Search NASA⌕ Search

SEARCH · Search NASA

Results for “computer aided 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

System, method, and computer program for creating an internal conforming structure

A system for creating an internal formation of a tubular structure having an inner surface via additive manufacturing. The system broadly includes a computer modeling system and an additive manufacturing system. The computer modeling system may include a processor for generating a lattice cellular component via computer-aided design software according to inputs received from a user. The processor may also generate an internal formation lattice structure based on the lattice cellular component and modify the lattice structure to follow and/or conform to the curvature of the inner surface of the outer wall of the tubular structure. The additive manufacturing system may be configured to produce the lattice structure and the tubular structure via additive manufacturing material deposited layer by layer according to the lattice structure.

42 ENGINEERING↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

The multiport autonomous reconfigurable solar (MARS) power plant is a promising solution to integrate renewable resources and energy storage systems into the alternating current (ac) power grid and an high-voltage direct current (HVdc) link. In the MARS system, various input power sources are connected to the individual submodules (SMs) through direct current (dc)–dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This article aims to address the gap by accurately determining the stability boundary of the MARS system. As such, a novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. Further, in conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both power systems computer aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criterion, efficient energy management is achieved for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high-efficiency standards.

14 SOLAR ENERGY↗
DOE OSTI2026

Design and Characterization of the 162.5 MHz RF Component Layout for Fermilab's PIP-II Reference Line

The Proton Improvement Plan II (PIP-II) Reference Line at Fermi National Accelerator Laboratory distributes phase-stable radio-frequency (RF) signals throughout the accelerator. PIP-II requires stable timing and phase reference signals so its accelerating cavities transfer energy to the particle beam at the correct point in each RF cycle. The full system includes 162.5, 325, and 650 MHz sections corresponding to the frequency sections of the PIP-II Linac, with this project focusing on the 162.5 MHz section. The Reference Line must provide a phase stable source signal while responding to phase changes caused by environmental conditions or system drift. Its RF components will be mounted on aluminum heat plates inside a temperature controlled enclosure to further limit temperature-driven phase changes. To prepare the system for manufacture, the project reviewed component functions and dimensions, developed a computer-aided design (CAD) model, and arranged the hardware to support short cable paths, grounding, fastener access, and maintenance. Several full scale, three dimensional printed prototypes allowed the available components to be mounted and inspected. These fit checks revealed mechanical conflicts and guided revisions to component placement, countersink geometry, labeling, and plate thickness. Electrical characterization was also performed on selected RF hardware to compare its measured behavior with the performance metrics that were set for our design. Overall, the project produced a manufacturable 162.5 MHz layout, physical fit-check prototypes, and documented electrical measurements that support review before metal fabrication. The 325 and 650 MHz layouts remain future work because they require additional minor mechanical changes.

Subedi, Harsheet [Unlisted, US, CA] (ORCID:0009000↗
DOE OSTI2026

Design and scale-up of 3D printed bat houses with biomass-derived polymer composites

Biomass (e.g., pine sawdust, especially high–ash content pine sawdust) is commonly disposed of as waste. Combining biomass with polymers to make composite feedstocks for 3D printing has been explored as a method to reduce or repurpose the biomass waste. Although not all biocomposite properties are known, the wood-based polylactic acid (PLA) composite has promising qualities for applications in ecological settings. In this work, pine wood–PLA composite feedstock was used to 3D print supplemental roost structures for endangered tree-roosting bats, which often face a paucity of suitable naturally occurring roosts. This material combination was selected because it is estimated to degrade faster than the synthetic material systems that are used widely in supplemental bat roosting structures to aid in the conservation of tree roosting bats. The layered, rough surface created by the 3D printing process serves as a surface that bats can grip while roosting. Computer-aided design (CAD) models were generated based on natural roost structures, and a full-size bat house was successfully additively manufactured using a pellet-fed large-scale 3D printing system. The 3D printed hexagon exhibited a tensile strength of 22–23 MPa and a Young’s modulus of 3202–3218 MPa in the x-direction. It has been demonstrated that the 3D printed bat house can be installed on a tree in a stable fashion. This successful demonstration of a bat roost manufactured using a bioderived composite should promote its use in other fish and wildlife structures and broader industrial applications such as construction and automobiles.

3D printing↗
DOE OSTI2020

Watertight Tensor-Product Spline Reconstruction (KCNSC PDRD Technical Report)

The standard for representing geometric models in Computer Aided Design (CAD) systems is boundary representation with trimmed surfaces. However, the approximate nature of surface-surface intersections in CAD systems leaves many small gaps between mating surfaces. These gaps lead to problems for downstream analysis and manufacturing applications, requiring time consuming repair operations and further approximation of the geometry as polygonal meshes, which do not support design iteration. In this study, nVariate, Inc. evaluated its watertight reconstruction technology on a test part. The resulting watertight model has the same absolute model accuracy of the original model zero gaps, providing a representation that requires no repair and supports further design iterations in the CAD system.

42 ENGINEERING↗
DOE OSTI2022

Hardware-Accelerated Ray Tracing of CAD-Based Geometry for Monte Carlo Radiation Transport

Monte Carlo radiation transport (MCRT) methods have been used to simulate radiation environments for many decades by tracking individual particles through a model to accumulate statistical information. MCRT geometry is historically formed using the constructive solid geometry (CSG). Recently, significant work has been performed to support simulations using computer-aided design (CAD)-based tessellated surfaces to support highly complex geometries. Ray tracing acceleration data structures from the rendering and visualization community are applied to accelerate particle tracking in CAD-based models. Despite these efforts, CSG representations provide the superior performance in surface intersection operations during particle flight. Concurrently, pseudo Monte Carlo methods have become prevalent in rendering applications to support more realistic models for scattering media, motivating innovations that are advantageous for MCRT simulations. Finally, the authors’ work extends these innovations by employing Intel’s Embree ray tracing kernel within a geometry toolkit for Monte Carlo to improve the simulation performance using CAD-based models by factors of 1.5 to 2.

42 ENGINEERING↗
DOE OSTI2021

Electromagnetic Transient Simulation Algorithms for Evaluation of Large-Scale Extreme Fast Charging Systems (Distribution Grid Models)

The distribution and transmission grids are observing an increased penetration of power electronics in loads and generations. For example, there is increasing interest in integrating in extreme fast charging (XFC) systems for fast charging of electrical vehicles. As these systems are integrated, developing high-fidelity electromagnetic transient model of XFC systems in distribution grids and evaluating their interactions with the power grid would be of significant interest. This model will be utilized for design of XFC systems, to identify upgrades in distribution and/or transmission grids, for planning purposes by transmission planners or operators or owners, among others. It can also be utilized in operations for improved reliable performance of the grid and/or XFC station. The challenge with simulating these models is the high computational complexity introduced by the large number of states present in the system and the time-step needed to simulate the system. In this paper, advanced simulations algorithms are applied to reduce the computational complexity of simulating large-scale XFC systems. The algorithms include numerical stiffness-based segregation, time constant-based segregation, clustering and aggregation on differential algebraic equations (DAEs), and multi-order integration approaches. While the first three algorithms split the matrix that needs to be inverted from a large matrix to much smaller matrices, the final algorithm reduces the computational burden of applying higher-order integration approaches in the complete system. The comparison made in the previous sentence is with respect to use of homogeneous integration approaches used in conventional electromagnetic transient simulators like power systems computer aided design (PSCAD). The approaches mentioned here have resulted in speed-up of 36x in the simulation of a single distribution system with 15 XFCs.

Debnath, Suman↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗
DOE OSTI2025

Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and Its Scalability for High-Density 3-D Ferroelectric Capacitor Memory

In this work, we present a comprehensive experimental and modeling study on the scaling of vertical 2T-nC ferroelectric random access memory (FeRAM) hybrid cells, comprising n metal-ferroelectric–metal (MFM) capacitors, to demonstrate a high-performance and high-density 3-D capacitor memory. Our contributions include: 1) successful process integration of vertical 2T-3C FeRAM cells by stacking MFM structures on top of Si CMOS transistors; 2) experimental validation of memory cell functionality, confirming the feasibility of the vertical 2T-nC FeRAM architecture; 3) an analysis of scaling effects on parasitic capacitance in densely integrated 3-D arrays, using 3-D technology computer-aided design (TCAD) simulations; 4) exploration of aggressive stacking of write bitlines (WBLs) to enhance memory density, where ferroelectric linear capacitance ( C FE ) enables self-boosted inhibition under the V W /2 scheme, but renders the V W /3 scheme ineffective due to intolerable write disturbances; and 5) assessment of horizontal scaling, revealing significant increases in read disturbances caused by interplane capacitance between adjacent WBLs ( C Z ). This work represents an early exploration into the potential of 2T-nC FeRAM as a scalable and efficient 3-D memory solution.

42 ENGINEERING↗
DOE OSTI2023

Methodology and Tool for the Physical Security Analysis of Micro and Advanced Reactors

This work proposes a dynamic evaluation methodology to relax the conservatism in physical security evaluation, by leveraging an ongoing work in the Light Water Reactor Sustainability pathway. This methodology is implemented in a dynamic risk assessment tool named Event Modeling Risk Assessment using Linked Diagrams (EMRALD). The work extends EMRALD’s capability to support a sandbox feature where analysts can easily create attack scenarios and modify advanced/small modular reactor (A/SMR) security and safety features using templates. This approach saves time and cost since the analysis does not require creating detailed computer-aided design models, as is commonly required in commercial force-on-force software tools. EMRALD is completely free to use at https://emraldapp.inl.gov. We have developed basic templates including physical barriers, intrusion sensors, physical areas, and safety actions, that can be downloaded from EMRALD’s GitHub site: https://github.com/idaholab/EMRALD. These templates use generic data commonly used for training purposes, which do not reflect any actual operating nuclear reactor. Users may adjust the data in the templates with their own dataset and/or create new templates in EMRALD. The proposed methodology combines security and safety by assessing sabotage effects up to the radiological consequence to the public instead of merely the core damage state. This practice follows the industry standard for advanced non-light-water reactors currently proposed for endorsement by the Nuclear Regulatory Commission. The combination of security and safety is expressed in an achievability-consequence chart. EMRALD can be used to generate data for this chart. A hypothetical case study using a representative sodium-cooled fast reactor (SFR) facility is presented in this report to demonstrate this methodology. This case study does not contain any actual nuclear plant information. This work will benefit A/SMR vendors and utilities to implement security by design during the reactor design iteration phase, such that they do not have to perform upgrades and retrofits to the reactor after it is installed to improve its physical protection system. The tool may also be used to analyze domestic or foreign reactor designs to support the International Nuclear Security Techniques for Advanced Reactors (INSTAR) bilateral missions. Future works are planned to implement the methodology on a reference SFR reactor and a reference high-temperature gas-cooled reactor to obtain insights and lessons-learned for the A/SMR community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Physical Compact Model for Three-Terminal SONOS Synaptic Circuit Element

A well-posed physics-based compact model for a three-terminal silicon–oxide–nitride–oxide–silicon (SONOS) synaptic circuit element is presented for use by neuromorphic circuit/system engineers. Based on technology computer aided design (TCAD) simulations of a SONOS device, the model contains a nonvolatile memristor with the state variable QM representing the memristor charge under the gate of the three-terminal element. By incorporating the exponential dependence of the memristance on QM and the applied bias V for the gate, the compact model agrees quantitatively with the results from TCAD simulations as well as experimental measurements for the drain current. The compact model is implemented through VerilogA in the circuit simulation package Cadence Spectre and reproduces the experimental training behavior for the source–drain conductance of a SONOS device after applying writing pulses ranging from –12 V to +11 V, with an accuracy higher than 90%.

42 ENGINEERING↗
DOE OSTI2023

Understanding the Breakdown Behavior of Ultrawide-Bandgap Boron Nitride Power Diodes Using Device Modeling

Herein, a device study using technology computer-aided design simulation to theoretically analyze the electrical performance of ultrawide-bandgap boron nitride (BN)-based vertical junction devices is performed, including h-BN Schottky diode, h-BN pn diode, and h-BN/AlN pn diode; this is also the first demonstration of the BN power devices in simulation. The material properties of BN are defined with recently reported data, and the physical mechanisms of the device performance are systematically investigated. Additionally, the h-BN junctions in this simulation shows excellent performance, especially for breakdown behaviors. Schottky diode shows a turn-on voltage of 0.6 V for Pt Schottky contact and breakdown voltages over 450 V for 5 μm, 6 × 10 15 cm –3 p-type-doped drift layer; The h-BN pn diode shows a turn-on voltage of 6 V and breakdown voltages over 3 kV with a critical electric field of 13.6 MV cm –1 for 2.5 μm, 2 × 10 16 cm –3 p-type-doped drift layer. The h-BN/AlN heterojunction pn diode shows a turn-on voltage of 5.8 V and breakdown voltage over 2 kV for 2.5 μm, 2 × 10 16 cm –3 n-type-doped AlN drift layer. Herein, an understanding of the device principles of vertical BN junctions is provided, which can serve as a reference for the future development of robust BN power electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An experimental investigation of the effectiveness of Ar-CO 2 shielding gas mixture for the wire arc additive process

Wire arc additive manufacturing (AM) is the process by which a large, metallic structure is built layer-by-layer using a welding arc to melt a wire feedstock. A novel opportunity exists to alter the shielding gas composition to fabricate distinct geometrical features without altering the other AM parameters. In this paper, shielding gases with varied concentrations of Argon (Ar) and CO 2 was used to deposit three distinct geometric shapes (walls, infill, and overhang) using a wire-based additive manufacturing system utilizing the gas metal arc welding (GMAW) surface tension transfer (STT) process. Computer-aided design (CAD) models were sliced with a custom-built slicer, and the sliced algorithm was converted into optimal robotic toolpaths. A custom virtual instrument (VI) was built in LabVIEW to compare the temperature profiles on the surface during each deposition process. After each deposition, the geometric features were scanned, and the surface waviness value was evaluated. Tensile and Charpy impact coupons were extracted from the wall geometries in the longitudinal and transverse directions and tested. The results indicated that a higher CO 2 content produced higher melt pool temperatures to an extent, while lower contents of CO 2 resulted in a dimensionally accurate geometry. The data also indicated that the 2%/98% CO 2 /Ar blend produced scatter in tensile strength and the analysis of variance (ANOVA) shows significant difference. However, the intermediate range of CO 2 (5–10%) resulted in uniform tensile properties. Altogether, these results indicate that a 5%/95% CO 2 /Ar blend is the ideal shielding gas for lowering process temperatures and improving mechanical properties in wire arc additive manufacturing using the gas metal arc welding surface tension transfer process. Additionally, varying concentrations of Ar/CO 2 can be used within the same part in order to modify the local properties or process parameters such as strength, toughness, temperature, or dimensional features. This may improve overall manufacturing quality without sacrificing specific properties.

42 ENGINEERING↗
DOE OSTI2025

An efficient second-order adaptive procedure for inserting CAD geometries into hexahedral meshes using volume fractions

Here, this paper is concerned with inserting three-dimensional computer-aided design (CAD) geometries into meshes composed of hexahedral elements using a volume fraction representation. An adaptive procedure for doing so is presented. The procedure consists of two steps. The first step performs spatial acceleration using a k-d tree. The second step involves subdividing individual hexahedra in an adaptive mesh refinement (AMR)-like fashion and approximating the CAD geometry linearly (as a plane) at the finest subdivision. The procedure requires only two geometric queries from a CAD kernel: determining whether or not a queried spatial coordinate is inside or outside the CAD geometry and determining the closest point on the CAD geometry’s surface from a given spatial coordinate. We prove that the procedure is second-order accurate for sufficiently smooth geometries and sufficiently refined background meshes. We demonstrate the expected order of accuracy is achieved with several verification tests and illustrate the procedure’s effectiveness for several exemplar CAD geometries.

Adaptive↗
DOE OSTI2022

Layered CAD/CSG geometry for spatially complex radiation transport scenarios

Many spatially complex fission, fusion, and national security Monte Carlo (MC) radiation transport scenarios involve combining computer-aided design (CAD) models with constructive solid geometry (CSG) models. A layered geometry method has been implemented in the Shift MC code to address this need. With layered geometry, multiple CAD and/or CSG models can be clipped, translated, rotated, and placed in overlapping layers to form transport-ready geometries. Here, the utility of this method is demonstrated with two problems: (1) a fixed-source simulation with a layered geometry consisting of a LiDAR-generated CAD model of the Combined Arms Collective Training Facility urban environment overlaid with CSG models of a mock hotel and a detector apparatus, and (2) a k-eigenvalue calculation using a layered geometry model of the Transformational Challenge Reactor consisting of CAD fuel elements placed in a CSG core. Tallied particle flux distributions match expectations, but tracking robustness must be improved prior to general-purpose use.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗