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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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NASA NTRS1982

An identification algorithm for linear stochastic systems with time delays

Linear discrete stochastic control systems containing unknown multiple time delays, plant parameters and noise variances are considered. An algorithm is established which uses the maximum-likelihood technique to identify the unknown parameters. An estimated likelihood function is evaluated based on the previous parameter estimates, which in turn generates a new descent direction vector to update the unknown parameters. The delays and plant parameters are identified in their respective parameter spaces. An example of a second-order stochastic system has been implemented by digital simulation to demonstrate the applicability of the algorithm.

Leondes, C. T.↗

DOE OSTI2014

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.

58 GEOSCIENCES↗

DOE OSTI2018

Climate Modeling and Causal Identification for Sea Ice Predictability

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.

"Sea ice" uncertainty↗

NASA NTRS1978

An adaptive learning control system for aircraft

A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

Mekel, R.↗

NASA NTRS2008

Tolerance and UQ4SIM: Nimble Uncertainty Documentation and Analysis Software

Ultimately, scientific numerical models need quantified output uncertainties so that modeling can evolve to better match reality. Documenting model input uncertainties and variabilities is a necessary first step toward that goal. Without known input parameter uncertainties, model sensitivities are all one can determine, and without code verification, output uncertainties are simply not reliable. The basic premise of uncertainty markup is to craft a tolerance and tagging mini-language that offers a natural, unobtrusive presentation and does not depend on parsing each type of input file format. Each file is marked up with tolerances and optionally, associated tags that serve to label the parameters and their uncertainties. The evolution of such a language, often called a Domain Specific Language or DSL, is given in [1], but in final form it parallels tolerances specified on an engineering drawing, e.g., 1 +/- 0.5, 5 +/- 10%, 2 +/- 10 where % signifies percent and o signifies order of magnitude. Tags, necessary for error propagation, can be added by placing a quotation-mark-delimited tag after the tolerance, e.g., 0.7 +/- 20% 'T_effective'. In addition, tolerances might have different underlying distributions, e.g., Uniform, Normal, or Triangular, or the tolerances may merely be intervals due to lack of knowledge (uncertainty). Finally, to address pragmatic considerations such as older models that require specific number-field formats, C-style format specifiers can be appended to the tolerance like so, 1.35 +/- 10U_3.2f. As an example of use, consider figure 1, where a chemical reaction input file is has been marked up to include tolerances and tags per table 1. Not only does the technique provide a natural method of specifying tolerances, but it also servers as in situ documentation of model uncertainties. This tolerance language comes with a utility to strip the tolerances (and tags), to provide a path to the nominal model parameter file. And, as shown in [1], having the ability to quickly mark and identify model parameter uncertainties facilitates error propagation, which in turn yield output uncertainties.

Kleb, Bil↗

DOE OSTI2018

Eddy Current Probe Parameters Identification Using a Genetic Algorithm and Simultaneous Perturbation Stochastic Approximation

This study tries to identify the coil parameters using numerical methods. The eddy current testing (ECT) is used for evaluation of a crack with the aid of numerical simulations by utilizing the identification of these parameters. In this study, a comparison of the performance of the GA and SPSA algorithms to identify the parameter values of the coil sensors are presented. So, the optimization probe geometry is introduced in the simulation with Three-dimensional finite element simulations (FLUX finite element code) were conducted to obtain eddy current signals resulting from a crack in a plate made of aluminium. The simulation results are compared with experimental measurements for the defect present in a plate.

42 ENGINEERING↗

DOE OSTI2021

Spread Spectrum Time Domain Reflectometry and Steepest Descent Inversion to Measure Complex Impedance

In this paper, we present a method for estimating complex impedances using reflectometry and a modified steepest descent inversion algorithm. We simulate spread spectrum time domain reflectometry (SSTDR), which can measure complex impedances on energized systems for an experimental setup with resistive and capacitive loads. A parametric function, which includes both a misfit function and stabilizer function, is created. The misfit function is a least squares estimate of how close the model data matches observed data. The stabilizer function prevents the steepest descent algorithm from becoming unstable and diverging. Steepest descent iteratively identifies the model parameters that minimize the parametric function. We validate the algorithm by correctly identifying the model parameters (capacitance and resistance) associated with simulated SSTDR data, with added 3 dB white Gaussian noise. With the stabilizer function, the steepest descent algorithm estimates of the model parameters are bounded within a specified range. Furthermore, the errors for capacitance (220pF to 820pF) and resistance (50 Ω to 270 Ω) are < 10%, corresponding to a complex impedance magnitude |R +1/jωC| of 53 Ω to 510 Ω.

14 SOLAR ENERGY↗

NASA NTRS1980

Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

Hotchkiss, G. B.↗

DOE OSTI2012

Some Issues in Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

The current tuning process of parameters in global climate models is often performed subjectively, or treated as an optimization procedure to minimize the difference between model fields and observations. The later approach may be generating a set of tunable parameters that approximate the observed climate but via an unrealistic balance of physical processes and/or compensating errors over different regions in the globe. In this study, we run the Weather Research and Forecasting (WRF) regional model constrained by the reanalysis data over the Southern Great Plains (SGP) where abundant observational data from various resources are available for calibration of the input parameters and validation of the model results. Our goal is to quantify the uncertainty ranges and identify the optimal values of five key input parameters in a new Kain-Frisch (KF) convective parameterization scheme incorporated in the WRF model. A stochastic sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA), is employed to efficiently sample the input parameters in KF scheme based on the skill score so that the algorithm progressively moves toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP show that the model bias for precipitation can be significantly reduced by using five optimal parameters identified by the MVFSA algorithm. The model performance is very sensitive to downdraft and entrainment related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreases as the ratio of downdraft to updraft flux increases. Larger CAPE consumption time results in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by only constraining precipitation generates positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25 km simulation, both the magnitude and spatial pattern of simulated precipitation are improved at 12-km spatial resolution. The optimal parameters identified from the SGP region have also improved the simulation of precipitation when moving model domain to another region with a different climate regime (i.e. North America monsoon region). These results suggest the improvement of precipitation simulation by using the optimal parameters remains when the model domain or spatial resolution is changed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Superheavy dark matter through Higgs portal operators

The WIMPzilla hypothesis states that the dark matter is a super-weakly-interacting and super-heavy particle. Conventionally, the WIMPzilla abundance is set by gravitational particle production during or at the end of inflation. In this study we allow the WIMPzilla to interact directly with Standard Model fields through the Higgs portal, and we calculate the thermal production (freeze-in) of WIMPzilla dark matter from the annihilation of Higgs boson pairs in the plasma. The two particle-physics model parameters are the WIMPzilla mass and the Higgs-WIMPzilla coupling. The two cosmological parameters are the reheating temperature and the expansion rate of the universe at the end of inflation. We delineate the regions of parameters space where either gravitational or thermal production is dominant, and within those regions we identify the parameters that predict the observed dark matter relic abundance. Allowing for thermal production opens up the parameter space, even for Planck-suppressed Higgs-WIMPzilla interactions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Unveiling the Essential Parameters Driving Mineral Reactions during CO2 Storage in Carbonate Aquifers through Proxy Models

Numerical simulation is a commonly employed technique for studying carbon dioxide (CO2) storage processes in porous media, particularly saline aquifers. It enables the representation of diverse trapping mechanisms and the assessment of CO2 retention capacity within the subsurface. The intricate physicochemical phenomena involved necessitate the incorporation of multiphase flow, accurate depiction of fluid and rock properties, and their interactions. Among these factors, geochemical reaction rates and mechanisms are pivotal for successful CO2 trapping in carbonate reactive rocks. However, research on kinetic parameters and the influence of lithology on CO2 storage remains limited. This limitation is partly due to the challenges faced in laboratory experiments, where the time scale of the reactions and the lack of in situ conditions hinder accurate measurement of mineral reaction rates. This study employs proxy models constructed using response surfaces calibrated with simulation results to address uncertainties associated with geochemical reactions. Monte Carlo simulation is utilized to explore a broader range of parameters and identify influential factors affecting CO2 mineralization. The findings indicate that an open database containing kinetic parameters can support uncertainty assessment. Additionally, the proxy models effectively represent objective functions related to CO2 injectivity and mineralization, with calcite dissolution playing a predominant role. pH, calcite concentration, and CO2 injection rate significantly impact dolomite precipitation, while quartz content remains unaffected.

58 GEOSCIENCES↗

NASA NTRS1974

The application of sensitivity analysis to models of large scale physiological systems

A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.

Leonard, J. I.↗

NASA NTRS1978

Economic comparison of fabric filters and electrostatic precipitators for particulate control on coal-fired utility boilers

The uncertainties and associated costs involved in selecting and designing a particulate control device to meet California's air emission regulations are considered. The basic operating principles of electrostatic precipitators and fabric filters are discussed, and design parameters are identified. The size and resulting cost of the control device as a function of design parameters is illustrated by a case study for an 800 MW coal-fired fired utility boiler burning a typical southwestern subbituminous coal. The cost of selecting an undersized particulate control device is compared with the cost of selecting an oversized device.

Cukor, P. M.↗

NASA NTRS1993

Radiative Cooling of Warm Molecular Gas

We consider the radiative cooling of warm (T >= 100 K), fully molecular astrophysical gas by rotational and vibrational transitions of the molecules H2O, CO, and H2. Using an escape probability method to solve for the molecular level populations, we have obtained the cooling rate for each molecule as a function of temperature, density, and an optical depth parameter. A four-parameter expression proves useful in fitting the run of cooling rate with density for any fixed values of the temperature and optical depth parameter. We identify the various cooling mechanisms which are dominant in different regions of the astrophysically relevant parameter space. Given the assumption that water is very abundant in warm regions of the interstellar medium, H2O rotational transitions are found to dominate the cooling of warm interstellar gas over a wide portion of the parameter space considered. While chemical models for the interstellar medium make the strong prediction that water will be produced copiously at temperatures above a few hundred degrees, our assumption of a high water abundance has yet to be tested observationally. The Infrared Space Observatory and the Submillimeter Wave Astronomy Satellite will prove ideal instruments for testing whether water is indeed an important coolant of interstellar and circumstellar gas.

Neufeld, David A.↗

DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Evaluating SWAT + model uncertainties for human and natural outcomes: Application in a Great Lakes agricultural watershed

Nutrient exports from agricultural lands in the Great Lakes Region pose significant threats to water quality and ecological health through eutrophication, hypoxia, and harmful algal blooms. Climate change and agricultural adaptation practices complicate future nutrient loading due to intensified hydrologic cycles and land use decisions. Our research focuses on evaluating the Soil and Water Assessment Tool (SWAT) plus model parametric uncertainties for human and natural outcomes across different scales. These factors are integral to ensuring a balance between productive agricultural practices and maintaining the health of watershed hydrology. However, uncertainties in modeling such complex interactions pose significant challenges, limiting our ability to precisely determine critical factors that influence crop yield and soil moisture. Our analysis employs Sobol global sensitivity analysis to evaluate first-order, second order, and total-order indices for SWAT crop growth parameter, ensuring comprehensive assessment of individual and interactive effects on model outputs. The objective is to identify the parameters that significantly affect model outputs for crop yield and soil moisture and improve our understanding of their interactions at the basin and hydrological response unit (HRU) scale. Our case study, the Portage River Watershed, which drains into Lake Erie, is chosen to better capture finer scale interactions crucial for predicting nutrient loading under future climate scenarios. This foundational work is aimed at setting the stage for the future development of an agent-based model (ABM). The ABM model would incorporate SWAT outputs to dynamically simulate decision-making processes.

Bunyon, Enock↗

NASA NTRS1987

A new method of boundary parameter estimation for a two-dimensional diffusion system under noisy observations

The purpose of this paper is to establish a method for identifying unknown parameters involved in the boundary state of a class of diffusion systems under noisy observations. A mathematical model of the system dynamics is given by a two-dimensional diffusion equation. Noisy observations are made by sensors allocated on the system boundary. Starting with the mathematical model mentioned above, an online parameter estimation algorithm is proposed within the framework of the maximum likelihood estimation. Existence of the optimal solution and related necessary conditions are discussed. By solving a local variation of the cost functional with respect to the perturbation of parameters, the estimation mechanism is proposed in a form of recursive computations. Finally, the feasibility of the estimator proposed here is demonstrated through results of digital simulation experiments.

Sunahara, Y.↗

NASA NTRS1988

A method of boundary parameter estimation for a two-dimensional diffusion system under noisy observations

Sunahara, Y.↗