SEARCH · Engineering Papers

Results for “data fidelity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

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

At least 613 records · Page 34

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Hyper-fidelity depletion coupled with discrete pebble motion in pebble bed reactors

Pebble bed reactors have raised new interest during the past decade due to their attractive characteristics. Therefore, accurate simulations must be performed to better understand these systems and ensure optimal and safe designs. Most current methods use lower fidelity approaches with representative unit-cells or macro-zones with uniform fluxes, which have accuracy and flexibility limitations. A novel hyper-fidelity method for pebble bed reactors depletion is presented and internally couples Serpent 2 and a pseudo-motion routine. Pseudo-motion is applied handling vertical shifts of compositions in a static pebble bed, random reinsertion of used pebbles, insertion of fresh pebbles and used pebbles discarding. Associated with individual depletion to correctly determine the flux spectrum and composition in each pebble, this hyper-fidelity approach paves the way towards more accurate depletion calculation in pebble bed reactors. Using this method, a demonstration is completed on a small-scale reactor. In this application, the core reaches equilibrium, and the following data is extracted: core-wise parameters evolution, pebble-wise spatial and statistical distribution. Discarded pebbles are analyzed, and relevant information is shown. This work proves the feasibility of hyper fidelity depletion with Serpent 2, and the range of use for this method: reactor design and analysis for equilibrium and slow transients, lower fidelity methods validation and feeding fuel performance, thermal-hydraulics, or waste management models. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.

42 ENGINEERING↗

DOE OSTI2023

On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 project Phase III

This study reports the results of the second round of analyses of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) project Phase III. While the first round investigated rotor aerodynamic loading, here, focus is given to the wake behavior of a floating wind turbine under large motion. Wind tunnel experimental data from the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project are compared with the results of simulations provided by participants with methods and codes of different levels of fidelity. The effect of platform motion on both the near and the far wake is investigated. More specifically, the behavior of tip vortices in the near wake is evaluated through multiple metrics, such as streamwise position, core radius, convection velocity, and circulation. Additionally, the onset of velocity oscillations in the far wake is analyzed because this can have a negative effect on stability and loading of downstream rotors. Results in the near wake for unsteady cases confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the rotor reduced frequency increases over 0.5. Additionally, differences across the simulations become significant, suggesting that further efforts are required to tune the currently available methodologies in order to correctly evaluate the aerodynamic response of a floating wind turbine in unsteady conditions. Regarding the far wake, it is seen that, in some conditions, numerical methods overpredict the impact of platform motion on the velocity fluctuations. Moreover, results suggest that the effect of platform motion on the far wake, differently from original expectations about a faster wake recovery in a floating wind turbine, seems to be limited or even oriented to the generation of a wake less prone to dissipation.

17 WIND ENERGY↗

DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗

DOE OSTI2025

60 years of science in ICF: from conception to scientific breakeven on the National Ignition Facility

The recent achievements of a burning plasma, fusion ignition, and scientific energy gain with deuterium-tritium (DT) fuel at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) represents a major milestone in the development of inertial confinement fusion (ICF) and all of fusion research. In these experiments, fuel pressures well in excess of hundreds of GBars were achieved in the compressed fuel, and robust alpha heating of the fuel, far in excess of the energy provided by the implosion, were demonstrated for the first time. These achievements occurred 60 years after the inception of ICF and the first laser demonstration, and were made possible by more than five decades of research at laser facilities around the world. Advances in laser technology both in wavelength and precision, motivated by improved understanding of laser-plasma interaction physics and the demands of targets; improvements in target fabrication inspired by the need to control and minimize hydrodynamic instabilities in the implosion; and multi-dimensional simulations and diagnostics have been critical to this achievement. This paper will summarize the scientific and technical advances, the surprises, and the challenges that had to be overcome to achieve these goals.

fusion↗

DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗

DOE OSTI2025

Can protein expression be ‘solved’?

Recombinant protein expression is central to biotechnology’s application in academic exploration as well as human health, climate applications and the bioeconomy in general. However, not all proteins can be expressed in all organisms, and the field lacks a predictive model of soluble protein overexpression that could replace laborious experimental trial-and-error. Here, we discuss the state of the field and identify the lack of large, high-fidelity datasets as the primary bottleneck to progress. We review possible assays that could be used for data collection to identify a path toward an extensible experimental platform for collecting soluble recombinant protein overexpression data across organisms. We suggest that the resulting dataset should be used to train increasingly generalizable predictive models of protein expression to answer the question: “How can predictive protein expression be solved?”.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Initial operation and data processing on a system for real-time evaluation of Thomson scattering signals on the Large Helical Device

A scalable system for real-time analysis of electron temperature and density based on signals from the Thomson scattering diagnostic, initially developed for and installed on the NSTX-U experiment, was recently adapted for the Large Helical Device and operated for the first time during plasma discharges. During its initial operation run, it routinely recorded and processed signals for four spatial points at the laser repetition rate of 30 Hz, well within the system’s rated capability for 60 Hz. We present examples of data collected from this initial run and describe subsequent adaptations to the analysis code to improve the fidelity of the temperature calculations.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Bichromatic Wave Selection for Validation of the Difference-Frequency Transfer Function for the OC6 Validation Campaign: Preprint

The focus of the Offshore Code Comparison Collaboration, Continuation, with Correlation and unCertainity (OC6) project, which operates under the International Energy Agency (IEA) Wind Task 30, is to refine the accuracy of engineering tools used to design offshore wind turbines. In support of this work, a new validation campaign is being developed that seeks to better understand the nonlinear wave loading that excites floating wind systems at their low-frequency, rigid-body modes in surge and pitch. The validation data will be used in a three-way validation between simplified engineering tools and higher-fidelity tools, such as computational fluid dynamics (CFD). Irregular wave spectrums, which are traditionally used to examine the nonlinear wave interaction with offshore structures, are too computationally expensive to be simulated in CFD tools, and so bichromatic wave cases will be used instead. This paper reviews the process used to choose the bichromatic wave pairs to be used in the campaign to validate the second-order difference-frequency quadratic and potential loads at the surge and pitch natural frequencies of a floating semisubmersible.

17 WIND ENERGY↗

DOE OSTI2024

Benchmarking a neutral-atom quantum computer

In this study, we simulated the algorithmic performance of a small neutral atom quantum computer and compared its performance when operating with all-to-all versus nearest-neighbor connectivity. This comparison was made using a suite of algorithmic benchmarks developed by the Quantum Economic Development Consortium. Circuits were simulated with a noise model consistent with experimental data from [Nature 604, 457 (2022)]. We find that all-to-all connectivity improves simulated circuit fidelity by [Formula: see text]–[Formula: see text], compared to nearest-neighbor connectivity.

Computer Science↗

DOE OSTI2021

Initial operation and data processing on a system for real-time evaluation of Thomson scattering signals on the Large Helical Device

A scalable system for real-time analysis of electron temperature and density based on signals from the Thomson scattering diagnostic, initially developed for and installed on the NSTX-U experiment, was recently adapted for the Large Helical Device (LHD) and operated for the first time during plasma discharges. During its initial operation run, it routinely recorded and processed signals for four spatial points at the laser repetition rate of 30 Hz, well within the system's rated capability for 60 Hz. We present examples of data collected from this initial run and describe subsequent adaptations to the analysis code to improve the fidelity of the temperature calculations.

Thomson scattering, real-time, LHD, plasma diagnos↗

DOE OSTI2026

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗

DOE OSTI2021

Multifidelity computing for coupling full and reduced order models

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

IER 516: Zirconium Test Assembly CED Phase-1: Preliminary Experiment Design

IER-516, Zirconium Test Assembly (ZTA), is a campaign to design, execute, and document a series of high-fidelity critical benchmark experiments to validate current and future zirconium (Zr) and zirconium hydride (ZrH x ) nuclear data evaluations. ZTA is a collaborative project between Los Alamos National Laboratory and the French Autoritè de Sûretè Nuclèaire et de Radioprotection. The experiments will be fueled with highly enriched uranium (HEU), and utilize the Comet critical assembly machine at the National Criticality Experiments Research Center. A total of ten preliminary critical experiments were designed and optimized for Zr and ZrHx nuclear data sensitivities using the MCNP-Particle Swarm Optimization methodology in the thermal, epithermal, intermediate, and fast neutron energy regions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

42 ENGINEERING↗

DOE OSTI2026

Neural entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws

We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.

Conservative flux form↗

DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗