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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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DOE OSTI2021

Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework

Accurate understanding and quantification of the risk to critical infrastructure posed by future large earthquakes continues to be a very challenging problem. Earthquake phenomena are quite complex and traditional approaches to predicting ground motions for future earthquake events have historically been empirically based whereby measured ground motion data from historical earthquakes are homogenized into a common data set and the ground motions for future postulated earthquakes are probabilistically derived based on the historical observations. This procedure has recognized significant limitations, principally due to the fact that earthquake ground motions tend to be dictated by the particular earthquake fault rupture and geologic conditions at a given site and are thus very site-specific. Historical earthquakes recorded at different locations are often only marginally representative. There has been strong and increasing interest in utilizing large-scale, physics-based regional simulations to advance the ability to accurately predict ground motions and associated infrastructure response. However, the computational requirements for simulations at frequencies of engineering interest have proven a major barrier to employing regional scale simulations. In a U.S. Department of Energy Exascale Computing Initiative project, the EQSIM application development is underway to create a framework for fault-to-structure simulations. This framework is being prepared to exploit emerging exascale platforms in order to overcome computational limitations. This article presents the essential methodology and computational workflow employed in EQSIM to couple regional-scale geophysics models with local soil-structure models to achieve a fully integrated, complete fault-to-structure simulation framework. Here, the computational workflow, accuracy and performance of the coupling methodology are illustrated through example fault-to-structure simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relative contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Feedback density and causal complexity of simulation model structure

Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the causal interactions present in the code. Causal structure diagrams facilitate comparison of simulation models, including those from different paradigms. Next, we develop metrics for evaluating a model’s causal complexity using its causal structure diagram. We discuss cyclomatic complexity as a measure of the intricacy of causal structure and introduce two new metrics that incorporate the concept of feedback, a fundamental component of causal structure. The first new metric introduced here is feedback density, a measure of the cycle-based interconnectedness of causal structure. The second metric combines cyclomatic complexity and feedback density into a comprehensive causal complexity measure. Finally, we demonstrate these complexity metrics on simulation models from multiple paradigms and discuss potential uses and interpretations. These tools enable direct comparison of models across paradigms and provide a mechanism for measuring and discussing complexity based on a model’s fundamental assumptions and design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources

Abstract. Here, computer models are ubiquitous tools used to represent systems across many scientific and engineering domains. For any given system, many computer models exist, each built on different assumptions and demonstrating variability in the ways in which these systems can be represented. This variability is known as epistemic uncertainty, i.e. uncertainty in our knowledge of how these systems operate. Two primary sources of epistemic uncertainty are (1) uncertain parameter values and (2) uncertain mathematical representations of the processes that comprise the system. Many formal methods exist to analyse parameter-based epistemic uncertainty, while process-representation-based epistemic uncertainty is often analysed post hoc, incompletely, informally, or is ignored. In this model description paper we present the multi-assumption architecture and testbed (MAAT v1.0) designed to formally and completely analyse process-representation-based epistemic uncertainty. MAAT is a modular modelling code that can simply and efficiently vary model structure (process representation), allowing for the generation and running of large model ensembles that vary in process representation, parameters, parameter values, and environmental conditions during a single execution of the code. MAAT v1.0 approaches epistemic uncertainty through sensitivity analysis, assigning variability in model output to processes (process representation and parameters) or to individual parameters. In this model description paper we describe MAAT and, by using a simple groundwater model example, verify that the sensitivity analysis algorithms have been correctly implemented. The main system model currently coded in MAAT is a unified, leaf-scale enzyme kinetic model of C 3 photosynthesis. In the Appendix we describe the photosynthesis model and the unification of multiple representations of photosynthetic processes. The numerical solution to leaf-scale photosynthesis is verified and examples of process variability in temperature response functions are provided. For rapid application to new systems, the MAAT algorithms for efficient variation of model structure and sensitivity analysis are agnostic of the specific system model employed. Therefore MAAT provides a tool for the development of novel or toy models in many domains, i.e. not only photosynthesis, facilitating rapid informal and formal comparison of alternative modelling approaches.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Oscillating water column structural model

An oscillating water column (OWC) wave energy converter is a structure with an opening to the ocean below the free surface, i.e. a structure with a moonpool. Two structural models for a non-axisymmetric terminator design OWC, the Backward Bent Duct Buoy (BBDB) are discussed in this report. The results of this structural model design study are intended to inform experiments and modeling underway in support of the U.S. Department of Energy (DOE) initiated Reference Model Project (RMP). A detailed design developed by Re Vision Consulting used stiffeners and girders to stabilize the structure against the hydrostatic loads experienced by a BBDB device. Additional support plates were added to this structure to account for loads arising from the mooring line attachment points. A simplified structure was designed in a modular fashion. This simplified design allows easy alterations to the buoyancy chambers and uncomplicated analysis of resulting changes in buoyancy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Model Structure and Climate Data Uncertainty in Historical Simulations of the Terrestrial Carbon Cycle (1850–2014)

Abstract The divergence among Earth system models in the terrestrial carbon cycle has prompted interest in how to reduce uncertainty. Previous studies have identified model structural uncertainty arising from process parameterizations and parameter values. The current study highlights the importance of climate forcing in generating carbon cycle uncertainty. We use simulations in which three models (Community Land Model version 4 (CLM4), CLM4.5, CLM5) with substantially different carbon cycles are forced with two climate reconstructions (CRUNCEPv7, Global Soil Wetness Project 3 version 1 (GSWP3v1)) to examine the contributions of model structure and climate to uncertainty in the carbon cycle over the period 1850–2014. Climate uncertainty for global annual net biome production exceeds one third of total uncertainty (defined as the sum of climate and model structure uncertainty) in the first half of the twentieth century, but declines after the 1950s. Global annual gross primary productivity, net primary productivity, heterotrophic respiration, and vegetation and soil carbon stocks have substantial climate uncertainty (relative to total uncertainty) throughout the simulation period. Climate forcing contributes more than one half of total uncertainty for these carbon cycle fluxes and stocks throughout boreal North America and Eurasia, some midlatitude regions, and in eastern Amazonia and western equatorial Africa during the decade 2000–2009. Comparison with observationally based data sets of the carbon cycle using model benchmarking methods provides insight into strengths and deficiencies among models and climate forcings, but we caution against overreliance on benchmarking to discriminate among models. The conceptualization of uncertainty arising from this study implies embracing multiple feasible model simulations rather than focusing on which model or simulation is best.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Screening sensitivity analysis of a PWR fuel assembly FEM structural model

Within the present project, computational methods to describe the static mechanical response of PWR fuel assemblies (FA) in the reactor core are developed. For this purpose a structural model of a PWR fuel assembly has been implemented with the Finite Element Method (FEM) using the ANSYS APDL Mechanical code. The purpose of the model is to investigate the long-term creep deformation of the fuel assemblies in the reactor core, commonly known as fuel assembly bow. FA bow is considered to be a complex process with a large number of influencing mechanisms and many only roughly known boundary conditions. Uncertainty and sensitivity analyses are a common way to investigate the predictive power and reliability of such complex models. Hence, as a first approach to understand the importance of the many model parameters, a qualitative sensitivity analysis of the model was conducted using the screening method proposed by Morris. In the first part of the paper, the FA structural model and its principal parameters are presented. The second part presents first the different simulation experiments used for the screening analyses. Based on the model description, relevant input parameters are then identified and a typical range of values is determined. The sensitivity analysis is performed in two subsequent steps. First, the model is screened for influential stiffness parameters with the objective of stiffening the FA structure. Second, the influence of the uncertain boundary conditions and material models on the FA creep deformation is tested. For the considered FA design it is concluded that the influence of the involved uncertainties is comparable to the influence of the parameters which can increase the stiffness. An integral approach accounting for these uncertainties is hence necessary to predict FA bow reliably in order to take additional measures for a further reduction of FA bow amplitudes. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances

Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python package for modeling protein structures from pairwise distances between any atoms, and optionally, dihedral angles. Finally, we provide a user-friendly input format for harnessing modern biomolecular force fields in an easy-to-install package that can efficiently make use of multiple compute cores.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

HAWC2 and BeamDyn: Comparison Between Beam Structural Models for Aero-Servo-Elastic Frameworks

This work presents a comparison of two beam codes for aero-servo-elastic frameworks: a new structural model for the aeroelastic code HAWC2 and a new nonlinear beam model, BeamDyn, for the aeroelastic modularization framework FAST v8. The main goal is to establish the suitability of the two approaches to model the structural behaviour of modern wind turbine blades in operation. Through a series of benchmarking structural cases of increasing complexity, the capability of the two codes to simulate highly nonlinear effects is investigated and analyzed. Results show that even though the geometrically exact beam theory can better model effects such as very large deflections, rotations, and structural couplings, an approach based on a multi-body formulation assembled through linear elements is capable of computing accurate solutions for typical nonlinear beam theory benchmarking cases.

17 WIND ENERGY↗

DOE OSTI2016

HAWC2 and BeamDyn: Comparison Between Beam Structural Models for Aero-Servo-Elastic Frameworks: Preprint

17 WIND ENERGY↗

DOE OSTI2023

Structural Models of the Rhodopseudomonas palustris Proteome

This dataset contains the structural models for the primary transcripts of the Rhodopseudomonas palustris proteome. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. palustris proteome to those available in the AlphaFold Protein Structure Database.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures

Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

3D WHOLE-PROMINENCE FINE STRUCTURE MODELING

We present the first 3D whole-prominence fine structure model. The model combines a 3D magnetic field configuration of an entire prominence obtained from nonlinear force-free field simulations, with a detailed description of the prominence plasma. The plasma is located in magnetic dips in hydrostatic equilibrium and is distributed along multiple fine structures within the 3D magnetic model. Through the use of a novel radiative transfer visualization technique for the Hα line such plasma-loaded magnetic field model produces synthetic images of the modeled prominence comparable with high-resolution observations. This allows us for the first time to use a single technique to consistently study, in both emission on the limb and absorption against the solar disk, the fine structures of prominences/filaments produced by a magnetic field model.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Harnessing expert knowledge: Defining a Bayesian network decision model with limited data-Model structure for the vibration qualification problem

As systems become more complex, systems engineers rely on experts to inform decisions. There are few experts and limited data in many complex new technologies. This challenges systems engineers as they strive to plan activities such as qualification in an environment where technical constraints are coupled with the traditional cost, risk, and schedule constraints. Bayesian network (BN) models provide a framework to aid systems engineers in planning qualification efforts with complex constraints by harnessing expert knowledge and incorporating technical factors. By quantifying causal factors, a BN model can provide data about the risk of implementing a decision supplemented with information on driving factors. This allows a systems engineer to make informed decisions and examine “what-if” scenarios. This paper discusses a novel process developed to define a BN model structure based primarily on expert knowledge supplemented with extremely limited data (25 data sets or less). The model was developed to aid qualification decisions—specifically to predict the suitability of six degrees of freedom (6DOF) vibration testing for qualification. The process defined the model structure with expert knowledge in an unbiased manner. Finally, validation during the process execution and of the model provided evidence the process may be an effective tool in harnessing expert knowledge for a BN model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A robust process-structure model for predicting the joint interface structure in impact welding

The impact welding is gaining popularity in joining of dissimilar metals as any intermetallics are often very thin or discontinous with little to no heat affected zone resulting from the process. This work focuses on a recently developed impact welding technique, known as the Vaporizing Foil Actuator Welding (VFAW), which uses the pressure created from the electrically driven rapid vaporization of a thin metallic conductor to drive the flyer. A typical characteristic of an impact-welded joint is the wavy weld interface which is attributed to the complex interfacial kinematics resulting from the high-speed oblique impact of the metal plates. A numerical simulation framework based on the finite element method (FEM) with Eulerian formalism is developed to model the high-speed impact between the metal plates. The model accounts for the thermomechanical interactions in the process and captures the complex interfacial deformations. A thorough validation of the model is achieved by comparing the wave characteristics obtained from numerical simulations with the experimental results from vaporizing foil actuator welding. The amplitude and wavelength of the interfacial waves and the resulting joint microstructure is shown to be strongly dependent on the process conditions and the specific material system, which is well captured by the model.

42 ENGINEERING↗

DOE OSTI2016

QUIESCENT PROMINENCES IN THE ERA OF ALMA: SIMULATED OBSERVATIONS USING THE 3D WHOLE-PROMINENCE FINE STRUCTURE MODEL

We use the detailed 3D whole-prominence fine structure model to produce the first simulated high-resolution ALMA observations of a modeled quiescent solar prominence. The maps of synthetic brightness temperature and optical thickness shown in the present paper are produced using a visualization method for synthesis of the submillimeter/millimeter radio continua. We have obtained the simulated observations of both the prominence at the limb and the filament on the disk at wavelengths covering a broad range that encompasses the full potential of ALMA. We demonstrate here extent to which the small-scale and large-scale prominence and filament structures will be visible in the ALMA observations spanning both the optically thin and thick regimes. We analyze the relationship between the brightness and kinetic temperature of the prominence plasma. We also illustrate the opportunities ALMA will provide for studying the thermal structure of the prominence plasma from the cores of the cool prominence fine structure to the prominence–corona transition region. In addition, we show that detailed 3D modeling of entire prominences with their numerous fine structures will be important for the correct interpretation of future ALMA observations of prominences.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Structural Models and Sequence Alignment Results of the Rhodospirillum rubrum Proteome

This dataset contains the structural models for the primary transcripts of the Rhodospirillum rubrum proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. rubrum proteome to those available in the AlphaFold Protein Structure Database. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHBlits: https://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: https://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Structural Models and Sequence Alignment Results of the Desulfovibrio vulgaris Proteome

This dataset contains the structural models for the primary transcripts of the Desulfovibrio vulgaris proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the D. vulgaris proteome to those available in the AlphaFold Protein Structure Database (AFDB). This is a bit more complicated since the proteins reporting in the AFDB originate from an outdated form of the D. vulgaris sequence. The different versions of the D. vulgaris gene annotation are collected in the Chronology subdirectory; further consideration of these changes on the structural space of the proteome are currently underway. For proteins that have been annotated as hypothetical, sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments. References AlphaFold v2 Multimer: https://doi.org/10.1101/2021.10.04.463034. References HHblits: hhtps://doi.org/10.1186/s12859-019-3019-7. References SAdLSA: hhtps://doi.org/10.3389/fbinf.2021.689960.

59 BASIC BIOLOGICAL SCIENCES↗