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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.

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At least 505 records · Page 28

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NASA NTRS2013

Forest Structure Characterization Using JPL's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

This paper concerns forest remote sensing using JPL's multi-baseline polarimetric interferometric UAVSAR data. It presents exemplary results and analyzes the possibilities and limitations of using SAR Tomography and Polarimetric SAR Interferometry (PolInSAR) techniques for the estimation of forest structure. Performance and error indicators for the applicability and reliability of the used multi-baseline (MB) multi-temporal (MT) PolInSAR random volume over ground (RVoG) model are discussed. Experimental results are presented based on JPL's L-band repeat-pass polarimetric interferometric UAVSAR data over temperate and tropical forest biomes in the Harvard Forest, Massachusetts, and in the La Amistad Park, Panama and Costa Rica. The results are partially compared with ground field measurements and with air-borne LVIS lidar data.

UAVSAR (Uninhabited Aerial Vehicle SAR)↗

NASA NTRS2013

Forest Structure Characterization Using Jpl's UAVSAR Multi-Baseline Polarimetric SAR Interferometry and Tomography

remote sensing↗

DOE OSTI2020

Exploring Hilbert space on a budget: Novel benchmark set and performance metric for testing electronic structure methods in the regime of strong correlation

This work explores the ability of classical electronic structure methods to efficiently represent (compress) the information content of full configuration interaction (FCI) wave functions. We introduce a benchmark set of four hydrogen model systems of different dimensionalities and distinctive electronic structures: a 1D chain, a 1D ring, a 2D triangular lattice, and a 3D close-packed pyramid. To assess the ability of a computational method to produce accurate and compact wave functions, we introduce the accuracy volume, a metric that measures the number of variational parameters necessary to achieve a target energy error. Using this metric and the hydrogen models, we examine the performance of three classical deterministic methods: (i) selected configuration interaction (sCI) realized both via an a posteriori (ap-sCI) and variational selection of the most important determinants, (ii) an a posteriori singular value decomposition (SVD) of the FCI tensor (SVD-FCI), and (iii) the matrix product state representation obtained via the density matrix renormalization group (DMRG). We find that the DMRG generally gives the most efficient wave function representation for all systems, particularly in the 1D chain with a localized basis. For the 2D and 3D systems, all methods (except DMRG) perform best with a delocalized basis, and the efficiency of sCI and SVD-FCI is closer to that of DMRG. For larger analogs of the models, the DMRG consistently requires the fewest parameters but still scales exponentially in 2D and 3D systems, and the performance of SVD-FCI is essentially equivalent to that of ap-sCI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perserverance rover to the surface of Mars at Jezero Crater. The entry capsule carried a set of instrumentation installed on the heat shield and backshell, named the Mars 2020 Entry, Descent, and Landing Instrumentation. The instruments include pressure transducers, thermocouples, heat flux gauges, and radiometers to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, are located in close proximity on the backshell. Each sensor is exposed to roughly the same environment, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that location on the backshell. The approach makes use of the Kalman filter methodology for solving state estimation problems. The filter has a predictor/corrector structure in which simplified process models are used to propagate the aeroheating states forward through time, which are then updated based on the measurement data. The method has been tested on simulated datasets.

Christopher D. Karlgaard↗

NASA NTRS2003

Collaborative Research: Atlantic Ocean Tropical/Subtropical Processes from Seasonal to Decadal Time Scales: Model/Data, Model/Model Comparison and Model/Data Synthesis Through Assimilation

The effort of this first year of research has been focused on the assimilation of TOPEX/Poseidon altimetric data into a primitive equation model of the Atlantic tropical/subtropical circulation. A reduced-rank, stationary Kalman filter has been constructed to assimilate the altimetric sea surface height anomaly (SHA) into the model. The goal is to assess how the inter-hemispheric transports between the Atlantic subtropics and tropics are affected by the assimilation and how the subsurface thermocline structure , and its variability ,is dynamically constrained by the SHA. The model is a reduced-gravity primitive equation GCM of the upper Atlantic Ocean between 30 S and 30 N. The assimilation scheme is an approximation to the extended Kalman filter in which the error covariances of the state estimates are calculated only in a reduced- dimension subspace. The subspace is defined by the leading empirical orthogonal functions calculated from an unconstrained model calculation. Both an identical twin experiment using simulated SHA observations and assimilation of the real TOPEX data were performed. Results from the twin experiments demonstrate the ability of the method to constrain the ocean circulation and the subsurface temperature structure. The impact on the subsurface temperature structure of TOPEX assimilation was assessed using data from expandable bathythermographs. This showed a substantial improvement in the estimated temperature variability only within 13 degrees in latitude around the equator. The impact of TOPEX SHA assimilation on zonally integrated meridional transport across different latitudes was also estimated. Again within 13 degrees from the equator both the mean amplitude and interannual variability of the surface and subsurface transports were significantly enhanced, while the transports were insensitive to the assimilation in the subtropics.

Malannotte-Rizzoli, Paola↗

NASA NTRS1976

Solar limb brightening in submillimeter wavelengths

Differential two-beam scans of the sun in submillimeter wavelengths (350 microns to 1 millimeter) indicate limb brightening approaching 1 percent when the cosine of the angle from the normal equals 0.60. The observations also show considerable chromospheric structure, both in active and quiet regions, but with less relative amplitude than at millimeter and centimeter wavelengths. The limited angular resolution of the observing system, together with photometric errors due to fluctuating atmospheric transparency, make the brightness profile of the extreme limb uncertain. The observed degree of limb brightening is considerably less than that consistent with spherically symmetric model atmospheres based on continuum brightness-temperature measurements. The suppression of limb brightening suggests the existence of irregular granular structure with both horizontal and vertical characteristic sizes of the order of 1500 km. High-resolution images in the wings of the K-line show granular structure of about this horizontal scale.

Lindsey, C.↗

DOE OSTI2022

Regression of exchangeable relational arrays

Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. Here we leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Efficient Space–Time Reduced Order Model for Linear Dynamical Systems in Python Using Less than 120 Lines of Code

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Removing Basis Set Incompleteness Error in Finite-Temperature Electronic Structure Calculations: Two-Electron Systems

We investigate the basis-set-size dependence for quantities related to interacting electrons in the canonical ensemble. Calculations are performed using exact diagonalization (finite temperature full configuration interaction method) on two-electron model systems–the uniform electron gas (UEG) and the helium atom. Our data reproduce previous observations of a competition for how the internal energy converges between the ground-state correlation energy and the high-temperature kinetic energy. We explore how this can be related to component parts of the internal energy including kinetic, exchange, and correlation energies and show there is surprising nuance in how this can be broken down into mostly monotonically converging quantities. We also show that separation of the free energy into a free energy with/without correlation allows for monotonic convergence with basis set size due to the variational principle. We find that the free energy convergence matches the previously observed convergence properties of the internal energy. We discuss the free energy divergence that happens when converging a finite basis analytical hydrogen atom to the complete basis set limit and compare this to the energies of a helium atom in a large periodic box. Reducing the box size, we saw convergence trends for the helium atom that were similar to the UEG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine

This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model's axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

17 WIND ENERGY↗

NASA NTRS1989

On the feasibility of a spaceborne fault-tolerant hypercube

The feasibility of implementing a fault-tolerant hypercube architecture for space applications is discussed. Node-level architectures and designs are considered and a first-order reliability model is presented. It is shown how error recovery can be implemented using program rollback or roll-forward techniques. Shared memory augmentations to the message-passing structure can be used to get around the inefficiencies of multicomputers to provide efficient use of hardware to achieve the needed reliabilities while maintaining performance.

Rennels, David A.↗

NASA NTRS1993

Automatic specification of reliability models for fault-tolerant computers

The calculation of reliability measures using Markov models is required for life-critical processor-memory-switch structures that have standby redundancy or that are subject to transient or intermittent faults or repair. The task of specifying these models is tedious and prone to human error because of the large number of states and transitions required in any reasonable system. Therefore, model specification is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model specification. Automation requires a general system description language (SDL). For practicality, this SDL should also provide a high level of abstraction and be easy to learn and use. The first attempt to define and implement an SDL with those characteristics is presented. A program named Automated Reliability Modeling (ARM) was constructed as a research vehicle. The ARM program uses a graphical interface as its SDL, and it outputs a Markov reliability model specification formulated for direct use by programs that generate and evaluate the model.

Liceaga, Carlos A.↗

NASA NTRS1998

Graphite Fiber Textile Preform/Cooper Matrix Composites

The purpose of this research was to produce a finned tube constructed of a highly conductive braided graphite fiber preform infiltrated with a copper matrix. In addition, the tube was to be fabricated with an integral geometry. The preform was integral in the sense that the tube and the fin could be braided to yield one continuous part. This composite component is a candidate for situations with high heat transmitting and radiation requirements. A proof-of-concept finned tube was braided and infiltrated with a copper matrix proving that a viable process was developed to fabricate the desired component. Braiding of high conductivity carbon fibers required much trial-and-error and development of special procedures. There are many tradeoffs between braidability and fiber conductivity. To understand the properties and structure of the braided finned tube, an geometric model of the braid structure was derived. This derivation set the basis for the research because knowing the tow orientations helped decipher the thermal as well as the mechanical and conduction tendencies. Infiltration of the fibers into a copper matrix was a complex procedure, and was performed by TRA, of Salt Lake City, Utah, using a proprietary process. Several batches were fabricated with a final, high quality batch serving as a confirming proof-of-concept.

Filatovs, George J.↗

NASA NTRSDate not supplied

Simulation of Deployable Composite Structures Based On Mechanics of Structure Genome

In this paper, a simulation method for analyzing deployable composite structures is presented. With a proper material model, effective plate/shell properties of the composites is obtained based on Mechanics of Structure Genome (MSG), and then implemented into a user subroutine UGENS for global structure simulation in ABAQUS. Column bending test (CBT) and composite boom and hub structure are studied for demonstration. A viscoelastic material model with direct integration implementation is adopted in this paper. CBT simulation shows good agreement with experiments during relaxation, while errors are observed when comparing residual deformation. This simulation can be potentially used as a calibration tool for material properties. After CBT simulation, a demonstrative model with a lenticular boom and the hub is created in ABAQUS. Complete process of flattening, coiling, stowage, deploying and recovery is simulated with the viscoelastic material model. Residual deformation of the boom is analyzed.

Multi-scale modeling↗