SEARCH · Engineering Papers

Results for “Models, Statistical”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2016

Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.

47 OTHER INSTRUMENTATION↗

NASA NTRS1970

Regional land use studies

Remote sensing technology and data from instrumented satellites and high altitude aircraft are proposed for mapping land use on a current national basis, for monitoring changes and trends, and for creating statistical models which can be manipulated to demonstrate the probable effects of proposed land use and of environmental changes over large areas. Both Apollo spacecraft and aircraft photography were used; the spacecraft pictures delineated the cropland and urban boundaries more clearly. A computer model is also proposed for statistical analysis and for printing out updated maps automatically; this model will include a data bank which can be updated rapidly with changes detected by the computer.

Place, J. L.↗

DOE OSTI2019

Strong one-neutron emission from two-neutron unbound states in β decays of the r -process nuclei Ga 86 , 87

β-delayed one-neutron and two-neutron branching ratios (P$_{1n}$ and P$_{2n}$) have been measured in the decay of A = 84 to 87 Ga isotopes at the Radioactive-Isotope Beam Factory (RIBF) at the RIKEN Nishina Center using a high-efficiency array of 3 He neutron counters (BRIKEN). Two-neutron emission was observed in the decay of 84, 85, 87 Ga for the first time and the branching ratios were measured to be P$_{2n}$ = 1.6 (2)%, 1.3 (2)%, and 10.2 ( 28 ) stat ( 5 ) sys %, respectively. One-neutron branching ratio of 87 Ga (P$_{1n}$= 81 (9) stat (8) sys %) and half-life of 29(4) ms were measured for the first time. The branching ratios of 86 Ga were also measured to be P$_{1n}$ = 74 (2) stat (8) sys % and 16.2 (9) stat ( 6 ) sys % with better precision than a previous study. The observation that P$_{1n}$ > P$_{2n}$ for both 86,87 Ga was unexpected and is interpreted as a signature of dominating one-neutron emission from the two-neutron unbound excited states in 86, 87 Ge. In order to interpret the experimental results, shell-model and Hauser-Feshbach statistical model calculations of delayed particle and γ-ray emission probabilities were performed. This model framework reproduces the experimental results. The shell model alone predicts P$_{2n}$ significantly larger than P$_{1n}$ for the 87 Ga decay, and it is necessary to invoke a statistical description to successfully explain the observation that P$_{1n}$ > P$_{2n}$. Our new results demonstrate the relevance and importance of a statistical description of neutron emission for the prediction of the decay properties of multineutron emitters and that it must be included in the r -process modeling.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

SIMPLIFIED PREDICTIVE MODELS FOR CO₂ SEQUESTRATION PERFORMANCE ASSESSMENT RESEARCH TOPICAL REPORT ON TASK #3 STATISTICAL LEARNING BASED MODELS

We compare two approaches for building a statistical proxy model (metamodel) for CO₂ geologic sequestration from the results of full-physics compositional simulations. The first approach involves a classical Box-Behnken or Augmented Pairs experimental design with a quadratic polynomial response surface. The second approach used a space-filling maxmin Latin Hypercube sampling or maximum entropy design with the choice of five different meta-modeling techniques: quadratic polynomial, kriging with constant and quadratic trend terms, multivariate adaptive regression spline (MARS) and additivity and variance stabilization (AVAS). Simulations results for CO₂ injection into a reservoir-caprock system with 9 design variables (and 97 samples) were used to generate the data for developing the proxy models. The fitted models were validated with using an independent data set and a cross-validation approach for three different performance metrics: total storage efficiency, CO₂ plume radius and average reservoir pressure. The Box-Behnken–quadratic polynomial metamodel performed the best, followed closely by the maximin LHS–kriging metamodel.

Mishra, Srikanta↗

DOE OSTI2021

Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical results from lattice field theory calculations. Although the general models used are often strongly motivated by physics, many model variations can frequently be considered for the same lattice data. Model averaging, which amounts to a probability-weighted average over all model variations, can incorporate systematic errors associated with model choice without being overly conservative. We discuss the framework of model averaging from the perspective of Bayesian statistics, and give useful formulae and approximations for the particular case of least-squares fitting, commonly used in modeling lattice results. In addition, we frame the common problem of data subset selection (e.g. choice of minimum and maximum time separation for fitting a two-point correlation function) as a model selection problem and study model averaging as a straightforward alternative to manual selection of fit ranges. Numerical examples involving both mock and real lattice data are given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗

NASA NTRS2004

Model for Solar Proton Risk Assessment

A statistical model for cumulative solar proton event fluences and for worst-case flux during space missions is presented. New features include the solar minimum time period and proton energy spectra that extend to higher energies.

Xapsos, M. A.↗

NASA NTRS2004

Model for Solar Proton Risk Assessment

A statistical model for cumulative solar proton event fluences during space missions is presented that covers both the solar minimum and solar maximum phases of the solar cycle. It is based on data from the IMP and GOES series of satellites that is integrated together to allow the best features of each data set to be taken advantage of. This allows fluence-energy spectra to be extended out to energies of 327 MeV.

Xapos, M. A.↗

DOE OSTI2017

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. Furthermore, the lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Benchmark of numerical modeling approaches on the systematic performance evaluation of wave energy converters

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

Fatigue↗

DOE OSTI2015

Degradation science: Mesoscopic evolution and temporal analytics of photovoltaic energy materials

Based on recent advances in nanoscience, data science and the availability of massive real-world datastreams, the mesoscopic evolution of mesoscopic energy materials can now be more fully studied. The temporal evolution is vastly complex in time and length scales and is fundamentally challenging to scientific understanding of degradation mechanisms and pathways responsible for energy materials evolution over lifetime. We propose a paradigm shift towards mesoscopic evolution modeling, based on physical and statistical models, that would integrate laboratory studies and real-world massive datastreams into a stress/mechanism/response framework with predictive capabilities. These epidemiological studies encompass the variability in properties that affect performance of material ensembles. Mesoscopic evolution modeling is shown to encompass the heterogeneity of these materials and systems, and enables the discrimination of the fast dynamics of their functional use and the slow and/or rare events of their degradation. As a result, we delineate paths forward for degradation science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

On the Power of Uncertainties in Microbial System Modeling: No Need To Hide Them Anymore

For decades, microbiologists have considered uncertainties as an undesired side effect of experimental protocols. As a consequence, standard microbial system modeling strives to hide uncertainties for the sake of deterministic understanding. However, recent studies have highlighted greater experimental variability than expected and emphasized uncertainties not as a weakness but as a necessary feature of complex microbial systems. We therefore advocate that biological uncertainties need to be considered foundational facets that must be incorporated in models. Not only will understanding these uncertainties improve our understanding and identification of microbial traits, it will also provide fundamental insights on microbial systems as a whole. Taking into account uncertainties within microbial models calls for new validation techniques. Formal verification already overcomes this shortcoming by proposing modeling frameworks and validation techniques dedicated to probabilistic models. However, further work remains to extract the full potential of such techniques in the context of microbial models. Herein, we demonstrate how statistical model checking can enhance the development of microbial models by building confidence in the estimation of critical parameters and through improved sensitivity analyses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

A Parameter Subset Selection Algorithm for Mixed-Effects Models

Mixed-effects models are commonly used to statistically model phenomena that include attributes associated with a population or general underlying mechanism as well as effects specific to individuals or components of the general mechanism. This can include individual effects associated with data from multiple experiments. However, the parameterizations used to incorporate the population and individual effects are often unidentifiable in the sense that parameters are not uniquely specified by the data. As a result, the current literature focuses on model selection, by which insensitive parameters are fixed or removed from the model. Model selection methods that employ information criteria are applicable to both linear and nonlinear mixed-effects models, but such techniques are limited in that they are computationally prohibitive for large problems due to the number of possible models that must be tested. To limit the scope of possible models for model selection via information criteria, we introduce a parameter subset selection (PSS) algorithm for mixed-effects models, which orders the parameters by their significance. In conclusion, we provide examples to verify the effectiveness of the PSS algorithm and to test the performance of mixed-effects model selection that makes use of parameter subset selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2012

Linking Plasma Conditions in the Magnetosphere with Ionospheric Signatures

Modeling of the full magnetosphere, ring current and ionosphere system has become an indispensable tool in analyzing the series of events that occur during geomagnetic storms. The CCMC has a full model suite available for the magnetosphere, together with visualization tools that allow a user to perform a large variety of analyses. The January, 21, 2005 storm was a moderate-size storm that has been found to feature a large penetration electric field and unusually large polar caps (low-latitude precipitation patterns) that are otherwise found in super storms. Based on simulations runs at CCMC we can outline the likely causes of this behavior. Using visualization tools available to the online user we compare results from different magnetosphere models and present connections found between features in the magnetosphere and the ionosphere that are connected magnetically. The range of magnetic mappings found with different models can be compared with statistical models (Tsyganenko) and the model's fidelity can be verified with observations from low earth orbiting satellites such as DMSP and TIMED.

Rastaetter, Lutz↗

NASA NTRS2013

Micromechanics Fatigue Damage Analysis Modeling for Fabric Reinforced Ceramic Matrix Composites

A micromechanics analysis modeling method was developed to analyze the damage progression and fatigue failure of fabric reinforced composite structures, especially for the brittle ceramic matrix material composites. A repeating unit cell concept of fabric reinforced composites was used to represent the global composite structure. The thermal and mechanical properties of the repeating unit cell were considered as the same as those of the global composite structure. The three-phase micromechanics, the shear-lag, and the continuum fracture mechanics models were integrated with a statistical model in the repeating unit cell to predict the progressive damages and fatigue life of the composite structures. The global structure failure was defined as the loss of loading capability of the repeating unit cell, which depends on the stiffness reduction due to material slice failures and nonlinear material properties in the repeating unit cell. The present methodology is demonstrated with the analysis results evaluated through the experimental test performed with carbon fiber reinforced silicon carbide matrix plain weave composite specimens.

Min, J. B.↗

NASA NTRS2016

Brief History of Agricultural Systems Modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

agricultural systems↗

DOE OSTI2013

On the effect of intermittency of turbulence on the parabolic relation between skewness and kurtosis in magnetized plasmas

This paper is aimed to contribute to the scientific discussions that have been triggered by the experimental observation of a quadratic relation between the kurtosis and skewness of turbulent fluctuations present in fusion plasmas and other nonlinear physical systems. In this paper, we offer a general statistical model which attributes the observed K=aS{sup 2}+b relation to the varying intermittency of the experimental signals. The model is a two random variable model constructed to catch the essential intermittent feature of the real signal. One of the variables is the amplitude of the underlying intermittent event (e.g., turbulent structure) while the other is connected to the intermittency level of the system. This simple model can attribute physical meaning to the a and b coefficients, as they characterize the spatio-temporal statistics of intermittent events. By constructing a particle-conserving Gaussian model for the underlying coherent structures, the experimentally measured a and b coefficients could be adequately reproduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗