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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

In situ synchrotron X-ray imaging and mechanical properties characterization of additively manufactured high-entropy alloy composites

Laser beam directed energy deposition has become an increasingly popular advanced manufacturing technique for materials discovery as a result of the in situ alloying capability. In this study, we leverage an additive manufacturing enabled high throughput materials discovery approach to explore the composition space of a graded W x (CoCrFeMnNi) 100–x sample spanning 0 ≤ x ≤ 21 at%. In addition to microstructural and mechanical characterization, synchrotron high speed x-ray computer aided tomography was conducted on a W 20 (CoCrFeMnNi) 80 composition to visualize melting dynamics, powder-laser interactions, and remelting effects of previously consolidated material. Results reveal the formation of the Fe 7 W 6 intermetallic phase at W concentrations> 6 at%, despite the high configurational entropy. Unincorporated W particles also occurred at W concentrations> 10 at% accompanied by a dissolution band of Fe 7 W 6 at the W/matrix interface and hardness values greater than 400 HV. In this work, the primary strengthening mechanism is attributed to the reinforcement of the Fe 7 W 6 and W phases as a metal matrix composite. The in situ high speed x-ray imaging during remelting showed that an additional laser pass did not promote further mixing of the Fe 7 W 6 or W phases suggesting that, despite the dissolution of the W into the Fe 7 W 6 phase being thermodynamically favored, it is kinetically limited by the thickness/diffusivity of the intermetallic phase, and the rapid solidification of the laser-based process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Parametrically designed surface topography on CAD models of additively manufactured lattice structures for improved design validation

Additively Manufactured (AM) lattice structures of the laser powder bed fusion (LPBF) process generally have different surface geometries depending on the overhang angle and location. This means that the design validation stage is often challenging, considering it is based on an ideal Computer Aided Design (CAD) model that is not truly representative, since the AM part will be different in terms of dimensional accuracy and surface finish. Previous studies have relied on the design of surface textures that are independent of the overhang angle, or techniques based on directly using X-ray Computed Tomography (XCT) data. In this paper, a new technique for designing surface texture on the CAD of lattices has been investigated and correlated with areal surface roughness parameters. After extracting areal surface parameters from the XCT data of the lattice, the method allowed for the design of this surface texture on the part CAD taking in consideration if its up skin or down skin, without using XCT data as the main input. By applying this method, it is possible to obtain a lattice CAD model with a designed surface texture and geometry that is more representative of the actual AM lattice. The mean deviation between the CAD model with the designed surface and the XCT was a third of the one between the XCT and the initial CAD. The proposed method allows for designing and replicating LPBF AM surfaces on the CAD of a lattice, taking into consideration the dimensional deviation caused by AM surfaces, especially on overhangs. Assuming that an LPBF AM process is stable and produces approximately the same AM surface, this method can be used to predict the geometry of a lattice, providing a cost-effective and efficient methodology that minimizes the time needed for design validation.

42 ENGINEERING↗
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2024

Advanced multimaterial shape optimization methods as applied to advanced manufacturing of wind turbine generators

Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.

17 WIND ENERGY↗
DOE OSTI2020

Process Heating Assessments Using DOE’s Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) Tool Suite

Process heating is the most energy-intensive manufacturing process for most sectors of industry. To quantify energy savings from various energy conservation measures, the Department of Energy (DOE) sponsored the development of the Process Heating Assessment and Survey Tool (PHAST) and similar tools for other industrial systems in the early 2000s. It has been used extensively in the Save Energy Now Program’s Energy Savings Assessments and the Better Plants Program’s In-Plant Trainings. Since the initial development of the legacy tools, both computer operating systems and software development have evolved significantly. Thus, DOE has invested in the modernization of PHAST and other legacy software tools to create the Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) tool suite. MEASUR offers a collection of software tools that can aid manufacturing facilities in improving the efficiency of energy systems and equipment (specifically pumps, fans, steam, and process heating) and in conducting “Energy Treasure Hunts”. Eventually, the tool will also add compressed air and process cooling systems. The Process Heating Assessment (PHA) module of MEASUR is an upgrade of the PHAST tool. PHA provides the means to model fuel-fired, steam-based, and electric process heating systems, covering process heating for most industrial plants in manufacturing sector. It also includes several key upgrades, including the ability to consider multi-component charge loads and account for several different areas of energy losses. The new tool includes a comprehensive flue gas calculator to quantify available heat and heat loss for various gaseous, liquid, and solid fuels and new heat loss calculators. It generates a report and a dynamic Sankey diagram to show the energy consumption in various areas of energy use. MEASUR has significantly improved the user experience by adopting a modern software design. This paper details the structure and workflow of PHA and presents a real-world case study to demonstrate energy savings quantification and MEASUR’s outstanding reporting capabilities.

Nimbalkar, Sachin U.↗
DOE OSTI2023

A combination interferometric and morphological image processing approach to rapid quality assessment of additively manufactured cellular truss core components

Advanced manufacturing (AM) processes such as laser powder bed fusion (LPBF) are increasingly capable of fabricating components with useful and unprecedented mechanical properties by incorporating complex internal bracing structures. From the standpoint of quality control and assessment, however, internally complex assemblies present significant build-verification challenges. Here we propose a hybrid approach to the inspection involving the application of computer-aided speckle interferometry (CASI) and morphological image processing as a rapid, inexpensive, and facile method for AM quality control. The described methodology has low capital equipment costs, is full-field and non-contact, can be used in an industrial setting, and has very low requirements in terms of operator training and expertise. Consisting primarily of the combination of image processing software with a simple optical system of variable sensitivity, the method is shown to be effective for inspection of a titanium honeycomb component subjected to differential pressure. Results are compared to those achieved with computed tomography (CT), immersion ultrasound testing (UT), and optical holographic interferometry. Here we propose several possible processing strategies for automated quality assessment based on this powerful hybrid approach.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

One System, Many Models: Designing a Surrogate Model for Sulfur Thermal Energy Storage: Preprint

Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.

computational fluid dynamics↗
DOE OSTI2023

How is statistical thinking useful for evaluating foundation models?

Good’s statement appears to be rather prescient – while humanity has marched on through to the 21st century, modern artificial intelligence (AI), driven by a menagerie of immense deep-neural-network architectures aided with parallel computation, seems positioned to change life as we know it. It would be an understatement to say that the world has been captivated by AI. Recent engineering feats, such as autonomous vehicles and generative AI, have gripped the public sphere, and AI is poised to disrupt a host of industries – including software, pharmaceuticals, healthcare, manufacturing, entertainment, and a slew of others; indeed, one is hard pressed to find any industry that does not claim to bear an impact from the so-called AI revolution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se 2 Solar Cells

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first used to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se 2 . Next, continuum reaction–diffusion models are developed to analyze the redistribution of defects during manufacturing processes. We estimate defect capture cross sections using a first-principles-based approach. Here, these cross sections are combined with our calculated defect profiles and trap energy levels to parameterize a Shockley–Read–Hall recombination model, which we implement into a device simulator to predict carrier lifetimes and device performance. In that way, a predictive technology computer aided design model is built to predict and optimize the performance of Cu(In,Ga)Se 2 solar cells.

14 SOLAR ENERGY↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

COMPUTER-AIDED LATTICE DESIGN AND ADVANCED MODELING FOR THE DEVELOPMENT OF LIGHTWEIGHT STRUCTURAL MATERIALS

The notion of a “fission battery” conveys a vision focused on realizing very simple “plug-and-play” nuclear systems that can be integrated into a variety of applications requiring affordable, reliable energy in the form of electricity and/or heat and function without operations and maintenance staff. Fission batteries require lightweight structural materials to increase their mobility, and the lightweight materials must demonstrate structural resilience under various conditions. The objective of this work is to develop lattice structured lightweight structural material featuring a good combination of mechanical properties using advanced modeling and simulation together with an advanced additive manufacturing technique such as laser powder bed fusion. The preliminary results show that different lattice structures and types can be successfully meshed using nTopology software, and the lattice structure data can be successfully transformed to Multiphysics Object-Oriented Simulation (MOOSE) Environment input. Finite Element Analysis (FEA) displays that, at macro/engineering scale simulation, the weight saving design has an obvious effect on tensile behavior such as effective elastic modulus and yield stress. The novel approaches of this work are (1) development of lattice structures for improved mechanical properties using advanced simulation and modeling techniques; and (2) model predictions of the mechanical properties (e.g., strength and stress distribution) of macroscopic materials in order to preliminarily select a lattice structure for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of Process Modeling Tools for Determining Variability in Additively Manufactured Parts

The Advanced Materials and Manufacturing Technologies (AMMT) program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy However, the unique aspects of additive manufacturing (AM) materials in terms of their processing history, microstructure, and properties, are a major barrier for qualification and certification of nuclear components. Much of this challenge may be attributed to component scale variations in microstructure and properties that are driven by local influences of process conditions and geometry on thermal history, melt pool dynamics, and corresponding microstructure evolution. Computational modeling tools may be helpful in this regard to aid in predicting and controlling this level of variability. The purpose of this report is to review the current state-of-the-art for process modeling with regards to metal AM. For this purpose, we consider specifically the case study of laser powder bed fusion (LPBF) processing of SS316, a family of alloys that are both commonly used in nuclear energy applications and suitable for AM processing. The report first introduces the necessary components of a process modeling workflow, followed by a review of the current status of each. At the end, application of these modeling tools to understanding variability in AM process given their current state are considered, and recommendations for future development are proposed

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction

Metal additive manufacturing (AM) offers flexibility and cost-effectiveness for printing complex parts but is limited to few alloys. Qualifying new alloys requires process parameter optimisation to produce consistent, high-quality components. High-resolution X-ray computed tomography (XCT) has not been effective for this task due to artifacts, slow scan speed, and costs. We propose a deep learning-based approach for rapid XCT acquisition and reconstruction of metal AM parts, leveraging computer-aided design models and physics-based simulations of nonlinear interactions between X-ray radiation and metals. This significantly reduces beam hardening and common XCT artifacts. We demonstrate high-throughput characterisation of over a hundred AlCe alloy components, quantifying improvements in characterisation time and quality compared to high-resolution microscopy and pycnometry. Our approach facilitates investigating the impact of process parameters and their geometry dependence in metal AM.

36 MATERIALS SCIENCE↗
DOE OSTI2026

JUSTIFI: Open-Source Software for Identifying and Quantifying Non-Energy Benefits

The integration of Non-Energy Benefits (NEBs) into energy efficiency initiatives is essential for operational excellence in manufacturing. This presentation and software demonstration explore how quantifying NEBs such as improved safety, increased quality, and enhanced productivity, can strengthen business cases for energy investments, leading to better payback periods and alignment with organizational goals. We introduce JUSTIFI, a free, open-source software by the U.S. Department of Energy that aids in the measurement of NEBs and enhances understanding of their impact on Key Performance Indicators (KPIs) and return on investment (ROI). JUSTIFI features an intuitive interface for identifying NEBs, customizable reporting tools, and comprehensive system cataloging, empowering companies to effectively communicate the value of energy efficiency projects. By leveraging this innovative tool, organizations can better navigate energy efficiency assessments and drive support for their energy management initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Surface chemistry models for GaAs epitaxial growth and hydride cracking using reacting flow simulations

Hydride vapor phase epitaxy (HVPE) is a promising technology that can aid in the cost reduction of III-V materials and devices manufacturing, particularly high-efficiency solar cells for space and terrestrial applications. However, recent demonstrations of ultrafast growth rates (~500 µm/h) via uncracked hydrides are not well described by present models for the growth. Therefore, it is necessary to understand the kinetics of the growth process and its coupling with transport phenomena, so as to enable fast and uniform epitaxial growth. In this work, we derive a kinetic model using experimental data and integrate it into a computational fluid dynamics simulation of an HVPE growth reactor. We also modify an existing hydride cracking model that we validate against numerical simulations and experimental data. Here, we show that the developed growth model and the improved cracking model are able to reproduce experimental growth measurements of GaAs in an existing HVPE system.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2023

Densification and microstructure features of lithium hydride fabrication

The manufacturing of lithium hydride (LiH) utilizing uniaxial pressing, which offers fabrication with tailorable properties via microstructure control, can lead to the expansion in application of LiH while bypassing the challenges presented by historical casting manufacturing techniques. Through control of consolidation conditions such as pressure, temperature, dwell time and powder load, the presented work highlights the densification of LiH, with an emphasis on quantifying oxygen content, for applications requiring a specific density range necessary for optimized material performance. Karl Fischer Titration and X-ray Diffraction proved useful in determining oxygen and phase content while Computed Tomography and Scanning Electron Microscopy provided structural analysis. The temperature dependent densification of LiH fit with an Arrhenius term resulted in an activation energy of 21.2 kJ/mol. Images of fractured surfaces of LiH pressed at 500 °C revealed drastic grain coarsening, aided by the presence of oxygen impurities.

36 MATERIALS SCIENCE↗
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↗