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All sourcesNASA NTRSDOE OSTI
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Portable Cleanroom for NASA OSIRIS-REx Mission Deintegration

NASA Johnson Space Center (JSC) Infrastructure and Astromaterials Acquisition & Curation Office completed construction and commissioning of the OSIRIS-REx (OREx) Deintegration portable cleanroom at the Utah Test and Training Range (UTTR). The new portable cleanroom was designed to receive the OREx sample return capsule from the landing point on the range to an ISO7 environment. Scientists used the portable clean-room to deintegrate the sample canister from the sample return capsule. Once separated, the sample canister was put in a container under nitrogen purge for transportation to B31 at the Johnson Space Center for astromaterial sample extraction, preliminary analysis, and long-term curation. The portable cleanroom was built by a subcontractor at their facility and then deconstructed to be transported to the remote location at UTTR. Since construction was completed in a remote location all tools and materials had to be transported from contractor site in Dallas, TX. The cleanroom was constructed within an existing facility, which provided conditioned air, electric power, and protection from the elements. Careful coordination was required between the host facility, cleanroom contractor, mission scientists, and JSC facilities and curation personnel. An existing anteroom at JSC was transported to UTTR and added to the portable cleanroom after there was concern about contamination without one for personnel entry/exit. The scientific study of organics is critical for the mission, so a stringent contamination control plan was implemented for low organics. Given these mission requirements the cleanroom construction materials were carefully selected to not hinder the scientific search for amino acids and the study of organics in the samples. The same cleanroom contractor that built the long-term astromaterial curation cleanroom back at JSC Houston, TX was selected to build the portable cleanroom and instructed to use the same materials. The cleanroom had double doors to open and allow the sample return capsule to fit into the cleanroom on its stand and be transferred to a clean stand already in the cleanroom. The portable cleanroom successfully completed its mission and the sample canister was safely deintegrated and transported to JSC under nitrogen purge.

astromaterials curation↗
DOE OSTI2025

Single Primary Heat Extraction and Removal Emulator (SPHERE) Long Duration Testing

For the development of heat-pipe cooled microreactors, it is crucial to thoroughly understand the characteristics and functioning of heat pipes across a wide spectrum of operating conditions. Passive heat removal and its long-term performance stability are critical factors in this context. Enhanced experimental data is vital for evaluating the operational lifespan of alkali metal heat pipes. Idaho National Laboratory (INL) has successfully conducted an extended duration test on a high-performance sodium-filled heat pipe, closely monitoring the axial temperature profile, power supplied by the heaters, and heat removed by a gas-gap calorimeter. The results from this testing provide valuable data that are instrumental in supporting heat pipe validation efforts. Specifically, this data aids in the development and validation of Sockeye, the Multiphysics Object-Oriented Simulation Environment (MOOSE) tool under the US-DOE NEAMS program designed for heat pipe modeling. By comparing experimental results with Sockeye’s predictions, the tool's accuracy and reliability can be assessed and improved, thereby enhancing its capability to simulate heat pipe operations under various conditions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Quantum efficiency of InGaN–GaN multi-quantum well solar cells: Experimental characterization and modeling

InGaN-based multi-quantum well (MQW) solar cells are promising devices for photovoltaics (e.g., for tandem solar cells and concentrator systems), space applications, and wireless power transfer. In order to improve the efficiency of these devices, the factors limiting their efficiency and stability must be investigated in detail. Due to the complexity of a MQW structure, compared with a simple pn junction, modeling the spectral response of these solar cells is not straightforward, and ad hoc methodologies must be implemented. In this paper, we propose a model, based on material parameters and closed-formula equations, that describes the shape of the quantum efficiency of InGaN/GaN MQW solar cells, by taking into account the layer thickness, the temperature dependence of the absorption coefficient, and quantum confinement effects. We demonstrate (i) that the proposed model can effectively reproduce the spectral response of the cells; in addition, (ii) we prove that the bulk p-GaN layer absorbs radiation, but the carriers photogenerated in this region do not significantly contribute to device current. Finally, we show that (iii) by increasing the temperature, there is a redshift of the absorption edge due to bandgap narrowing, which can be described by Varshni law and is taken into account by the model, and a lowering in the extraction efficiency due to the increase in recombination (mostly Shockley–Read–Hall) inside the quantum wells, which is also visible by decreasing light intensity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Development of a Structure for Lossless Ion Manipulations (SLIM) High Charge Capacity Array of Traps

Enhancing the sensitivity of low abundance ions in a complex mixture without sacrificing experiment throughput is highly desirable. This work demonstrates a way to greatly improve the sensitivity of ion mobility (IM) selected ions by accumulating them in an array of high-capacity ion traps located inside a novel structures for lossless ion manipulations ion mobility spectrometer (SLIM-IMS) module. The array of ion traps used in this work consisted of seven independently controllable traps. Each trap was 386 mm long and possessed a charge capacity of ~4.5×10 8 charges, with a linear range extending to ~2.5×10 8 charges. Each ion trap could be used to extract a peak (or ions over a mobility range) from an ion mobility separation based on arrival time. Ions could be stored without losses for long times (>100 s) and then released all at once or one trap at a time. It was possible to accumulate large ion populations by extracting and storing ions over repeated IM separations. We report enrichment of up to seven individual ion distributions could be performed using the seven independently controllable ion traps. Additionally, the ion trapping process effectively compressed ion populations into narrow peaks, which provides a greatly improved basis for subsequent ion manipulations. The array of high charge-capacity ion traps provides a flexible addition to SLIM and a powerful tool for IMS-MS applications requiring high sensitivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2025

Fermilab PIP II Machine Protection System Digitized Data Noise Elimination Scheme and Its FPGA Implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can con-taminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is re-quired. Several real-time functional blocks were simu-lated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid base-line by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
NASA NTRS1988

Intelligent data reduction for autonomous power systems

Since 1984 Marshall Space Flight Center was actively engaged in research and development concerning autonomous power systems. Much of the work in this domain has dealt with the development and application of knowledge-based or expert systems to perform tasks previously accomplished only through intensive human involvement. One such task is the health status monitoring of electrical power systems. Such monitoring is a manpower intensive task which is vital to mission success. The Hubble Space Telescope testbed and its associated Nickel Cadmium Battery Expert System (NICBES) were designated as the system on which the initial proof of concept for intelligent power system monitoing will be established. The key function performed by an engineer engaged in system monitoring is to analyze the raw telemetry data and identify from the whole only those elements which can be considered significant. This function requires engineering expertise on the functionality of the system, the mode of operation and the efficient and effective reading of the telemetry data. Application of this expertise to extract the significant components of the data is referred to as data reduction. Such a function possesses characteristics which make it a prime candidate for the application of knowledge-based systems' technologies. Such applications are investigated and recommendations are offered for the development of intelligent data reduction systems.

Floyd, Stephen A.↗
NASA NTRS1992

Dynamics of ultraharmonic resonances in spiral galaxies

The mildly nonlinear response of a fluid disk with pressure, viscosity, and self-gravity to spiral stellar forcing is considered as a model of the interstellar medium in spiral galaxies. Nonlinear effects are analyzed through a quasi-linear flow analysis ordered by successive powers of a dimensionless spiral perturbing force, which is the ratio of imposed nonaxisymmetric gravitational to axisymmetric gravitational forces. Waves with mn arms are launched from a position where the wavenumber of a free wave matches n times the wavenumber of the spiral forcing. The launched short wave in the gas is an interarm feature that is more tightly wrapped than the stellar wave. The gas wave extracts energy and angular momentum from the stellar wave, causing it to damp. The application of the results to the stellar disk alone reveals even stronger damping, as stars undergo Landau damping of the short wave. For parameters in M81, damping times are less than 10 exp 9 yr.

Artymowicz, Pawel↗
NASA NTRS2012

Development and Applications of a Stage Stacking Procedure

The preliminary design of multistage axial compressors in gas turbine engines is typically accomplished with mean-line methods. These methods, which rely on empirical correlations, estimate compressor performance well near the design point, but may become less reliable off-design. For land-based applications of gas turbine engines, off-design performance estimates are becoming increasingly important, as turbine plant operators desire peaking or load-following capabilities and hot-day operability. The current work develops a one-dimensional stage stacking procedure, including a newly defined blockage term, which is used to estimate the off-design performance and operability range of a 13-stage axial compressor used in a power generating gas turbine engine. The new blockage term is defined to give mathematical closure on static pressure, and values of blockage are shown to collapse to curves as a function of stage inlet flow coefficient and corrected shaft speed. In addition to these blockage curves, the stage stacking procedure utilizes stage characteristics of ideal work coefficient and adiabatic efficiency. These curves are constructed using flow information extracted from computational fluid dynamics (CFD) simulations of groups of stages within the compressor. Performance estimates resulting from the stage stacking procedure are shown to match the results of CFD simulations of the entire compressor to within 1.6% in overall total pressure ratio and within 0.3 points in overall adiabatic efficiency. Utility of the stage stacking procedure is demonstrated by estimation of the minimum corrected speed which allows stable operation of the compressor. Further utility of the stage stacking procedure is demonstrated with a bleed sensitivity study, which estimates a bleed schedule to expand the compressors operating range.

Kulkarni, Sameer↗
NASA NTRS1996

Silicon Carbide Epitaxial Films Studied by Atomic Force Microscopy

Silicon carbide (SiC) holds great potential as an electronic material because of its wide band gap energy, high breakdown electric field, thermal stability, and resistance to radiation damage. Possible aerospace applications of high-temperature, high-power, or high-radiation SiC electronic devices include sensors, control electronics, and power electronics that can operate at temperatures up to 600 C and beyond. Commercially available SiC devices now include blue light-emitting diodes (LED's) and high-voltage diodes for operation up to 350 C, with other devices under development. At present, morphological defects in epitaxially grown SiC films limit their use in device applications. Research geared toward reducing the number of structural inhomogeneities can benefit from an understanding of the type and nature of problems that cause defects. The Atomic Force Microscope (AFM) has proven to be a useful tool in characterizing defects present on the surface of SiC epitaxial films. The in-house High-Temperature Integrated Electronics and Sensors (HTIES) Program at the NASA Lewis Research Center not only extended the dopant concentration range achievable in epitaxial SiC films, but it reduced the concentration of some types of defects. Advanced structural characterization using the AFM was warranted to identify the type and structure of the remaining film defects and morphological inhomogeneities. The AFM can give quantitative information on surface topography down to molecular scales. Acquired, in part, in support of the Advanced High Temperature Engine Materials Technology Program (HITEMP), the AFM had been used previously to detect partial fiber debonding in composite material cross sections. Atomic force microscopy examination of epitaxial SiC film surfaces revealed molecular-scale details of some unwanted surface features. Growth pits propagating from defects in the substrate, and hillocks due, presumably, to existing screw dislocations in the substrates, were imaged. Away from local defects, step bunching was observed to yield step heights of hundreds of angstroms, with possible implications for the uniformity of dopants incorporated in SiC devices during fabrication. The quantitative topographic data from the AFM allow the relevant defect information to be extracted, such as the size and distribution of step bunching and the Burgers vector of screw dislocations. These atomic force microscopy results have furthered the understanding of the dynamic epitaxial SiC growth process. A model describing the observed hillock step bunching has been proposed. This cooperation between researchers involved in crystal growth, electronic device fabrication, and surface structural characterization is likely to continue as atomic force microscopy is used to improve SiC films for high-temperature electronic devices for NASA's advanced turbine engines and space power devices, as well as for future applications in the automotive industry.

Source record↗
DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2025

Rapid Event Detection via Synchro-Waveform Based Temporal Attention Network in Distributed Grid

Compared with the information collected from phasor measurement units, synchro-waveforms contain high-fidelity disturbances of the grid, which can be a granular and authentic representation of measurements in the modern power system. However, the dynamic changing morphology makes it challenging to effectively capture various disturbance information from the synchro-waveforms. To tackle this issue, this paper proposes a Synchro-waveform based Temporal Attention (STA) network to achieve rapid event detection. First, a multi-scenario distributed model with renewable integration is established to generate synchro-waveforms under various uncertainties. Then, three typical temporal features are extracted directly from the synchro-waveform measurements. Additionally, the lightweight STA network is deployed to identify the most common event types in renewable energy systems via the self-attention based vision transformer module. The results from simulated experiments demonstrate that the proposed approach can achieve rapid and real-time detection within 0.81 ms and over 96.27 % accuracy.

Dong, Yuqing [University of Tennessee (UT)]↗
DOE OSTI2022

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Shedding Light on the Darkness of the Actinide Properties [Slides]

Did you know that 19% of America’s electricity comes from nuclear power? Yeah… and despite this is one of the cleanest energies, we have to deal with the nuclear waste. This nuclear waste is composed of lanthanides, actinides (which are radioactive). The goal is to recycle the actinides which is currently a challenge. Regarding lanthanides and actinides, these elements are located at the bottom of the periodic table. They are similar in terms of size, which makes the actinide extraction complicated. As you see, actinides are the problem here…but what do we know about actinides?

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Development of Nanocrystalline Graphite from Lignin Sources

Carbon composites are attractive to a variety of high-impact applications, such as carbon fibers, batteries, and vehicle parts, due to their multifunctional properties. The properties of carbon are highly dependent on the allotrope the carbon takes and the functionality, impurities, and defects contained within the structure. The increase in demand for sustainable carbon sources in energy storage devices motivates interest in understanding synthesis parameters of lignin value-added products. Also, as the dependence on oil for fuel decreases, alternative sources for carbon in many applications will be needed. In this work, the thermochemical conversion of lignin powders from different feedstocks was evaluated via small and wide-angle X-ray scattering techniques to resolve the amorphous, disordered, and crystalline domains present in the lignin carbons. Scattering analyses indicated an evolution of hierarchical structures along with an increase in ordered domains as a function of carbonization temperature. Qualitative and quantitative methods were used to describe isotropic scattering intensity profiles at multiple length scales. The use of power law models in the mesoscopic region served as the basis to describe morphological changes related to structural features, for example, graphene stacking, degree of roughness, and surface fractals. Kraft softwood and switchgrass produced carbon powder with the most crystalline domains and the least surface roughness. Softwoods reached the highest degree of crystallinity followed by switchgrass samples and had less variability in particle sizes. These results suggest lignin carbons extracted from softwoods and switchgrass are viable substitutes for graphite. Interpretation of X-ray scattering data from lignin carbon powders elucidates feedstock- and processing-dependent morphological features across multiple length scales providing a straightforward framework to evaluate the feasibility of leveraging lignin carbons for producing tunable application-specific materials.

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

Effect of ZnO and PEDOT:PSS charge selective layers on photovoltage of cuprous oxide (Cu 2 O) heterojunction solar cells

Electrochemical deposition (ECD) of Cu 2 O provides a scalable and low temperature pathway to solar cells with a theoretical high energy conversion efficiency of 23%, based on the 2.0–2.2 eV band gap of Cu 2 O. However, existing ECD-Cu 2 O devices are plagued by poor crystallinity and low selectivity of the electron and hole transport layers. Here we use Vibrating Kelvin Probe Surface Photovoltage Spectroscopy (VKP-SPV) to probe the charge transfer selectivity in FTO/ZnO/Cu 2 O/PEDOT:PSS/Ni heterojunction solar cells. Selective electron extraction is achieved at the ZnO back contact to Cu 2 O, as confirmed by a negative surface photovoltage signal. While the uncoated Cu 2 O surface is electron-selective due to the formation of a hole depletion layer, spin coating of a PEDOT:PSS film turns it into a hole-selective interface. After adding nickel metal ink top electrodes, functional 1.0 cm 2 solar cells with power conversion efficiency (PCE) of up to 0.07%, 195 mV open-circuit voltage, and 1.28 mA cm –2 short-circuit photocurrent are achieved. The photovoltaic performance is higher for aluminum doped zinc oxide (AZO) substrates than for fluorine doped tin oxide (FTO) due to the presence of a n-/p-junction that further increases the electron selectivity of the AZO/ZnO/Cu 2 O contacts. Overall, this work demonstrates the first application of PEDOT:PSS as a hole transport layer (HTL) for Cu 2 O and the use of VKP-SPV to measure the photovoltage contributions of the Cu 2 O interfaces. Furthermore, the ability to fabricate Cu 2 O solar cells at near room temperature without the use of vacuum methods or rare elements is an important step towards a scalable Cu 2 O PV technology.

14 SOLAR ENERGY↗
DOE OSTI2023

Microstructure characterization of electric field assisted sintering (EFAS) sintered metallic and ceramic materials using local thermal diffusivity measurement

Electric Field Assisted Sintering (EFAS, also referred to as spark plasma sintering) is a powerful technology for the consolidation of powder materials. The high heating rate during the sintering process is critical for minimizing energy consumption, but it can also cause microstructure heterogeneities in sintered parts, such as spatially varied porosity. The examination of localized porosity usually requires the use of a scanning electron microscope with a carefully prepared surface. In this paper, photothermal radiometry is used to measure local thermal diffusivity and extract localized porosity of EFAS-sintered parts by using a percolation-threshold model. Applying this approach, we identified the radial position-dependent porosity variation in EFAS parts, which is likely formed due to the large temperature gradient during the sintering process. This approach has a unique advantage because it can measure samples with minimal or no surface preparation, enabling the possibility of in situ characterization in EFAS with proper system modification. Necessary modifications on the measurement approach for EFAS deployment and in situ characterization are also discussed.

36 MATERIALS SCIENCE↗