Results for “Rapid Process Design” — Page 8

NASA NTRS1992

Application of integrated structural analysis to the high speed civil transport

The Integrated Structural Analysis (ISA) system was developed in response to the need to define lightweight structures early in the design process for military aircraft. The objective of the ISA system is to permit rapid development of an optimized skin and big-bones structure that insures that the requirements of strength, dynamics, loads, and weight are met. While the system was originally developed for fighter aircraft, it is applicable to commercial transports or any other structures. The design requirements dictate the structural arrangement and sizing limitations to be imposed.

Saff, C. R.↗

NASA NTRS2011

Incorporating Ameriflux Data into LVT

This paper describes a new generic data reader that was developed in Fortran to handle the Ameriflux data for the LIS Verification Toolkit (LVT). Researchers at the Hydrological Sciences Branch of NASA Goddard Space Flight Center have created a high resolution land surface modeling and data assimilation system known as the Land Information System (LIS), which provides an infrastructure to integrate state-of-the-art land surface models, data assimilation algorithms, observations of land surface from satellite and remotely sensed platforms to provide estimates of land surface conditions such as soil moisture, evaporation, snowpack and runoff. These model predictions are typically evaluated by comparing them with data from observational networks. The observational data; however, are usually available in disparate data formats and require significant effort to process them into a structure amenable for use with the model data. The motivation to develop a uniform approach for land surface verification as a way to alleviate these processing efforts has led to the development of LVT which is designed to enable the rapid evaluation of land surface modeling and analysis products from LIS. LVT focuses on the use of observational datasets in their native format. As the formats of these datasets vary widely, a major part of LVT is creating programs to read and process the native datasets. The primary goal of this project is to enhance LVT capabilities by incorporating observational datasets from Ameriflux

Georgiev, Teodor↗

NASA NTRS2011

Integration of Off-Track Sonic Boom Analysis in Conceptual Design of Supersonic Aircraft

A highly desired capability for the conceptual design of aircraft is the ability to rapidly and accurately evaluate new concepts to avoid adverse trade decisions that may hinder the development process in the later stages of design. Evaluating the robustness of new low-boom concepts is important for the conceptual design of supersonic aircraft. Here, robustness means that the aircraft configuration has a low-boom ground signature at both under- and off-track locations. An integrated process for off-track boom analysis is developed to facilitate the design of robust low-boom supersonic aircraft. The integrated off-track analysis can also be used to study the sonic boom impact and to plan future flight trajectories where flight conditions and ground elevation might have a significant effect on ground signatures. The key enabler for off-track sonic boom analysis is accurate computational fluid dynamics (CFD) solutions for off-body pressure distributions. To ensure the numerical accuracy of the off-body pressure distributions, a mesh study is performed with Cart3D to determine the mesh requirements for off- body CFD analysis and comparisons are made between the Cart3D and USM3D results. The variations in ground signatures that result from changes in the initial location of the near-field waveform are also examined. Finally, a complete under- and off-track sonic boom analysis is presented for two distinct supersonic concepts to demonstrate the capability of the integrated analysis process.

Ordaz, Irian↗

DOE OSTI2012

Survey and evaluate Uncertainty Quantification Methodologies

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2012

LLNL ARRA Report on CCSI Uncertainty Quantification Capabilities with Applications on MEA Process Models

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Annual Report: Carbon Capture Simulation Initiative

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that is developing and deploying state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models, with uncertainty quantification (UQ), optimization, risk analysis and decision making capabilities. The CCSI Toolset incorporates commercial and open-source software currently in use by industry and is also developing new software tools as necessary to fill technology gaps identified during execution of the project. Ultimately, the CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that is developing and deploying state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models, with uncertainty quantification (UQ), optimization, risk analysis and decision making capabilities. The CCSI Toolset incorporates commercial and open-source software currently in use by industry and is also developing new software tools as necessary to fill technology gaps identified during execution of the project. Ultimately, the CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. CCSI is led by the National Energy Technology Laboratory (NETL) and leverages the Department of Energy (DOE) national laboratories’ core strengths in modeling and simulation, bringing together the best capabilities at NETL, Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), and Pacific Northwest National Laboratory (PNNL). The CCSI’s industrial partners provide representation from the power generation industry, equipment manufacturers, technology providers and engineering and construction firms. The CCSI’s academic participants (Carnegie Mellon University, Princeton University, West Virginia University, Boston University and the University of Texas at Austin) bring unparalleled expertise in multiphase flow reactors, combustion, process synthesis and optimization, planning and scheduling, and process control techniques for energy processes. During Fiscal Year (FY) 13, CCSI announced the initial release of its first set of computational tools and models during the October 2012 meeting of its Industry Advisory Board. This initial release led to five companies licensing the CCSI Toolset under a Test and Evaluation Agreement this year. By the end of FY13, the CCSI Technical Team had completed development of an updated suite of computational tools and models. The list below summarizes the new and enhanced toolset components that were released following comprehensive testing during October 2013. 1. FOQUS. Framework for Optimization and Quantification of Uncertainty and Sensitivity. Package includes: FOQUS Graphic User Interface (GUI), simulation-based optimization engine, Turbine Client, and heat integration capabilities. There is also an updated simulation interface and new configuration GUI for connecting Aspen Plus or Aspen Custom Modeler (ACM) simulations to FOQUS and the Turbine Science Gateway. 2. A new MFIX-based Computational Fluid Dynamics (CFD) model to predict particle attrition. 3. A new dynamic reduced model (RM) builder, which generates computationally efficient RMs of the behavior of a dynamic system. 4. A completely re-written version of the algebraic surrogate model builder for optimization (ALAMO). The new version is several orders of magnitude faster than the initial release and eliminates the MATLAB dependency. 5. A new suite of high resolution filtered models for the hydrodynamics associated with horizontal cylindrical objects in a flow path. 6. The new Turbine Science Gateway (Cluster), which supports FOQUS for running multiple simulations for optimization or UQ using a local computer or cluster. 7. A new statistical tool (BSS-ANOVA-UQ) for calibration and validation of CFD models. 8. A new basic data submodel in Aspen Plus format for a representative high viscosity capture solvent, 2-MPZ system. 9. An updated RM tool for CFD (REVEAL) that can create a RM from MFIX. A new lightweight, stand-alone version will be available in late 2013. 10. An updated RM integration tool to convert the RM from REVEAL into a CAPE-OPEN or ACM model for use in a process simulator. 11. An updated suite of unified steady-state and dynamic process models for solid sorbent carbon capture included bubbling fluidized bed and moving bed reactors. 12. An updated and unified set of compressor models including steady-state design point model and dynamic model with surge detection. 13. A new framework for the synthesis and optimization of coal oxycombustion power plants using advanced optimization algorithms. This release focuses on modeling and optimization of a cryogenic air separation unit (ASU). 14. A new technical risk model in spreadsheet format. 15. An updated version of the sorbent kinetic/equilibrium model for parameter estimation for the 1st generation sorbent model. 16. An updated process synthesis superstructure model to determine optimal process configurations utilizing surrogate models from ALAMO for adsorption and regeneration in a solid sorbent process. 17. Validation models for NETL Carbon Capture Unit utilizing sorbent AX. Additional validation models will be available for sorbent 32D in 2014. 18. An updated hollow fiber membrane model and system example for carbon capture. 19. An updated reference power plant model in Thermoflex that includes additional steam extraction and reinjection points to enable heat integration module. 20. An updated financial risk model in spreadsheet format.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2018

Advanced Reaction System Program: Materials for ARS Reaction Chambers (FY18 Milestone, Subtask 3.3)

The objective of this effort is to identify materials and manufacturing technology that will allow the rapid construction and evaluation of computer modeled advanced reactor system designs to process specific carbon feedstock materials. Before full scale reaction systems can be built, prototypes need to be constructed and operated to validate the proposed new system performance against target criteria. The first steps, however, are to understand what type of materials are needed that can withstand the targeted process environments and determine how structures using those materials can be built. A wide range of reaction vessel conditions may exist in the processes being considered that could involve temperatures up to 1200°C, abrasive materials (high velocity coal and coal ash), different carbon feedstock materials (coal, biomass, and process wastes such as petcoke), corrosive and oxidizing/reducing gases, different material phases (solid, liquid, and gas), cyclic temperatures, microwave energy, customized vessel construction material needs (process stability, cost, weight, and compatibility with other materials), along with other known/unknown variables – all of which must be considered in selecting materials and considering manufacturing processes. The current focus of the research is to develop a reaction vessel sidewall structure consisting of a metal outer support and an inner ceramic (or cermet) protective surface. Ceramic coatings are desired because of their good corrosion and wear resistance, and because they are the material of choice to reduce metal shell temperature in high temperature processes. However significant property mismatches between metals and ceramics have been a roadblock to the joining of metals and ceramics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Automated Systems for Solvent Extraction

An automated solvent extraction system for the eventual handling of radioactive materials has been designed and procured for rapid, efficient and safe liquid-liquid extraction processes. This system will be configured to carry out liquid-liquid separations from beginning to end, automating tedious and time-consuming tasks such as organic/aqueous phase prep, pH checks, phase separation, and metal ion analysis. The system features advanced liquid handling capabilities, a robotic arm for precise sample transfer, and sophisticated analytical tools including UV-VIS spectrophotometry for real-time monitoring. Additionally, it incorporates automated capping of vials, vortex mixing, and centrifugation to ensure thorough mixing and phase separation. This report provides an overview of the system and general capabilities as well as initial efforts to develop an automated workflow for liquid-liquid separations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

NASA NTRS1991

The display of tactile information

There are a number of examples of natural tactile displays that can five us some insights about the solid geometry of touch, and recent experimental work on the subject has extended our thinking considerably. The concern of here is, however, more with synthetic or artificial displays for the production of a virtual environment. Features of synthetic displays that have enjoyed some success in one of the following two enterprises are discussed: the study of the spatio-temporal dimensions of stimuli that afford accurate and rapid processing of environmental information, or the use of displays in the design of sensory aids for disabled persons.

Sherrick, Carl E.↗

DOE OSTI2014

Emerging Technologies in the Built Environment: Geographic Information Science (GIS), 3D Printing, and Additive Manufacturing

Abstract 1: Geographic information systems emerged as a computer application in the late 1960s, led in part by projects at ORNL. The concept of a GIS has shifted through time in response to new applications and new technologies, and is now part of a much larger world of geospatial technology. This presentation discusses the relationship of GIS and estimating hourly and seasonal energy consumption profiles in the building sector at spatial scales down to the individual parcel. The method combines annual building energy simulations for city-specific prototypical buildings and commonly available geospatial data in a GIS framework. Abstract 2: This presentation focuses on 3D printing technologies and how they have rapidly evolved over the past couple of years. At a basic level, 3D printing produces physical models quickly and easily from 3D CAD, BIM (Building Information Models), and other digital data. Many AEC firms have adopted 3D printing as part of commercial building design development and project delivery. This presentation includes an overview of 3D printing, discusses its current use in building design, and talks about its future in relation to the HVAC industry. Abstract 3: This presentation discusses additive manufacturing and how it is revolutionizing the design of commercial and residential facilities. Additive manufacturing utilizes a broad range of direct manufacturing technologies, including electron beam melting, ultrasonic, extrusion, and laser metal deposition for rapid prototyping. While there is some overlap with the 3D printing talk, this presentation focuses on the materials aspect of additive manufacturing and also some of the more advanced technologies involved with rapid prototyping. These technologies include design of carbon fiber composites, lightweight metals processing, transient field processing, and more.

New, Joshua Ryan↗

DOE OSTI2015

Using Advanced Modeling to Accelerate the Scale-Up of Carbon Capture Technologies

Carbon capture and storage (CCS) is one of many approaches that are critical for significantly reducing domestic and global CO2 emissions. The U.S. Department of Energy’s Clean Coal Technology Program Plan envisions 2nd generation CO2 capture technologies ready for demonstration-scale testing around 2020 with the goal of enabling commercial deployment by 2025 [1]. Third generation technologies have a similarly aggressive timeline. A major challenge is that the development and scale-up of new technologies in the energy sector historically takes up to 15 years to move from the laboratory to pre-deployment and another 20 to 30 years for widespread industrial scale deployment. In order to help meet the goals of the DOE carbon capture program, the Carbon Capture Simulation Initiative (CCSI) was launched in early 2011 to develop, demonstrate, and deploy advanced computational tools and validated multi-scale models to reduce the time required to develop and scale up new carbon capture technologies. The CCSI Toolset (1) enables promising concepts to be more quickly identified through rapid computational screening of processes and devices, (2) reduces the time to design and troubleshoot new devices and processes by using optimization techniques to focus development on the best overall process conditions and by using detailed device-scale models to better understand and improve the internal behavior of complex equipment, and (3) provides quantitative predictions of device and process performance during scale up based on rigorously validated smaller scale simulations that take into account model and parameter uncertainty[2]. This article focuses on essential elements related to the development and validation of multi-scale models in order to help minimize risk and maximize learning as new technologies progress from pilot to demonstration scale.

Miller, David↗

NASA NTRS2006

Solid Modeling of Crew Exploration Vehicle Structure Concepts for Mass Optimization

Parametric solid and surface models of the crew exploration vehicle (CEV) command module (CM) structure concepts are developed for rapid finite element analyses, structural sizing and estimation of optimal structural mass. The effects of the structural configuration and critical design parameters on the stress distribution are visualized, examined to arrive at an efficient design. The CM structural components consisted of the outer heat shield, inner pressurized crew cabin, ring bulkhead and spars. For this study only the internal cabin pressure load case is considered. Component stress, deflection, margins of safety and mass are used as design goodness criteria. The design scenario is explored by changing the component thickness parameters and materials until an acceptable design is achieved. Aluminum alloy, titanium alloy and an advanced composite material properties are considered for the stress analysis and the results are compared as a part of lessons learned and to build up a structural component sizing knowledge base for the future CEV technology support. This independent structural analysis and the design scenario based optimization process may also facilitate better CM structural definition and rapid prototyping.

Mukhopadhyay, Vivek↗

NASA NTRSDate not supplied

Integration of Design Data Into Automated Fiber Placement Process Planning Metrics

With the ever-expanding aviation industry, a need is arising for more rapid production of composite aircraft to meet increasing demand. State-of-the-art aircraft such as the Boeing 787showcase 50% composite material usage by weight, highlighting this emerging industry-wide adoption of the material system. Currently, many of the large structures associated with these aircraft are manufactured additively via Automated Fiber Placement (AFP). The AFP process shows great potential for efficient manufacturing, however unavoidable defects still occur because of tool surface geometry, placement errors, or poor process planning, resulting in decreased quality and throughput. Due to such effects, it is critical to incorporate design for manufacturing (DFM) principles to achieve the optimal manufacturing plan and resulting structure. This work will develop a methodology for incorporating design information into process planning metrics in an automated fashion to achieve an optimal set of process inputs.The analysis incorporates HyperX, Computer Aided Process Planning (CAPP) and Vericut Composite Programming (VCP). Safety margins from HyperX are imported into CAPP where AFP defects are mapped to the values. The resulting margins are then incorporated into the CAPP manufacturability algorithms, creating a design informed process planning analysis

Automated Fiber Placement↗

DOE OSTI2022

Network Uncertainty Quantification for Analysis of Multi-Component Systems

To impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.

42 ENGINEERING↗

DOE OSTI2018

Feedstock powder processing research needs for additive manufacturing development

Additive manufacturing (AM) promises to redesign traditional manufacturing by enabling the ultimate in agility for rapid component design changes in commercial products and for fabricating complex integrated parts. Here, by significantly increasing quality and yield of metallic alloy powders, the pace for design, development, and deployment of the most promising AM approaches can be greatly accelerated, resulting in rapid commercialization of these advanced manufacturing methods. By successful completion of a critical suite of processing research tasks that are intended to greatly enhance gas atomized powder quality and the precision and efficiency of powder production, researchers can help promote continued rapid growth of AM. Finally, other powder-based or spray-based advanced manufacturing methods could also benefit from these research outcomes, promoting the next wave of sustainable manufacturing technologies for conventional and advanced materials.

36 MATERIALS SCIENCE↗

NASA NTRS1990

Synthesis: Intertwining product and process

Synthesis is a proposed systematic process for rapidly creating different members of a program family. Family members are described by variations in their requirements. Requirements variations are mapped to variations on a standard design to generate production quality code and documentation. The approach is made feasible by using principles underlying design for change. Synthesis incorporates ideas from rapid prototyping, application generators, and domain analysis. The goals of Synthesis and the Synthesis process are discussed. The technology needed and the feasibility of the approach are also briefly discussed. The status of current efforts to implement Synthesis methodologies is presented.

Weiss, David M.↗

DOE OSTI2026

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin [Fermilab] (ORCID:0000000157000288↗