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Results for “optimal experimental design”

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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Additive manufacturing and testing of a ceramic heat exchanger for high-temperature and high-pressure applications for concentrating solar power

Heat exchangers with excellent corrosion and oxidation resistances are essential for the next-generation concentrating solar power (CSP) plants using a molten salt heat transfer fluid and a supercritical CO 2 Brayton power cycle that can operate at temperatures > 700 °C for higher efficiencies. Techniques were developed for additively manufacturing ceramic materials for applications involving high temperature, high pressure, and high corrosion resistance as needed for the CSP application. Based on a previous ceramic heat exchanger design with its cross-section geometry of the flow channels optimized for heat transfer and mechanical strength, lab-scale prototype heat exchangers, with integrated headers and incorporated flow channel dimensional compensations, were fabricated by using the binder jetting process. The printed prototype heat exchangers were successfully densified through the processes of liquid polymer infiltration and pyrolysis. In addition, thermophysical properties of the densified silicon carbide parts were measured to provide necessary design information. Experimental heat transfer testing of the lab-scale prototype heat exchanger was conducted, and the experimental data agreed reasonably well with the simulated results. Furthermore, this agreement provides validation of the simulation models for their applicability to the development of a full-scale ceramic heat exchanger.

14 SOLAR ENERGY↗
DOE OSTI2026

Synergistic Solvent-Surface Interactions Enable Alkyne Semihydrogenation at Palladium

Enabling higher yield and better selectivity for fine-chemical synthesis through heterogeneous catalysis is intricately linked to the interplay of active sites, reaction conditions, and mass transfer influence provided by the catalyst. Alkyne semihydrogenation is ubiquitous in the production of bulk chemicals in the pharmaceutical, polymer, or fine-chemical industries, but product selectivity remains a major challenge. Here, in this study, we demonstrate that the design of catalysts encompassing nickel (Ni) foams as contiguous monolith supports, decorated with ultralow loading of Pd/PdO x nanoparticles on a carbonized polydopamine interface and tuned with a thin layer of Al 2 O 3 , in conjunction with an optimized reaction environment leads to highly selective alkyne semihydrogenation. The reactions demonstrate good functional group tolerance and applicability to flow reactor systems. Combined computational and experimental studies are presented to describe the synergistic effect between the solvent-surface interaction and the degree of Pd surface reduction that are necessary to promote this selectivity. The system highlights the opportunity for catalyst-solvent codesign as a benign alternative to more complex reactants featuring extrinsic poisons or less-favored dopants.

atomic layer deposition↗
DOE OSTI2023

DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering

With advances in machine learning (ML)-assisted protein engineering, models based on data, biophysics, and natural evolution are being used to propose informed libraries of protein variants to explore. Synthesizing these libraries for experimental screens is a major bottleneck, as the cost of obtaining large numbers of exact gene sequences is often prohibitive. Degenerate codon (DC) libraries are a cost-effective alternative for generating combinatorial mutagenesis libraries where mutations are targeted to a handful of amino acid sites. However, existing computational methods to optimize DC libraries to include desired protein variants are not well suited to design libraries for ML-assisted protein engineering. To address these drawbacks, we present DEgenerate Codon Optimization for Informed Libraries (DeCOIL), a generalized method which directly optimizes DC libraries to be useful for protein engineering: to sample protein variants that are likely to have both high fitness and high diversity in the sequence search space. Using computational simulations and wet-lab experiments, we demonstrate that DeCOIL is effective across two specific case studies, with potential to be applied to many other use cases. DeCOIL offers several advantages over existing methods, as it is direct, easy-to-use, generalizable, and scalable. With accompanying software, DeCOIL can be readily implemented to generate desired informed libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigating the Impact of Thickness, Calendering and Channel Structures of Printed Electrodes on the Energy Density of LIBs - 3D Simulation and Validation

Current lithium ion batteries (LIBs) are expensive and bulky, limited by relatively low charging rates. To increase the rate of charging and reduce weight, thin electrodes with high energy density are required. The increase in energy density can be achieved by several techniques including boosting electrolyte transport, high loading/utilization of active material, employing high conductive electrolytes and electrodes with advanced architectures, and increasing cell temperature. In this paper, a 3D physics-based electrochemical model of LIBs is developed in COMSOL simulation software for different thickness, calendering steps as well as channel structures (conical, cylindrical) to optimize the electrode design and in turn maximize volumetric energy density. The simulation results demonstrated that calendering the electrodes with high initial porosity increases the volumetric energy density of the cell. In addition, cylindrical channel structures with relatively lower edge-to-edge distance also results in increased volumetric energy density. The simulation results of the 3D model was validated by comparing it with experimental results.

improving volumetric energy density↗
DOE OSTI2024

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage (CRADA Report)

NREL and Element 16 collaborated on sulfur thermal energy storage modeling using NREL’s high performance computing (HPC) resources to assist its application in industrial processes. Industrial process heat (IPH) accounts for ~70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately, 1500 TWht (~60% Terawatt hour thermal) of IPH demand is in the temperature range of 100-300°C. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. This HPC4EI project facilitated Element 16’s development of low-cost and high-impact molten sulfur TES for dispatchable IPH. The development of a high-fidelity model validated by experimental data and HPC simulations enabled the successful resolution of the complex interplay between fluid dynamics and heat transfer processes during transient operation of sulfur TES, overcoming the numerical challenges posed by the non-linear temperature-dependent physical properties of sulfur. The project helped accelerate Element 16’s molten sulfur TES product design and support its broad applications.

25 ENERGY STORAGE↗
DOE OSTI2026

A generative machine learning model for designing metal hydrides applied to hydrogen storage

Developing new metal hydrides is a critical step toward efficient hydrogen storage in carbon-neutral energy systems. However, existing materials databases, such as the Materials Project, contain a limited number of well-characterized hydrides, which constrains the discovery of optimal candidates. This work presents a framework that integrates causal discovery with a lightweight generative machine learning model to generate novel metal hydride candidates that may not exist in current databases. Using a dataset of 450 samples (270 training, 90 validation, and 90 testing), the model generates 1000 candidates. After ranking and filtering, six previously unreported chemical formulas and crystal structures are identified, four of which are validated by density functional theory simulations and show strong potential for future experimental investigation. Overall, the proposed framework provides a scalable and time-efficient approach for expanding hydrogen storage datasets and accelerating materials discovery.

generative model↗
DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗
DOE OSTI2025

Experiment-modeling studies comparing energy dissipation in the DIII-D SAS and SAS-VW divertors

Recent DIII-D experiments on Small Angle Slot (SAS) divertors have confirmed that a combination of divertor closure and target shaping can enhance cooling across the divertor target and increase energy dissipation, but with significant dependence on B T (toroidal magnetic field) direction. In these novel divertors, the roles of closure, target shaping, drifts, and scale lengths are all interconnected in optimizing dissipation, with the separatrix electron density n eSEP being the key parameter associated with the level of dissipation/detachment. After modifying the original flat-targeted graphite SAS to include a V shape with a tungsten coating on the outer side of the divertor (SAS-VW), matched series of discharges were run to compare to detailed SOLPS-ITER modeling. Experimentally, when run as designed with the outer strike point at the slot vertex, SAS-VW requires nearly identical n eSEP for detachment as the original SAS, with little difference in dissipation for the new geometry. This is in contrast to (1) earlier modeling predictions that a small change of the SAS geometry to a V shape should enhance dissipation at the same n eSEP for magnetic configurations having better H-mode access (ion B × ∇B drift directed into the divertor), and (2) despite the achievement of significantly higher (2-7x) neutral pressures and compression in the SAS-VW slot. Comparisons of experimental density scans to the most recent SOLPS-ITER modeling with ExB drifts show reasonable agreement for dissipation/detachment onset when using separatrix density as the independent parameter. In order to help understand the discrepancy in modeled vs actual performance for the new configuration, additional measurements varying gas injection location and impurity injection were undertaken. In-slot D 2 gas fueling is more effective (5–22 %) in promoting detachment, in accord with modeling. In-slot impurity injection (N 2 or Ne) can yield 30 % lower core Z eff and 15 % less confinement degradation after detachment compared to main chamber puffing, as well as relatively lower tungsten leakage from the divertor. Modeling can also reproduce the improved detachment seen as the strike point moves inboard of the slot vertex. While we can explain the effects of the most important parameters causing energy dissipation in these slot divertors, it remains that many aspects of their behavior cannot be accurately modeled using state-of-art codes such as SOLPS-ITER. This is of concern for future model-driven designs utilizing similar V-shaped geometries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Designing optimal core–shell MOFs for direct air capture

Metal–organic frameworks (MOFs), along with other novel adsorbents, are frequently proposed as candidate materials to selectively adsorb CO 2 for carbon capture processes. However, adsorbents designed to strongly bind CO 2 nearly always bind H 2 O strongly (sometimes even more so). Given that water is present in significant quantities in the inlet streams of most carbon capture processes, a method that avoids H 2 O competition for the CO 2 binding sites would be technologically valuable. Here, in this paper, we consider a novel core–shell MOF design strategy, where a high-CO 2 -capacity MOF “core” is protected from competitive H 2 O-binding via a MOF “shell” that has very slow water diffusion. We consider a high-frequency adsorption/desorption cycle that regenerates the adsorbents before water can pass through the shell and enter the core. To identify optimal core–shell MOF pairs, we use a combination of experimental measurements, computational modeling, and multiphysics modeling. Our library of MOFs is created from two starting MOFs-UiO-66 and UiO-67-augmented with 30 possible functional group variations, yielding 1740 possible core–shell MOF pairs. After defining a performance score to rank these pairs, we identified 10 core–shell MOF candidates that significantly outperform any of the MOFs functioning alone.

42 ENGINEERING↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Treatment of Problematic Reactive Metal Wastes Using the GeoMelt{sup R} In-Container Vitrification (ICV{sup TM}) Process - 20326

Decommissioning of sodium-cooled reactors and fast reactor technologies has generated a number of reactive metal waste configurations that are problematic to treat and typically lack cost effective treatment methods and disposition options. As a result, Veolia Nuclear Solutions, under contract with Idaho National Laboratory (owned by the U.S. Department of Energy and managed and operated by Battelle Energy Alliance, LLC) demonstrated its GeoMelt{sup R} In-Container Vitrification (ICV){sup TM} technology to safely convert sodium metal to a non-reactive vitrified oxide form. The demonstration project, supported by glass formulation and crucible testing, consisted of a series of ICV{sup TM} melts that processed elemental sodium into stable non-reactive glass. INL is currently implementing GeoMelt{sup R} technology as a means to safely and reliably convert radioactive reactive metal residues that contaminate sodium cooled reactor components into waste forms that comply with existing disposition pathways. Reactive metal wastes require treatment in order to remove the Resource Conservation and Recovery Act (RCRA) reactivity and ignitability characteristics to comply with land disposal restrictions. GeoMelt{sup R}, which is an alternative to other potential treatment approaches, provides a robust approach that chemically converts the reactive metals to an inert oxide while also immobilizing radionuclides in a vitrified waste form with durability equal to or better than vitrified nuclear fuel reprocessing wastes (very robust and inert waste forms). Most other treatment approaches generate hydrogen gas which is problematic. In 2016, Veolia Nuclear Solutions first demonstrated the effectiveness of the GeoMelt{sup R} ICV{sup TM} process in deactivating reactive sodium metal. Crucible, bench-scale, and engineering-scale demonstrations were conducted on several surrogate waste configurations with various ratios of sodium metal and glass formers. Each ratio and configuration demonstrated complete deactivation of the surrogate sodium metal. Follow-on work in 2017 demonstrated the deactivation of reactive sodium by GeoMelt{sup R} ICV{sup TM} at a higher waste loading relative to previously demonstrated work performed in 2016; the higher waste loading optimized glass chemistry while enhancing the economical full-scale treatment of reactive metals. Additionally, follow-on demonstration testing in 2018 and 2019 focused on more complex shapes and other reactive-metals (mocked up Experimental Breeder Reactor II [EBR-II] subassembly, sodium filled heat exchanger, and a can containing sodium potassium alloy) which were all performed at engineering scale. Veolia Nuclear Solutions designed, installed, and commissioned in September 2018, at Perma-Fix Northwest in Richland Washington, a 10-metric ton full-scale GeoMelt unit (GeoMelt{sup R} Richland) for the treatment of reactive metal wastes. As of September 2019, over 900 55-gallon drums containing a total of around 3,500 lb of sodium with low levels of radioactivity have been treated at GeoMelt{sup R} Richland, with resulting glass monoliths disposed at the Nevada National Security Site (NNSS). A full-scale radiological demonstration melt on an actual EBR-II subassembly has also been performed using the full-scale melter in 2019. The GeoMelt{sup R} technology is a proven radioactive waste treatment technology capable of immobilizing radioactive wastes, including bulk rubble such as drums and other steel vessels usually without pretreatment. Utilizing the GeoMelt{sup R} technology to treat reactive metals eliminates pretreatment steps resulting from having to separate the reactive metal from steel containers or jackets as GeoMelt{sup R} can easily operate at temperatures sufficient to melt the steel and expose the reactive metal for treatment. Eliminating handling steps of reactive metals is a significant safety advantage since reactive metals are pyrophoric. The results generated as a part of the 2018-2019 demonstration program are presented in the paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Development of a Testbed for Pipeline Flushing - 20425

Pipelines which carry high-level waste within the DOE complex should be properly flushed in the cases where stationary or moving beds of solid sediment occur, or lines are prone to hydrogen gas buildup. Present guidelines establish a minimum for the amount of water (flush volume) and flush velocity values used for post-transfer flushing operations to achieve satisfactory cleaning of pipelines. However, further studies are needed to find optimal operating velocity modes/values in order to minimizing the flush volume and consequent downstream waste. These efforts are significantly helpful to DOE waste remediation sites by preserving tank storage, preventing additional waste processing, and minimizing dilution and changes in waste chemistry. An experimental test loop was recently developed for study of non-Newtonian slurry flushing at the Florida International University (FIU). This loop was designed to create sediment beds of various materials and bed heights and investigate parameters that effect the efficiency of flushing operations. The objective is to find flush velocity values/modes which lead to satisfactory cleaning of transport lines with a minimum amount of water usage. A 165 ft experimental test loop made of 3-inch carbon steel pipes was constructed and used in initial testing campaigns. This pipe loop was equipped with real-time monitoring instruments to record variation of pressure, density, mass flow rate, and temperature during testing. Post-flush in-situ no-flow evaluations were performed using ultrasonic, endoscopy, and visual inspection. Additional post-flush evaluations were conducted using filtration and density monitoring of residuals within a designated circulations loop. These provisions enable comparison of pipeline cleanness before the start and after termination of flushing operations for efficiency evaluation. This paper presents efforts associated with flushing of kaolin-water mixtures at different concentrations and with various flush volume and flush velocity values/modes. In initial testing, efforts focused on creating repeatable sediment beds inside the pipeline in both fully-flooded (no pre-flush drainage) and gravity-drained (pre-flush drained) systems. Bed characterization (i.e., measurement of sediment height and solids concentration) was conducted to ensure about consistency of initial conditions between tests. Preliminary results for flushing study of 10% vol. kaolin-water mixtures starting from a fully-flooded initial condition showed that flushing with 1-line volume (65 gallons) could remove up to 72 percent of solids from the pipeline. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

CFD modeling of crystallization fouling with CO 2 desorption incorporated for a falling-film evaporator in thermal desalination

Seawater flowing around horizontal tubes in falling-film evaporators is a common configuration for thermal desalination. Heat is transferred from in-tube steam condensation to the shell-side seawater by conduction through the tube wall and scaling layer, and conduction and convection to the evaporating liquid film. CO 2 is simultaneously released from seawater and mixed with the produced vapor. Fouling coupled with CO 2 desorption has deleterious impacts on both evaporation and condensation. The evaluation of spatiotemporal dependent crystallization fouling and CO 2 desorption is vital to selecting optimal operating conditions. Here, in this work, a CO 2 desorption model is integrated into CFD modeling for predictions of coupled heat and mass transfer, to understand and predict scale formation and CO 2 desorption with local and transient profiles of temperature, carbonate species concentrations, pH, and total alkalinity. The porosity of the scale layer is experimentally determined as 67.7% and invoked in the calculation of effective thermal conductivity. The simulation results reveal that high steam temperatures increase seawater total alkalinity and accelerate scale formation. The scale thickness on the bottom tube reaches 0.30 μm and 5.24 μm for steam temperatures of 60 °C and 80 °C, respectively. High salinity leads to a large CO 2 desorption rates. The CO 2 desorption rate increases 43% when the seawater salinity increases from 35 to 55 g/kg. The effects of operating conditions on carbonate speciation and pH have been compared and analyzed. This model can serve as a comprehensive tool for the design of thermal desalination systems and optimal operation.

42 ENGINEERING↗
DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗
DOE OSTI2020

Sample spinning to mitigate polarization artifact and interstitial-vacancy imbalance in ion-beam irradiation

Accelerator-based ion-beam irradiation has been widely used to mimic the effects of neutron radiation damage in nuclear reactors. However, ion radiation is most often monodisperse in the incoming ions’ momentum direction, leading to excessive polarization in defect distribution, while the scattering under neutron irradiation is often more isotropic and has less radiation-induced polarization. Mitigation of the excess-polarization as well as the damage non-uniformity artifact might be crucial for making the simulation of neutron radiation by ion-beam radiation more realistic. In this work, a general radiation polarization theory in treating radiation as external polar stimuli is established to understand the natural material responses in different contexts, and the possibility to correct the defect polarization artifact in ion-beam irradiation. Inspired by Magic Angle Spinning in Nuclear Magnetic Resonance, we present a precise sample spinning strategy to reduce the point-defect imbalance effect in ion-beam irradiation. It can be seen that with optimized surface inclination angle and the axis of sample rotation, the vacancy-interstitial population imbalance, as well as the damage profile non-uniformity in a designated region in the target are both reduced. It is estimated that sample spinning frequency on the order of kHz should be sufficient to scramble the ion momentum monodispersity for commonly taken ion fluxes and dose rates, which is experimentally feasible.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: Defect formation, geometric precision, and process mapping

Recent developments in additive manufacturing (AM) technology have reignited interest in the fabrication of the Nb-based refractory C103 alloy offering solutions to the challenges posed by traditional manufacturing methods. However, the limited numerical and experimental studies on laser direct energy deposition (DED) of C103 have hindered the understanding of the relationships between process parameters and build quality. This has made it challenging to consistently produce parts with the desired quality and microstructure suitable for critical applications. In this study, we focus on optimizing the laser DED process for C103 by employing a hybrid approach that combines experimental techniques and computational fluid dynamics (CFD). This approach facilitates the development of process maps for defect detection and geometric precision. To achieve this, multi-layer C103 samples were fabricated using laser DED under various process parameters, enabling the creation of a process map for defect detection. Additionally, a multi-physics, multiphase simulation framework was developed within a high-performance computing (HPC) environment to establish process maps for geometric precision. Using these process maps, printability windows were identified for achieving both the desired geometric accuracy and defect-free prints. It was observed that prints with a power-to-velocity (P/V) ratio close to unity resulted in defect-free outcomes. This study provides a foundation for reducing design lead time and rejected parts, ultimately optimizing the laser DED process for C103.

Defect formation and geometric precision↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗