Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model based development”

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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Development of a Prototype Model-Form Uncertainty Knowledge Base

Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.

Green, Lawrence L.↗
NASA NTRS2018

Exploring Li-Air Batteries for High Specific Energy and High Power Applications: A Simulation Study

Commercialization of lithium-air batteries face many challenges, such as electrolyte decomposition, short cycle life, low energy efficiency, low power density, etc. However, commercialization of Li-air batteries for mass sensitive applications such as electric vehicles, portable power source, and drones is more challenging due to additional constraints of safety, electrolyte evaporation, high specific energy requirements, and reliable discharge times. In this presentation, we will present our finite element simulation results comparing Li-O2 and Li-air batteries using power density, energy density, specific power, and discharge times as metrics to evaluate different electrolytes and electrode geometry to reduce total mass and maximize discharge current. We use a finite element model and a discharge product model developed in which is based on porous electrode and concentrated electrolyte theories and the discharge product is modeled using quantum tunneling; for reaction kinetics and oxygen diffusion, an improved model was used. The electrolyte properties such as ion conductivity and ion diffusion were obtained from Molecular Dynamics (MD) simulations while the other parameters for the finite element model were calibrated to match experiments at high discharge current densities (>1.5 mA/cm2). The mass densities of different electrolytes were computed using MD simulations as well. For this presentation, we examine the practical electrochemical mass of a system at different current ratings, the sensitivity of mass to the use of ambient air as compared to pure oxygen as well as the electrolyte, which affects maximum current density and the total mass associated with the electrolyte (which includes the mass of additional components), and, the optimization of battery geometry for total discharge time, average discharge voltage, maximum discharge current, and minimum electrochemical mass.

Mehta, M.↗
DOE OSTI2022

The HUNTER Dynamic Human Reliability Analysis Tool: Development of a Module for Performance Shaping Factors

Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) in communication with a variety of methods and tools. In this paper, how we have developed one of the HUNTER modules, the individual module for evaluating performance shaping factors (PSFs), is introduced. The PSF refers to any factor that influences human performance such as workload or complexity. It has been used for highlighting human errors and adjusting the error probabilities in the existing HRA. We consider the eight PSFs suggested in the Standardized Plant Analysis Risk-HRA (SPAR-H) method, which is the representative HRA method widely used in the nuclear field. To support our dynamic modeling using the eight SPAR-H PSFs, we reviewed human performance literature and developed data-based mathematical models to rate and quantify PSFs in the context of dynamic HRA. We also design the individual module composed of the two functions: 1) the PSF qualification function that automatically or manually evaluates a PSF level, and 2) the PSF quantification function that dynamically or statically determines the PSF multiplier values and integrate them to adjust human error probabilities (HEPs). How each function works with the SPAR-H PSFs and how the PSFs adjust the HEPs are investigated through literature and discussed in this paper.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Molecular insights into the complex mechanics of plant epidermal cell walls

Computational analysis of cell walls Layers of intertwined fibers make up plant cell walls. The various types of fibers respond differently to deformation. Cellulose microfibrils, for example, can stretch or curve, changing their end-to-end length, and can also slide past each other, reorient relative directions, and bundle with neighboring microfibrils. Zhang et al. developed a computational model based on observations of onion skin epidermis that describes how these complex changes in space govern cell wall mechanics. The results inform ways to engineer multifunctional fibrous materials. Science , this issue p. 706

Science & Technology - Other Topics↗
DOE OSTI2018

DOE Final Report: A Unified Understanding of Residual Stress in Thin Films: Kinetic Models, Experiments and Simulations

Thin films are critical for a wide range of advanced technologies. However, the deposited films often have high levels of residual stress that can limit their performance or lead to failure. The stress is known to depend on many variables, including the processing conditions, type of material, deposition technique and the film’s microstructure. The goal of this DOE program was to develop a fundamental understanding of how the different processes that control thin film growth under different conditions can be related to the development of stress. In the program, systematic experiments were performed or analyzed that related the stress to the processing conditions that were used. Measurements of stress were obtained for films that were grown at different rates, different solutions (for electrodeposition), different particle energies (for sputter deposition) and different microstructures. Based on this data, models were developed to explain the observed dependence on the different parameters. The models were based on considering the balance among different stress-inducing mechanism occurring as the film grows (for both non-energetic and energetic deposition). Comparison of the model predictions with the experiments enabled the kinetic parameters to be determined for different materials. The resulting model equations provide a comprehensive picture of how stress changes with the processing conditions that can be used to optimize the growth of thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Multiple indicators, multiple causes measurement error models

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Wetland Water Cooling Partnership: The Use of Constructed Wetlands to Enhance Thermoelectric Power Plant Cooling and Mitigate the Demand of Surface Water Use

Through the Phase I study segment of contract #DE-NT0006644 with the U.S. Department of Energy’s National Energy Technology Laboratory, Applied Ecological Services, Inc. and Sterling Energy Services, LLC (the AES/SES Team) explored the use of constructed wetlands to help address stresses on surface water and groundwater resources from thermoelectric power plant cooling and makeup water requirements. The project objectives were crafted to explore and develop implementable water conservation and cooling strategies using constructed wetlands (not existing, naturally occurring wetlands), with the goal of determining if this strategy has the potential to reduce surface water and groundwater withdrawals of thermoelectric power plants throughout the country. Our team’s exploratory work has documented what appears to be a significant and practical potential for augmenting power plant cooling water resources for makeup supply at many, but not all, thermoelectric power plant sites. The intent is to help alleviate stress on existing surface water and groundwater resources through harvesting, storing, polishing and beneficially re-using critical water resources. Through literature review, development of conceptual created wetland plans, and STELLA-based modeling, the AES/SES team has developed heat and water balances for conventional thermoelectric power plants to evaluate wetland size requirements, water use, and comparative cooling technology costs. The ecological literature on organism tolerances to heated waters was used to understand the range of ecological outcomes achievable in created wetlands. This study suggests that wetlands and water harvesting can provide a practical and cost-effective strategy to augment cooling waters for thermoelectric power plants in many geographic settings of the United States, particularly east of the 100th meridian, and in coastal and riverine locations. The study concluded that constructed wetlands can have significant positive ancillary socio-economic, ecosystem, and water treatment/polishing benefits when used to complement water resources at thermoelectric power plants. Through the Phase II pilot study segment of the contract, the project team partnered with Progress Energy Florida (now Duke Energy Florida) to quantify the wetland water cooling benefits at their Hines Energy Complex in Bartow, Florida. The project was designed to test the wetland’s ability to cool and cleanse power plant cooling pond water while providing wildlife habitat and water harvesting benefits. Data collected during the monitoring period was used to calibrate a STELLA model developed for the site. It was also used to inform management recommendations for the demonstration site, and to provide guidance on the use of cooling wetlands for other power plants around the country. As a part of the pilot study, Duke Energy is scaling up the demonstration project to a larger, commercial scale wetland instrumented with monitoring equipment. Construction is expected to be finalized in early 2014.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2013

Multiaxial and Thermomechanical Fatigue of Materials: A Historical Perspective and Some Future Challenges

Structural materials used in engineering applications routinely subjected to repetitive mechanical loads in multiple directions under non-isothermal conditions. Over past few decades, several multiaxial fatigue life estimation models (stress- and strain-based) developed for isothermal conditions. Historically, numerous fatigue life prediction models also developed for thermomechanical fatigue (TMF) life prediction, predominantly for uniaxial mechanical loading conditions. Realistic structural components encounter multiaxial loads and non-isothermal loading conditions, which increase potential for interaction of damage modes. A need exists for mechanical testing and development verification of life prediction models under such conditions.

Kalluri, Sreeramesh↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 kilometers, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-kilometer scales are resolved in horizontal domains as large as 10,000 kilometers in two dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to Using a Cloud-resolving Model to Study the Interactions Between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as l0,OOO km in two-dimensions, and 1,OOO x 1,OOO km2 in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
NASA NTRS2005

A New Approach to using a Cloud-Resolving Model to Study the Interactions between Clouds, Precipitation and Aerosols

Numerical cloud models, which are based the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades. Because cloud-scale dynamics are treated explicitly, uncertainties stemming from convection that have to be parameterized in (hydrostatic) large-scale models are obviated, or at least mitigated, in cloud models. Global models will use the non-hydrostatic framework when their horizontal resolution becomes about 10 km, the theoretical limit for the hydrostatic approximation. This juncture will be reached one to two decades from now. Over the past generation, voluminous datasets on atmospheric convection have been accumulated from radar, instrumented aircraft, satellites, and rawinsonde measurements in field campaigns, enabling the detailed evaluation of models. Improved numerical methods have resulted in more accurate and efficient dynamical cores in models. Improvements have been made in the parameterizations of microphysical processes, radiation, boundary-layer effects, and turbulence; however, microphysical parameterizations remain a major source of uncertainty in all classes of atmospheric models. In recent years, exponentially increasing computer power has extended cloud-resolving-model integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 square kilometers in three-dimensions. Cloud models now provide statistical information useful for developing more realistic physically-based parameterizations for climate models and numerical weather prediction models. A review of developments and applications of cloud models in the past, present and future will be presented in this talk. In particular, a new approach to using cloud-resolving models to study the interactions between clouds, precipitation and aerosols will be presented.

Tao, Wei-Kuo↗
DOE OSTI2017

Modeling cookoff of HMX based PBX explosives

We have previously developed a PBX 9501 cookoff model for the plastic bonded explosive PBX 9501 consisting of 95 wt% octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazoncine (HMX), 2.5 wt% Estane® 5703 (a polyurethane thermoplastic), and 2.5 wt% of a nitroplasticizer (NP): BDNPA/F, a 50/50 wt% eutectic mixture bis(2,2-dinitropropyl)-acetal (BDNPA) and bis(2,2-dinitropropyl)-formal (BDNPF). This fivestep model includes desorption of water, decomposition of the NP to form NO 2 , reaction of the NO 2 with Estane and HMX, and decomposition of HMX [1]. This model has been successfully validated with data from six laboratories with scales ranging from 2 g to more than 2.5 kg of explosive. We have determined, that the PBX 9501 model can be used to predict cookoff of other plastic bonded explosives containing HMX and an inert binder, such as LX-04 consisting of 85 wt% HMX and 15 wt% Viton A (vinylidine fluoride/hexafluoropropylene copolymer), LX-07 (90 wt% HMX and 10 wt% Viton A), LX- 10-0 (95 wt% HMX and 5 wt% Viton A), and LX-14 consisting of 95.5 wt % HMX and 4.5 wt% Estane® 5702-F1 (a polyurethane thermoplastic). Normally our cookoff models are verified using Sandia’s Instrumented Thermal Initiation (SITI) experiment. However, SITI data for LX-04, LX-07, LX-10-0, and LX-14 are not available at pressed density; although, some molding powder SITI data on LX-10-0 and LX-14 exists. Tarver and Tran [2] provide some one-dimensional time-to-explosion (ODTX) data for these explosives. The applicability of the PBX 9501 model to LX-04, LX-07, LX-10-0, AND LX-14 was made using this ODTX data [2]. The PBX 9501 model is applied to these other explosives by accounting for the correct amount of HMX in the explosive and limiting the NP reaction. We have found the PBX 9501 model to be useful for predicting the response of these PBXs to abnormal thermal environments such as fire.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗