Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MODEL SIMULATION DATA”

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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluation of enzymatic depolymerization of PET, PTT, and PBT polyesters

Millions of tons of waste polyester plastics, including polyethylene terephthalate (PET), polytrimethylene terephthalate (PTT), and polybutylene terephthalate (PBT), end up in the environment as soil and water contaminants. Recent advances in enzymatic polyester degradation have motivated researchers toward biorecycling of plastic wastes. Leaf-branch compost cutinase (LCC) enzymes have been proven to be effective in the biodegradation of PET. This study focuses on enzymatic depolymerization of waste PET, PTT, and PBT materials by using an ICCG variant of LCC (LCC ICCG ) produced from Escherichia coli BL21(DE3). The degradation efficiency of the polyesters was determined by the monomer terephthalic acid (TPA) released from the depolymerization reaction. It was found that the most efficient depolymerization was achieved for PET, followed by PTT and PBT. A kinetic model based on Langmuir adsorption and the Michaelis-Menten equation was developed to describe the enzymatic depolymerization of PET, PTT, and PBT with various enzyme and substrate loadings. The model simulation results revealed that the LCCICCG enzyme loading should be linearly increased as the work capacity of the polyester substrate increases. A specific enzyme loading of 0.91 mg/g PET is suggested to achieve 90% depolymerization of PET within three days. Furthermore, the experimental data and model simulation results can be used to help further engineer the enzyme and process to achieve a complete biodegradation of polyester wastes at large scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Inference finds consistency between a neutrino flavor evolution model and Earth-based solar neutrino measurements

We continue examining statistical data assimilation (SDA), an inference methodology, to infer solutions to neutrino flavor evolution, for the first time using real - rather than simulated - data. The model represents neutrinos streaming from the Sun's center and undergoing a Mikheyev-Smirnov-Wolfenstein (MSW) resonance in flavor space, due to the radially-varying electron number density. The model neutrino energies are chosen to correspond to experimental bins in the Sudbury Neutrino Observatory (SNO) and Borexino experiments, which measure electron-flavor survival probability at Earth. In conclusion, the procedure successfully finds consistency between the observed fluxes and the model, if the MSW resonance - that is, flavor evolution due to solar electrons - is included in the dynamical equations representing the model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1979

Effects of simulated forward flight on jet noise, shock noise and internal noise

Inflight simulation experiments are conducted in an anechoic free-jet facility to examine the flight effects on various combinations of jet noise, shock noise and internal noise. The jet mixing noise component reduces with forward velocity at all angles and frequencies. When jet mixing noise is contaminated with internal noise, forward motion provides a noise reduction in the rear arc and a noise increase in the forward arc, with little change at 90 deg. The results are similar for shock-containing jets. It is found that the existing anomalies between full-scale flight data and model-scale flight simulation data could well be due to the contamination of the flight data by internal noise.

Ahuja, K. K.↗
NASA NTRS2016

Role of Surface Wind and Vegetation Cover in Multi-decadal Variations of Dust Emission in the Sahara and Sahel

North Africa, the world's largest dust source, is non-uniform, consisting of a permanently arid region (Sahara), a semi-arid region (Sahel), and a relatively moist vegetated region (Savanna), each with very different rainfall patterns and surface conditions. This study aims to better understand the controlling factors that determine the variation of dust emission in North Africa over a 27-year period from 1982 to 2008, using observational data and model simulations. The results show that the model-derived Saharan dust emission is only correlated with the 10-m winds (W10m) obtained from reanalysis data, but the model-derived Sahel dust emission is correlated with both W10m and the Normalized Difference Vegetation Index (NDVI) that is obtained from satellite. While the Saharan dust accounts for 82 of the continental North Africa dust emission (1340-1570 Tg year(exp -1) in the 27-year average, the Sahel accounts for 17 with a larger seasonal and inter-annual variation (230-380 Tg year(exp -1), contributing about a quarter of the transatlantic dust transported to the northern part of South America. The decreasing dust emission trend over the 27-year period is highly correlated with W10m over the Sahara (R equals 0.92). Over the Sahel, the dust emission is correlated with W10m (R 0.69) but is also anti-correlated with the trend of NDVI (R equals 0.65). W10m is decreasing over both the Sahara and the Sahel between 1982 and 2008, and the trends are correlated (R equals 0.53), suggesting that Saharan Sahelian surface winds are a coupled system, driving the inter-annual variation of dust emission.

Inter-annual variation↗
DOE OSTI2015

Improving in vitro to in vivo extrapolation by incorporating toxicokinetic measurements: A case study of lindane-induced neurotoxicity

Approaches for extrapolating in vitro toxicity testing results for prediction of human in vivo outcomes are needed. The purpose of this case study was to employ in vitro toxicokinetics and PBPK modeling to perform in vitro to in vivo extrapolation (IVIVE) of lindane neurotoxicity. Lindane cell and media concentrations in vitro, together with in vitro concentration-response data for lindane effects on neuronal network firing rates, were compared to in vivo data and model simulations as an exercise in extrapolation for chemical-induced neurotoxicity in rodents and humans. Time- and concentration-dependent lindane dosimetry was determined in primary cultures of rat cortical neurons in vitro using “faux” (without electrodes) microelectrode arrays (MEAs). In vivo data were derived from literature values, and physiologically based pharmacokinetic (PBPK) modeling was used to extrapolate from rat to human. The previously determined EC{sub 50} for increased firing rates in primary cultures of cortical neurons was 0.6 μg/ml. Media and cell lindane concentrations at the EC{sub 50} were 0.4 μg/ml and 7.1 μg/ml, respectively, and cellular lindane accumulation was time- and concentration-dependent. Rat blood and brain lindane levels during seizures were 1.7–1.9 μg/ml and 5–11 μg/ml, respectively. Brain lindane levels associated with seizures in rats and those predicted for humans (average = 7 μg/ml) by PBPK modeling were very similar to in vitro concentrations detected in cortical cells at the EC{sub 50} dose. PBPK model predictions matched literature data and timing. These findings indicate that in vitro MEA results are predictive of in vivo responses to lindane and demonstrate a successful modeling approach for IVIVE of rat and human neurotoxicity. - Highlights: • In vitro to in vivo extrapolation for lindane neurotoxicity was performed. • Dosimetry of lindane in a micro-electrode array (MEA) test system was assessed. • Cell concentrations at the MEA EC{sub 50} equaled rat brain levels associated with seizure. • PBPK-predicted human brain levels at seizure also equaled EC{sub 50} cell concentrations. • In vitro MEA results are predictive of lindane in vivo dose–response in rats/humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Ionic solvation phenomena in liquids involve intense interactions in the inner solvation shell. For interactions beyond the first shell, the ion–solvent interaction energies result from the sum of many smaller-magnitude contributions that can still include polarization effects. Deep neural network (DNN) methods have recently found wide application in developing efficient molecular models that maintain near-quantum accuracy. In this work, we extend the DeePMD-kit code to produce accurate molecular multipole moments in the bulk and near interfaces. The new method is validated by comparing the DNN moments with those generated by ab initio simulations. The moments are used to compute the electrostatic potential at the center of a molecular-sized hydrophobic cavity in water. The results show that the fields produced by the DNN models are in quantitative agreement with the AIMD-derived values. These efficient methods will open the door to more accurate solvation models for large solutes such as proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

A distributed analysis and visualization system for model and observational data

Software was developed with NASA support to aid in the analysis and display of the massive amounts of data generated from satellites, observational field programs, and from model simulations. This software was developed in the context of the PATHFINDER (Probing ATmospHeric Flows in an Interactive and Distributed EnviRonment) Project. The overall aim of this project is to create a flexible, modular, and distributed environment for data handling, modeling simulations, data analysis, and visualization of atmospheric and fluid flows. Software completed with NASA support includes GEMPAK analysis, data handling, and display modules for which collaborators at NASA had primary responsibility, and prototype software modules for three-dimensional interactive and distributed control and display as well as data handling, for which NSCA was responsible. Overall process control was handled through a scientific and visualization application builder from Silicon Graphics known as the Iris Explorer. In addition, the GEMPAK related work (GEMVIS) was also ported to the Advanced Visualization System (AVS) application builder. Many modules were developed to enhance those already available in Iris Explorer including HDF file support, improved visualization and display, simple lattice math, and the handling of metadata through development of a new grid datatype. Complete source and runtime binaries along with on-line documentation is available via the World Wide Web at: http://redrock.ncsa.uiuc.edu/ PATHFINDER/pathre12/top/top.html.

Wilhelmson, Robert B.↗
NASA NTRS1986

Identification of differences between finite element analysis and experimental vibration data

An important problem that has emerged from combined analytical/experimental investigations is the task of identifying and quantifying the differences between results predicted by F.E. analysis and results obtained from experiment. The objective of this study is to extend and evaluate the procedure developed by Sidhu for correlation of linear F.E. and modal test data to include structures with viscous damping. The desirability of developing this procedure is that the differences are identified in terms of physical mass, damping, and stiffness parameters instead of in terms of frequencies and modes shapes. Since the differences are computed in terms of physical parameters, locations of modeling problems can be directly identified in the F.E. model. From simulated data it was determined that the accuracy of the computed differences increases as the number of experimentally measured modes included in the calculations is increased. When the number of experimental modes is at least equal to the number of translational degrees of freedom in the F.E. model both the location and magnitude of the differences can be computed very accurately. When the number of modes is less than this amount the location of the differences may be determined even though their magnitudes will be under estimated.

Lawrence, C.↗
NASA NTRS1988

Identification of differences between finite element analysis and experimental vibration data

An important problem that has emerged from combined analytical/experimental investigations is the task of identifying and quantifying the differences between results predicted by F.E. analysis and results obtained from experiment. The objective of this study is to extend and evaluate the procedure developed by Sidhu for correlation of linear F.E. and modal test data to include structures with viscous damping. The desirability of developing this procedure is that the differences are identified in terms of physical mass, damping, and stiffness parameters instead of in terms of frequencies and modes shapes. Since the differences are computed in terms of physical parameters, locations of modeling problems can be directly identified in the F.E. model. From simulated data it was determined that the accuracy of the computed differences increases as the number of experimentally measured modes included in the calculations is increased. When the number of experimental modes is at least equal to the number of translational degrees of freedom in the F.E. model both the location and magnitude of the differences can be computed very accurately. When the number of modes is less than this amount the location of the differences may be determined even though their magnitudes will be underestimated.

Lawrence, Charles↗
NASA NTRS1993

Physiological water model development

The water of the human body can be categorized as existing in two main compartments: intracellular water and extracellular water. The intracellular water consists of all the water within the cells and constitutes over half of the total body water. Since red blood cells are surrounded by plasma, and all other cells are surrounded by interstitial fluid, the intracellular compartment has been subdivided to represent these two cell types. The extracellular water, which includes all of the fluid outside of the cells, can be further subdivided into compartments which represent the interstitial fluid, circulating blood plasma, lymph, and transcellular water. The interstitial fluid surrounds cells outside of the vascular system whereas plasma is contained within the blood vessels. Avascular tissues such as dense connective tissue and cartilage contain interstitial water which slowly equilibrates with tracers used to determine extracellular fluid volume. For this reason, additional compartments are sometimes used to represent these avascular tissues. The average size of each compartment, in terms of percent body weight, has been determined for adult males and females. These compartments and the forces which cause flow between them are presented. The kidneys, a main compartment, receive about 25 percent of the cardiac output and filters out a fluid similar to plasma. The composition of this filtered fluid changes as it flows through the kidney tubules since compounds are continually being secreted and reabsorbed. Through this mechanism, the kidneys eliminate wastes while conserving body water, electrolytes, and metabolites. Since sodium accounts for over 90 percent of the cations in the extracellular fluid, and the number of cations is balanced by the number of anions, considering the renal handling sodium and water only should sufficiently describe the relationship between the plasma compartment and kidneys. A kidney function model is presented which has been adapted from a previous model of normal renal function in man. To test the validity of the proposed kidney model, results predicted by the model will be compared to actual data involving injected or ingested fluids and subsequent urine flow rates. Comparison of the model simulation to actual data following the ingestion of 1 liter of water is shown. The model simulation is also shown with actual data following the intravenous infusion of hypertonic saline.

Doty, Susan↗
DOE OSTI2019

Contributions of Different Combinations of the IPO and AMO to Recent Changes in Winter East Asian Jets

Recent concurrent shifts of the East Asian polar-front jet (EAPJ) and the East Asian subtropical jet (EASJ) in the boreal winter have raised concerns, since they could result in severe weather events over East Asia. However, the possible mechanisms are not fully understood. In this study, the roles of the interdecadal Pacific oscillation (IPO) and the Atlantic multidecadal oscillation (AMO) are investigated by analyzing reanalysis data and model simulations. Results show that combinations of opposite phases of the IPO and AMO can result in significant shifts of the two jets during 1920–2014. This relationship is particularly evident during 1999–2014 and 1979–98 in the reanalysis data. A combination of a negative phase of the IPO (-IPO) and a positive phase of the AMO (+AMO) since the late 1990s has enhanced the meridional temperature gradient and the Eady growth rate and thus westerlies over the region between the two jets, but weakened them to the south and north of the region, thereby contributing to the equatorward and poleward shifts of the EAPJ and EASJ, respectively. Atmospheric model simulations are further used to investigate the relative contribution of -IPO and +AMO to the jet shifts. The model simulations show that the combination of -IPO and +AMO favors the recent jet changes more than the individual -IPO or +AMO. Finally, under a concurrent -IPO and +AMO, the meridional eddy transport of zonal momentum and sensitive heat strengthens, and more mean available potential energy converts to the eddy available potential energy over the region between the two jets, which enhances westerly winds there.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A model to calculate fatigue damage caused by partial waking during wind farm optimization

Abstract. Wind turbines in wind farms often operate in waked or partially waked conditions, which can greatly increase the fatigue damage. Some fatigue considerations may be included, but currently a full fidelity analysis of the increased damage a turbine experiences in a wind farm is not considered in wind farm layout optimization because existing models are too computationally expensive. In this paper, we present a model to calculate fatigue damage caused by partial waking on a wind turbine that is computationally efficient and can be included in wind farm layout optimization. The model relies on analytic velocity, turbulence, and load models commonly used in farm research and design, and it captures some of the effects of turbulence on the fatigue loading. Compared to high-fidelity simulation data, our model accurately predicts the damage trends of various waking conditions. We also perform example wind farm layout optimizations with our presented model in which we maximize the annual energy production (AEP) of a wind farm while constraining the damage of the turbines in the farm. The results of our optimization show that the turbine damage can be significantly reduced, more than 10 %, with only a small sacrifice of around 0.07 % to the AEP, or the damage can be reduced by 20 % with an AEP sacrifice of 0.6 %.

17 WIND ENERGY↗
NASA NTRS2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

land data assimilation↗
NASA NTRS1978

Simulation gravity modeling to spacecraft-tracking data - Analysis and application

It is proposed that line-of-sight gravity measurements derived from spacecraft-tracking data can be used for quantitative subsurface density modeling by suitable orbit simulation procedures. Such an approach avoids complex dynamic reductions and is analogous to the modeling of conventional surface gravity data. This procedure utilizes the vector calculations of a given gravity model in a simplified trajectory integration program that simulates the line-of-sight gravity. Solutions from an orbit simulation inversion and a dynamic inversion on Doppler observables compare well (within 1% in mass and size), and the error sources in the simulation approximation are shown to be quite small. An application of this technique is made to lunar crater gravity anomalies by simulating the complete Bouguer correction to several large young lunar craters. It is shown that the craters all have negative Bouguer anomalies.

Phillips, R. J.↗
DOE OSTI2022

Macrovoid resolved simulations of transport through HPRO relevant membrane geometries

Modeling the transport properties such as diffusivity and permeability of high pressure reverse osmosis (HPRO) membranes is critical for the selection and manufacture of membranes suitable for operation under high pressure. These properties can be significantly affected by the changes in heterogeneous pore structures due to compaction. Here, the modeling platform presented in this work resolves the two scale porosity in HPRO relevant membranes. A synthetic membrane geometry is constructed based on available experimental visualizations of pore structures. The simulations directly capture the flow channeling that results from a combination of material properties and geometric features of the macrovoids. A parametric study is presented to account for the transition of flow characteristics from a material governed regime to a macrovoid governed regime. Permeability of the membrane is evaluated using the simulation data and compared with an existing model that scales with the square of tortuosity over a range of material properties. The model is found to perform well under a narrow range of tortuosity while deviating from the calculated permeabilities at extreme conditions. The effective permeability of the membrane is found to vary by at least two orders of magnitude between the two flow regimes. It is also observed that tortuosity is a bounded property with its upper limit determined by the macrovoid geometry. Consequently, the tortuosity based correlations fail near a flow regime that is mainly governed by the macrovoids. The modeled permeability can be more than an order of magnitude smaller than the simulation result. A new model based on flux-weighted porosity of a membrane is introduced and its correlation with tortuosity is studied. The model agrees with the simulated data as, in addition to tortuosity, it also accounts for the flux partition within and outside the flow channels. Such correlations enable extending the existing understanding of flow characteristics to enhance predictability of porous media models.

42 ENGINEERING↗
DOE OSTI2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗
NASA NTRS2021

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically-triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) and Gravity Recovery and Climate Experiment (GRACE) remote sensing data, Catchment Land Surface Model (CLSM) simulations and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300-km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011-2016, unless the CLSM model-only soil water content is already high (≥ 50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015-2019) more generally updates the soil water conditions towards higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Anne Felsberg↗
DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗