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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.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Application of stochastic weighted algorithms to a multidimensional silica particle model

Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associated majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Role of Solvation on Diffusion of Ions in Diblock Copolymers: Understanding the Molecular Weight Effect through Modeling

Salt-doped diblock copolymers with microphase-separated domains of both an ion conductive and a mechanically strong polymer have been extensively studied due to their potential in transport applications. Several unusual or counterintuitive trends regarding their transport properties have been observed experimentally, such as increasing ion conduction as a function of molecular weight. A vital feature of these systems is the strong solvation of ions in the conducting microphase due to its higher dielectric constant. Herein, we perform molecular dynamics simulations using a coarse-grained model that includes a 1/r 4 potential form to generically represent ion solvation, allowing us to reproduce experimentally observed trends and explore their molecular underpinnings. We find that increasing ion concentration can increase or decrease ion diffusion, depending on solvation strength. We further show that the trend of increasing diffusion with molecular weight becomes more dramatic as ions are solvated in one polymer block more strongly or as the ion-ion interactions get stronger. In contrast to expectations, the interfacial width or the overlap of ions with the nonconductive polymer block do not adequately explain this phenomenon; instead, local ion agglomeration best explains reduced diffusion. Interfacial sharpening, controlled by the Flory chi parameter and molecular weight, tends to allow ions to spread more uniformly, and this increases their diffusion.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Model reduction by weighted Component Cost Analysis

Component Cost Analysis considers any given system driven by a white noise process as an interconnection of different components, and assigns a metric called 'component cost' to each component. These component costs measure the contribution of each component to a predefined quadratic cost function. A reduced-order model of the given system may be obtained by deleting those components that have the smallest component costs. The theory of Component Cost Analysis is extended to include finite-bandwidth colored noises. The results also apply when actuators have dynamics of their own. Closed-form analytical expressions of component costs are also derived for a mechanical system described by its modal data. This is very useful to compute the modal costs of very high order systems. A numerical example for MINIMAST system is presented.

Kim, Jae H.↗
NASA NTRSDate not supplied

Evaluation of Human Spaceflight-Related Tissue Weight Relief Using Whole Body Finite Element Model Simulations

Tissue Weight Relief (TWR) is a physiological condition observed in human spaceflight. It not only impacts the injury biomechanics of soft tissue but also the physiological responses of the cardiovascular system both due to fluid redistribution and the effect of tissue-related transmural pressure on the large venous blood vessels. Understanding the effects of tissue weight relief is especially important because of the role it may play in understanding the cause of Space Associated Neuro-Ocular Syndrome (SANS). SANS can be characterized by a number of ocular changes which reduce visual acuity and SANS related symptoms occur in up to 51% of astronauts. A prevailing theory for the causation of SANS is that of headward (cephalad) fluid shift and a prolonged increase of Intracranial Pressure (ICP) similar to intracranial hypertension, which is not fully supported by the experimental data or astronaut symptom reporting. However, it is still believed that SANS is caused by a pressure change in the eye and the surrounding tissues. It has been proposed that TWR plays a substantial role in affecting internal pressures and fluid shifts in microgravity. In this effort, two whole-body Finite Element (FE) models – Elemance and THUMS – are used to ascertain the microgravity-associated TWR of the musculature surrounding the lower body veins. Elemance and THUMS are physics-based computational models that have been validated and verified for several automotive and domestic applications, and as such, can simulate the relief of soft tissue weight due to changes in the gravitation vector. Specifically, the current effort modeled the transition of the gravitational vector from 1G to 0G, applied across the whole-body model in a supine position. For each 1G to 0G transition simulation, the lower body vein’s transmural pressure-time profile was extracted and averaged around the anterior portion of the thigh muscle. The ascertained transmural pressure changes from 1G to 0G transition are given in Figure 1 for the Elemance and the THUMS FE models. The transmural pressure changes of 10 mmHg and 21 mmHg are in the same order of magnitude as Lu’s value of 44 mmHg. It is to be noted that Lu implemented a 0D to 1D lumped parameter model and the Elemance and THUMS are 3D higher order computational models. This proof-of-concept approach demonstrates that TWR pressure can be adequately estimated with in silico techniques however, further in silico investigations need to be conducted to address the unique contributions to the transmural pressure from each of the computational models.

Finite element modeling↗
DOE OSTI2015

TU-EF-304-05: Anterior Proton Beams for Prostate Treatments Lead to Substantial Elevations in Modeled RBE-Weighted Rectal Dose

Purpose: If a constant RBE of 1.1 is assumed, range-verified anterior oblique (AO) proton beams appear to offer the potential to reduce the mean dose delivered to the rectum, anterior rectal wall and penile bulb by a factor of ∼2 relative to standard bilateral (SB) proton beam arrangements. Additionally, AO proton beams targeted at the prostate avoid the femoral heads and so form a particularly appealing option for patients with hip prostheses. This study investigates LET enhancement at the distal edge of AO SOBPs, applying RBE models to consider the extent to which AO beams lead to hotspots in rectum biological dose. Methods: Eight patients were selected, all treated with passively scattered, SB proton beams to 79.2Gy(E) within the prostate and 50.4Gy(E) within the proximal 5–15mm of seminal vesicles. Additional plans utilizing AO beams (±35°) were created in XiO (Elekta) assuming a fixed RBE of 1.1. Voxelised dose and LET distributions were calculated using Monte Carlo (TOPAS). Three different LET/RBE models were applied (Carabe 2012, Wedenberg 2013, McNamara 2015). Results: Across the eight patients, the mean LET within the rectal wall was found to be 3.5(3.2–4.0)keV/µm for the SB plans compared to 10.5(8.6–13.0)keV/µm for the AO plans. Application of the median LET/RBE model to the AO plans, rather than a fixed RBE of 1.1, resulted in an increase of 13.6(11.9–14.7)GyRBE in maximum rectal wall (1cc) RBEw dose, leading to values of 90.4(90.0–91.3)GyRBE. Conclusion: relative to SB proton beams, AO proton beams exhibit substantially increased mean LET values within the rectal wall. For the passively scattered AO beams, modeling indicates that this enhancement is likely to translate into unacceptable RBEw dose hotspots. Whilst no RBE- LET model has yet been fully validated in-vivo, caution must be applied if AO beams are to be considered for prostate patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Future projections and uncertainty assessment of precipitation extremes in the Korean peninsula from the CMIP5 ensemble

Abstract Projections of changes in extreme climate are sometimes predicted by multimodel ensemble methods that combine forecasts from individual simulation models using weighted averaging. One method to assign weight to each model is the Bayesian model averaging (BMA) in which posterior probability is used. For the cases of extreme climate, the generalized extreme value distribution (GEVD) is typically used. We applied the approach of GEV‐embedded BMA to a series of 35 years of the annual maximum daily precipitation data (both historical data and data gathered from simulation experiments for future periods) over the Korean peninsula as simulated by the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulation data under two Representative Concentration Pathway (RCP) scenarios, namely RCP4.5 and RCP8.5, were used. Observed data and 17 CMIP5 models for 12 gird cells in Korea have been examined to predict future changes in precipitation extremes. A simple regional frequency analysis of pooling observations from three stations in each cell was employed to reduce the estimation variance and local fluctuations. A bias correction technique using the regression‐type transfer function was applied to these simulation data. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1971–2005), were estimated for two future periods, namely Period 1 (2021–2050) and Period 2 (2066–2095). From these analyses, relative increase observed in the spatially averaged 20‐year (50‐year) return level was approximately 23% (16%) and 45% (36%) in the RCP4.5 and RCP8.5 experiments, respectively, by the end of the 21st century. We concluded that extreme rainfalls will likely occur two times and four times more frequently in the RCP4.5 and RCP8.5 scenarios, respectively, as compared to in the reference years by the end of the 21st century.

Lee, Youngsaeng↗
NASA NTRS1990

Optimal model reduction and frequency-weighted extension

In this paper the quadratically optimal model reduction problem for single-input, single-output systems is considered. The reduced order model is determined by minimizing the integral of the magnitude-squared of the transfer function error. It is shown that the numerator coefficients of the optimal approximant satisfy a weighted least squares problem and, on this basis, a two-step iterative algorithm is developed combining a least squares solver with a gradient minimizer. The existence of globally optimal stable solutions to the optimization problem is established, and convergence of the algorithm to stationary values of the cost function is proved. The formulation is extended to handle the frequency-weighted optimal model reduction problem. Three examples demonstrate the optimization algorithm.

Spanos, J. T.↗
DOE OSTI2014

SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the left femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A thin-walled composite beam model for light-weighted structures interacting with fluids

A thin-walled beam model is proposed for structures of variable cross-section, which can be either open or closed and includes multicellular cross-sections with either isotropic or orthotropic materials. The proposed model does not require any priori definition of cross-sectional warping which instead results from the solution of the problem. To achieve that a special deformation pattern is superimposed on the bending deformation described by Euler–Bernoulli beam theory. All sectional properties are automatically incorporated in the analysis as a result of the usual variational formulation of the system of equations. The proposed model is specifically designed to simulate the dynamics of wind/hydrokinetic turbine blade with low computational cost, especially in fluid–structure interaction (FSI) simulation. A number of test cases have been carried out to validate the proposed structural model which show good agreement between the results obtained her e and the solutions available in literature. Finally, FSI simulation of a hydrokinetic blade under field condition is carried out to illustrate the capability of the current thin-walled beam model in practice.

42 ENGINEERING↗
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
DOE OSTI2013

Energy-weighted sum rules connecting ΔZ = 2 nuclei within the SO(8) model

Energy-weighted sum rules associated with ΔZ = 2 nuclei are obtained for the Fermi and the Gamow-Teller operators within the SO(8) model. It is found that there is a dominance of contribution of a single state of the intermediate nucleus to the sum rule. The results confirm founding obtained within the SO(5) model that the energy-weighted sum rules of ΔZ = 2 nuclei are governed by the residual interactions of nuclear Hamiltonian. A short discussion concerning some aspects of energy weighted sum rules in the case of realistic nuclei is included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 species across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.

Zhao, Kaiguang↗
DOE OSTI2020

A radiative transfer model for solar induced fluorescence using spectral invariants theory

Solar Induced chlorophyll Fluorescence (SIF) shows promise as an approach for estimating gross primary production (GPP) remotely. However, sun-target-sensor geometry and within-canopy absorption of SIF can alter the relationship between measured SIF and GPP, because sensors can only retrieve some unknown fraction of the total emitted SIF. Radiative transfer models that allow for variation in canopy structure and sensor angles are therefore needed to properly interpret SIF measurements. Spectral invariants allow decoupling of the wavelength-independent canopy structure and the wavelength-dependent leaf and soil spectrum in the radiative transfer process. Here we develop a simple analytical Fluorescence Radiative Transfer model based on Escape and Recollision probability (FluorRTER) to investigate the impact of canopy structure and sun-target-sensor geometry on SIF emissions. SIF simulations using the FluorRTER model agreed well the one-dimensional Soil-Canopy Observation of Photochemistry and Energy balance (SCOPE) model and the three-dimensional Fluorescence model with Weighted Photon Spread (FluorWPS) model. The fractional vegetation cover (FVC) and clumping effect have a large influence the SIF emission of 3D discontinuous canopies. For a moderate solar zenith angle (30°) and a clumped canopy (FVC?=?0.6), the difference between the directional observed SIF of a 3D discontinuous canopy and a 1D homogeneous canopy was as large as 43.2% and 38.4% for Photosystem I?+?II fluorescence at 685?nm and at 740?nm, respectively. By bridging the gap between observed SIF and total emitted SIF over 3D heterogeneous vegetation canopies, the FluorRTER model can assist with the angular normalization of SIF measurements and enable the more robust interpretation of how variations in SIF from directional and hemispherical in-situ, airborne and satellite observations relate to leaf and whole-canopy physiological processes.

Zeng, Yelu↗
DOE OSTI2020

Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature

Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.

14 SOLAR ENERGY↗
DOE OSTI2017

Multivariate analysis of gamma spectra to characterize used nuclear fuel

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.

Multivariate Analysis for Characterization of Used↗