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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 235 records · Page 13

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NASA NTRS1985

Impact resistance of fiber composites - Energy-absorbing mechanisms and environmental effects

Energy absorbing mechanisms were identified by several approaches. The energy absorbing mechanisms considered are those in unidirectional composite beams subjected to impact. The approaches used include: mechanic models, statistical models, transient finite element analysis, and simple beam theory. Predicted results are correlated with experimental data from Charpy impact tests. The environmental effects on impact resistance are evaluated. Working definitions for energy absorbing and energy releasing mechanisms are proposed and a dynamic fracture progression is outlined. Possible generalizations to angle-plied laminates are described.

Chamis, C. C.↗

DOE OSTI2022

An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data

This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.

42 ENGINEERING↗

NASA NTRS1990

Statistical mechanics models for motion and force planning

The models of statistical mechanics provide an alternative to the methods of classical mechanics more traditionally used in robotics. They have a potential to: improve analysis of object collisions; handle kinematic and dynamic contact interactions within the same frmework; and reduce the need for perfect deterministic world model information. The statistical mechanics models characterize the state of the system as a probability density function (p.d.f.) whose time evolution is governed by a partial differential equation subject to boundary and initial conditions. The boundary conditions when rigid objects collide reflect the conservation of momentum. The models are being developed to embedd in remote semi-autonomous systems with a need to reason and interact with a multiobject environment.

Rodriguez, G.↗

DOE OSTI2014

Statistical mechanical models for dissociative adsorption of O{sub 2} on metal(100) surfaces with blocking, steering, and funneling

We develop statistical mechanical models amenable to analytic treatment for the dissociative adsorption of O{sub 2} at hollow sites on fcc(100) metal surfaces. The models incorporate exclusion of nearest-neighbor pairs of adsorbed O. However, corresponding simple site-blocking models, where adsorption requires a large ensemble of available sites, exhibit an anomalously fast initial decrease in sticking. Thus, in addition to blocking, our models also incorporate more facile adsorption via orientational steering and funneling dynamics (features supported by ab initio Molecular Dynamics studies). Behavior for equilibrated adlayers is distinct from those with finite adspecies mobility. We focus on the low-temperature limited-mobility regime where analysis of the associated master equations readily produces exact results for both short- and long-time behavior. Kinetic Monte Carlo simulation is also utilized to provide a more complete picture of behavior. These models capture both the initial decrease and the saturation of the experimentally observed sticking versus coverage, as well as features of non-equilibrium adlayer ordering as assessed by surface-sensitive diffraction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Accelerated statistical failure analysis of multifidelity TRISO fuel models

Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Correct Interpretations of ENDF-102 Definitions for Resonance Effects

My Uncle Willie circa 1600 wrote “What’s in a name; a rose by any other name would smell as sweet.” I fear in this case we have a somewhat similar problem in that we may be using the same word but are not using the same definition; specifically, the word Unresolved. The simplest physics definition as it applies to neutron resonances, is the energy point where we can no longer see/measure ALL – let me repeat that – ALL - of the individual resonances. That seems simple and clear, but the question is: how to represent resonances beyond this point in order to accurately reproduce the effects we have seen in measurements and expect/need to reproduce in our applications. We know there are more, unseen resonances, otherwise we wouldn’t say Unresolved. The ENDF approach is well defined in ENDF-102 and simple: for ENDF data the only way to represent Unresolved data is by using a theoretical model to define the distribution of resonances, including those that are too narrow to measure (i.e., are unresolved). It is important to note that in ENDF this is the one and only Unresolved model, e.g., there is no provision in ENDF to accurately define individually ALL resonances above the Resolved energy range – by ALL here I mean both those that we can measure and those that we cannot individually measure, but that theory and integral measurements tells us are present. An alternative approach, which would appear to be equally valid, would be to include the latest measured data as tabulated energy expendent data extending upwards in energy above the Resolved energy range. In this approach the evaluation would not include an ENDF style Unresolved energy range; it would only include a Resolved resonance region, followed by tabulated higher energy points, representing the resonances that could be measured beyond the Resolved range. But an important point to note: By listing these resonances above the resolved energy one admits that at least some resonances in this energy range are missing as Unresolved; i.e., they are too narrow or overlapping to measure. The purpose of this paper is to illustrate that the later approach, while done with good intentions, and appearing to be valid/adequate in plots, does not meet the need of our engineering applications. Why? As we will see below, of these two possible approaches, only the ENDF use of a model to statistically include the missing, i.e., unresolved, resonances, can meet our engineering needs to reproduce the integral effects we have measured and understand. Only with this statistical model can we predict and include in our calculated results the important effects of temperature (Doppler broadening), and energy integrals (self-shielding). Below I will first present results using two ENDF/B-VIII.1 evaluations, U235 and U238, that use the correct ENDF-102 definition of an Unresolved resonance region, using a statistical model to include the effects of resonances that theory predicts are present, but are too narrow to measure. These two evaluations reproduce the expected temperature (Doppler) and energy integral (self-shielding) effects that we expect. Next I will present results using one ENDF/B-VIII.1 evaluation, 26-Fe-56, that does not use an ENDF-102 Unresolved resonance region; instead above its Resolved energy range it lists many tabulated energy points, that look like measured data, but by definition, since they are included above the ENDF Resolved energy range there are missing Unresolved resonances, i.e., there are missing the resonances that are too narrow to resolve, i.e., are unresolved. My conclusion, and I hope yours, is that the below figures illustrate that this approach does not reproduce the temperature and energy integrals that we expect and need to accurately calculate results for our fission reactor calculations. As such this approach should not be used in ENDF formatted evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1993

Design and analysis of numerical experiments

Methods for the design and analysis of numerical experiments that are especially useful and efficient in multidimensional parameter spaces are presented. The analysis method, which is similar to kriging in the spatial analysis literature, fits a statistical model to the output of the numerical model. The method is applied to a fully nonlinear, global, equivalent-barotropic dynamical model. The statistical model also provides estimates for the uncertainty of predicted numerical model output, which can provide guidance on where in the parameter space to conduct further experiments, if necessary. The method can provide significant improvements in the efficiency with which numerical sensitivity experiments are conducted.

Bowman, Kenneth P.↗

DOE OSTI2022

Solving Statistical Flowgraph Models: A Brief Review

Statistical flowgraph models (SFGMs) have proven useful for analysis and modeling of multistate processes in various fields. In this paper we briefly review flowgraph models, describe how to set up a model, and provide a fully worked example from biostatistics. References are provided for more details.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1971

Nonelastic nuclear reactions and accompanying gamma radiation

Several aspects of nonelastic nuclear reactions which proceed through the formation of a compound nucleus are dealt with. The full statistical model and the partial statistical model are described and computer programs based on these models are presented along with operating instructions and input and output for sample problems. A theoretical development of the expression for the reaction cross section for the hybrid case which involves a combination of the continuum aspects of the full statistical model with the discrete level aspects of the partial statistical model is presented. Cross sections for level excitation and gamma production by neutron inelastic scattering from the nuclei Al-27, Fe-56, Si-28, and Pb-208 are calculated and compared with avaliable experimental data.

Snow, R.↗

DOE OSTI2015

Turning statistical physics models into materials design engines

Despite the success statistical physics has enjoyed at predicting the properties of materials for given parameters, the inverse problem, identifying which material parameters produce given, desired properties, is only beginning to be addressed. Recently, several methods have emerged across disciplines that draw upon optimization and simulation to create computer programs that tailor material responses to specified behaviors. However, so far the methods developed either involve black-box techniques, in which the optimizer operates without explicit knowledge of the material's configuration space, or require carefully tuned algorithms with applicability limited to a narrow subclass of materials. Here we introduce a formalism that can generate optimizers automatically by extending statistical mechanics into the realm of design. The strength of this approach lies in its capability to transform statistical models that describe materials into optimizers to tailor them. Here, by comparing against standard black-box optimization methods, we demonstrate how optimizers generated by this formalism can be faster and more effective, while remaining straightforward to implement. The scope of our approach includes possibilities for solving a variety of complex optimization and design problems concerning materials both in and out of equilibrium.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Estimation of pollen productivity and dispersal: How pollen assemblages in small lakes represent vegetation

Abstract Quantitative understanding of vegetation dynamics over timespans beyond a century remains limited. In this regard, the pollen‐based reconstruction of past vegetation enables unique research opportunities by quantifying changes in plant community compositions during hundreds to thousands of years. Critically, the methodological basis for most reconstruction approaches rests upon estimates of pollen productivity and dispersal. Previous studies, however, have reached contrasting conclusions concerning these estimates, which may be perceived to challenge the applicability and reliability of pollen‐based reconstruction. Here we show that conflicting estimates of pollen production and dispersal are, at least in part, artifacts of fixed assumptions of pollen dispersal and insufficient spatial resolution of vegetation data surrounding the pollen‐collecting lake. We implemented a Bayesian statistical model that related pollen assemblages in surface sediments of 33 small lakes (<2 ha) in the northeastern United States, with surrounding vegetation ranging from 10 1 to >10 5 m from the lake margin. Our analysis revealed three key insights. First, pollen productivity is largely conserved within taxa and across forest types. Second, when local (within a 1‐km radius) vegetation abundances are not considered, pollen‐source areas may be overestimated for some common taxa (Cupressaceae, Pinus , Quercus , and Tsuga ). Third, pollen dispersal mechanisms may differ between local and regional scales; this is missed by pollen‐dispersal models used in previous studies. These findings highlight the complex interactions between vegetation heterogeneity on the landscape and pollen dispersal. We suggest that, when estimating pollen productivity and dispersal, both detailed local and extended regional vegetation must be taken into account. Also, both deductive (mechanistic models) and inductive (statistical models) approaches are needed to better understand the emergent properties of pollen dispersal in heterogeneous landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Scalable statistical inference of photometric redshift via data subsampling

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift—the stretching effect in observing the light of far-away galaxies—given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.

data subsampling↗

NASA NTRS2005

Regression Analysis of Long-term Profile Ozone Data Set from BUV Instruments

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9,11, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUV instrument, which operated from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established from comparisons with SSBUV shuttle flight data. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. We use statistical time-series analysis of the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. The Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. We extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the statistics of the residuals from the fit for the Nimbus 4 period to those obtained from the 1978-2003 period over which the statistical model coefficients were estimated.

Frith, Stacey↗

DOE OSTI2016

The statistical multifragmentation model: Origins and recent advances

We review the Statistical Multifragmentation Model (SMM) which considers a generalization of the liquid-drop model for hot nuclei and allows one to calculate thermodynamic quantities characterizing the nuclear ensemble at the disassembly stage. We show how to determine probabilities of definite partitions of finite nuclei and how to determine, through Monte Carlo calculations, observables such as the caloric curve, multiplicity distributions, heat capacity, among others. Some experimental measurements of the caloric curve confirmed the SMM predictions of over 10 years before, leading to a surge in the interest in the model. However, the experimental determination of the fragmentation temperatures relies on the yields of different isotopic species, which were not correctly calculated in the schematic, liquid-drop picture, employed in the SMM. This led to a series of improvements in the SMM, in particular to the more careful choice of nuclear masses and energy densities, specially for the lighter nuclei. With these improvements the SMM is able to make quantitative determinations of isotope production. We show the application of SMM to the production of exotic nuclei through multifragmentation. These preliminary calculations demonstrate the need for a careful choice of the system size and excitation energy to attain maximum yields.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1999

Model Error Estimation for the CPTEC Eta Model

Statistical data assimilation systems require the specification of forecast and observation error statistics. Forecast error is due to model imperfections and differences between the initial condition and the actual state of the atmosphere. Practical four-dimensional variational (4D-Var) methods try to fit the forecast state to the observations and assume that the model error is negligible. Here with a number of simplifying assumption, a framework is developed for isolating the model error given the forecast error at two lead-times. Two definitions are proposed for the Talagrand ratio tau, the fraction of the forecast error due to model error rather than initial condition error. Data from the CPTEC Eta Model running operationally over South America are used to calculate forecast error statistics and lower bounds for tau.

Tippett, Michael K.↗

DOE OSTI2017

Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Statistical Shape Modeling of Cam Femoroacetabular Impingement

In this study, statistical shape modeling (SSM) was used to quantify three-dimensional (3D) variation and morphologic differences between femurs with and without cam femoroacetabular impingement (FAI). 3D surfaces were generated from CT scans of femurs from 41 controls and 30 cam FAI patients. SSM correspondence particles were optimally positioned on each surface using a gradient descent energy function. Mean shapes for control and patient groups were defined from the resulting particle configurations. Morphological differences between group mean shapes and between the control mean and individual patients were calculated. Principal component analysis was used to describe anatomical variation present in both groups. The first 6 modes (or principal components) captured statistically significant shape variations, which comprised 84% of cumulative variation among the femurs. Shape variation was greatest in femoral offset, greater trochanter height, and the head-neck junction. The mean cam femur shape protruded above the control mean by a maximum of 3.3 mm with sustained protrusions of 2.5-3.0 mm along the anterolateral head-neck junction and distally along the anterior neck, corresponding well with reported cam lesion locations and soft-tissue damage. This study provides initial evidence that SSM can describe variations in femoral morphology in both controls and cam FAI patients and may be useful for developing new measurements of pathological anatomy. SSM may also be applied to characterize cam FAI severity and provide templates to guide patient-specific surgical resection of bone.

statistical shape modeling↗