Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model structural error”

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.

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

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Theory of the Miscibility of Fullerenes in Random Copolymer Melts

We combine polymer integral equation theory and computational chemistry methods to study the interfacial structure, effective interactions, miscibility and spatial dispersion mechanism of fullerenes dissolved in specific random AB copolymer melts characterized by strong non-covalent electron donor-acceptor interactions with the nanofiller. A statistical mechanical basis is developed for designing random copolymers to optimize fullerene dispersion at intermediate copolymer compositions. Pair correlation calculations reveal a strong sensitivity of interfacial packing near the fullerene to copolymer composition and adsorption energy mismatch. The potential of mean force between fullerenes displays rich trends, often non-monotonic with copolymer composition, reflecting a non-additive competition between direct filler attractions and polymer-mediated bridging and steric stabilization. The spinodal phase diagrams are in qualitative agreement with recent solubility limit experimental observations on three systems, and testable predictions are made for other random copolymers. The distinctive non-monotonic variation of miscibility with copolymer composition is found to be primarily a consequence of composition-dependent, spatially short-range attractions between the A and B monomers with the fullerene. A remarkably rich, polymer-specific temperature dependence of the spinodal diagram is predicted which reflects the thermal sensitivity of spatial correlations which can result in fullerene miscibility either increasing or decreasing with cooling. The calculations are contrasted with a simpler effective homopolymer model and the random structure Flory-Huggins model. The former appears to be qualitatively reasonable but can incur large quantitative errors since it misses preferential packing of monomers near nanoparticles, while the latter appears to fail qualitatively due to its neglect of all spatial correlations.

Dadmun, Mark D↗
DOE OSTI2019

Decorrelating the errors of the galaxy correlation function with compact transformation matrices

Abstract Covariance matrix estimation is a persistent challenge for cosmology, often requiring a large number of synthetic mock catalogues. The off-diagonal components of the covariance matrix also make it difficult to show representative error bars on the 2-point correlation function (2PCF) since errors computed from the diagonal values of the covariance matrix greatly underestimate the uncertainties. We develop a routine for decorrelating the projected and anisotropic 2PCF with simple and scale-compact transformations on the 2PCF. These transformation matrices are modelled after the Cholesky decomposition and the symmetric square root of the Fisher matrix. Using mock catalogues, we show that the transformed projected and anisotropic 2PCF recover the same structure as the original 2PCF while producing largely decorrelated error bars. Specifically, we propose simple Cholesky-based transformation matrices that suppress the off-diagonal covariances on the projected 2PCF by ${\sim } 95{{\ \rm per\ cent}}$ and that on the anisotropic 2PCF by ${\sim } 87{{\ \rm per\ cent}}$. These transformations also serve as highly regularized models of the Fisher matrix, compressing the degrees of freedom so that one can fit for the Fisher matrix with a much smaller number of mocks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters

Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.

Simulation, autotuning↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Direct numerical simulations in solid mechanics for quantifying the macroscale effects of microstructure and material model-form error

Two fundamental approximations in macroscale solid-mechanics modeling are (1) the assumption of scale separation in homogenization theory and (2) the use of a macroscopic plasticity material model that represents, in a mean sense, the multitude of inelastic processes occurring at the microscale. With the goal of quantifying the errors induced by these approximations on engineering quantities of interest, we perform a set of direct numerical simulations (DNS) in which polycrystalline microstructures are embedded throughout a macroscale structure. The largest simulations model over 50,000 grains. The microstructure is idealized using a randomly close-packed Voronoi tessellation in which each polyhedral Voronoi cell represents a grain. An face centered cubic crystal-plasticity model is used to model the mechanical response of each grain. The overall grain structure is equiaxed, and each grain is randomly oriented with no overall texture. The detailed results from the DNS simulations are compared to results obtained from conventional macroscale simulations that use homogeneous isotropic plasticity models. The macroscale plasticity models are calibrated using a representative volume element of the idealized microstructure. Furthermore, we envision that DNS modeling will be used to gain new insights into the mechanics of material deformation and failure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Process-Based Balance-of-System Cost Modeling for Offshore Wind Power Plants in the United States: Preprint

This work describes the development of a process based and open source balance of system cost model which provides the capability to evaluate both existing and novel offshore wind technologies. Individual design and installation steps are represented with bottom-up engineering models that compute the times and costs associated with the process; furthermore, operational constraints are assigned to each process so that delays due to weather and presence of marine mammals may be accounted for in the overall project timeline. The model structure, assumptions, inputs, and results are vetted with industry partners and compared against actual projects for validation; installation times are shown to agree with real data with relative errors of less than 35\%. Case studies are presented to demonstrate the functionality of the model. First, individual vessel efficiencies are computed for varying numbers of installation vessels and weather time series to show the diminishing returns of more than two feeder barges. Then, array cable capital costs and installation times are determined for a representative project with different turbine sizes. This quantifies the cost benefit tradeoffs of decreasing number of turbines, increased turbine spacing, and fewer turbine terminations and shows a net cost savings. These results demonstrate that the BOS model features the accuracy, functionality, and accessibility to serve as the foundation for a wide range of analyses to identify cost reduction potentials for offshore wind energy in the United States.

17 WIND ENERGY↗
DOE OSTI2024

Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation

The multivariate adaptive regression spline (MARS) approach of Friedman and its Bayesian counterpart are effective approaches for the emulation of computer models. The traditional assumption of Gaussian errors limits the usefulness of MARS, and many popular alternatives, when dealing with stochastic computer models. Here, we propose a generalized Bayesian MARS (GBMARS) framework which admits the broad class of generalized hyperbolic distributions as the induced likelihood function. This allows us to develop tools for the emulation of stochastic simulators which are parsimonious, scalable, and interpretable and require minimal tuning, while providing powerful predictive and uncertainty quantification capabilities. GBMARS is capable of robust regression with t distributions, quantile regression with asymmetric Laplace distributions, and a general form of “Normal-Wald” regression in which the shape of the error distribution and the structure of the mean function are learned simultaneously. We demonstrate the effectiveness of GBMARS on various stochastic computer models, and we show that it compares favorably to several popular alternatives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors

Introduction: Measurement errors are ubiquitous in epidemiological studies, especially for doses arising from environmental and occupational exposures, which are difficult to assess. Correction methods for independent measurement errors, have been comprehensively described elsewhere, including regression calibration, simulation extrapolation (SIMEX), etc. However, when exposure estimates are constructed using complicated physical and biological models, uncertainties in the dosimetry system can become very complex. In such systems, uncertainties in some parameters may affect a large group of study participants simultaneously, and hence are “shared” as opposed to independent dosimetric uncertainties. Representing the structure of the uncertainty in the dosimetry by providing multiple realizations of possible dose has been described for several studies. The goal of this paper is to implement, and explore the properties of, a generalized estimating equation-based approach to shared uncertainties based on principles described in an earlier paper. Methods: The effects of shared and unshared dosimetry error on parameter estimates and confidence intervals in a generalized excess relative risk model are examined. A post-hoc correction method is implemented that constructs corrected confidence intervals based on an underlying variance structure described in an earlier paper. In this correction method it is assumed that the estimated slope parameter in a linear excess relative risk model is distributed as a mixture of normal and lognormal components. A simulation study of the behavior of this correction was developed based on multiple realizations provided by the Monte Carlo dosimetry system used for the plutonium exposures in the Mayak Worker Cohort and a risk model with close similarity to that used in analyses of lung cancer data in that cohort. Results: In the simulation study we found that shared dose errors severely degraded the coverage probabilities of confidence limits for the slope parameter in the linear excess relative risk model relating uncertain exposure to lung cancer risk. Using our correction method to adjust the confidence intervals provided much improved coverage probabilities for this parameter. A smaller amount of degradation in confidence interval coverage for other parameters (e.g. those in the baseline and dose modification terms) was noted, and the proposed correction method also improved coverage for these parameters as well. Discussion: Compared to other recently described methods our proposed correction method relies on relatively familiar software to fit dose response models. The basic modeling philosophy described here first uses existing Poisson regression methods to estimate parameters in the excess relative risk model; in this first stage mean dose (computed from the multiple realizations) is used as the independent variable in the regression. Once the model is fit we then correct (widen) the standard errors for all parameters using the proposed correction method. The simulation studies indicate that the methods described here are an important contribution to the available tools for dealing with shared measurement errors in complex risk analysis.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Improved Earthquake Source Parameters with 3D Wavespeed Models in California and Nevada

Seismic tomography harnesses earthquake data to explore the inaccessible structure of the Earth. Adjoint waveform tomography (AWT), a method of seismic tomography, updates the tomographic model by optimizing the fit between observed earthquake data and synthetic waveforms. The synthetic data are calculated by solving the wave equation through a given 3D model. An important requirement to calculating synthetics is the source information (location, centroid time, depth, and moment tensor). Errors in source information affect the quality of the synthetics produced, which in turn can limit how structure can be inferred in the AWT workflow. Here, to test the effect of updating source information, we used MTTime (Chiang, 2020), a time-domain full-waveform moment tensor inversion code, to calculate the moment tensors and depths of 118 earthquakes that occurred in California and Nevada over a 20-yr period. We calculated 3D Green’s functions using a 3D seismic wavespeed model of California and Nevada (Doody et al., 2023b). We show that the inverted solutions provide better waveform fits than the Global Centroid Moment Tensor catalog and increase usable, well-correlated data by up to 7%. Therefore, we argue that recalculating source parameters should be considered in AWT workflows, particularly for smaller magnitude events (⁠M w > 5.0).

58 GEOSCIENCES↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
DOE OSTI2016

On the general constraints in single qubit quantum process tomography

In this study, we briefly review single-qubit quantum process tomography for trace-preserving and nontrace-preserving processes, and derive explicit forms of the general constraints for fitting experimental data. These forms provide additional insight into the structure of the process matrix. We illustrate this with several examples, including a discussion of qubit leakage error models and the intuition which can be gained from their process matrices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Solving local structure around dopants in metal nanoparticles with ab initio modeling of X-ray absorption near edge structure

We adopted ab-initio X-ray Absorption Near Edge Structure (XANES) modelling for structural refinement of local environments around metal impurities in a large variety of materials. Our method enables both direct modelling, where the candidate structures are known, and the inverse modelling, where the unknown structural motifs are deciphered from the experimental spectra. We present also estimates of systematic errors, and their influence on the stability and accuracy of the obtained results. We illustrate our approach by following the evolution of local environment of palladium atoms in palladium-doped gold thiolate clusters upon chemical and thermal treatments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure of the Ndc80 complex and its interactions at the yeast kinetochore–microtubule interface

The conserved Ndc80 kinetochore complex, Ndc80c, is the principal link between mitotic spindle microtubules and centromere-associated proteins. We used AlphaFold 2 (AF2) to obtain predictions of the Ndc80 ‘loop’ structure and of the Ndc80 : Nuf2 globular head domains that interact with the Dam1 subunit of the heterodecameric DASH/Dam1 complex (Dam1c). The predictions guided design of crystallizable constructs, with structures close to the predicted ones. The Ndc80 ‘loop’ is a stiff, α-helical ‘switchback’ structure; AF2 predictions and positions of preferential cleavage sites indicate that flexibility within the long Ndc80c rod occurs instead at a hinge closer to the globular head. Conserved stretches of the Dam1 C terminus bind Ndc80c such that phosphorylation of Dam1 serine residues 257, 265 and 292 by the mitotic kinase Ipl1/Aurora B can release this contact during error correction of mis-attached kinetochores. We integrate the structural results presented here into our current molecular model of the kinetochore–microtubule interface. The model illustrates how multiple interactions between Ndc80c, DASH/Dam1c and the microtubule lattice stabilize kinetochore attachments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Generating a high-confidence reference genome map of the Greater Duckweed by integration of cytogenomic, optical mapping, and Oxford Nanopore technologies

Duckweeds are the fastest growing angiosperms and have the potential to become a new generation of sustainable crops. Although a seed plant, Spirodela polyrhiza clones rarely flower and multiply mainly through vegetative propagation. Whole-genome sequencing using different approaches and clones yielded two reference maps. One for clone 9509, supported in its assembly by optical mapping of single DNA molecules, and one for clone 7498, supported by cytogenetic assignment of 96 fingerprinted bacterial artificial chromosomes (BACs) to its 20 chromosomes. However, these maps differ in the composition of several individual chromosome models. We validated both maps further to resolve these differences and addressed whether they could be due to chromosome rearrangements in different clones. For this purpose, we applied sequential multicolor fluorescence in situ hybridization (mcFISH) to seven S. polyrhiza clones, using 106 BACs that were mapped onto the 39 pseudomolecules for clone 7498. Furthermore we integrated high-depth Oxford Nanopore (ON) sequence data for clone 9509 to validate and revise the previously assembled chromosome models. We found no major structural rearrangements between these seven clones, identified seven chimeric pseudomolecules and Illumina assembly errors in the previous maps, respectively. A new S. polyrhiza genome map with high contiguity was produced with the ON sequence data and genome-wide synteny analysis supported the occurrence of two Whole Genome Duplication events during its evolution. This work generated a high confidence genome map for S. polyrhiza at the chromosome scale, and illustrates the complementarity of independent approaches to produce whole-genome assemblies in the absence of a genetic map.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A quick guide for student-driven community genome annotation

High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the course of many years. The majority of gene models present in biological databases today have been identified in draft genome assemblies using automated annotation pipelines that are frequently based on orthologs from distantly related model organisms and usually have minor or major errors. Manual curation is time consuming and often requires substantial expertise, but is instrumental in improving gene model structure and identification. Manual annotation may seem to be a daunting and cost-prohibitive task for small research communities but involving undergraduates in community genome annotation consortiums can be mutually beneficial for both education and improved genomic resources. We outline a workflow for efficient manual annotation driven by a team of primarily undergraduate annotators. This model can be scaled to large teams and includes quality control processes through incremental evaluation. Moreover, it gives students an opportunity to increase their understanding of genome biology and to participate in scientific research in collaboration with peers and senior researchers at multiple institutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Bayesian inference of nonlinear unsteady aerodynamics from aeroelastic limit cycle oscillations

A Bayesian model selection and parameter estimation algorithm is applied to investigate the influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) of a pitching airfoil in the transitional Reynolds number regime. At small angles of attack, laminar boundary layer trailing edge separation causes negative aerodynamic damping leading to the LCO. The fluid–structure interaction of the rigid, but elastically mounted, airfoil and nonlinear unsteady aerodynamics is represented by two coupled nonlinear stochastic ordinary differential equations containing uncertain parameters and model approximation errors. Several plausible aerodynamic models with increasing complexity are proposed to describe the aeroelastic system leading to LCO. The likelihood in the posterior parameter probability density function (pdf) is available semi-analytically using the extended Kalman filter for the state estimation of the coupled nonlinear structural and unsteady aerodynamic model. The posterior parameter pdf is sampled using a parallel and adaptive Markov Chain Monte Carlo (MCMC) algorithm. The posterior probability of each model is estimated using the Chib–Jeliazkov method that directly uses the posterior MCMC samples for evidence (marginal likelihood) computation. The Bayesian algorithm is validated through a numerical study and then applied to model the nonlinear unsteady aerodynamic loads using wind-tunnel test data at various Reynolds numbers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Sensitivity Analysis of Material Model Parameters to Reproduce Crushing of Composite Tubes

The ability to predict the behavior of composite structures under impact conditions is an important challenge for engineering analysis since it requires modeling beyond the elasticity and into failure initiation and propagation. Crushing of laminate composites is the result of a combination of several and complex failure mechanisms, which are not always possible to reproduce together in a single finite element analysis (FEA). In this paper, models based on lamina-level failure criteria have been used to predict the onset of damage; once failure initiates, the mechanisms of failure propagation require reducing the material properties using several degradation schemes. Among the composite material models available in LS-DYNA, MAT-ENHANCED-COMPOSITE-DAMAGE (MAT-54) has been chosen for modeling the axial crushing of composite tubes with various resistant sections and wall slope, given the reduced experimental input parameters to insert into the card definition. Comparison with experimental data shows that such material model is able to successfully reproduce the crushing behavior, even if the numerical results are strictly correlated with some model parameters which cannot be measured experimentally or are purely mathematical expedients. Therefore, extensive calibration of these parameters by trial-and-error is necessary to achieve successful simulation results. Once done it for a structure made of the same composite material, it is possible to adopt the same card configuration for all the other cases, obtaining good agreement between numerical and experimental results. After, the attempt to model the same geometries using the MAT-LAMINATED-COMPOSITE-FABRIC (MAT-58) card with the same parameter values previously calibrated has been done obtaining results comparable with the experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST

Abstract. This article presents a validation study of the popular aeroservoelastic code suite OpenFAST leveraging weeks of measurements obtained during normal operation of a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate one-to-one turbulent flow fields (i.e., comparing simulation to measurement in 10 min increments, or bins) through unconstrained and constrained assimilation methods using the kinematic turbulence generators TurbSim and PyConTurb. A total of 253 bins of 10 min of normal turbine operation were selected for analysis, and a statistical comparison in terms of performance and loads is presented. We show that successful validation of the model was not strongly dependent on the type of inflow assimilation method used for mean quantities of interest, which had median modeling errors per wind-speed interval generally within 5 %–10 % of the measurement. The type of inflow assimilation method did have a larger effect on the fatigue predictions for blade-root flapwise and tower-base fore–aft quantities, which surprisingly saw larger errors from the assumed higher-fidelity assimilation methods. Avenues for further work are discussed and include possible improvements to the aerodynamic, structural, and controller modeling that may offer insight on the origin of the up to ∼ 40 % median overprediction of fatigue for these quantities.

17 WIND ENERGY↗