Injection from the Linac into the Booster is a critical area for high-power performance of the Fermilab proton complex. The Booster is a high-intensity proton ring with extreme space-charge forces which necessitates precise control over the beam losses through the acceleration cycle. The main challenge for the reliability of Booster performance is compensating for drifting conditions in the beam from the Linac, which can drift daily in energy by up to O(1) MeV w.r.t. design. Drifts in Linac orbit and energy must be corrected to match the Booster, while simultaneously accommodating interdependent drifts in transverse and longitudinal beam quality. Operationally, compensation for these changes is addressed by manual tuning of the Linac output energy and/or Booster acceptance, which can be inefficient and time-consuming. This works describes contextual Bayesian Optimization for injection tuning that takes into account the state of Linac beam via information from instrumentation in the injection line (Beam position monitors (BPMs), beam loss monitors (BLMs), wire scanners for transverse profiles (WSs)), as well as RF cavity setting parameters from the Linac.

Sharankova, R. [Fermilab] (ORCID:000000027014593X)↗
DOE OSTI2025

Lattice Anisotropy-Driven Reduction of Phonon Velocities in Black Phosphorus

Phonon dynamics and transport determine how heat is utilized and dissipated in materials. In 2D systems for optoelectronics and thermoelectrics, the impact of nanoscale material structure on phonon propagation is central to controlling thermal conduction. Here, we directly observe in-plane coherent acoustic phonon propagation in black phosphorus (BP) using ultrafast electron microscopy. We identify a significant reduction of the group velocities in directions intermediate to the armchair and zigzag lattice directions. Using a machine learning-based model with an >8000 atom supercell, we find that this slowing results from the mixing of in-plane transverse and longitudinal acoustic phonons and is independent of broken symmetries of edge reconstructions. In conclusion, this work demonstrates how coherent phonon transport is sensitive to propagation direction in the lattice plane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The damping of terahertz acoustic modes in aqueous nanoparticle suspensions

In this work, we investigate the possibility of controlling the acoustic damping in a liquid when nanoparticles are suspended in it. To shed light on this topic, we performed Inelastic X-Ray Scattering (IXS) measurements of the terahertz collective dynamics of aqueous suspensions of nanospheres of various materials, size, and relative concentration, either charged or neutral. A Bayesian analysis of measured spectra indicates that the damping of the two acoustic modes of water increases upon nanoparticle immersion. This effect seems particularly pronounced for the longitudinal acoustic mode, which, whenever visible at all, rapidly damps off when increasing the exchanged wavevector. Results also indicate that the observed effect strongly depends on the material the immersed nanoparticles are made of.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effects of an External Magnetic Field on Keyhole Mode Laser Melting of 316 Stainless Steel

Keyhole-mode laser melting is an efficient method for joining or cutting large, thick components, but controlling keyhole depth and fluctuations has remained challenging. Applying an external magnetic field can control melt pool flows and indirectly influence keyhole morphology and dynamics. The induced Lorentz force, comprising Seebeck and damping components, plays a crucial role in the melt pool dynamics, depending on temperature gradient, flow rates, and magnetic field orientation and magnitude. This research investigates the effects of an external magnetic field on keyhole behavior during laser spot melting of 316 stainless steel using synchronized high-speed synchrotron X-ray and thermal imaging. Findings revealed that a longitudinal magnetic field (120 mT) increased keyhole depth but exacerbated lateral fluctuations, resulted in a 20% increase in the melt pool temperature gradient and a 27% decrease in cooling rate. Conversely, a transverse magnetic field (760 mT) reduced keyhole depth and improved porosity formation. The findings suggest that a decrease in keyhole depth correlates with a decrease in fluctuations, and vice versa. These insights enhance understanding of external magnetic fields’ impact on laser melting, with implications for improving part quality.

Alamdari, Aslan Bafahm [The Ohio State Univ., Colu↗
DOE OSTI2023

Revealing the dynamics of ultrarelativistic non-equilibrium many-electron systems with phase space tomography

The description of physical processes with many-particle systems is a key approach to the modeling of numerous physical systems. For example in storage rings, where ultrarelativistic particles are agglomerated in dense bunches, the modeling and measurement of their phase-space distribution is of paramount importance: at any time the phase-space distribution not only determines the complete space-time evolution but also provides fundamental performance characteristics for storage ring operation. Here, we demonstrate a non-destructive tomographic imaging technique for the 2D longitudinal phase-space distribution of ultrarelativistic electron bunches. For this purpose, we utilize a unique setup, which streams turn-by-turn near-field measurements of bunch profiles at MHz repetition rates. To demonstrate the feasibility of our method, we induce a non-equilibrium state and show that the phase-space distribution microstructuring as well as the phase-space distribution dynamics can be observed in great detail. Our approach offers a pathway to control ultrashort bunches and supports, as one example, the development of compact accelerators with low energy footprints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum

Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Stimulated Rayleigh Scattering Enhanced by a Longitudinal Plasma Mode in a Periodically Driven Dirac Semimetal Cd 3 As 2

Using broadband (12–45 THz) multi-terahertz spectroscopy, we show that stimulated Rayleigh scattering dominates the transient optical conductivity of cadmium arsenide, a Dirac semimetal, under an optical driving field at 30 THz. Here, the characteristic dispersive line shape with net optical gain is accounted for by optical transitions between light-induced Floquet subbands, strikingly enhanced by the longitudinal plasma mode. Stimulated Rayleigh scattering with an unprecedentedly large refractive index change may pave the way for slow light generation in conductive solids at room temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Beam and Spin Optics Simulation Tutorials, Using Zgoubi Exercises and Solutions 2 - RHIC Run 22 Spin Transparency Experiment

High electron and ion polarizations are paramount design requirements for the future Electron Ion Collider (EIC) at BNL. A “spin transparency” mode of operation, inherited from the “figure-8” ring design [1], allows preservation and control of electron and ion spin polarization in a collider or storage ring [2, 3]. It makes the ring lattice “invisible” to the spin and allows for polarization control by small quasi-static magnetic fields with practically no effect on beam orbital parameters. Beam polarization can then be fully controlled by small adjustments of snake axis orientation (a few degrees) and snake strengths (a few percent); polarization orientation at IPs can be controlled without spin rotators, spins can be adiabatically flipped without the need of ac magnets. The transparent spin mode has been demonstrated in simulations [2] for the purpose of being tested experimentally during RHIC Run 22. This experiment was aimed at providing data to validate the principles. Yellow RHIC ring (Fig. 1.1) was configured in the transparent spin mode by aligning the axes of its two helical snakes (located respectively in the 3 o’clock and 9 o’clock sextants) parallel to the longitudinal axis. Experiment goals and expected results, ad hoc procedures and hardware requirements, are addressed in Refs. [2, 3].

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Control of Weld Residual Stress in a Thin Steel Plate through Low Transformation Temperature Welding Consumables

Low transformation temperature welding (LTTW) consumables are characterized by a low martensite start temperature and a large fraction of martensite forming in the weld. It can efficiently reduce the tensile residual stress because the volume expansion associated with the martensitic transformation compensates for the thermal contraction during cooling. In this work, a LTTW wire, designated as EH200B, was created for the arc welding of advanced high-strength steel thin plates. In comparison to conventional ER70S-3 wires, this LTTW wire generated an opposite distortion pattern. Neutron diffraction measurements along the center thickness of the welded plates showed the maximum residual stress along the longitudinal direction (LD) in the weld region, and the heat-affected zone (HAZ) immediately adjacent to the weld region was reduced from ~330 MPa to below 240 MPa by using the LTTW wire. A finite element (FE) model was developed to predict the residual stress distributions of the plates welded under these two wires. The simulation results showed reasonable agreement with the volume-average neutron diffraction data. Compressive residual stress in the weld region using the LTTW wire was predicted by the FE method. Electron backscattered diffraction and x-ray diffraction measurements confirmed ~90% martensite was present in the LTTW weld. The fatigue life of DP980 steel lap joint panels using EH200B wire nearly doubled that of ER70S-3 wire. This improvement was attributed to the high strength and low LD residual stress in the weld and HAZ immediately adjacent to the weld.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models f(c) for two groups, with a faster misfolding for the Alzheimer's group, f(c) = 0.23c 3 – 1.34c 2 + 1.11c, than for the healthy control group, f(c) = –c 3 + 0.62c 2 + 0.39c. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. Furthermore, we expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Sound Speed as a Candidate for Internal Temperature Monitoring During Solid Phase Processing of Materials

Internal temperature is an important parameter for understanding the solid phase processing (SPP) of materials and improving feedback control of these processes. Friction stir processes, shear assisted processing and extrusion (ShAPE ™ ), and ultrahigh velocity (UHV) cold spray are examples of such processes. Simple thermal models based on temperatures measured at the boundary of the deforming body cannot capture the dynamic nature of temperature generation within the deforming solid. One possibly way of understanding internal temperature is by measuring sound speed, which varies as a function of temperature for materials and directing ultrasonic wave propagation through selected material volumes of interest. In this project, longitudinal wave speed is estimated for aluminum 6061. Piezoelectric materials bonded to a specimen provided data from which longitudinal wave speed was estimated. A discussion evaluated the linear relationship of wave speed versus temperature and postulated how wave speed measurements could be used to estimate the internal temperature of selected material volumes for feedback during SPP of materials. Instrumentation, data and analysis, and a discussion are reviewed.

36 MATERIALS SCIENCE↗