Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Engineering Model”

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

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

At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Precision Structural Engineering of Self-Rolled-up 3D Nanomembranes Guided by Transient Quasi-Static FEM Modeling

Micro- and nanoscale tubular structures can be formed by strain-induced self-rolled-up nanomembranes. Precision engineering of the shape and dimension determines the performance of devices based on this platform for electronic, optical, and biological applications. A transient quasi-static finite element method (FEM) with moving boundary conditions is proposed as a general approach to design diverse types of three-dimensional (3D) rolled-up geometries. Furthermore, this method captures the dynamic release process of membranes through etching driven by mismatch strain and accurately predicts the final dimensions of rolled-up structures. Guided by the FEM modeling, experimental demonstration using silicon nitride membranes was achieved with unprecedented precision including controlling fractional turns of a rolled-up membrane, anisotropic rolling to form helical structures, and local stress control for 3D hierarchical architectures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. Additionally, the method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

58 GEOSCIENCES↗
DOE OSTI2016

Coupling the System Analysis Module with SAS4A/SASSYS-1

SAS4A/SASSYS-1 is a simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced reactors, with an emphasis on sodium fast reactors. SAS4A/SASSYS-1 has been under development and in active use for nearly forty-five years, and is currently maintained by the U.S. Department of Energy under the Office of Advanced Reactor Technology. Although SAS4A/SASSYS-1 contains a very capable primary and intermediate system modeling component, PRIMAR-4, it also has some shortcomings: outdated data management and code structure makes extension of the PRIMAR-4 module somewhat difficult. The user input format for PRIMAR-4 also limits the number of volumes and segments that can be used to describe a given system. The System Analysis Module (SAM) is a fairly new code development effort being carried out under the U.S. DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM is being developed with advanced physical models, numerical methods, and software engineering practices; however, it is currently somewhat limited in the system components and phenomena that can be represented. For example, component models for electromagnetic pumps and multi-layer stratified volumes have not yet been developed. Nor is there support for a balance of plant model. Similarly, system-level phenomena such as control-rod driveline expansion and vessel elongation are not represented. This report documents fiscal year 2016 work that was carried out to couple the transient safety analysis capabilities of SAS4A/SASSYS-1 with the system modeling capabilities of SAM under the joint support of the ART and NEAMS programs. The coupling effort was successful and is demonstrated by evaluating an unprotected loss of flow transient for the Advanced Burner Test Reactor (ABTR) design. There are differences between the stand-alone SAS4A/SASSYS-1 simulations and the coupled SAS/SAM simulations, but these are mainly attributed to the limited maturity of the SAM development effort. The severe accident modeling capabilities in SAS4A/SASSYS-1 (sodium boiling, fuel melting and relocation) will continue to play a vital role for a long time. Therefore, the SAS4A/SASSYS-1 modernization effort should remain a high priority task under the ART program to ensure continued participation in domestic and international SFR safety collaborations and design optimizations. On the other hand, SAM provides an advanced system analysis tool, with improved numerical solution schemes, data management, code flexibility, and accuracy. SAM is still in early stages of development and will require continued support from NEAMS to fulfill its potential and to mature into a production tool for advanced reactor safety analysis. The effort to couple SAS4A/SASSYS-1 and SAM is the first step on the integration of these modeling capabilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bayesian calibration of strength model parameters from Taylor impact data

Materials strength plays a key role in determining the mechanical response of engineered structures. As such, accurate strength models are crucial in simulations involving complex loading conditions, particularly when deformation in the plastic regime is deemed important. In this work, a Gaussian process based surrogate for a finite element simulation of a Taylor impact test is developed and used for Bayesian calibration of the Preston–Tonks–Wallace strength model. Here, the surrogate model is shown to closely approximate the salient features of the Taylor cylinder deformation and is validated against simulation output before being used in the strength model calibration routine. The results show that Taylor impact test data can be used in the calibration of constitutive equations through the use of a combination of data science techniques, namely Gaussian processes and Bayesian inference.

36 MATERIALS SCIENCE↗
DOE OSTI2013

The Full Scale Seal Experiment - A Seal Industrial Prototype for Cigeo - 13106

The Full Scale Seal (FSS) Experiment is one of various experiments implemented by Andra, within the frame of the Cigeo (the French Deep Geological Repository) Project development, to demonstrate the technical construction feasibility and performance of seals to be constructed, at time of Repository components (shafts, ramps, drifts, disposal vaults) progressive closure. FSS is built inside a drift model fabricated on surface for the purpose. Prior to the scale 1:1 seal construction test, various design tasks are scheduled. They include the engineering work on the drift model to make it fit with the experimental needs, on the various work sequences anticipated for the swelling clay core emplacement and the concrete containment plugs construction, on the specialized handling tools (and installation equipment) manufactured and delivered for the purpose, and of course on the various swelling clay materials and low pH (below 11) concrete formulations developed for the application. The engineering of the 'seal-as-built' commissioning means (tools and methodology) must also be dealt with. The FSS construction experiment is a technological demonstrator, thus it is not focused on the phenomenological survey (and by consequence, on the performance and behaviour forecast). As such, no hydration (forced or natural) is planned. However, the FSS implementation (in particular via the construction and commissioning activities carried out) is a key milestone in view of comforting phenomenological extrapolation in time and scale. The FSS experiment also allows for qualifying the commissioning methods of a real sealing system in the Repository, as built, at time of industrial operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Toward predictive and computationally affordable Lagrangian–Eulerian modeling of spray–wall interaction

The work presented in this article focuses on improving the understanding of spray–wall interaction phenomena and the predictivity of computational models for modern internal combustion engine–like conditions. Previous work from the authors highlighted some of the limitations of the currently available spray–wall interaction sub-models, especially those based on experimental observations carried out on single droplet impingement of non-hydrocarbon fluids. Previous experimental observations and direct numerical simulations provided unique qualitative and quantitative details that were leveraged to improve Lagrangian–Eulerian simulations of spray–wall interaction. In this work, Yarin and Weiss’ theory for droplet train impingements, as interpreted by Stanton and Rutland, was implemented in the CONVERGE software. A novel and mathematically rigorous calculation of the impingement frequency for Lagrangian parcels was proposed to replace the zero-droplet-spacing assumption present in the original model and improve the prediction of droplet splash. Preliminary testing and validation of the new formulation against experiments carried out in a constant volume vessel showed encouraging results. The feedback loop between experiments, Lagrangian–Eulerian simulations, and direct numerical simulations also motivated the investigation of the surface roughness model available in the Lagrangian–Eulerian framework. This part of the study pointed out the importance of correctly predicting the interaction between liquid and surrounding gas in the near-wall region. Overall, the results shown indicate that correctly capturing the pre-impingement physics and liquid–gas interaction, together with improving splash predictions, was key for improving the accuracy of Lagrangian–Eulerian computational fluid dynamics simulations of spray–wall impingement.

Torelli, Roberto↗
DOE OSTI2024

Temperature effect on turbulent burning velocity of lean premixed hydrogen/air flames

Hydrogen has drawn great attention in recent years as a carbon-free fuel. The turbulent burning velocity (S T ) is an important parameter for the design and modeling of hydrogen-fueled engines given the high propagation speed of hydrogen flames. It has been well documented that S T of hydrogen flames can be dramatically increased by thermo-diffusive effects which are sensitive to thermodynamic conditions. Previous studies have mainly focused on the pressure effect on S T of lean hydrogen flames, while the temperature effect has been largely ignored. Here, in the present study, the turbulent burning velocity for a lean hydrogen/air mixture over a wide range of temperatures (300–641 K) and pressures (1–15 atm) is investigated through direct numerical simulations of statistically planar turbulent premixed flames. Results show that the variation of normalized turbulent burning velocity (S T /S L , where S L is the laminar flame speed) with temperature and pressure is mainly controlled by the variation of the stretching factor I 0 . While S T /S L is only marginally dependent on temperature at the atmospheric pressure, it exhibits a decreasing trend with temperature at an elevated pressure (10 atm). This is associated with different temperature dependencies of flame surface area enlargement at the two different pressures, despite the monotonically decreasing trends of I 0 with temperature at both pressures. In addition, under engine-relevant conditions where the temperature and pressure increase simultaneously, the promotion effect of pressure is found to be largely canceled out by the suppression effect of temperature, leading to only a slight increase in I 0 and S T /S L . The observed trends are further explained through detailed flame dynamic analysis. Furthermore, I 0 at different temperatures and pressures is found to correlate very well with the enhancement of fuel consumption rate in the critically strained laminar flames. The present study elucidates the strong impact of temperature on S T of lean premixed hydrogen/air flames at elevated pressures and provides new insights into the modeling of S T , especially for engine-relevant conditions.

Wang, Yiqing [Argonne National Laboratory (ANL), A↗
DOE OSTI2019

A Modified Johnson–Cook Constitutive Model for the Compressive Flow Behaviors of the SnSbCu Alloy at High Strain Rates

The SnSbCu alloy is widely used as the material for the main bearing in low-speed marine engines, and an accurate constitutive model is the foundation for studying the frictional behaviors of bearings. In this work, the compressive flow behaviors of the SnSbCu alloy were considered under the different strain rates (1000-5000 s{sup −1}) and temperatures (20-110 °C) by quasi-static and split-Hopkinson pressure bar dynamic compression tests. First, the original Johnson–Cook model was used to describe the constitutive relation of the SnSbCu alloy at high strain rates, and the results predicted by the original model showed relatively large errors compared with the experimental results since the coupled effect of temperature and strain rate was omitted. Then, a modified Johnson–Cook constitutive model was developed to describe the compressive flow behaviors of the SnSbCu alloy, and the results predicted by this modified model agreed well with the experimental data. Moreover, a finite element analysis was also conducted to verify the accuracy of the modified Johnson–Cook model.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Numerical modeling of a 2K J-T heat exchanger used in Fermilab Vertical Test Stand VTS-1

Fermilab Vertical Test Stand-1 (VTS-1) is in operation since 2007 for testing the superconducting RF cavities at 2 K. This test stand has single layer coiled finned tubes heat exchanger before J-T valve. A finite difference based thermal model has been developed in Engineering Equation Solver (EES) to study its thermal performance during filling and refilling to maintain the constant liquid level of test stand. The model is also useful to predict its performance under other various operating conditions and will be useful to design the similar kind of heat exchanger for future needs. Present paper discusses the different operational modes of this heat exchanger and its thermal characteristics under these operational modes. Results of this model have also been compared with the experimental data gathered from the VTS-1 heat exchanger and they are in good agreement with the present model.

2 Kelvin J-T Heat Exchanger↗
DOE OSTI2013

DOE Chair of Excellence Professorship in Environmental Disciplines

The United States (US) nuclear weapons program during the Cold War left a legacy of radioactive, hazardous, chemical wastes and facilities that may seriously harm the environment and people even today. Widespread public concern about the environmental pollution has created an extraordinary demand for the treatment and disposal of wastes in a manner to protect the public health and safety. The pollution abatement and environmental protection require an understanding of technical, regulatory, economic, permitting, institutional, and public policy issues. Scientists and engineers have a major role in this national effort to clean our environment, especially in developing alternative solutions and evaluation criteria and designing the necessary facilities to implement the solutions. The objective of the DOE Chair of Excellence project is to develop a high quality educational and research program in environmental engineering at North Carolina A&T State University (A&T). This project aims to increase the number of graduate and undergraduate students trained in environmental areas while developing a faculty concentrated in environmental education and research. Although A&T had a well developed environmental program prior to the Massie Chair grant, A&T's goal is to become a model of excellence in environmental engineering through the program's support. The program will provide a catalyst to enhance collaboration of faculty and students among various engineering departments to work together in a focus research area. The collaboration will be expanded to other programs at A&T. The past research focus areas include: hazardous and radioactive waste treatment and disposal fate and transport of hazardous chemicals in the environment innovative technologies for hazardous waste site remediation pollution prevention Starting from 2005, the new research focus was in the improvement of accuracy for radioactive contaminant transport models by ensemble based data assimilation. The specific objectives are to: 1). improve model accuracy for use in minimizing health and environmental risk, and 2). improve the decision making process in the selection and application of available technologies for long-term monitoring and safeguard operation at NNSA sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Initial Verification and Validation Assessment for VERA

The Virtual Environment for Reactor Applications (VERA) code suite is assessed in terms of capability and credibility against the Consortium for Advanced Simulation of Light Water Reactors (CASL) Verification and Validation Plan (presented herein) in the context of three selected challenge problems: CRUD-Induced Power Shift (CIPS), Departure from Nucleate Boiling (DNB), and Pellet-Clad Interaction (PCI). Capability refers to evidence of required functionality for capturing phenomena of interest while capability refers to the evidence that provides confidence in the calculated results. For this assessment, each challenge problem defines a set of phenomenological requirements against which the VERA software is assessed. This approach, in turn, enables the focused assessment of only those capabilities relevant to the challenge problem. The evaluation of VERA against the challenge problem requirements represents a capability assessment. The mechanism for assessment is the Sandia-developed Predictive Capability Maturity Model (PCMM) that, for this assessment, evaluates VERA on 8 major criteria: (1) Representation and Geometric Fidelity, (2) Physics and Material Model Fidelity, (3) Software Quality Assurance and Engineering, (4) Code Verification, (5) Solution Verification, (6) Separate Effects Model Validation, (7) Integral Effects Model Validation, and (8) Uncertainty Quantification. For each attribute, a maturity score from zero to three is assigned in the context of each challenge problem. The evaluation of these eight elements constitutes the credibility assessment for VERA.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Updated Verification and Validation Assessment for VERA

The Virtual Environment for Reactor Applications (VERA) code suite is assessed in terms of capability and credibility against the Consortium for Advanced Simulation of Light Water Reactors (CASL) Verification and Validation Plan (presented herein) in the context of three selected challenge problems: CRUD-Induced Power Shift (CIPS), Departure from Nucleate Boiling (DNB), and Pellet-Clad Interaction (PCI). Capability refers to evidence of required functionality for capturing phenomena of interest while credibility refers to the evidence that provides confidence in the calculated results. For this assessment, each challenge problem defines a set of phenomenological requirements against which the VERA software is assessed. This approach, in turn, enables the focused assessment of only those capabilities relevant to the challenge problem. The evaluation of VERA against the challenge problem requirements represents a capability assessment. The mechanism for assessment is the Sandia-developed Predictive Capability Maturity Model (PCMM) that, for this assessment, evaluates VERA on 8 major criteria: (1) Representation and Geometric Fidelity, (2) Physics and Material Model Fidelity, (3) Software Quality Assurance and Engineering, (4) Code Verification, (5) Solution Verification, (6) Separate Effects Model Validation, (7) Integral Effects Model Validation, and (8) Uncertainty Quantification. For each attribute, a maturity score from zero to three is assigned in the context of each challenge problem. The evaluation of these eight elements constitutes the credibility assessment for VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Rapid Design and Engineering of Smart and Secure Microbiological Systems (Final Report)

The design and application of successfully engineered biosystems requires an understanding of how engineered microbes will interact with other organisms – either as one-on-one competitors or in the context of microbial consortia. Engineering microorganisms from first principles for non-laboratory, environmental applications is inherently challenging because: (1) engineered systems tend to quickly revert back to their wild-type behaviors; and (2) these systems typically pay a price in reduced fitness, making them uncompetitive against invasive contaminating species (i.e., metabolic burden). For this project, we used a synthetic biology-based strategy to investigate the organization, control, stabilization, and destabilization of natural and engineered microbes. This approach enabled development of (1) single-strain systems capable of detecting and responding to target organisms in the environment; (2) a pipeline for refining and engineering biological constructs in new, non-model host organisms; and (3) improved systems for rapidly designing, engineering, and assaying new biological modules. This coupled approach to safeguard system design is predictable and portable across bacterial species and is focused on microbes that are part of the beneficial plant microbiome. A long-term goal beyond the proposed research is to enable the rational engineering of microbial communities based on first principles of biological design that mimic the smart performance of microorganisms observed in natural systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Updated Verification and Validation Assessment for VERA

The Virtual Environment for Reactor Applications (VERA) code suite is assessed in terms of capability and credibility against the Consortium for Advanced Simulation of Light Water Reactors (CASL) Verification and Validation Plan (presented herein) in the context of three selected challenge problems: CRUD-Induced Power Shift (CIPS), Departure from Nucleate Boiling (DNB), and Pellet-Clad Interaction (PCI). Capability refers to evidence of required functionality for capturing phenomena of interest while credibility refers to the evidence that provides confidence in the calculated results. For this assessment, each challenge problem defines a set of phenomenological requirements against which the VERA software is assessed. This approach, in turn, enables the focused assessment of only those capabilities relevant to the challenge problem. The evaluation of VERA against the challenge problem requirements represents a capability assessment. The mechanism for assessment is the Sandia-developed Predictive Capability Maturity Model (PCMM) that, for this assessment, evaluates VERA on 8 major criteria: (1) Representation and Geometric Fidelity, (2) Physics and Material Model Fidelity, (3) Software Quality Assurance and Engineering, (4) Code Verification, (5) Solution Verification, (6) Separate Effects Model Validation, (7) Integral Effects Model Validation, and (8) Uncertainty Quantification. For each attribute, a maturity score from zero to three is assigned in the context of each challenge problem. The evaluation of these eight elements constitutes the credibility assessment for VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Improving Solar Cell Performance Using Quantum Dot Triad Charge-Separation Engines

Here, we use kinetic modeling to explore the current–voltage, power–voltage, and power conversion efficiency characteristics of quantum dot dyads and triads as possible light absorption and charge separation engines in quantum dot, bulk heterojunction solar cells. The external and internal power conversion quantum efficiencies are significantly enhanced by introducing a third quantum dot between the donor and acceptor quantum dots. Given the constraint of comparable charge-recombination and charge-separation rates, open-circuit voltages for triads are predicted to be about 10%–17% larger than those for dyads, and short-circuit currents for triads are about 400% larger than those for dyads. These improvements in the efficiencies can be further enhanced by tuning the band-edge energy offset of the middle-position quantum dot from its neighbors. The band-edge energies of the middle quantum dot should be tuned so that they form a cascading band-edge energy alignment from the band-edge energies of the left CdTe QD to the right CdSe QD. To produce the most favorable solar cell performance, the middle quantum dot’s conduction (valence) band edge should be closer to the right quantum dot’s band edge when the charge recombination rates are low (high) and near the conduction (valence) band edge of the left quantum dot when the charge recombination rates are high (low). This analysis identifies prime strategies to design multi-QD assemblies for solar energy harvesting and conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗