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Results for “nonlinear water modeling”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Assimilation of Geosat Altimetric Data in a Nonlinear Shallow-Water Model of the Indian Ocean by Adjoint Approach. Part II: Some Validation and Interpretation of the Assimilated Results: Some Validation and Interpretation of the Assimilated Results - Part 2

This paper examines the results of assimilating Geosat sea level variations relative to the November 1986-November 1988 mean reference, in a nonlinear reduced-gravity model of the Indian Ocean, Data have been assimilated during one year starting in November 1986 with the objective of optimizing the initial conditions and the yearly averaged reference surface. The thermocline slope simulated by the model with or without assimilation is validated by comparison with the signal, which can be derived from expandable bathythermograph measurements performed in the Indian Ocean at that time. The topography simulated with assimilation on November 1986 is in very good agreement with the hydrographic data. The slopes corresponding to the South Equatorial Current and to the South Equatorial Countercurrent are better reproduced with assimilation than without during the first nine months. The whole circulation of the cyclonic gyre south of the equator is then strongly intensified by assimilation. Another assimilation experiment is run over the following year starting in November 1987. The difference between the two yearly mean surfaces simulated with assimilation is in excellent agreement with Geosat. In the southeastern Indian Ocean, the correction to the yearly mean dynamic topography due to assimilation over the second year is negatively correlated to the one the year before. This correction is also in agreement with hydrographic data. It is likely that the signal corrected by assimilation is not only due to wind error, because simulations driven by various wind forcings present the same features over the two years. Model simulations run with a prescribed throughflow transport anomaly indicate that assimilation is rather correcting in the interior of the model domain for inadequate boundary conditions with the Pacific.

Greiner, Eric↗
NASA NTRS1986

Using Hough harmonics to validate and assess nonlinear shallow-water models

The implementation of a technique for locating programming errors in shallow-water codes, establishing the correctness of the code, and assessing the performance of the numerical model under various flow conditions is described. The right-hand side of the differential equations is modified in such a way that the exact solution of the nonlinear initial-value problem is known, so that the truncation errors of the numerical scheme can be studied in detail. The exact solution is prescribed to be any linear combination of Hough harmonics which propagate in time according to their natural frequencies.

Dee, Dick P.↗
DOE OSTI2014

Topography-based Flood Planning and Optimization Capability Development Report

Globally, water-related disasters are among the most frequent and costly natural hazards. Flooding inflicts catastrophic damage on critical infrastructure and population, resulting in substantial economic and social costs. NISAC is developing LeveeSim, a suite of nonlinear and network optimization models, to predict optimal barrier placement to protect critical regions and infrastructure during flood events. LeveeSim currently includes a high-performance flood model to simulate overland flow, as well as a network optimization model to predict optimal barrier placement during a flood event. The LeveeSim suite models the effects of flooding in predefined regions. By manipulating a domain’s underlying topography, developers altered flood propagation to reduce detrimental effects in areas of interest. This numerical altering of a domain’s topography is analogous to building levees, placing sandbags, etc. To induce optimal changes in topography, NISAC used a novel application of an optimization algorithm to minimize flooding effects in regions of interest. To develop LeveeSim, NISAC constructed and coupled hydrodynamic and optimization algorithms. NISAC first implemented its existing flood modeling software to use massively parallel graphics processing units (GPUs), which allowed for the simulation of larger domains and longer timescales. NISAC then implemented a network optimization model to predict optimal barrier placement based on output from flood simulations. As proof of concept, NISAC developed five simple test scenarios, and optimized topographic solutions were compared with intuitive solutions. Finally, as an early validation example, barrier placement was optimized to protect an arbitrary region in a simulation of the historic Taum Sauk dam breach.

42 ENGINEERING↗
NASA NTRS1990

Recurrence in truncated Boussinesq models for nonlinear waves in shallow water

The rapid spatial recurrence of weakly nonlinear and weakly dispersive progressive shallow-water waves is examined using a numerical integration technique on the discretized and truncated form of the Boussinesq equations. This study primarily examines recurrence in wave fields with Ursell number O(1) and characterizes the sensitivity of recurrence to initial spectral shape and number of allowed frequency modes. It is shown that the rapid spatial recurrence is not an inherent property of the considered Boussinesq systems for evolution distances of 10-50 wavelengths. The main result of the study is that highly truncated Boussinesq models of resonant shallow-water ocean surface gravity waves predict rapid multiple recurrence cycles, but that this is an artifact dependent on the number of allowed modes. For initial conditions consisting of essentially all energy concentrated in a single mode, damping of the recurrence cycles increases as the number of low-power background modes increases. When more than 32 modes are allowed, the recurrence behavior is relatively insensitive to the number of allowed modes.

Elgar, Steve↗
NASA NTRS1980

Mountain winds (revisited)

The prediction of extremely high wind speeds, at ground level on the downstream side of a mountain range, is possible by solving the initial value problem for a two-layered nonlinear shallow water model of the atmosphere. Three different numerical methods are described to find the solutions which may involve shocks: (1) the vonNeumann-Richtmyer artificial viscosity method, (2) a filtering scheme, and (3) a hybrid method.

Isaacson, E.↗
NASA NTRS1997

Applications of Data Assimilation to Analysis of the Ocean on Large Scales

It is commonplace to begin talks on this topic by noting that oceanographic data are too scarce and sparse to provide complete initial and boundary conditions for large-scale ocean models. Even considering the availability of remotely-sensed data such as radar altimetry from the TOPEX and ERS-1 satellites, a glance at a map of available subsurface data should convince most observers that this is still the case. Data are still too sparse for comprehensive treatment of interannual to interdecadal climate change through the use of models, since the new data sets have not been around for very long. In view of the dearth of data, we must note that the overall picture is changing rapidly. Recently, there have been a number of large scale ocean analysis and prediction efforts, some of which now run on an operational or at least quasi-operational basis, most notably the model based analyses of the tropical oceans. These programs are modeled on numerical weather prediction. Aside from the success of the global tide models, assimilation of data in the tropics, in support of prediction and analysis of seasonal to interannual climate change, is probably the area of large scale ocean modeling and data assimilation in which the most progress has been made. Climate change is a problem which is particularly suited to advanced data assimilation methods. Linear models are useful, and the linear theory can be exploited. For the most part, the data are sufficiently sparse that implementation of advanced methods is worthwhile. As an example of a large scale data assimilation experiment with a recent extensive data set, we present results of a tropical ocean experiment in which the Kalman filter was used to assimilate three years of altimetric data from Geosat into a coarsely resolved linearized long wave shallow water model. Since nonlinear processes dominate the local dynamic signal outside the tropics, subsurface dynamical quantities cannot be reliably inferred from surface height anomalies. Because of its potential for large scale synoptic coverage of the deep ocean, acoustic travel time data should be a natural complement to satellite altimetry. Satellite data give us vertical integrals associated with thermodynamic and dynamic processes.

Miller, Robert N.↗
NASA NTRS1996

On the Evolution-Dynamics of Tropical Ocean-Atmosphere Annual-Cycle Variability

The structure of ocean-atmosphere annual-cycle variability across the global tropics is extracted from the Comprehensive Ocean-Atmosphere Data Set (COADS) surface winds and SSTs, and oceanic heat-content simulation from a nonlinear shallow water model (forced by COADS wind stress) using the co-variance based rotated principal component analysis technique.

COADS↗
NASA NTRS1975

Initialization procedures for primitive equation models

A linear analysis and comparison of the damping properties of six dynamic initialization schemes is presented, indicating that the Okamura-Rivas scheme has the most efficient damping properties over the whole frequency range, and suggesting that it should be faster than the other methods and given more stable results. The results obtained with a nonlinear shallow water equations model agree well with the linear analysis. The Okamura-Rivas scheme attains complete balance in the equivalent of 5 to 6 hours of leapfrog forecasting, and requires in this model an order of magnitude less computation than the balance equation solution.

Grant, W. K. F.↗
NASA NTRS1995

Mean Sea Level Derived from Altimetry and Wind-Driven Numerical Models in the Indian Ocean

Wind-driven model skill in simulating sea level variations in the Indian Ocean depends on our knowledge of the mean ocean dynamic topography. This is demonstrated by running the nonlinear or linear version of a shallow-water model driven by observed winds over Geosat and TOPEX periods. Geosat variations are assimilated in the nonlinear shallow-water model with the objective of obtaining topography data.

numerical models Indian ocean mean sea level remot↗
NASA NTRS2015

Determining the Water Ice Content of Martian Regolith by Nonlinear Spectral Mixture Modeling

In the search for evidence of life, Icebreaker will drill in to the Martian ice-rich regolith to collect samples, which will then be analyzed by an array of instruments designed to identify biomarkers. In addition, drilling into the subsurface will provide the opportunity to assess the vertical distribution of ice to a depth of 1 meter. The purpose of this particular project was to understand the uncertainties involved in the use of the imaging system to constrain the water ice content in regolith samples.

Subtractive Kramers-Kronig analysis↗
DOE OSTI2017

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗
NASA NTRS1988

A simple model describing the nonlinear dynamics of the dusk/dawn asymmetry in the high-latitude thermospheric flow

A simple nonlinear, axisymmetric, shallow-water numerical model has been used to study the asymmetry in the neutral flow between the dusk and dawn sides of the auroral oval. The results indicate that the Coriolis force and the curvature terms are nearly in balance on the evening side and require only a small pressure gradient to effect adjustment. The result is smaller neutral velocities near dawn and larger velocities near dusk than would be the case for a linearized treatment. A consequence is that more gravity wave energy is produced on the morning side than on the evening side.

Gundlach, J. P.↗
NASA NTRS1972

Nonlinear interaction of resonant plasma oscillations.

Using the water-bag model of an electron plasma the nonlinear interaction of two plasma wave pulses (each of sufficiently narrow wave-number spectrum) is discussed. It is shown that for the special case of a resonant (zero group velocity) pulse interacting with a nonresonant pulse, the interaction produces an effective slowing down of the group velocity of the nonresonant pulse. An application of this theory to the explanation of the apparent resonances at fractions of the plasma frequency observed in the ionosphere is discussed.

Lewak, G. J.↗
DOE OSTI2019

On the variable effects of climate change on Pacific salmon

Pacific salmon are sensitive to changes in water temperature and streamflow. Climate change and water management may alter the streamflow and temperature regimes to which local species have adapted, hence affecting the survival and growth of juvenile salmon. This study estimates the changes in smolt survival rates of Chinook (Oncorhynchus tshawytscha) and Steelhead (Oncorhynchus mykiss) in Columbia River Basin (CRB) due to climatic changes and water management practices (reservoir regulation and water extraction). A multivariate nonlinear regression model is developed between juvenile survival rate and streamflow as well as stream temperature, and incorporates the inherent nonlinearity between survival rate and stream temperature. Using streamflow and stream temperature simulations from a state-of-the-art integrated earth system model, results indicate that climatic changes may reduce basin average juvenile Steelhead survival rates and smolt Chinook survival in Snake River by the 2080s. Water management, on the other hand, can alleviate the reduction in Chinook and Steelhead survival rates induced by climate change through mitigation of stream temperature warming. Integration of the nonlinear response of survival rate to stream temperature in addition to considering water management impacts on streamflow and temperature reduces the overestimate of the negative impacts from climate change. Furthermore, stream warming likely benefits juvenile survival in areas where the current stream temperature is lower than the optimal temperature zone. Our findings add new insights on climate and human impacts on juvenile salmon survival in CRB that are useful for conservation planning.

Zhang, Xiao↗
DOE OSTI2025

Experimental observation of nonlinear relation between pressure and water flux is consistent with the solution-diffusion model

In several recent studies, it has been proposed that the fundamental understanding of penetrant transport in dense polymer membranes occurring via the solution-diffusion model, which has been the generally accepted theoretical framework for describing penetrant transport in such materials for the past several decades, is flawed. An alternate mechanistic framework based on the idea of two-phase flow in a porous medium (i.e., pore-flow) has been broadly advanced instead, with proponents of this approach claiming that the pore-flow theoretical framework provides the necessary mechanistic insight to design novel polymeric membrane materials for emerging applications. In this study, we show experimental results for hydraulic permeation of water that are entirely consistent with the solution-diffusion theory, without modification, for three dense polymeric membranes: crosslinked poly(ethylene glycol diacrylate) (XLPEGDA), Nafion 117 ionomer in the sodium counterion form (Nafion 117-Na), and cellulose acetate (CA). By measuring water flux at transmembrane pressures up to 240 bar, we observe a nonlinear relationship between the transmembrane pressure (TMP) and water flux, J w , for XLPEGDA and Nafion 117-Na, while this relationship is linear for CA. We demonstrate that the behavior of these three materials is described via the solution-diffusion model. According to the solution-diffusion model, flux is, to a good approximation, proportional to the transmembrane concentration difference induced by the pressure difference across the membrane, rather than to TMP itself. Water sorption isotherms are reported for all three materials. They further justify the nonlinear relationship between TMP and J w observed in XLPEGDA and Nafion 117-Na, emphasizing that the nonlinearity in the flux/TMP relationship stems from nonlinearities in the sorption isotherm with pressure. Additionally, the relationship between water flux and TMP can be predicted, a priori, with no adjustable parameters when a predictive model for the diffusion coefficient of water is employed in conjunction with the experimental water sorption isotherms in the solution-diffusion model. Furthermore, our results demonstrate the validity of the solution-diffusion model to describe transport of penetrants in dense polymer membranes, while highlighting the sensitivity of the solution-diffusion model to the many physical and mathematical simplifications commonly applied to the theory in literature.

materials↗
DOE OSTI2016

CAMPUS: Fully Coupled Multiphysics Modeling and Simulation of LWR and FBR Nuclear Fuel Behavior

Nuclear fuels operate in an environment that involves complex multiphysics phenomena, and the multiphysics behavior is often tightly coupled. The prediction of fuel behavior under irradiation involves very complex material behavior that is not seen in any other engineering discipline. Understanding and predicting the evolving material properties and phenomenal behaviors of nuclear fuels are major challenges for fuel design and behavior prediction. The multiphysics behavior is often a tightly coupled thermal, mechanical and chemical phenomenon associated with a large number of control parameters and uncertainties, which requires an advanced fuel performance modeling and simulation capability to improve the existing reactors performance, to address environmental and reactor safety concerns. Nuclear fuel performance modeling and simulation capability is necessary as a result of the high cost and difficulty in experimental measurements. So, a fuel performance code called CityU Advanced Multiphysics Nuclear Fuels Performance with User-defined Simulations (CAMPUS) was developed to predict both light water reactor and fast reactor fuel behavior. An effective approach was developed using self-defined multiphysics models based on the framework of COMSOL Multiphysics to manage the nonlinearities associated with light water reactor and fast reactor fuels performance analysis. The modelling and simulations approach is of particular importance since it demonstrates highly interrelated and nonlinear fuel performance models can be developed and implemented into a multiphysics platform, and thus more efforts can be put into physics model development and improvement. The modelling results can be used to identify the important phenomena and parameters, to direct and design experiments and to assist in post analysis of expensive nuclear experiments for light water reactor and fast reactor nuclear fuel performance analysis. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗