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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Exploration ToolKit (ExTK)

The Exploration Toolkit (ExTK) is a reusable Extended Reality (XR) system developed for incorporating and exploring 3-Dimensional (3-D) computer aided design (CAD) models in XR, with a primary focus on Augmented Reality (AR). The ExTK consists of a Developer Mode and a User Mode. In Developer Mode, ExTK provides developers with the ability to easily import 3-D CAD models and activate desired exploration functionality and layout. Multiple models can be added to a single instantiation of the ExTK using Unity's Scene capability. Exploration functions include scaling, rotating, explode/contract, animations, hiding parts, submodules, and measurement functions. In User Mode, ExTK provides a menu system that allows users to select models and initiate exploration functions. ExTK is architected for reusability and developers can customize the ExTK layout and functions according to application needs. ExTK is designed to be hardware agnostic, although initial development focused on the Microsoft HoloLens as the primary deployment platform. The ExTK is developed using the Unity Game Engine Development Platform. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-1506 O

Klein, BrandonThorin↗
DOE OSTI2022

NucMesh: nuclear reactor geometry creation and mesh generation module in NEMoSys

NucMesh is a parameterized geometry and mesh generator for nuclear reactors developed within the Nuclear Energy Modeling System NEMoSys at Illinois Rocstar. NEMoSys is a platform developed for mesh generation, adaptive refinement, and solution verification. NucMesh is implemented to be generalized and extensible with a robust computer aided design engine and multiple mesh generation algorithms for unstructured triangular, quad-dominant, and structured quadrilateral meshing. In this paper, we present the geometric and meshing features of NucMesh. Geometrically objects are constructed bottom-to-top and overlaps are addressed automatically. A sophisticated object tracking algorithm prevents data from being lost for segmented objects. We discuss the primitive objects of circle and polygons that constitute the module and show how they are used with example inputs. Arrays of primitives and arrays of arrays are utilized to build large assemblies of objects. The concept of saved objects is discussed to demonstrate how repetitive objects can be reused easily and augmented in place. Three dimensional meshes can be obtained through mesh extrusion where all materials and side sets are extended to three dimensions. We show that side sets can be defined nearly anywhere within the geometry and can then be applied to the mesh. Finally, example reactor meshes are demonstrated for the Idaho National Laboratory Advanced Test Reactor and Los Alamos National Lab Empire reactor, both of which use control drums that NucMesh handles easily. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Use of Commercial Off-The-Shelf Parts with 3-D Printed Connectors for Powered Air Purifying Respirators

This report presents preliminary results from the performance evaluation of a powered air purifying respirator (PAPR) assembled from commercial off-the-shelf (COTS) parts. Cost is a major limiting factor to PAPR adoption in healthcare. Moreover, the lack of standardization for filters and batteries poses a challenge for clinics and hospitals. Rapid prototyping of connectors, both mechanical and electrical, offers a possible solution in addressing both issues. We used 3-D printed connectors to assemble COTS components into a prototype PAPR. Large-scale manufacturing of 3-D printed connectors may be accomplished by converting the computer-aided design (CAD) drawings to pressure-injection molds.

42 ENGINEERING↗
DOE OSTI2022

Cubit for MCNP Unstructured Mesh Analysis of Oktavian Benchmarks

The Monte Carlo N-Particle (MCNP) transport code developed by Los Alamos National Laboratory (LANL) can be used to transport various particles across user defined three dimensional (3D) geometries. Traditionally, these geometries are created as constructive solid geometry (CSG), involving the use of Boolean operators on defined surfaces to create 3D regions known as cells. A newer method of geometry definition in the MCNP code is unstructured mesh (UM) embedded within a CSG cell using the universe and fill repeated-structure features. An MCNP UM calculation requires an MCNP input file and mesh geometry file. The MCNP code cannot be used to generate UM geometry models. A computer-aided design (CAD) software is typically used to construct a solid geometry model, and a CAD file is then imported into a mesh generation software to prepare and generate a mesh model. Some mesh generation software packages have the ability to create a solid geometry and thus a separate CAD software for creating a CAD model is not needed. In this work, Cubit, a geometry creation and meshing software developed by Sandia National Laboratories, is used to construct UM models for MCNP simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Modeling and Analysis for Spent Nuclear Fuel Seismic Testing

Note: This is the final public release version of PNNL-31671 Draft, which was previously released to the sponsor for review. The intent is the public release version will be PNNL-31671. There are no significant changes from the earlier draft version. This unlimited distribution report is the deliverable for M3SF-21PN010202014: Modeling and Analysis for Spent Nuclear Fuel Seismic Testing. This report summarizes the modeling, analysis, and test plan support completed by Pacific Northwest National Laboratory for the spent nuclear fuel dry storage system seismic test plan through May of 2021. Test plan preparation is planned to continue until the seismic test is completed in July of 2022. This report covers preliminary structural dynamic model development and computer-aided design of test hardware. Based on preliminary modeling, the strongest earthquakes under consideration in this test program provide mechanical loading on the spent nuclear fuel that is comparable to the 30 cm cask drop scenario, although the loads do not appear to be strong enough to cause significant permanent deformation of the fuel assembly spacer grids. The potential for grid deformation during the test will be assessed when the proposed shake table motion becomes available. The weakest earthquakes considered in this test are expected to be comparable to the mechanical loads witnessed in the multimodal transportation test of 2017. The horizontal canister system is predicted to provide nearly-uniform loading condition on the fuel assemblies it contains, while the vertical cask system is predicted to cause non-uniform dynamic loads on the fuel assemblies. In the horizontal case, gravity keeps the fuel assemblies in contact with one basket wall surface unless the loads are strong enough to cause a separation. In the vertical case, the fuel assemblies are relatively long and slender, and seismic motion in the anticipated test range is predicted to cause the assemblies to lean, tilt, and impact the basket walls throughout the seismic event. These gap closures are a nonlinear force transmission condition, so the vertical system is expected to have more variation and variability than the horizontal system. While the horizontal system is expected to have a relatively more linear response than the vertical system, there is still the potential for nonlinear behavior in the horizontal system because the fuel assemblies are free to slide, bounce, and impact the basket walls if the seismic loads are strong enough. One major conclusion of this study is that the use of mixed fuel assemblies in the canister will be acceptable. There are differences in overall system response when the mass, center of gravity, or gaps are changed, but the changes in response are within the bounds of a system that contains completely homogeneous fuel assemblies. One important observation is that the loading for each individual fuel assembly within the vertical canister is expected to be different from that of the others because of nonlinearities in the system. The horizontal canister system is expected to have a more uniform and more predictable response than the vertical canister, making variations in fuel assembly characteristics easier to account for. One important recommendation that comes from this modeling work is to repeat some of the strongest shake tests at different angles, particularly for the vertical cask system. Modeling predicts that strong seismic motion will cause the fuel assemblies to close initial gaps and impact the fuel basket walls when the canister is in the vertical cask configuration. Each shake test will impose a pre-defined three-dimensional time history on the cask system.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

High-Voltage DC MULTI-terminal SIMulation (HVDC MULTISIM): Technical Program Summary

Expansion of the power grid in the USA is essential to fulfilling the rising energy demand of the country. In particular, the transmission grid, a network of electrical energy corridors that enable the flow of large amounts of energy from the point of generation to the point of consumption, needs significant expansion to cater to this growth of electrification. The current infrastructure is also dated, and any new transmission corridor should be based on a vision of building a modern infrastructure that is futureproof. High voltage DC (HVDC) transmission technology falls in this category: it is the most economical way to build large transmission lines and relies on sophisticated electronics and flexible controls, rather than just passive components like switchgear and transformers. This project is aimed at establishing the technical and economic feasibility of a network of HVDC lines that are interconnected to form a multi-terminal DC network. HVDC transmission is not a new technology and United States has a number of such lines; however, these are point to point transmissions and do not form a DC grid. Modeling and analyzing DC grids is challenging since there are no established modeling tools and the traditional ways of modeling AC grids fall short of providing the fidelity required the fast dynamics of the DC grid. This project aims to build a software-in-the-loop simulator for a multi-terminal HVDC grid, called MULTISIM and, establish new control and protection algorithms to operate such a connected DC system. NLR, a key partner, established the core framework of the MULTISIM simulator and validated its functionality. The GE Vernova team, created a full-scale model of a four-terminal HVDC network using PSCAD (Power Systems Computer Aided Design) software and established the baseline performance of the system during normal and fault operation. The project was started on 10/12024 with a kick-off meeting held on 12/11/2024 and ran through two quarters till termination. All the tasks, milestones and deliverables during this period were met. Several interim reports detailing the various tasks were submitted. The following sections provide details on the completed tasks till the project pause and termination after Q2.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Directional Dark Matter Detection With Scintillating Crystals

Dark matter is in every galaxy including our own. Dark matter is composed of non-visible particles which makes it difficult to measure. During this research project I used a Computer Aided Design Software (CAD) called AutoDesk Fusion 360 to create a housing component for a scintillating crystal that would be compatible with the front end of the Astro Dewar holding a CCD. This component allowed us to measure the dark matter UV energy given off by the scintillating crystal.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physics-based information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.

Ziabari, Amir↗
DOE OSTI2022

How Can Construction Process Simulation Modeling Aid the Integration of Lean Principles in the Factory-Built Housing Industry?

New and existing factories that produce and deliver factory-built housing can benefit from construction process simulation modeling to explore the integration of Lean principles in their operations. Construction process simulation modeling provides digital or virtual recreations of the real-world factory environments to visualize, quantify, analyze, and optimize their underlying behavior, including factory productivity, material flow, labor dynamics, bottlenecks, and work scope. One of the key benefits of process simulation modeling is the ability to create and compare "what-if" scenarios, including integrating Lean principles such as reducing waste (for example, transportation, waiting), line balancing, and just-in-time concepts. In general, three process simulation methods are widely used: discrete event simulation (DES), agentbased modeling (ABM), and system dynamics (SD). Myriad process simulation software also is available, but depending on the industry, complexity of the system, and purposes of the simulation, some software might be more appropriate. Similar to how computer-aided design (CAD) software such as AutoCAD and Rhinoceros enable building design of modular or factory-built housing, process simulation modeling software such as jStrobe, ProModel, and AnyLogic can enable factory design of new and existing factories to deliver modular affordable housing at scale, as opposed to traditional site-built construction. Software with DES capabilities can help generate a process model that is a logical representation of resources and activities in a factory. Software with CAD-DES integration can leverage product-process data integration to help spatially visualize a DES model of the factory in the CAD environment. Software with multimethod simulation capabilities, widely used in the manufacturing industry, brings together DES, ABM, and SD in a single platform that allows visualization, quantification, analyses, and optimization at varying data fidelities. Near-real-time data from an existing factory can be directly plugged into multimethod simulation software so that the construction process simulation model is a near-accurate representation of the real-world factory conditions. This report provides insights into the use of simulation as an aid to integrate Lean concepts in factories, including guidelines for selecting the appropriate process simulation modeling method and software. These insights have been developed as part of ongoing process simulation modeling research, development, and demonstration projects at the U.S. Department of Housing and Urban Development, the U.S. Department of Energy, and the National Renewable Energy Laboratory focused on how process simulation models can enable better integration of resilience, energy efficiency, and low-carbon design strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Chapter 10: Advances in Protein Engineering and its Application in Synthetic Biology

Protein engineering has been used successfully in fields ranging from medicine to food science to biofuels. Applications of protein engineering include developing antiviral peptides or other protein therapeutics, antibody engineering, designing protein-based logic circuits, engineering enzymes to be more specific or to function under industrially relevant conditions such as at higher temperatures or high/low pH, modifying cell signaling or regulatory functions, and so on. Advances in recombinant DNA, "omics," and CRISPR-Cas (clustered regularly interspaced short palindromic repeats and its associated proteins) technologies, combined with high-throughput screening facilities, will lead to improved methods for protein engineering, enabling easy modification of more proteins/enzymes for new specific applications. New methods for rational design, directed evolution, and computer-aided protein design will further accelerate the speed of protein evolution and expand the scope for protein engineering. In this chapter we discuss general protein engineering strategies and advances in engineering proteins with desired functions, focusing on the "design" and "build" part of the design-build-test-learn cycle.

BIOMASS FUELS↗
DOE OSTI2024

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Poster for Foundations of Computer Aided Process Design (FOCAPD) 2024 describing the formality of model-based design of experiments for batch crystallization. This paper focuses on the implementation of model-based design of experiments for parameter precision, enabled by Pyomo.DoE, a package in Python. Specifically, this work outlines an identifiability analysis for the estimability of the model parameters in a batch crystallization system.

Lynch, Hailey↗
DOE OSTI2025

A Simple, Scalable Large Deformation Solid Mechanics Implementation in the MOOSE Framework

This article describes a large deformation solid mechanics solver implemented as part of the freely available and open source MOOSE finite element simulation framework. The article documents the choices made in developing the solid mechanics framework and describes novel formulations for the gradient operator and constitutive modeling framework made to simplify implementations of different coordinate systems, stabilized gradient operators, and different constitutive model inputs and outputs. In the process, the article describes a new formulation that casts objective integration of the Cauchy stress as a linear transformation of the small stress rate. Finally, the article presents key implementation details and examines the parallel efficiency of the solid mechanics solver implemented in MOOSE. The implementation retains a good weak scaling efficiency beyond 1,000 parallel processes. The article includes a discussion of the factors limiting the parallel efficiency of implicit, large deformation solid mechanics codes on current high-performance computers, with the main current limitation being the scalability of the algebraic multigrid methods used to solve the linearized equilibrium equations.

Applied computing → Computer-aided design↗
DOE OSTI2020

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. Finally, we provide an analysis of the results and discuss insights into determined best practice methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers

Membrane polymers are a promising technology for use in many challenging gas separation applications. Here, the techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R 2 scores exceeding 0.82.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Unified Nanotechnology Format: One Way to Store Them All

The domains of DNA and RNA nanotechnology are steadily gaining in popularity while proving their value with various successful results, including biosensing robots and drug delivery cages. Nowadays, the nanotechnology design pipeline usually relies on computer-based design (CAD) approaches to design and simulate the desired structure before the wet lab assembly. To aid with these tasks, various software tools exist and are often used in conjunction. However, their interoperability is hindered by a lack of a common file format that is fully descriptive of the many design paradigms. Therefore, in this paper, we propose a Unified Nanotechnology Format (UNF) designed specifically for the biomimetic nanotechnology field. UNF allows storage of both design and simulation data in a single file, including free-form and lattice-based DNA structures. By defining a logical and versatile format, we hope it will become a widely accepted and used file format for the nucleic acid nanotechnology community, facilitating the future work of researchers and software developers. Together with the format description and publicly available documentation, we provide a set of converters from existing file formats to simplify the transition. Finally, we present several use cases visualizing example structures stored in UNF, showcasing the various types of data UNF can handle.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH 3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of Timing in the Design Process on Students’ Application of Design for Additive Manufacturing Heuristics

The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules or heuristics. It can be challenging for novice designers to create successful designs for additive manufacturing (AM), due to its recent surge in popularity and lack of formal education or training. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process: before the initial design, between the initial design and redesign, and after the redesign. The novelty and quality of each of the resulting designs were evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better-suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants who received heuristic information between the design sessions experienced a decrease in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students’ perceptions of their ability to perform DfAM-related tasks. Furthermore, these results validate the practicality of design heuristics in lecture form as AM training tools while also emphasizing the importance of iteration in the design process.

computer-aided design↗