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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Parsimonious Potential Energy Surface Expansions Using Dictionary Learning with Multipass Greedy Selection

Potential energy surfaces fit with basis set expansions have been shown to provide accurate representations of electronic energies and have enabled a variety of high-accuracy dynamics, kinetics, and spectroscopy applications. The number of terms in these expansions scales poorly with system size, a drawback that challenges their use for systems with more than similar to 10 atoms. A solution is presented here using dictionary learning. Subsets of the full set of conventional basis functions are optimized using a newly developed multipass greedy regression method inspired by forward and backward selection methods from the statistics, signal processing, and machine learning literatures. Here, the optimized representations have accuracies comparable to the full set but are 1 or more orders of magnitude smaller, and notably, the number of terms in the optimized multipass greedy expansions scales approximately linearly with the number of atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Evaluation of LPBF Steels for Nuclear Applications

This report provides an update on the prioritization of existing reactor materials for advanced manufacturing. This report is a Milestone 3 deliverable in FY2023, under work package CT-23AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). The focus of FY23 for ANL included the determination of a decision criteria matrix for the prioritization of existing materials, the literature review of a select few Fe-based alloys, and working with vendors to obtain customized powders. The work package also includes fabricating test samples in a Renishaw AM400 Laser Powder Bed Fusion (LPBF) system and optimizing the process parameters. The major outcomes of this work package are: With collaborations from PNNL, ORNL, and INL, we were able to establish a decision criteria matrix containing a total of 6 categories and 31 different criteria. These will be used to downselect alloys for further evaluation; As part of that, 6 different Fe-based alloys were selected, 3 austenitic stainless steels (A709, D9, AFA) and 3 ferritic/martensitic steels (HT9, Grade 91, Grade 92). Customized powders were obtained from vendors in small quantities to fabricate initial prints to check printability of these specific alloys; A total of 72 single track experiments were performed on two alloy systems, 1 austenitic stainless steel (A709) and 1 ferritic/martensitic steel (Grade 91) in order to optimize the process parameters for the full 3d prints. The optimization led to the selection of 20 different processing conditions, 10 for each class of alloys; D9 and AFA alloys showed extensive cracking and porosity in the samples. This was due to less-than-ideal conditions present in the chamber during the deposition. A709 printed using the same process parameters showed almost fully dense samples with no noticeable porosity or any other defects. SEM and EBSD analysis revealed single phase FCC microstructure with cellular structure within the grains; HT9, Grade 91, Grade 92 alloys also showed no noticeable signs of cracking and ImageJ analysis showed porosity <0.5% in all conditions. While Grade 91 and Grade 92 showed single phase BCC microstructures, the presence of martensite laths was noted in HT9 alloy; Future work will include further characterization of these alloys to better understand the microstructural evolution during the 3d printing process.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Alchemy: A Model-Based Approach for 2D to 3D Autonomous Nuclear System Design

Engineering design of nuclear power plant (NPP) piping and equipment systems frequently bypasses crucial 2D system planning, instead moving straight to 3D modeling. This often leads to designs that exceed building envelope constraints, forcing expensive and time-consuming redesigns. When 2D modeling is employed, it typically involves labor-intensive manual workflows that convert 2D drawings into 3D models, resulting in inefficiencies and errors across design iterations. These workflows further suffer from poor software interoperability and dependence on proprietary software ecosystems, thereby contributing to schedule delays and cost overruns. This paper presents Alchemy, an autonomous framework that transforms 2D system definitions into Industry Foundation Classes (IFC)-compliant 3D building information models (BIMs) for expediting nuclear facility design at the conceptual preliminary phase. Using a model-based approach, the framework treats the 2D system diagram as the central reference model employed to automatically generate all subsequent outputs, ensuring consistency between the system definition and the resulting physical design. A web-based interface enables engineers to define hierarchical system topologies including associated equipment, geometric properties, and connectivity requirements. A two-phase equipment layout optimization algorithm automatically computes collision-free spatial configurations within predefined building envelopes. An artificial intelligence (AI)-assisted pipe routing module then generates orthogonal, collision-free routing paths, allowing the user to select either an A* search-based method or an Ant Colony Optimization (ACO)-based method. All outputs are authored natively in IFC format, relying on open-source technologies and standardized formats in order to ensure extensibility and eliminate proprietary software dependencies. The proposed framework is validated on two representative pressurized-water reactor (PWR)-based case studies, for which it autonomously generates IFC-compliant 3D models in minutes, drastically reducing workflows that typically require hours of manual effort. The generated model demonstrates topologically correct equipment placement, physically plausible spatial relationships, and collision-free pipe routing consistent with known PWR loop configurations. This work represents a foundational step toward digital engineering for nuclear facility preliminary design, with future ongoing development targeting design code compliance and expanded system complexity.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Composition-Based Approach for Predicting Hanford Tank Waste Liquid Viscosity

Liquid viscosity is an important Hanford waste physical property that governs, in part, the performance of key Hanford flowsheet unit operations and processes, including waste filtration, ion exchange, pumping and transport, and mixing. Evaluation and optimization of these processes rely on liquid viscosity correlations subject to substantial error (at least ±60%) that can lead to overly conservative, and sometimes physically unachievable, limits for validation and operation of these unit operations. The current liquid viscosity correlations used for the Hanford flowsheet attempt to capture the effects of specific waste chemistry (namely, the speciation of various salts) on liquid viscosity using liquid density alone. It is postulated that the ±60% or greater error associated with these “density-based” liquid viscosity predictions is driven in part by the inability of waste density to capture the full impact of waste chemistry on liquid viscosity, and that improved prediction accuracy could be achieved by implementing a composition-based approach to evaluating liquid viscosity for Hanford wastes. Recent publications, namely those of Lencka et al. (1998), Hu (2004), and Laliberté (2007a), provide correlations and comprehensive, consolidated databases of single-salt solution viscosity that enable composition-based predictions for Hanford-type liquids (i.e., concentrated multi-component aqueous electrolyte solutions). In the current study, the viscosities of Hanford wastes and waste simulants were predicted using a composition-based model developed by Laliberté (2007a) (hereafter referred to as the “Laliberté viscosity model”) to evaluate its accuracy relative to the current density-based models used to predict waste viscosity for engineering applications at the Hanford Site. A viscosity database that captured the composition and viscosity of wastes and waste simulants was developed to support this evaluation. To facilitate application of the Laliberté viscosity model, waste and waste simulant compositions were converted to an equivalent salt recipe composed of salts with known viscosity coefficients using a number of simplifying assumptions that have the potential to impact the fidelity of the model prediction. Despite these simplifications, the Laliberté viscosity model was able to predict viscosity more accurately than the density-based correlations currently available for Hanford flowsheet use, such as that proposed in RPP-RPT- 51652. The use of composition-based viscosity models, like that proposed by Laliberté (2007a), shows promise for leading to improved prediction of liquid waste viscosity. As implemented in this study, application of the Laliberté viscosity model could lead to a decrease in the uncertainty associated with liquid viscosity prediction by as much as a factor of two, allowing for reduction in unit operation design conservatism and for the more realistic selection of test simulants for flowsheet process evaluation and optimization, all of which could potentially reduce flowsheet design margins, lower costs and reduce duration of the Hanford mission, and reduce Hanford flowsheet risks.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Optimizing squeezing in a coherent quantum feedback network of optical parametric oscillators

Advances in the emerging field of coherent quantum feedback control (CQFC) have led to the development of new capabilities in the areas of quantum control and quantum engineering, with a particular impact on the theory and applications of quantum optical networks. For this study, we consider a CQFC network consisting of two coupled optical parametric oscillators (OPOs) and study the squeezing spectrum of its output field. The performance of this network as a squeezed-light source with desired spectral characteristics is optimized by searching over the space of model parameters with experimentally motivated bounds. We use the QNET package to model the network’s dynamics and the PyGMO package of global optimization algorithms to maximize the degree of squeezing at a selected sideband frequency or the average degree of squeezing over a selected bandwidth. The use of global search methods is critical for identifying the best possible performance of the CQFC network, especially for squeezing at higher-frequency sidebands and higher bandwidths. The results demonstrate that the CQFC network of two coupled OPOs makes it possible to vary the squeezing spectrum, effectively utilize the available pump power, and overall significantly outperform a single OPO. Additionally, the Hessian eigenvalue analysis shows that the squeezing generation performance of the optimally operated CQFC network is robust to small variations of phase parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

High Energy High Power Battery Exceeding PHEV40 Requirements

TIAX has developed long-life lithium-ion cells that can meet and exceed the energy and power targets (200Wh/kg and 800W/kg pulse power) set out by DOE for PHEV40 batteries. To achieve these targets, we selected and scaled-up a high capacity version of our proprietary high energy and high power CAM-7® cathode material. We paired the cathode with a blended anode containing Si-based anode material capable of delivering high capacity and long life. Furthermore, we optimized the anode blend composition, cathode and anode electrode design, and selected binder and electrolyte compositions to achieve not only the best performance, but also long life. By implementing CAM-7 with a Si-based blended anode, we built and tested prototype 18650 cells that delivered measured specific energy of 198Wh/kg total energy and 845W/kg at 10% SOC (projected to 220Wh/kg in state-of-the-art 18650 cell hardware and 250Wh/kg in 15Ah pouch cells). These program demonstration cells achieved 90% capacity retention after 500 cycles in on-going cycle life testing. Moreover, we also tested the baseline CAM-7/graphite system in 18650 cells showing that 70% capacity retention can be achieved after ~4000 cycles (20 months of on-going testing). Ultimately, by simultaneously meeting the PHEV40 power and energy targets and providing long life, we have developed a Li-ion battery system that is smaller, lighter, and less expensive than current state-of-the-art Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Concentric Ring Gas Atomization Die Design for Optimized Particle Production

In partnership with Linde plc (formerly Praxair, Inc.), Ames Laboratory will further develop and commercialize its concentric-ring high pressure gas atomization (CR-HPGA) gas-die technology with a goal to improve the precision of metal powder production in a desired size range and quality. The successful commercialization of this technology could reduce production cost of metal powders and improve reliability of this industrial process, increasing specialty alloy powder availability for new applications and adoption into additive manufacturing and multiple materials sectors. The project scope is to [1] utilize AMES compressible gas flow and melt break-up models to explore a wide swath of parameter space available for the CR-HPGA technology, identifying the most promising gas-die designs for fabrication, [2] verify and optimize the new gas-die design, fabricate it and select preferred operating parameters by gas-only flow imaging and aspiration pressure measurements, down-selecting gas compositions and atomization parameters for full scale testing, [3] perform pilot-scale atomization trials with Al and/or Cu alloys of a new CR-HPGA gas-die at selected parameters, [4] evaluate gas-die performance by comparing resulting powder size distribution and powder quality attributes with equivalent powders made by conventional close-coupled HPGA technology, and [5] assess (through Praxair partnership) reduction in operational costs of new CR-HPGA gas-die through the use of an inert gas recovery/recycling system. In partnership with Praxair, Inc., the team, led by Ames Laboratory senior metallurgist Iver Anderson, intend to demonstrate the performance of an optimized CR-HPGA gas-die design in a pilot scale atomizer and better understand the design and operational controls to increase overall benefits for precision metal powder production.

42 ENGINEERING↗
DOE OSTI2019

Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Selected Negative Linear Compressibilities in the Metal–Organic Framework of [Cu(4,4'-bpy) 2 (H 2 O) 2 ]·SiF 6

Negative linear compressibility (NLC) is a rare high-pressure observation that lattice contraction is accompanied by the structural expansion along a specific direction. Generally, this counterintuitive phenomenon only derives from the intrinsic structural characteristics of materials and cannot be tuned by external perturbations. Searching for an effective method to control NLC effect is still a challenge in both chemical and material science. In this work, we successfully discover and select the NLC behaviors in the metal-organic framework (MOF) of [Cu(4,4'-bpy) 2 (H 2 O) 2 ]·SiF 6 (Cu(bpy)·SiF) through controlling the pressure conditions therein. The NLC effect of Cu(bpy)·SiF originates from the wine-rack mechanism that quasi-square grids transfer to rhombic ones with the structural expansion along the diagonal direction at high pressure. Meanwhile, both of the pressure range and magnitude of the NLC responses are enlarged with optimized pressure conditions. This study not only presents the intriguing selected NLC behaviors of a MOF but also proves the effects of pressure conditions on NLC, which offers promising strategies for further design and applications of NLC materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Promoting propane dehydrogenation over PtFe bimetallic catalysts by optimizing the state of Fe species

Optimizing the structure of Pt-based bimetallic catalysts is of utmost importance toward improving the propane dehydrogenation performance. It is challenging to precisely synthesize uniform PtFe alloy nanoparticles without excess unalloyed Fe species on the support as these Fe species lead to low propylene selectivity, coke deposition, and poor stability. Herein, we report an effective strategy to optimize the structure of PtFe bimetallic catalysts with minimal coke and high turnover frequency (8.2 s -1 ). For the optimized catalyst, 1Pt3Fe@S-1, most Fe species is in the framework of the zeolite S-1, which significantly suppresses the formation of coke. In addition, the extra-framework Fe and Pt species encapsulated in the channel of zeolite form uniform PtFe alloy nanoparticles, which significantly improves the C 3 H 6 selectivity, catalytic stability, and recycling performance. In conclusion, these findings provide insights into the structure-performance relationship of PtFe bimetallic catalysts and shall be beneficial to future design and optimization of similar catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT)

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from the noisy observation of a single solution trajectory. Identifying unknown differential equations from noisy data is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The proposed algorithm, GP-IDENT, has three ingredients: (i) we use B-spline bases to express the unknown space and time varying coefficients, (ii) we propose Group Projected Subspace Pursuit (GPSP) to find a sequence of candidate PDEs with various levels of complexity, and (iii) we propose a new criterion for model selection using the Reduction in Residual (RR) to choose an optimal one among a pool of candidates. The new GPSP considers group projected subspaces which is more robust than existing methods in distinguishing correlated group features. We test GP-IDENT on a variety of PDEs and PDE systems, and compare it with the state-of-the-art parametric PDE identification algorithms under different settings to illustrate its outstanding performance. Furthermore, our experiments show that GP-IDENT is effective in identifying the correct terms from a large dictionary, and our model selection scheme is robust to noise.

Data-driven method↗
DOE OSTI2023

Selective and Scalable Control of Spin Quantum Memories in a Photonic Circuit

A central goal in many quantum information processing applications is a network of quantum memories that can be entangled with each other while being individually controlled and measured with high fidelity. This goal has motivated the development of programmable photonic integrated circuits (PICs) with integrated spin quantum memories using diamond color center spin-photon interfaces. However, this approach introduces a challenge into the microwave control of individual spins within closely packed registers. Here, we present a quantum memory-integrated photonics platform capable of (i) the integration of multiple diamond color center spins into a cryogenically compatible, high-speed programmable PIC platform, (ii) selective manipulation of individual spin qubits addressed via tunable magnetic field gradients, and (iii) simultaneous control of qubits using numerically optimized microwave pulse shaping. The combination of localized optical control, enabled by the PIC platform, together with selective spin manipulation opens the path to scalable quantum networks on intrachip and interchip platforms.

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

Magnesium‐Mediated Electrochemical Synthesis of Ammonia

Metal-mediated electrochemical synthesis of ammonia (NH3) is a promising method to activate N2 at room temperature. While a Li-mediated approach has been optimized to produce NH3 at high current density and selectivity, Li's scarcity and its highly negative plating potential limit scalability and energy efficiency. Alternative mediators have been proposed, but only Ca has shown some promise, achieving ≈50% Faradaic efficiency (FE), though requiring voltages beyond -3 V. Here, we report a Mg-mediated nitrogen reduction reaction (Mg-NRR), where N2 is activated on Mg to form Mg3N2, followed by protolysis to release NH3 and regenerate Mg. A notable NH3 FE of 25.28 ± 3.80% is achieved at a current density of -45 mA cm-2, corresponding to an NH3 partial current density of -11.30 ± 1.77 mA cm-2 under 6 bar N2. Isotope-labeled experiments confirm that NH3 originates from N2, with similar FE (25.15 ± 1.01%). Importantly, NH3 production is demonstrated at a total cell potential as low as -3 V. This Li-free Mg-NRR system offers key advantages, including lower energy input and use of earth-abundant materials, making it a scalable route for sustainable NH3 synthesis.

Goyal, Ishita↗
DOE OSTI2020

Gold Catalysts Synthesized Using a Modified Incipient Wetness Impregnation Method for Propylene Epoxidation

Propylene epoxidation in the presence of oxygen and hydrogen were measured for a series of Au/TS-1 catalysts prepared by a modified incipient wetness impregnation (mIWI) method. This method enables precise control of the Au : Ti ratio in the Au/TS-1 catalysts. The optimized Au/TS-1 catalyst exhibited 12 % propylene conversion, 87 % PO selectivity, and 25 % hydrogen efficiency. The particle size of gold nanoparticles prepared by the modified IWI was between 2 and 3 nm, as demonstrated by XRD patterns, STEM images, and X-ray absorption spectroscopy at the Au L(3)edge. XPS spectra showed that the surface species on the catalysts were similar. UV-Vis spectra suggested that in the modified IWI method, the chlorine ligands in Au(Cl)(4)(-)were replaced by hydroxyl groups, which contributes to form small gold nanoparticles. Kinetic studies showed that the active sites of Au(mIWI)/TS-1 are similar to the Au(DP)/TS-1 prepared by deposition precipitation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Review of Physical and Numerical Approaches for the Study of Gas Stirring in Ladle Metallurgy

This article presents a review of the research into gas stirring in ladle metallurgy carried out over the past few decades. Herein, the physical modeling experiments are divided into four major areas: (1) mixing and homogenization in the ladle; (2) gas bubble formation, transformation, and interactions in the plume zone; (3) inclusion behavior at the steel–slag interface and in the molten steel; and (4) open eye formation. Several industrial trials have also been carried out to optimize gas stirring and open eye formation. Approaches for selecting criteria for scaling to guarantee flow similarity between industrial trials and physical modeling experiments are discussed. To describe the bubble behavior and two-phase plume structure, four main mathematical models have been used in different research fields: (1) the quasi-single-phase model, (2) the volume of fluid (VOF) model, (3) the Eulerian multiphase (E–E) model, and (4) the Eulerian–Lagrangian (E–L) model. In recent years, the E–E model has been used to predict gas stirring conditions in the ladle, and specific models in commercial packages, as well as research codes, have been developed gradually to describe the complex physical and chemical phenomena. Furthermore, the coupling of turbulence models with multiphase models is also discussed. For physical modeling, some general empirical rules have not been analyzed sufficiently. Based on a comparison with the available experimental results, it is found that the mathematical models focusing on the mass transfer phenomenon and inclusion behaviors at the steel-slag interface, vacuum degassing at the gas–liquid interface, dissolution rate of the solid alloy at the liquid–solid interface, and the combination of fluid dynamics and thermodynamics need to be improved further. To describe industrial conditions using mathematical methods and improve numerical modeling, the results of physical modeling experiments and industrial trials must offer satisfactory validations for the improvement of numerical modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Computational Simulation of an Additively Manufactured Marine Component

Early work in finite-element analysis of additive manufacturing (AM) components was not able to include distortion and residual stress of the entire build, yielding uncertainty in the computational model predictions. To address this, current versions of recently developed computational tools are being used to understand their role in validation at the Naval Surface Warfare Center, Carderock Division, for laser powder-bed fusion (L-PBF) AM. A 316L stainless steel L-PBF build containing multiple parts, which exhibited the same local part failure over multiple AM fabrication runs, was selected for computational analysis. Single-part simulations analyzed optimization of computational speed and prediction accuracy. Full-build simulations exposed the smallest re-coater tolerances early in part fabrication as a result of part shrinkage and subsequent cupping. This cupping was predicted to change the powder spreading and result in the non-consolidation of the support structures, which paralleled the failures seen in the physical builds. Part redesign of the conventionally produced part to align with AM fabrication requirements was recommended to alleviate the build failures. This project is part of a larger program focused on validation of the computational tools to determine best practices for insertion of AM simulation into the greater Naval enterprise.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ethanol Monitoring Gas Sensor Based on Flower-Shaped Copper Sulfide by a Facile Hydrothermal Method for Marine Transportation

Flower-shaped CuS as a p-type gas-sensing material was synthesized by a facile hydrothermal method in this study. Morphology, structure, and chemical composition of the synthesized CuS gas-sensing material were analyzed by SEM, XRD, XPS, and N{sub 2} adsorption adsorption–desorption technique. Gas-sensing properties of the as-prepared CuS sensors were also investigated toward ethanol monitoring. The results showed that the flower-like CuS nanostructures consisted of interconnected nanosheets and exhibited good crystallinity. With the increase in ethanol concentration, the sensitivity of the CuS sensor significantly increased and indicated a roughly linear relationship at the optimal operating temperature of 260 °C. The ethanol-selective characteristics of the CuS sensor against other interfering gases including methanol, benzene, dichloromethane, and hexane were studied, and the gas response of the CuS sensor synthesized at 170 °C toward 100 ppm ethanol was 5.22, which was significantly higher than all the other gases. Moreover, 14-day continuous measurement further confirmed the excellent stability of the CuS sensor.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermal characterization of the build chamber in electron beam melting

Electron beam powder bed fusion, commonly termed electron beam melting (EBM), offers great versatility in multiple-part processes and can produce high quality as-built components due to low residual stresses. The EBM process is complex and requires careful thermal management, including uniform and consistent preheating of the powder bed, in order to ensure quality and consistency of the product. However, most of the simulations in the literature focus on the selective melting stage of the process. As of today, optimal conditions for initial pre-heating temperatures are only available for specific materials. Thus, in order to extend the EBM technology to other desired build materials, a much better understanding of the pre-heating stages is required. In this work, numerical and experimental approaches are combined in order to investigate the effects and sensitivities of heat removal from the build plate during EBM pre-heating stages. For this purpose, a carefully reconstructed numerical model of the build chamber of an ARCAM Q20 + machine is developed. It includes all main parts of the chamber and all relevant heat transfer mechanisms, whereas special attention is paid to radiation heat exchange between various bodies. In order to validate the model, dedicated experiments are performed, in which a system of thermocouples is installed in the build chamber, allowing direct measurement of the local temperatures of the start-plate and heat shields. A good agreement between the simulation and experimental findings is achieved, leading to a better understanding of the thermal processes characteristic to the pre-heating stages. This basic analysis is followed by a representative pre-heating case, where a powder bed is also considered. The energy required to obtain the desired pre-heating temperatures is evaluated, and the role of the powder bed in heat transfer within the chamber is assessed. The pre-heating stage, simulated in the present work, is supposed to create proper conditions for sintering and consequent melting of the powder, which are highly dependent on the local temperatures and heat transfer features. Thus, the findings of the reported work present a step towards a better understanding of the thermal processes that characterize EBM. The reported model can be further used to provide realistic boundary condition inputs for other meso- or macro-scale models as a function of time and geometry. The model can serve also for verification of machine settings, i.e., jump-safe and melt-safe ones which actually provide desired preheat, and for development of settings for new powders.

36 MATERIALS SCIENCE↗