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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Observations of modal coupling due to bolted joints in an experimental benchmark structure

The goal of this paper is to present a set of measurements from a benchmark structure containing two bolted joints to support future efforts to predict the damping due to the joints and to model nonlinear coupling between the first two elastic modes. Bolted joints introduce nonlinearities in structures, typically causing a softening in the natural frequency and an increase in damping because of frictional slip between the contact interfaces within the joint. These nonlinearities pose significant challenges when characterizing the response of the structure under a large range of load amplitudes, especially when the modal responses become coupled, causing the effective damping and natural frequency to not only depend on the excitation amplitude of the targeted mode, but also the relative amplitudes of other modes. In this work, two nominally identical benchmark structures, known in some prior works as the S4 beam, are tested to characterize their nonlinear properties for the first two elastic modes. Detailed surface measurements are presented and validated through finite element analysis and reveal distinct contact interactions between the two sets of beams. The free-free test structures are excited with an impact hammer and the transient response is analyzed to extract the damping and frequency backbone curves. A range of impact amplitudes and drive points are used to isolate a single mode or to excite both modes simultaneously. Differences in the nonlinear response correlate with the relative strength of the modes that are excited, allowing one to characterize mode coupling. Each of the beams shows different nonlinear properties for each mode, which is attributed to the different contact pressure distributions between the parts, although the mode coupling relationship is found to be consistent between the two. Lastly, the test data key finding are presented in this paper and the supporting data is available on a public repository for interested researchers.

42 ENGINEERING↗
NASA NTRS1991

Multi-disciplinary optimization of aeroservoelastic systems

New methods were developed for efficient aeroservoelastic analysis and optimization. The main target was to develop a method for investigating large structural variations using a single set of modal coordinates. This task was accomplished by basing the structural modal coordinates on normal modes calculated with a set of fictitious masses loading the locations of anticipated structural changes. The following subject areas are covered: (1) modal coordinates for aeroelastic analysis with large local structural variations; and (2) time simulation of flutter with large stiffness changes.

Karpel, Mordechay↗
NASA NTRS1998

Evaluation of Landscape Structure Using AVIRIS Quicklooks and Ancillary Data

Currently the best tool for examining landscape structure is remote sensing, because remotely sensed data provide complete and repeatable coverage over landscapes in many climatic regimes. Many sensors, with a variety of spatial scales and temporal repeat cycles, are available. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has imaged over 4000 scenes from over 100 different sites throughout North America. For each of these scenes, one-band "quicklook" images have been produced for review by AVIRIS investigators. These quicklooks are free, publicly available over the Internet, and provide the most complete set of landscape structure data yet produced. This paper describes the methodologies used to evaluate the landscape structure of quicklooks and generate corresponding datasets for climate, topography and land use. A brief discussion of preliminary results is included at the end. Since quicklooks correspond exactly to their parent AVIRIS scenes, the methods used to derive climate, topography and land use data should be applicable to any AVIRIS analysis.

Sanderson, Eric W.↗
DOE OSTI2022

Efficient and universal characterization of atomic structures through a topological graph order parameter

Abstract A graph-based order parameter, based on the topology of the graph itself, is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system and is transferable to all structural geometries. Four sets of data are used to validate both the generalizability and accuracy of the algorithm: (1) liquid lithium configurations spanning up to 300 GPa, (2) condensed phases of carbon along with nanotubes and buckyballs at ambient and high temperature, (3) a diverse set of aluminum configurations including surfaces, compressed and expanded lattices, point defects, grain boundaries, liquids, nanoparticles, all at nonzero temperatures, and (4) eleven niobium oxide crystal phases generated with ab initio molecular dynamics. We compare our proposed method to existing, state-of-the-art methods for the cases of aluminum and niobium oxide. Our order parameter uniquely classifies every configuration and outperforms all studied existing methods, opening the door for its use in a multitude of complex application spaces that can require fine structure-level characterization of atomistic graphs.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Computer-assisted knowledge acquisition for hypermedia systems

The usage of procedural and declarative knowledge to set up the structure or 'web' of a hypermedia environment is described. An automated knowledge acquisition tool was developed that helps a knowledge engineer elicit and represent an expert's knowledge involved in performing procedural tasks. The tool represents both procedural and prerequisite, declarative knowledge that supports each activity performed by the expert. This knowledge is output and subsequently read by a hypertext scripting language to generate the link between blank, but labeled cards. Each step of the expert's activity and each piece of supporting declarative knowledge is set up as an empty node. An instructional developer can then enter detailed instructional material concerning each step and declarative knowledge into these empty nodes. Other research is also described that facilitates the translation of knowledge from one form into a form more readily useable by computerized systems.

Steuck, Kurt↗
NASA NTRS1991

The three-dimensional evolution of a plane mixing layer. Part 1: The Kelvin-Helmholtz roll-up

The Kelvin Helmholtz roll up of three dimensional, temporally evolving, plane mixing layers were simulated numerically. All simulations were begun from a few low wavenumber disturbances, usually derived from linear stability theory, in addition to the mean velocity profile. The spanwise disturbance wavelength was taken to be less than or equal to the streamwise wavelength associated with the Kelvin Helmholtz roll up. A standard set of clean structures develop in most of the simulations. The spanwise vorticity rolls up into a corrugated spanwise roller, with vortex stretching creating strong spanwise vorticity in a cup shaped region at the vends of the roller. Predominantly streamwise rib vortices develop in the braid region between the rollers. For sufficiently strong initial three dimensional disturbances, these ribs collapse into compact axisymmetric vortices. The rib vortex lines connect to neighboring ribs and are kinked in the opposite direction of the roller vortex lines. Because of this, these two sets of vortex lines remain distinct. For certain initial conditions, persistent ribs do not develop. In such cases the development of significant three dimensionality is delayed. When the initial three dimensional disturbance energy is about equal to, or less than, the two dimensional fundamental disturbance energy, the evolution of the three dimensional disturbance is nearly linear (with respect to the mean and the two dimensional disturbances), at least until the first Kelvin Helmholtz roll up is completed.

Rogers, Michael M.↗
NASA NTRS2010

Mud Volcanoes - Analogs to Martian Cones and Domes (by the Thousands!)

Mud volcanoes are mounds formed by low temperature slurries of gas, liquid, sediments and rock that erupt to the surface from depths of meters to kilometers. They are common on Earth, with estimates of thousands onshore and tens of thousands offshore. Mud volcanoes occur in basins with rapidly-deposited accumulations of fine-grained sediments. Such settings are ideal for concentration and preservation of organic materials, and mud volcanoes typically occur in sedimentary basins that are rich in organic biosignatures. Domes and cones, cited as possible mud volcanoes by previous authors, are common on the northern plains of Mars. Our analysis of selected regions in southern Acidalia Planitia has revealed over 18,000 such features, and we estimate that more than 40,000 occur across the area. These domes and cones strongly resemble terrestrial mud volcanoes in size, shape, morphology, associated flow structures and geologic setting. Geologic and mineralogic arguments rule out alternative formation mechanisms involving lava, ice and impacts. We are studying terrestrial mud volcanoes from onshore and submarine locations. The largest concentration of onshore features is in Azerbaijan, near the western edge of the Caspian Sea. These features are typically hundreds of meters to several kilometers in diameter, and tens to hundreds of meters in height. Satellite images show spatial densities of 20 to 40 eruptive centers per 1000 square km. Many of the features remain active, and fresh mud flows as long as several kilometers are common. A large field of submarine mud volcanoes is located in the Gulf of Cadiz, off the Atlantic coasts of Morocco and Spain. High-resolution sonar bathymetry reveals numerous km-scale mud volcanoes, hundreds of meters in height. Seismic profiles demonstrate that the mud erupts from depths of several hundred meters. These submarine mud volcanoes are the closest morphologic analogs yet found to the features in Acidalia Planitia. We are also conducting laboratory analyses of surface samples collected from mud volcanoes in Azerbaijan, Taiwan and Japan. X-ray diffraction, visible / near infrared reflectance spectroscopy and Raman spectroscopy show that the samples are dominated by mixed-layer smectite clays, along with quartz, calcite and pyrite. Thin section analysis by optical and scanning electron microscopy confirms the mineral identifications. These samples also contain chemical and morphological biosignatures, including common microfossils, with evidence of partial replacement by pyrite. The bulk samples contain approximately 1 wt% total organic carbon and 0.4 mg / gm volatile hydrocarbons. The thousands of features in Acidalia Planitia cited as analogous to terrestrial mud volcanoes clearly represent an important element in the sedimentary record of Mars. Their location, in the distal depocenter for massive Hesperian-age floods, suggests that they contain fine-grained sediments from a large catchment area in the martian highlands. We have proposed these features as a new class of exploration target that can provide access to minimally-altered material from significant depth. By analogy to terrestrial mud volcanoes, these features may also be excellent sites for the sampling martian organics and subsurface microbial life, if such exist or ever existed.

Allen, Carlton C.↗
DOE OSTI2022

AbacusSummit Cleaned Halo Catalogs

This is a data release from the AbacusSummit suite of cosmological N-body simulations. It contains dark matter halo catalogs and particle samples from those halos. The halo catalogs have been augmented (cleaned) using halo merger tree information. Descriptions of AbacusSummit, the cleaned halo catalogs, and instructions for manipulating these catalogs can be found at the following URL: abacussummit.readthedocs.io/ The halo catalog cleaning is structured as a set of auxiliary files that are ingested by the reader code at runtime. But this release contains both the original catalogs and the auxiliary files, thus forming a self-contained dataset. The files in this release are aggregated by redshift. We define sets of simulations (like all c000 sims or all hugebase sims), and then store one redshift from all simulations per tarball. This is designed to support the use-case where a user wants to analyze multiple simulations at a certain redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Symmetry-specific orientational order parameters for complex structures

A comprehensive framework of characterizing complex self-assembled structures with a set of orientational order parameters is presented. It is especially relevant in the context of using anisotropic building blocks with various symmetries. Two classes of tensor order parameters are associated with polyhedral nematic and bond orientational order. For the latter, a variation of classical bond order parameters (BOPs) is introduced, which takes advantage of the symmetry of constituent particles and/or expected crystalline phases. These symmetrized BOPs can be averaged over an entire system or assigned locally to an individual bond. By combining that with the bond percolation procedure, one is able to identify coherent domains within a self-assembled structure. As a demonstration of the proposed framework, we apply it to a simulated hybrid system that combines isotropic and patchy particles with octahedral symmetry. In conclusion, not only does the methodology allow one to identify individual crystalline domains but also it detects coherent clusters of a peculiar compact amorphous structure that is not space-filling and lacks any long-range order.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Thermal and structural analysis of the GOES scan mirror's on orbit performance

The on-orbit performance of the GOES satellite's scan mirror has been predicted by means of thermal, structural, and optical models. A simpler-than-conventional thermal model was used to reduce the time required to obtain orbital predictions, and the structural model was used to predict on-earth gravity sag and on-orbit distortions. The transfer of data from the thermal model to the structural model was automated for a given set of thermal nodes and structural grids.

Zurmehly, G. E.↗
NASA NTRS2004

Comparing NASA and ESA Cost Estimating Methods for Human Missions to Mars

To compare working methodologies between the cost engineering functions in NASA Marshall Space Flight Center (MSFC) and ESA European Space Research and Technology Centre (ESTEC), as well as to set-up cost engineering capabilities for future manned Mars projects and other studies which involve similar subsystem technologies in MSFC and ESTEC, a demonstration cost estimate exercise was organized. This exercise was a direct way of enhancing not only cooperation between agencies but also both agencies commitment to credible cost analyses. Cost engineers in MSFC and ESTEC independently prepared life-cycle cost estimates for a reference human Mars project and subsequently compared the results and estimate methods in detail. As a non-sensitive, public domain reference case for human Mars projects, the Mars Direct concept was chosen. In this paper the results of the exercise are shown; the differences and similarities in estimate methodologies, philosophies, and databases between MSFC and ESTEC, as well as the estimate results for the Mars Direct concept. The most significant differences are explained and possible estimate improvements identified. In addition, the Mars Direct plan and the extensive cost breakdown structure jointly set-up by MSFC and ESTEC for this concept are presented. It was found that NASA applied estimate models mainly based on historic Apollo and Space Shuttle cost data, taking into account the changes in technology since then. ESA used models mostly based on European satellite and launcher cost data, taking into account the higher equipment and testing standards for human space flight. Most of NASA's and ESA s estimates for the Mars Direct case are comparable, but there are some important, consistent differences in the estimates for: 1) Large Structures and Thermal Control subsystems; 2) System Level Management, Engineering, Product Assurance and Assembly, Integration and Test/Verification activities; 3) Mission Control; 4) Space Agency Program Level activities.

Hunt, Charles D.↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
DOE OSTI2026

Tunable Nanostructures from Inverse Surfactants

Hierarchical materials in the natural world are often made through the self-assembly of amphiphilic molecules. Achieving similar structural complexity in synthetic materials requires understanding how various molecular parameters affect assembly behavior. In recent years, inverse surfactants─molecules with hydrophobic head groups and hydrophilic macromolecular tails─have been shown to self-assemble into supramolecular assemblies in aqueous solutions that show promise for a number of applications, including drug delivery. Here, we build an understanding of the morphological phase diagram of inverse surfactants using insights from scattering experiments, computer simulations, and statistical mechanics. The scattering and simulation results reveal that changing the headgroup size is an important molecular knob in controlling morphological transitions. The molecular size ratio of the hydrophobic group to the hydrophilic group emerges as a crucial dimensionless quantity in our theory and plays a determining role in setting the micelle structure and the transition from mesoscale to macroscale aggregates. Our minimal theory is able to qualitatively explain the key features of the morphological phase diagram, including the prevalence of fiber-like structures in comparison to spherical and planar micelles. Together, these findings provide a more complete picture of the molecular dependencies of assemblies of inverse surfactants, which we hope may aid in the de novo design of supramolecular structures.

Christakopoulos, Panagiotis [ORNL] (ORCID:00090004↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Subduction, Underplating, and Return Flow Recorded in the Cycladic Blueschist Unit Exposed on Syros, Greece

Exhumed high-pressure/low-temperature (HP/LT) metamorphic rocks provide insights into deep (~20–70 km) subduction interface dynamics. On Syros Island (Cyclades, Greece), the Cycladic Blueschist Unit preserves blueschist-to-eclogite facies oceanic- and continental-affinity rocks that record the structural and thermal evolution linked to Eocene subduction. Despite decades of research, the metamorphic and deformation history (P-T-D) and timing of subduction and exhumation are matters of ongoing discussion. We suggest that Syros comprises three coherent tectonic slices and that each slice underwent subduction, underplating, and syn-subduction return flow along similar P-T trajectories, but at progressively younger times. Subduction and exhumation are distinguished by lineations and ductile fold axis orientations, and are kinematically consistent with previous studies that document top-to-the-S-SW shear (prograde-to-peak subduction), top-to-the-NE shear (blueschist facies exhumation), and then E-W coaxial stretching (greenschist facies exhumation). Amphibole zonations record cooling during decompression, indicating return flow above a cold slab. Multi-mineral Rb-Sr isochrons and compiled metamorphic geochronology show that the three slices record distinct stages of peak subduction (53–52, ~50, and 45 Ma) that young with structural depth. Retrograde blueschist and greenschist facies fabrics span ~50–40 and ~43–20 Ma, respectively, and also young with structural depth. Synthesized data sets support a revised tectonic framework for Syros, involving subduction of structurally distinct coherent slices and simultaneous return flow of previously accreted tectonic slices in the subduction channel shear zone. Distributed, ductile, dominantly coaxial return flow in an Eocene-Oligocene subduction channel proceeded at rates of ~1.5–5 mm/yr and accommodated ~80% of the total exhumation of this HP/LT complex.

58 GEOSCIENCES↗
DOE OSTI2023

Neutral gas pressure dependence of ion–ion mutual neutralization rate constants using Landau–Zener theory coupled with trajectory simulations

In this computational study, we describe a self-consistent trajectory simulation approach to capture the effect of neutral gas pressure on ion–ion mutual neutralization (MN) reactions. The electron transfer probability estimated using Landau–Zener (LZ) transition state theory is incorporated into classical trajectory simulations to elicit predictions of MN cross sections in vacuum and rate constants at finite neutral gas pressures. Electronic structure calculations with multireference configuration interaction and large correlation consistent basis sets are used to derive inputs to the LZ theory. The key advance of our trajectory simulation approach is the inclusion of the effect of ion-neutral interactions on MN using a Langevin representation of the effect of background gas on ion transport. For H + – H – and Li + – H(D) – , our approach quantitatively agrees with measured speed-dependent cross sections for up to ~10 5 m/s. For the ion pair Ne + – Cl – , our predictions of the MN rate constant at ~1 Torr are a factor of ~2 to 3 higher than the experimentally measured value. Similarly, for Xe + – F – in the pressure range of ~20 000–80 000 Pa, our predictions of the MN rate constant are ~20% lower but are in excellent qualitative agreement with experimental data. Here, the paradigm of using trajectory simulations to self-consistently capture the effect of gas pressure on MN reactions advanced here provides avenues for the inclusion of additional nonclassical effects in future work.

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

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

Structures technology applications for the National AeroSpace Plane

The National AeroSpace Plane (NASP) presents a unique set of very complex structural problems that challenge our computational capabilities. Complex analyses are required in the conceptual design phase to achieve sufficient accuracy to address the extreme load conditions and to adequately evaluate vehicle weight. The computational capability must be available to perform these analyses in a rapid manner to accommodate the design process.

Little, T. E.↗