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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Abstract Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.

Annoni, Jennifer↗
DOE OSTI2023

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

17 WIND ENERGY↗
DOE OSTI2017

Vector models and generalized SYK models

Here, we consider the relation between SYK-like models and vector models by studying a toy model where a tensor field is coupled with a vector field. By integrating out the tensor field, the toy model reduces to the Gross-Neveu model in 1 dimension. On the other hand, a certain perturbation can be turned on and the toy model flows to an SYK-like model at low energy. Furthermore, a chaotic-nonchaotic phase transition occurs as the sign of the perturbation is altered. We further study similar models that possess chaos and enhanced reparameterization symmetries.

1/N Expansion↗
DOE OSTI2026

Toward real-time optimization through model reduction and model discrepancy sensitivities

Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.

PDE-constrained optimization↗
DOE OSTI2016

Integrating a reservoir regulation scheme into a spatially distributed hydrological model

During the past several decades, numerous reservoirs have been built across the world for a variety of purposes such as flood control, irrigation, municipal/industrial water supplies, and hydropower generation. Consequently, natural streamflow timing and magnitude have been altered significantly by reservoir operations. In addition, the hydrological cycle can be modified by land use/land cover and climate changes. To understand the fine scale feedback between hydrological processes and water management decisions, a distributed hydrological model embedded with a reservoir component is of desire. In this study, a multi-purpose reservoir module with predefined complex operational rules was integrated into the Distributed Hydrology Soil Vegetation Model (DHSVM). Conditional operating rules, which are designed to reduce flood risk and enhance water supply reliability, were adopted in this module. The performance of the integrated model was tested over the upper Brazos River Basin in Texas, where two U.S. Army Corps of Engineers reservoirs, Lake Whitney and Aquilla Lake, are located. The integrated DHSVM model was calibrated and validated using observed reservoir inflow, outflow, and storage data. The error statistics were summarized for both reservoirs on a daily, weekly, and monthly basis. Using the weekly reservoir storage for Lake Whitney as an example, the coefficients of determination (R2) and the Nash-Sutcliff Efficiency (NSE) are 0.85 and 0.75, respectively. These results suggest that this reservoir module has promise for use in sub-monthly hydrological simulations. Enabled with the new reservoir component, the DHSVM model provides a platform to support adaptive water resources management under the impacts of evolving anthropogenic activities and substantial environmental changes.

Zhao, Gang↗
DOE OSTI2014

Modeling of thermal storage systems in MILP distributed energy resource models

Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO 2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Ultimately,results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.

25 ENERGY STORAGE↗
DOE OSTI2018

Importance of filter’s microstructure in dynamic filtration modeling of gasoline particulate filters (GPFs): Inhomogeneous porosity and pore size distribution

The state-of-the-art multiscale modeling of GPFs including channel scale, wall scale, and pore scale is described. The microstructures of two GPFs were experimentally characterized. The pore size distributions of the GPFs were determined by mercury porosimetry. The porosity was measured by X-ray computed tomography (CT) and found to be inhomogeneous across the substrate wall. The significance of pore size distribution with respect to filtration performance was analyzed. The predictions of filtration efficiency were improved by including the pore size distribution in the filtration model. A dynamic heterogeneous multiscale filtration (HMF) model was utilized to simulate particulate filtration on a single channel particulate filter with realistic particulate emissions from a spark-ignition direct-injection (SIDI) gasoline engine. The dynamic evolution of filter’s microstructure and macroscopic filtration characteristics including mass- and number-based filtration efficiencies and pressure drop were predicted and discussed. The microstructure of the GPF substrate including inhomogeneous porosity and pore size distribution is found to significantly influence local particulate deposition inside the substrate and macroscopic filtration performance and is recommended to be resolved in the filtration model to simulate and evaluate the filtration performance of GPFs.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

A Methodology and Implementation of Automated Emissions Harmonization for Use in Integrated Assessment Models

Emissions harmonization refers to the process used to match greenhouse gas (GHG) and air pollutant results from Integrated Assessment Models (IAMs) against a common source of historical emissions. To date, harmonization has been performed separately by individual modeling teams. For the hand-over of emission data for the Shared Socio-economic Pathways (SSPs) to climate model groups, a new automated approach based on commonly agreed upon algorithms was developed. This work describes the novel methodology for determining such harmonization methods and an open-source Python software library implementing the methodology. Results are shown for two example scenarios (with and without climate policy cases) using the MESSAGE-GLOBIOM IAM that satisfactorily harmonize over 96% of the total emissions trajectories while having a negligible eect on key long-term climate indicators. This new capability enhances the comparability across dierent models, increases transparency and robustness of results, and allows other teams to easily participate in intercomparison exercises by using the same, openly available harmonization mechanism.

intergrated assessment, climate change, Harmonizat↗
DOE OSTI2020

Evaluation of the cytotoxic and cellular proteome impacts of food-grade TiO2 (E171) using simulated gastrointestinal digestions and a tri-culture small intestinal epithelial model

Engineered nanomaterials (ENMs) are widely used in the food industry; however, regulations for ENMs in food are still in the early stages of development due to insufficient health data. This study investigated the cytotoxicity and changes to the proteomic profile in an in vitro small intestinal epithelium model after exposure to digested food models containing the ubiquitous engineered particulate food additive, TiO2 (E171). TiO2 at 0.75% or 1.5% (w/w) concentrations in either a fasting food model (FFM) or a standardized food model (SFM) based on American diet were digested using an in vitro oral-gastric-small intestinal simulator, and the resulting digestas were applied to a small intestinal epithelium tri-culture cellular model. Effects on cell layer integrity, cytotoxicity, and oxidative stress were assessed. In order to explore impact on cellular processes beyond basic cytotoxicity, mass spectrometrybased quantitative proteomic analyses of control and exposed tri-culture cells was performed. TiO2 in FFM, but not in SFM, produced significant, dose-dependent cytotoxicity, and at the higher dose caused significant oxidative stress, indicative of a food matrix effect. No significant perturbations of the cellular proteome were observed with TiO2 in either FFM or SFM food models. However, proteins involved in the tricarboxylic acid (TCA) cycle, gluconeogenesis, and translation were up-regulated by digestas from SFM compared to those from FFM, also indicative of food matrix effects in the case of food models alone. Interestingly, the differences in profiles between the two food models was more pronounced in the presence of TiO2 indicative of food matrix effects. Together, these results indicate that TiO2 in a fasting diet may be slightly cytotoxic, and that ingested TiO2 does not significantly alter the epithelial proteome, whereas the food matrix alone can have a dramatic effect on the proteome.

Cao, Xiaoqiong↗
DOE OSTI2014

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and EnKF algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Revisiting low-fidelity two-fluid models for gas–solids transport

Two-phase gas–solids transport models are widely utilized for process design and automation in a broad range of industrial applications. Some of these applications include proppant transport in gaseous fracking fluids, air/gas drilling hydraulics, coal-gasification reactors and food processing units. Systems automation and real time process optimization stand to benefit a great deal from availability of efficient and accurate theoretical models for operations data processing. However, modeling two-phase pneumatic transport systems accurately requires a comprehensive understanding of gas–solids flow behavior. In this study we discuss the prevailing flow conditions and present a low-fidelity two-fluid model equation for particulate transport. The model equations are formulated in a manner that ensures the physical flux term remains conservative despite the inclusion of solids normal stress through the empirical formula for modulus of elasticity. A new set of Roe–Pike averages are presented for the resulting strictly hyperbolic flux term in the system of equations, which was used to develop a Roe-type approximate Riemann solver. The resulting scheme is stable regardless of the choice of flux-limiter. The model is evaluated by the prediction of experimental results from both pneumatic riser and air-drilling hydraulics systems. We demonstrate the effect and impact of numerical formulation and choice of numerical scheme on model predictions. We illustrate the capability of a low-fidelity one-dimensional two-fluid model in predicting relevant flow parameters in two-phase particulate systems accurately even under flow regimes involving counter-current flow.

42 ENGINEERING↗
DOE OSTI2024

Mesoscale Modeling of the Effects of Accelerated Burnup on UO2 Microstructural Evolution

Accelerating the nuclear fuel qualification process will rely on some combination of advanced modeling and simulation techniques with accelerated irradiation testing and separate effects experiments to enable the development of new fuel concepts in a shorter time frame. One of the key challenges to successfully leveraging accelerated irradiation tests will be understanding the artifacts that may be introduced with accelerated accumulation of dose and/or burnup. This work presents phase field (MARMOT) simulations of the evolution of representative 2D UO2 microstructures up to 40 MWd/kgU. Simulations were performed under both commercial light water reactor fuel conditions as well as those that would be expected for highly accelerated (~10x) burnup conditions similar to those used in the MiniFuel irradiations in Oak Ridge National Laboratory’s High Flux Isotope Reactor. The phase field model was coupled with a discrete nucleation algorithm to model re- structuring at high burnup. The effect of the different fission rates in both microstructures was investigated at two temperatures: 650?C and 800?C. The lower temperature simulations both showed an onset of restructuring at nearly 60 MWd/kgU. More extensive restructuring was obtained in the MiniFuel microstructure compared with that of the PWR fuel. At 800?C, no restructuring was obtained as a result of the thermally activated diffusion of Xe atoms and U vacancies to fission gas bubbles, which reduces the nucleation driving force. These results highlight the importance of using modeling and simulation tools to inform the environmental conditions during targeted accelerated irradiation tests to extract the most useful fuel performance data.

accelerated fuel qualification, Phase Field, Restr↗
DOE OSTI2018

Radiation Anomaly Detection and Classification with Bayes Model Selection

We present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. The method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations.

Anomaly detection, Gamma-ray spectroscopy, Radiati↗
DOE OSTI2013

Modeling Tidal Stream Energy Extraction and its Effects on Transport Processes in a Tidal Channel and Bay System Using a Three-dimensional Coastal Ocean Model

This paper presents a numerical modeling study for simulating in-stream tidal energy extraction and assessing its effects on the hydrodynamics and transport processes in a tidal channel and bay system connecting to coastal ocean. A marine and hydrokinetic (MHK) module was implemented in a three-dimensional (3-D) coastal ocean model using the momentum sink approach. The MHK model was validated with the analytical solutions for tidal channels under one-dimensional (1-D) conditions. Model simulations were further carried out to compare the momentum sink approach with the quadratic bottom friction approach. The effects of 3-D simulations on the vertical velocity profile, maximum extractable energy, and volume flux reduction across the channel were investigated through a series of numerical experiments. 3-D model results indicate that the volume flux reduction at the maximum extractable power predicted by the 1-D analytical model or two-dimensional (2-D) depth-averaged numerical model may be overestimated. Maximum extractable energy strongly depends on the turbine hub height in the water column, and which reaches a maximum when turbine hub height is located at mid-water depth. Far-field effects of tidal turbines on the flushing time of the tidal bay were also investigated. Model results demonstrate that tidal energy extraction has a greater effect on the flushing time than volume flux reduction, which could negatively affect the biogeochemical processes in estuarine and coastal waters that support primary productivity and higher forms of marine life.

Yang, Zhaoqing↗
DOE OSTI2016

Development of a PBPK model of thiocyanate in rats with an extrapolation to humans: A computational study to quantify the mechanism of action of thiocyanate kinetics in thyroid

Thyroid homeostasis can be disturbed due to thiocyanate exposure from the diet or tobacco smoke. Thiocyanate inhibits both thyroidal uptake of iodide, via the sodium-iodide symporter (NIS), and thyroid hormone (TH) synthesis in the thyroid, via thyroid peroxidase (TPO), but the mode of action of thiocyanate is poorly quantified in the literature. The characterization of the link between intra-thyroidal thiocyanate concentrations and dose of exposure is crucial for assessing the risk of thyroid perturbations due to thiocyanate exposure. We developed a PBPK model for thiocyanate that describes its kinetics in the whole-body up to daily doses of 0.15 mmol/kg, with a mechanistic description of the thyroidal kinetics including NIS, passive diffusion, and TPO. The model was calibrated in a Bayesian framework using published studies in rats. Goodness-of-fit was satisfactory, especially for intra-thyroidal thiocyanate concentrations. Thiocyanate kinetic processes were quantified in vivo, including the metabolic clearance by TPO. The passive diffusion rate was found to be greater than NIS-mediated uptake rate. The model captured the dose-dependent kinetics of thiocyanate after acute and chronic exposures. Model behavior was evaluated using a Morris screening test. The distribution of thiocyanate into the thyroid was found to be determined primarily by the partition coefficient, followed by NIS and passive diffusion; the impact of the latter two mechanisms appears to increase at very low doses. Extrapolation to humans resulted in good predictions of thiocyanate kinetics during chronic exposure. The developed PBPK model can be used in risk assessment to quantify dose-response effects of thiocyanate on TH. - Highlights: • A PBPK model of thiocyanate (SCN{sup −}) was calibrated in rats in a Bayesian framework. • The intra-thyroidal kinetics of thiocyanate including NIS and TPO was modeled. • Passive diffusion rate for SCN{sup −} seemed to be greater than the NIS-mediated uptake. • The dose-dependent kinetics of SCN{sup −} was captured after an acute and chronic exposure. • The PBPK model of thiocyanate was successfully extrapolated to humans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Challenges in Modeling Electrochemical Reaction Energetics with Polarizable Continuum Models

A major challenge in the modeling of electrochemical phenomena is the accurate description of the interface between an electrolyte and a charged conductor. Polarizable continuum models (PCM) have been gaining popularity because they offer a computationally inexpensive method of modeling the electrolyte. In this Perspective, we discuss challenges from using one such model which treats the ions using a linearized Poisson–Boltzmann (LPB) distribution. From a physical perspective, this model places charge unphysically close to the surface and adsorbates, and it includes excessively steep ramping of the dielectric constant from the surface to the bulk solvent. Both of these issues can be somewhat mitigated by adjusting parameters built into the model, but in doing so, the resultant capacitance deviates from experimental values. Likewise, hybrid explicit-implicit approaches to the solvent may offer a more realistic description of hydrogen bonding and solvation to reaction intermediates, but the corresponding capacitances also deviate from experimental values. These deviations highlight the need for a careful adjustment of parameters in order to reproduce not only solvation energies but also other physical properties of solid–liquid interfaces. Continuum approaches alone also necessarily do not capture local variations in the electric field from cations at the interface, which can affect the energetics of intermediates with substantial dipoles or polarizability. Finally, since the double-layer charge can be varied continuously, LPB/PCM models provide a way to determine electrochemical barriers at constant potential. However, double-layer charging and the atomic motion associated with reaction events occur on significantly different timescales. As a result, we suggest that more detailed approaches, such as the modified Poisson–Boltzmann model and/or the addition of a Stern layer, may be able to mitigate some but not all of the challenges discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models

Biomass is a renewable and sustainable energy resource. Current design of biomass handling and feeding equipment leverage both experiments and numerical modeling. This paper reviews the state-of-the-art discrete element methods (DEM) for the flow of milled biomass (Part I), accompanied by a comprehensive review on continuum-based computational models (Part II). The present review on DEM is primarily focused on the features and suitability of various particle shape models for different types of milled biomass because particle shape is the predominant attribute controlling the flow behavior of complex-shaped granular material. The general strengths and weaknesses in the applicability of those models for the milled biomass modeling are summarized. In particular, comments are provided to balance the numerical model capabilities and the computational cost for the development of DEM models. To our best knowledge, this is the first-of-its-kind review on DEM specifically for biomass. Our study indicates that the current DEM models require further development, calibration, and validation based on a deep understanding of biomass particle contact mechanics and experimental data support before they can be reliably used for predictive simulations in handling and feeding systems.

42 ENGINEERING↗