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DOE OSTI2016

Fe kα profiles from simulations of accreting black holes

We present the first results from a new technique for the prediction of Fe Kα profiles directly from general relativistic magnetohydrodynamic (GRMHD) simulations. Data from a GRMHD simulation are processed by a Monte Carlo global radiation transport code, which determines the X-ray flux irradiating the disk surface and the coronal electron temperature self-consistently. With that irradiating flux and the disk’s density structure drawn from the simulation, we determine the reprocessed Fe Kα emission from photoionization equilibrium and solution of the radiation transfer equation. We produce maps of the surface brightness of Fe Kα emission over the disk surface, which—for our example of a 10M{sub ⊙} Schwarzschild black hole accreting at 1% the Eddington value—rises steeply one gravitational radius outside the radius of the innermost stable circular orbit and then falls ∝r {sup −2} at larger radii. We explain these features of the Fe Kα radial surface brightness profile as consequences of the disk’s ionization structure and an extended coronal geometry, respectively. We also present the corresponding Fe Kα line profiles as would be seen by distant observers at several inclinations. Both the shapes of the line profiles and the equivalent widths of our predicted Kα lines are qualitatively similar to those typically observed from accreting black holes. Most importantly, this work represents a direct link between theory and observation: in a fully self-consistent way, we produce observable results—iron fluorescence line profiles—from the theory of black hole accretion with almost no phenomenological assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Study on efficiency of time computation in x-ray imaging simulation base on Monte Carlo algorithm using graphics processing unit

Monte Carlo (MC) is one of the powerful techniques for simulation in x-ray imaging. MC method can simulate the radiation transport within matter with high accuracy and provides a natural way to simulate radiation transport in complex systems. One of the codes based on MC algorithm that are widely used for radiographic images simulation is MC-GPU, a codes developed by Andrea Basal. This study was aimed to investigate the time computation of x-ray imaging simulation in GPU (Graphics Processing Unit) compared to a standard CPU (Central Processing Unit). Furthermore, the effect of physical parameters to the quality of radiographic images and the comparison of image quality resulted from simulation in the GPU and CPU are evaluated in this paper. The simulations were run in CPU which was simulated in serial condition, and in two GPU with 384 cores and 2304 cores. In simulation using GPU, each cores calculates one photon, so, a large number of photon were calculated simultaneously. Results show that the time simulations on GPU were significantly accelerated compared to CPU. The simulations on the 2304 core of GPU were performed about 64 -114 times faster than on CPU, while the simulation on the 384 core of GPU were performed about 20 – 31 times faster than in a single core of CPU. Another result shows that optimum quality of images from the simulation was gained at the history start from 10{sup 8} and the energy from 60 Kev to 90 Kev. Analyzed by statistical approach, the quality of GPU and CPU images are relatively the same.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Effect of Aluminum Dissolved in Hematite on Formation of Calcium Ferrites at 1473 K

Silico-ferrite of calcium and aluminum (SFCA) is a primary bonding phase, which determines the quality of high basicity sinter used widely as raw materials in blast furnace ironmaking. Calcium ferrite containing aluminum (CFA) is a key precursor during the formation process of SFCA and is influenced by Al{sub 2}O{sub 3} content in iron ore fines. In this work, the experiment was conducted to investigate the effect of the aluminum occurrence state on the formation of calcium ferrite by the simulating sintering process of different types of aluminum-bearing hematite at 1473 K. X-ray diffraction, optical microscope, scanning electron microscopy, and energy-dispersive spectroscopy were used to characterize the mineral change in sintered samples. The result shows that the formation of CFA was promoted by both granular Al{sub 2}O{sub 3} wrapped in Fe{sub 2}O{sub 3} and aluminum dissolved in Fe{sub 2}O{sub 3} solid solution, especially for the latter, due to the formation of the CFA directly from the reaction between Fe{sub 2}O{sub 3}-Al{sub 2}O{sub 3} solid solution and CaO, and the aluminum content in CFA formed was influenced obviously by the solid solution amount of alumina into Fe{sub 2}O{sub 3} as raw materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On-chip non-Hermitian optical parametric amplifiers with a large bandwidth

Recently, our groups have introduced the notion of optical parametric amplification based on non-Hermitian phase matching wherein the incorporation of loss can lead to gain in this nonlinear optical process. Previous simulation results using second-order nonlinear optical coupled-mode theory have demonstrated the potential of this technique as an alternative to the stringent phase-matching condition, which is often difficult to achieve in semiconductor platforms. Here we fortify this notion for the case of third-order nonlinearity by considering parametric amplification in silicon nanowires and illustrate the feasibility of these devices by employing rigorous finite-difference time-domain analysis using realistic materials and geometric parameters. Particularly, we demonstrate that by systematic control of the optical loss of the idler in a four-wave mixing process, we can achieve efficient unidirectional energy conversion from the pump to the signal component even when the typical phase-matching condition is violated. Importantly, our simulations show that a signal gain of ∼ <#comment/> 9 d B for a waveguide length of a few millimeters is possible over a large bandwidth of several hundreds of nanometers ( ∼ <#comment/> 600 n m ). This bandwidth is nearly 2 orders of magnitude larger than what can be achieved in the conventional silicon-photonics-based four-wave mixing process.

Ahmed, Asif (ORCID:0000000152234392)↗

DOE OSTI2025

Effect of Ice Number Concentration on the Evolution of Boundary Layer Clouds During Arctic Marine Cold‐Air Outbreaks

Abstract Marine cold‐air outbreaks (MCAOs) are crucial for Arctic Ocean heat loss, featuring convective cloud rolls that transition into convection cells downstream. Understanding factors controlling this transformation is the key for improving MCAO cloud representation in climate models. This study employs large‐eddy simulations to investigate how cloud ice number concentrations () affect cloud evolution using a case from the Cold‐Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. The simulations, performed in a Lagrangian framework following an air mass trajectory, are driven by ERA5 reanalysis data. Initially, all simulations produce similar cloud patterns, but higher leads to earlier breakup of cloud rolls. Between 4 and 10 hr, surface precipitation rates are similar across simulations, but precipitation initiates earlier, and the cloud‐base precipitation rates are higher when is higher. The stronger precipitation evaporation leads to increased stability of the boundary layer and reduced intensity of vertical mixing between the surface and cloud layer. An increased sink of cloud layer moisture via precipitation and decreased source through diminished vertical transport result in earlier cloud breakup in higher conditions. Simulations with different sea surface temperatures (SST) indicate that this cloud breakup mechanism remains valid for MCAOs of different strengths, although the cloud organization is more sensitive to SST changes in low environments. This work highlights the importance of accurate representations of ice processes in simulating MCAO clouds and suggests the need for observational constraints of ice nucleating particles and over the mixed‐phase cloud regimes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

New particle pusher with hadronic interactions for modeling multimessenger emission from compact objects

We propose novel numerical schemes based on the Boris method in curved spacetime, incorporating both hadronic and radiative interactions for the first time. Once the proton has lost significant energy due to radiative and hadronic losses, and its gyroradius has decreased below typical scales on which the electromagnetic field varies, we apply a guiding center approximation (GCA). We fundamentally simulate collision processes either with a Monte-Carlo method or, where applicable, as a continuous energy loss, contingent on the local optical depth. To test our algorithm for the first time combining the effects of electromagnetic, gravitational, and radiation fields including hadronic interactions, we simulate highly relativistic protons traveling through various electromagnetic fields and proton backgrounds. We provide unit tests in various spatially dependent electromagnetic and gravitational fields and background photon and proton distributions, comparing the trajectory against analytic results. We propose that our method can be used to analyze hadronic interactions in black hole accretion disks, jets, and coronae to study the neutrino abundance from active galactic nuclei.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

FIREX-Related Biomass Burning Research Using ARM Single-Particle Soot Photometer Field Campaign Report

The scientific focus of this study was to investigate and quantify the mass loadings, chemical compositions, and optical properties of biomass burning particulate emissions generated in the laboratory from Western U.S. fuels using a similar instrument suite to the one deployed on the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility Gulfstream-1 (G-1) aircraft during the 2013 Biomass Burning Observation Project (BBOP) field study (Kleinman and Sedlacek, 2013). We deployed the single-particle soot photometer (SP2) to make measurements of biomass burning refractory black carbon (rBC) mass loadings and size distributions to correlate with non-refractory particulate matter (NR-PM; i.e., HR-AMS) and rBC (SP-AMS) measurements as a function of photo-oxidation processes in an environmental chamber. With these measurements, we will address the following scientific questions: 1. What are the emission indices (g/kg fuel) of rBC from various wildland fuels from the Pacific Northwest (i.e., relevant to BBOP analysis) as a function of combustion conditions and simulated atmospheric processing in an environmental chamber? 2. What are the optical properties (e.g., mass-specific absorption cross-section [MAC], single-scattering albedo [SSA], and absorption Angstrom exponent [AAE)] of rBC emitted from various wildland fuels and how are they impacted by atmospheric processing? 3. How does the mixing state of rBC in biomass-burning plumes relate to the optical properties? 4. How does the emitted rBC affect radiative forcing?

09 BIOMASS FUELS↗

DOE OSTI2018

Efficient Recovery of Copper and Cobalt from the Matte–Slag Mixture of ISA Furnace by Injection of Coke and Pyrite

During ISA copper smelting process, ISASMELT furnace discharges a large amount of matte and slag mixture and the separation of them has an important influence on the recovery of valuable metals. This paper presented a reduction-sulfurization sedimentation process for recovering copper and cobalt from the matte–slag mixture of ISA furnace. Firstly, matte–slag mixture and traditional static sedimentation slag are characterized to determine their mineral composition and occurrence state. It indicates that Cu is primarily lost in slag in the form of sulfide, while Co is mostly lost in the form of oxide. With coke and pyrite as the reducing agent and vulcanizing agent, an orthogonal laboratory experiment was conducted to determine the effects of the smelting temperature and additive dosage on the recovery process. The optimum slag cleaning conditions were found to be: a coke dosage of 2 pct, a pyrite dosage of 2 pct, and a smelting temperature of 1260 °C lasting for 2 hours. In order to improve on the low utilization ratio of additives associated with the industrial sedimentation process, an innovative additive introducing method was put forward which enables the additives to mix and react with slag more adequately. A powder injection device was purpose-made to inject additives into molten slag in dispersion state, and a laboratory experiment was carried out to simulate this process. By injecting 2 pct coke and 2 pct pyrite, the contents of Cu and Co in cleaned slag decreased to 0.46 and 0.01 pct, respectively. It proves that the injection of additives into molten slag is an effective method to recover Cu and Co from the matte–slag mixture of ISA furnace.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Modelling the complex evaporated gas flow and its impact on particle spattering during laser powder bed fusion

The additive manufacturing (AM) of metals is becoming an increasingly important production process with the potential to replace traditional techniques such as casting. Laser Powder Bed Fusion (LPBF) is used in many applications to print metal parts from powder. The metal powder is heated locally with sufficient laser radiation that the liquid melt easily reaches its boiling temperature, which leads to a metallic vapour jet that can entrain both powder bed particles and molten droplets. The small size of laser-matter interaction site makes a detailed experimental analysis of the process challenging. Synchrotron X-ray imaging experiments are one of the few methods which can capture the dynamic melting and solidification processes. Comparing such experiments with computer simulations of the process is an important approach in order to better understand the manufacturing process and to analyse the influence of process parameters on the evaporated gas jet and the subsequent impact on particle ejection, leading to potentially reduced AM component quality. The melting and solidification of the metal powder is simulated using an Eulerian multiphase approach based on a control volume discretization of powder bed and substrate and a volume of liquid separation from melt and gas phase. The gas phase modelled as an ideal gas reaches velocities up to 100 m/s. Lagrangian particle tracking in the simulation demonstrates that the velocity fields calculated by the Eulerian multi-phase approach in combination with a standard drag-force model lead to particle accelerations in good agreement with those measured experimentally. In order to avoid numerical laborious Lagrangian calculations, a direct method to compare an Eulerian multiphase simulation with synchrotron X-ray experiments was introduced and validated. This approach is used to analyse the influence of process parameters including laser power and laser speed on the maximal acceleration of particles from the melt pool area. While the particle acceleration increases linearly with line energy in the conduction mode, a linear decrease of the acceleration with increasing line energy can be found in the transition mode before the accel-eration increases again with line energy in the keyhole mode.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Convective Biases in the US DOE Global Storm‐Resolving Model: Insights From Regionally Refined Simulations During the CACTI Campaign

Accurately simulating convective processes in complex terrain remains a critical challenge for global storm-resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud-Resolving E3SM Atmosphere Model (RRM-SCREAM) using comprehensive observations and large-eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high-resolution observations and reanalysis data indicate that RRM-SCREAM effectively captures large-scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM-SCREAM disproportionately produces smaller precipitation clusters referred to as “popcorn convection,” and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow-to-deep convective transition case shows that RRM-SCREAM delays initial shallow convection growth due to lower-tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low-level moistening and excessive upper-level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM-SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high-resolution atmospheric models.

Su, Tianning [Lawrence Livermore National Laborato↗

DOE OSTI2019

Improvement of aerosol activation/ice nucleation in a source-oriented WRF-Chem model to study a winter Storm in California

The source-oriented Weather Research and Forecasting chemistry (SOWC) model is modified to improve mixed-phase cloud parameterization by including aerosol aging processes (external mixture) in cloud activation/nucleation. The improved SOWC model is applied to investigate how aerosol mixing state influences cloud and ice formation and atmospheric optical properties during a winter storm. SOWC tracks 6-dimensional chemical variables (X, Z, Y, Size Bins, Source Types, Species) through an explicit simulation of atmospheric chemistry and physics. A new module is implemented into the enhanced SOWC model to simulate microphysics processes with all source-oriented hydrometeors (cloud, ice, rain, snow and graupel) by using the modified Morrison two-moment microphysics scheme. In this study, all aerosol source types can activate to form cloud droplets based on the Köhler theory, but the dust source type is the only source of ice nuclei (IN). Here, the Goddard shortwave and longwave radiation schemes are modified to use a new geometric optics method to estimate cloud optical properties by considering the chemistry components and the physical shape of each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Phase-shift speckle-shearing interferometry

We have numerically simulated the process of measuring stress – strain states by the method of speckle-shearing interferometry using the phase-shift technique. A computer model with the possibility of setting its strain and roughness is developed, which includes a model of a diffusely reflecting test object corresponding to the characteristics of a real membrane, as well as a speckle interferometer model that allows speckle interferograms to be obtained for different speckle sizes and angles between interfering beams. The process of reconstructing the object surface topogram from model speckle interferograms by the phase-shift technique is implemented. Using the developed models, a two-dimensional shearogram are obtained, which is a derivative of the strain field of a circular membrane. Comparison of the results of numerical simulation with experimental data shows that the differences (rms deviations) do not exceed 0.02 μm. It is also shown that the error of interferogram reconstruction by the phase-shift technique increases significantly when the test object strains exceed 12 μm. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗

DOE OSTI2017

Upgrades for the CMS simulation

We report the current status of the full simulation application developed by the CMS experiment. For LHC run-II, CMS is using Geant4 10.0p02 built in sequential mode. About 16 billion events were produced for run-II analysis during 2015-2016. A new method to handle pileup events has been developed and is now used for regular processing of simulation events. We plan to use Geant4 10.2p02 for 2017 production. In this work, we present CPU and memory performance of the CMS full simulation for different configurations and Geant4 versions considered during our testing. We also discuss technical aspects of the migration to Geant4 10.2 and a new premixing scheme for the simulation of pileup.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Galaxy properties and UV escape fractions during the epoch of reionization: results from the renaissance simulations

Cosmic reionization is thought to be primarily fueled by the first generations of galaxies. We examine their stellar and gaseous properties, focusing on the star formation rates and the escape of ionizing photons, as a function of halo mass, redshift, and environment using the full suite of the Renaissance Simulations with an eye to provide better inputs to global reionization simulations. This suite probes overdense, average, and underdense regions of the universe of several hundred comoving Mpc{sup 3}, each yielding a sample of over 3000 halos in the mass range of 10{sup 7}–10{sup 9.5} M{sub ⊙} at their final redshifts of 15, 12.5, and 8, respectively. In the process, we simulate the effects of radiative and supernova feedback from 5000 to 10,000 Population III stars in each simulation. We find that halos as small as 10{sup 7} M{sub ⊙} are able to host bursty star formation due to metal-line cooling from earlier enrichment by massive Population III stars. Using our large sample, we find that the galaxy-halo occupation fraction drops from unity at virial masses above 10{sup 8.5} M{sub ⊙} to ∼50% at 10{sup 8} M{sub ⊙} and ∼10% at 10{sup 7} M{sub ⊙}, quite independent of redshift and region. Their average ionizing escape fraction is ∼5% in the mass range of 10{sup 8}–10{sup 9} M{sub ⊙} and increases with decreasing halo mass below this range, reaching 40%–60% at 10{sup 7} M{sub ⊙}. Interestingly, we find that the escape fraction varies between 10%–20% in halos with virial masses of ∼3 × 10{sup 9} M{sub ⊙}. Taken together, our results confirm the importance of the smallest galaxies as sources of ionizing radiation contributing to the reionization of the universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Simulating strongly correlated molecules with a superconducting quantum processor

Many of the biggest challenges in expanding the nation’s access to clean and low-cost energy resources are fundamentally chemistry or materials challenges. An important case is the development of new catalysts for the up-conversion of cheap and readily available materials such as methane or water into materials suitable for use as a fuel such as methanol or oxygen. To understand and exploit such processes, computer simulations of chemical reactions provide a natural complement to experimental studies. Unfortunately, most catalytic reactions involve so-called “strongly correlated” molecules which are notoriously difficult to study with simulation algorithms that can be executed on existing (classical) computers. The recent growth in quantum information science offers an alternative potential route for simulating these difficult systems. As a result, an increasing number of computational chemists are becoming interested in quantum computing. At the same time, quantum information scientists have identified chemistry simulation as a possible first demonstration of a quantum computer providing an improvement over a classical computer. The objective of this project is to accurately simulate strongly correlated molecules on a quantum processor. To meet the high challenges of this objective, new hybrid quantum/classical algorithms will be co-designed with advanced quantum gate developments and computed on customized quantum hardware. Some of the developed techniques will be transferable to study other molecular systems, while the project as a whole will help define better strategies for advancing the quantum simulation of matter more generally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands

Wetlands are one of the most significant natural sources of methane (CH 4 ) to the atmosphere. They emit CH 4 because decomposition of soil organic matter in waterlogged anoxic conditions produces CH 4 , in addition to carbon dioxide (CO 2 ). Production of CH 4 and how much of it escapes to the atmosphere depend on a multitude of environmental drivers. Models simulating the processes leading to CH 4 emissions are thus needed for upscaling observations to estimate present CH 4 emissions and for producing scenarios of future atmospheric CH 4 concentrations. Aiming at a CH 4 model that can be added to models describing peatland carbon cycling, we composed a model called HIMMELI that describes CH 4 build-up in and emissions from peatland soils. It is not a full peatland carbon cycle model but it requires the rate of anoxic soil respiration as input. Driven by soil temperature, leaf area index (LAI) of aerenchymatous peatland vegetation, and water table depth (WTD), it simulates the concentrations and transport of CH 4 , CO 2 , and oxygen (O 2 ) in a layered one-dimensional peat column. Here, we present the HIMMELI model structure and results of tests on the model sensitivity to the input data and to the description of the peat column (peat depth and layer thickness), and demonstrate that HIMMELI outputs realistic fluxes by comparing modeled and measured fluxes at two peatland sites. As HIMMELI describes only the CH 4 -related processes, not the full carbon cycle, our analysis revealed mechanisms and dependencies that may remain hidden when testing CH 4 models connected to complete peatland carbon models, which is usually the case. Our results indicated that (1) the model is flexible and robust and thus suitable for different environments; (2) the simulated CH 4 emissions largely depend on the prescribed rate of anoxic respiration; (3) the sensitivity of the total CH 4 emission to other input variables is mainly mediated via the concentrations of dissolved gases, in particular, the O 2 concentrations that affect the CH 4 production and oxidation rates; (4) with given input respiration, the peat column description does not significantly affect the simulated CH 4 emissions in this model version.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Thermal modeling of directed energy deposition additive manufacturing using graph theory

Purpose: The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach: Here, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings: The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications: This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications: The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value: This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

42 ENGINEERING↗