FERMI: A MULTI-PHYSICS SIMULATION ENVIRONMENT FOR FUSION REACTOR BLANKET
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Nuclear science and engineering, Mathematics and C↗
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Types of spatter and their features and formation mechanisms in laser powder bed fusion additive manufacturing process
Spatter causes defect formation, powder redistribution and contamination in laser powder bed fusion (LPBF) additive manufacturing process. It is critical to distinguish different types of spatter and understand their features and formation mechanisms. This work reveals the features and formation mechanisms of five unique types of spatter during the LPBF process by in-situ high-speed, high-energy x-ray imaging. Spatters observed during LPBF testing are quantified by their speed, size, and direction. Distinct quantifiable characteristics for each type of spatter are identified. Finally, effects of the laser power, scan speed, and ambient pressure on spatter formation and features are unraveled. A spatter formation map for AlSi10Mg alloy is constructed.
36 MATERIALS SCIENCE↗
Modeling and characterizing the elastodynamic response of octet-truss lattice structures using resonance techniques
Characterization of additively manufactured materials and structures is an ongoing effort for the advanced manufacturing community. This article will investigate an approach using resonant spectral ultrasound (RUS) to measure the effective elastic constants of an Octet Truss lattice and apply the results to continuum bases models representing the lattice regions in different structures. The study is focused on simple lattices structures fabricated from Ti5553 using a laser powder bed fusion process (LBPF). Solid and lattice samples are measured to determine RUS estimates for the elastic properties of the bulk material and the effective elastic properties of the lattice structure. The estimated elastic properties are then incorporated into 3d finite element models using a continuum approximation of the physical AM parts. Comparisons show good agreement between the experimentally measured eigen frequencies of the Ti5553 LBPF parts and the eigen frequencies calculated using the continuum approximations based on the effective RUS elastic properties. The current results suggest there is additional physics and geometrical effects that are accounted for in the RUS continuum approximation of the lattice that are not captured in the full 3d finite element model of the parts utilizing only the base material properties.
36 MATERIALS SCIENCE↗
MOOSE: A Modular Platform for Fission and Fusion Multiphysics
The Multiphysics Object-Oriented Simulation Environment (MOOSE) Framework, as well as MOOSE-based simulation tools, have accelerated the development of fission energy and advanced reactor technologies through the United States Department of Energy, Office of Nuclear Science, Nuclear Energy Advanced Modeling & Simulation (NEAMS) Program. MOOSE contains a complete platform of multiphysics simulation capabilities, capable of running on massively parallel systems, and is developed in an open-source manner with great attention paid to high-quality software quality assurance practices. This overall approach could greatly benefit the fusion energy community, which requires rapid design iteration and improvement in order to facilitate the successful development of fusion as an alternative energy source to fossil fuels. In the first half of this talk, applications of MOOSE and MOOSE-based tools for advanced reactor designs will be showcased, as well as MOOSE ecosystem infrastructure (such as the NEAMS Virtual Test Bed) that enables and accelerates fission reactor design. In the second half, a discussion of how the MOOSE approach to modeling and simulation is currently being applied internationally in fusion energy research and development at the United Kingdom Atomic Energy Authority will be discussed, and ongoing/future domestic research efforts will be highlighted.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A Graphical Model for Fusing Diverse Microbiome Data
This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.
59 BASIC BIOLOGICAL SCIENCES↗
Oxidation Behavior of Heat-Resistant Type $\mathrm{HK}$ Steel ($\mathrm{HK30Nb}$) at 800 °C
We report the cyclic oxidation behavior of HK30Nb heat-resistant steel processed by laser powder bed fusion (LPBF) was compared to its cast counterpart during exposures in air and air + 10% H 2 O at 800 °C. The specific finer microstructure and lower Mn of the LPBF alloy resulted in lower oxidation rates in dry air and faster establishment of a continuous Cr 2 O 3 scale in air + 10% H 2 O compared to coarse-grained cast HK30Nb with higher Mn. Differences in alloy mechanical strength and therefore their ability to accommodate high temperature and oxidation-induced stresses as well as differences in thermal expansion coefficients between the alloy and the formed oxides (Cr 2 O 3 only for the LPBF and Cr 2 O 3 and MnCr 2 O 4 for the cast specimens) during temperature cycling were found to result in a greater extent of spallation for the LPBF than for the cast alloy in dry air at 800 °C.
36 MATERIALS SCIENCE↗
Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning
One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.
30 DIRECT ENERGY CONVERSION↗
Detection of Foreign Materials on Broiler Breast Meat Using a Fusion of Visible Near-Infrared and Short-Wave Infrared Hyperspectral Imaging
Foreign material (FM) found on a poultry product lowers the quality and safety of the product. We developed a fusion method combining two hyperspectral imaging (HSI) modalities in the visible-near infrared (VNIR) range of 400–1000 nm and the short-wave infrared (SWIR) range of 1000–2500 nm for the detection of FMs on the surface of fresh raw broiler breast fillets. Thirty different types of FMs that could be commonly found in poultry processing plants were used as samples and prepared in two different sizes (5 × 5 mm 2 and 2 × 2 mm 2 ). The accuracies of the developed Fusion model for detecting 2 × 2 mm 2 pieces of polymer, wood, and metal were 95%, 95%, and 81%, respectively, while the detection accuracies of the Fusion model for detecting 5 × 5 mm 2 pieces of polymer, wood, and metal were all 100%. The performance of the Fusion model was higher than the VNIR- and SWIR-based detection models by 18% and 5%, respectively, when F1 scores were compared, and by 38% and 5%, when average detection rates were compared. The study results suggested that the fusion of two HSI modalities could detect FMs more effectively than a single HSI modality.
36 MATERIALS SCIENCE↗
Ultrasonic Testing (UT) and Computed Tomography (CT) Comparative Scanning of Proposed Additive Manufacturing Reference Standard
This report presents qualitative results of ultrasonic full matrix capture/total focusing method (FMC/TFM) scanning of two series of blocks, additively manufactured by powder bed fusion, containing a variety of internal features and structures that would not be achievable by conventional manufacturing techniques. The purpose of the first series of additively manufactured (AM) blocks was to explore the possibility of building calibration blocks for FMC/TFM ultrasonic testing (UT). It was confirmed that AM is a suitable candidate for generation of unusual and novel reflector forms, such as rotating slots, purposely embedded voids, and tapering holes, and that FMC/TFM was very capable of characterizing them. The intended use of the second series of AM blocks was as UT reference blocks to characterize the AM process quality or the interrogating UT technique. The larger block in this series was scanned from multiple faces, using multiple UT methods (conventional UT and FMC/TFM) and X-ray computed tomography for comparative purposes. The performance of each method was quantified by a metric corresponding to the detection limit of each feature, and the quantitative results are discussed.
36 MATERIALS SCIENCE↗
Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography
Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.
36 MATERIALS SCIENCE↗
Gas pore correlations in laser powder bed fusion of Al6061
Additive manufacturing (AM) of metal materials based on powder bed fusion technology is widely used now in many industries. A known limitation of this type of manufacturing is the formation of gas pores in the bulk material. Here we present a combined X-ray imaging and mid-infrared pyrometry study of pore formation in side-by-side tracks of Al6061 for different processing conditions using both in-situ and post-processing analysis. By carefully quantifying the distributions and correlations of pore positions, we show that an existing pore in one track often catalyzes the formation of another pore in an adjacent track. In a raster scan strategy commonly used to construct bulk material, this phenomenon has the result of forming subsurface perforations, or lines of pores transverse to the scanning direction in a rastered patch. If controlled, this effect can be eliminated to improve the yield strength of the build, or exploited to create programmable failures for specific purposes.
36 MATERIALS SCIENCE↗
An experimental process parameter study on the identification of defects in additively fabricated Al6061 with laser powder bed fusion
Additively fabricated metal parts using laser powder bed fusion (L-PBF) possess sophisticated morphology due to the recurrent use of laser-induced metal powder melting and solidification. The surface and 3D morphology of these parts often include defects in the form of protrusions, depressions, pores, voids, keyholes, or cracks that are known to be influenced by laser scanning paths and layer-to-layer processing. Such inconsistent part quality hampers the extensive adoption of L-PBF. Pores and cracks are detrimental to the fatigue life of the parts and components. Quantifying and controlling part defects and optimizing processing and scanning strategy parameters adaptively in real-time through in situ monitoring systems are highly desired. This study investigates the optimization of experimental process parameters (power, scan velocity, and hatch spacing) and their effects on the cracking and porosity of Al6061 alloy using machine learning techniques. Multi-objective optimization is formulated and conducted to determine the L-PBF parameters that minimize both porosity and crack densities.
36 MATERIALS SCIENCE↗
Rapid extraction of short-lived isotopes from a buffer gas cell for use in gas-phase chemistry experiments, Part II: On-line studies with short-lived accelerator-produced radionuclides
We report a novel combination of advanced gas-chromatography and detection systems coupled to a buffer-gas cell was characterized on-line to allow gas-phase chemical studies of accelerator-produced short-lived α -decaying mercury, francium, and astatine isotopes. These were produced in 40 Ar- and 48 Ca-induced nuclear fusion–evaporation reactions, subsequently isolated in the recoil separators MARS at Texas A&M University, USA, and TASCA at GSI Darmstadt, Germany, before being thermalized in a buffer-gas-stopping cell. From the latter, the nuclear reaction products were extracted into gas-phase chromatographic systems, suitable for registering α-decaying short-lived radionuclides, such as isotopes of superheavy elements. Efficiencies of 21(3)% for 204-209 Fr were reached for the extraction into the optimized miniCOMPACT gas-chromatography setup, indicating that this technique enables the identification of isotopes of volatile as well as non-volatile elements. These studies guide the path towards chemical investigations of superheavy elements beyond flerovium, which are out of reach with currently used setups.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Uncovering acoustic signatures of pore formation in laser powder bed fusion
Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.
36 MATERIALS SCIENCE↗
Enhanced mechanical performance via laser induced nanostructure formation in an additively manufactu
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Martin, Aiden A.↗
Effect of Volumetric Energy Density on Microstructure and Properties of Grade 300 Maraging Steel Fabricated by Laser Powder Bed Fusion
Here this study investigated the effect of laser powder bed fusion process parameters on the microstructure and properties of maraging steel. The results show that the process parameters can be finetuned to achieve both a high build rate/low hardness and low build rate/high hardness during fabrication, enabling the co-design of components where property gradients are desirable such as an injection molding tool where surface hardness is desired to be higher than the bulk, which needs to have higher fracture toughness.
36 MATERIALS SCIENCE↗
Improved modeling of the solid-to-plasma transition of polystyrene ablator for laser direct-drive inertial confinement fusion hydrocodes
We report that the target performance of laser direct-drive inertial confinement fusion (ICF) can be limited by the development of hydrodynamic instabilities resulting from the nonhomegeneous laser absorption at the target surface, i.e., the laser imprint on the ablator. To understand and describe the formation of these instabilities, the early ablator evolution during the laser irradiation should be considered. In this work, an improved modeling of the solid-to-plasma transition of a polystyrene ablator for laser direct-drive ICF is proposed. This model is devoted to be implemented in hydrocodes dedicated to ICF which generally assume an initial plasma state. The present approach consists of the two-temperature model coupled to the electron, ion and neutral dynamics including the chemical fragmentation of polystyrene. The solid-to-plasma transition is shown to significantly influence the temporal evolution of both free electron density and temperatures, which can lead to different shock formation and propagation compared with an initial plasma state. The influence of the solid-to-plasma transition on the shock dynamics is evidenced by considering the scaling law of the pressure with respect to the laser intensity. The ablator transition is shown to modify the scaling law exponent compared with an initial plasma state.
36 MATERIALS SCIENCE↗
In-Situ Characterization of Pore Formation Dynamics in Pulsed Wave Laser Powder Bed Fusion
Laser powder bed fusion (LPBF) is an additive manufacturing technology with the capability of printing complex metal parts directly from digital models. Between two available emission modes employed in LPBF printing systems, pulsed wave (PW) emission provides more control over the heat input compared to continuous wave (CW) emission, which is highly beneficial for printing parts with intricate features. However, parts printed with pulsed wave LPBF (PW-LPBF) commonly contain pores, which degrade their mechanical properties. In this study, we reveal pore formation mechanisms during PW-LPBF in real time by using an in-situ high-speed synchrotron x-ray imaging technique. We found that vapor depression collapse proceeds when the laser irradiation stops within one pulse, resulting in occasional pore formation during PW-LPBF. We also revealed that the melt ejection and rapid melt pool solidification during pulsed-wave laser melting resulted in cavity formation and subsequent formation of a pore pattern in the melted track. The pore formation dynamics revealed here may provide guidance on developing pore elimination approaches.
36 MATERIALS SCIENCE↗