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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Final Report (October 2024): University of Tennessee, Knoxville (UTK) contribution to: FusMatML: Machine Learning Atomistic Modeling for Fusion Materials Collaborative Project led by Dr. Aidan Thompson, Sandia National Laboratory

The rapid growth of the field of Machine Learning Inter-Atomic Potentials (MLIAP) has lead to a profusion of methods, all of which have some similarity to each other, but each also restricted to particular design choices, often arrived at in a rather ad hoc fashion. Beyond anecdotal evidence, and some benchmarking studies on specific problems, little progress has been made in developing design principles for MLIAPs. The goal of this project is to use machine learning, data science, and uncertainty quantification methods to optimize the design choices for MLIAP.

Density functional theory, Helium and Hydrogen↗
DOE OSTI2026

Dynamic Transmission Line Switching Amid Wildfire-Prone Weather Under Decision-Dependent Uncertainty

During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multistage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for decision-dependent uncertainty, where line survival probabilities depend on usage, we employ distributionally robust optimization to model uncertainty in line survival distributions. We adapt the stochastic nested decomposition algorithm and derive a deterministic upper bound for its finite convergence. To enhance computational efficiency, we exploit the Lagrangian dual problem structure for a faster generation of Lagrangian cuts. Using realistic data from the California transmission grid, we demonstrate the superior performance of dynamic response policies against two-stage alternatives through a comprehensive case study. In addition, after solving the multistage formulation, we construct easy-to-implement policies that significantly reduce computational burden while maintaining good performance in real-time deployment. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms and Applications. Funding: This work was supported by the U.S. Department of Energy, Office of Electricity [Grant DE-AC02-05CH11231]. The work of R. Jiang was supported in part by the U.S. National Science Foundation, Division of Electrical, Communications and Cyber Systems [Grant ECCS-1845980] and the U.S. Air Force Office of Scientific Research [Grant FA9550-23-1-0323]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Estrada-Garcia, Juan-Alberto↗
DOE OSTI2019

Surrogate Model Selection for Design Space Approximation And Surrogatebased Optimization

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for sensitivity analysis, uncertainty propagation and surrogate based optimization. This work evaluates the performance of eight surrogate modeling techniques for design space approximation and surrogate based optimization applications over a set of generated datasets with known characteristics. With this work, we aim to provide general rules for selecting an appropriate surrogate model form solely based on the characteristics of the data being modeled. Furthermore, the computational experiments revealed that, in general, multivariate adaptive regression spline models (MARS) and single hidden layer feed forward neural networks (ANN) yielded the most accurate predictions over the design space while Random Forest (RF) models most reliably identified the locations of the optimums when used for surrogate-based optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Statistical uncertainties of a chiral interaction at next-to-next-to leading order

In this paper, we have quantified the statistical uncertainties of the low-energy coupling-constants (LECs) of an optimized nucleon–nucleon interaction from chiral effective field theory at next-to-next-to-leading order. Finally, in addition, we have propagated the impact of the uncertainties of the LECs to two-nucleon scattering phase shifts, effective range parameters, and deuteron observables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗
DOE OSTI2013

Effectiveness of robust optimization in intensity-modulated proton therapy planning for head and neck cancers

Purpose: Intensity-modulated proton therapy (IMPT) is highly sensitive to uncertainties in beam range and patient setup. Conventionally, these uncertainties are dealt using geometrically expanded planning target volume (PTV). In this paper, the authors evaluated a robust optimization method that deals with the uncertainties directly during the spot weight optimization to ensure clinical target volume (CTV) coverage without using PTV. The authors compared the two methods for a population of head and neck (H and N) cancer patients. Methods: Two sets of IMPT plans were generated for 14 H and N cases, one being PTV-based conventionally optimized and the other CTV-based robustly optimized. For the PTV-based conventionally optimized plans, the uncertainties are accounted for by expanding CTV to PTV via margins and delivering the prescribed dose to PTV. For the CTV-based robustly optimized plans, spot weight optimization was guided to reduce the discrepancy in doses under extreme setup and range uncertainties directly, while delivering the prescribed dose to CTV rather than PTV. For each of these plans, the authors calculated dose distributions under various uncertainty settings. The root-mean-square dose (RMSD) for each voxel was computed and the area under the RMSD-volume histogram curves (AUC) was used to relatively compare plan robustness. Data derived from the dose volume histogram in the worst-case and nominal doses were used to evaluate the plan optimality. Then the plan evaluation metrics were averaged over the 14 cases and were compared with two-sided paired t tests. Results: CTV-based robust optimization led to more robust (i.e., smaller AUCs) plans for both targets and organs. Under the worst-case scenario and the nominal scenario, CTV-based robustly optimized plans showed better target coverage (i.e., greater D{sub 95%}), improved dose homogeneity (i.e., smaller D{sub 5%}- D{sub 95%}), and lower or equivalent dose to organs at risk. Conclusions: CTV-based robust optimization provided significantly more robust dose distributions to targets and organs than PTV-based conventional optimization in H and N using IMPT. Eliminating the use of PTV and planning directly based on CTV provided better or equivalent normal tissue sparing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗
DOE OSTI2024

Multi-Timescale Optimal Operation Framework for Integrated Economic and Reliability Analysis of Hybrid Power Plants

This paper introduces a hierarchical modeling framework for hybrid power plants (HPP) to facilitate the operation of HPP in power systems similar to conventional generators (Congens) in the integrated multi-timescale optimal operation framework. To consider the uncertainties of HPP renewable power in the day-ahead scheduling, distributionally robust optimization (DRO) is used. To ensure that the state-of-charge (SOC) of energy storage systems in HPPs aligns closely with the planned value for long-term reliability, real-time SOC management is incorporated. In addition, an adjustable real-time control is designed for the robust delivery of HPP real-time services. Case studies performed on a revised IEEE 39-bus system demonstrate the effectiveness of the proposed framework for HPP operation. Simulation results highlight that the proposed framework not only can help operators schedule HPP similar to Congens in varying weather conditions but can also maintain the frequency reliability of the system.

frequency stability↗
NASA NTRS2020

Toward Comprehensive Uncertainty Predictions for Remote Imaging Spectroscopy

Remote imaging spectroscopy’s role in Earth science will grow in the coming decade as a series of globe-spanning spectroscopy missions launch from NASA, ESA, and other agencies. The nature of remote imaging spectroscopy will change, advancing from short regional studies to address global multi-year questions. The diversity of data will also grow with exposure to a wider range of biomes and atmospheric conditions. To execute these new investigations we must reconcile diverse observing conditions to derive consistent global maps. To this end, rig- orous uncertainty quantification and propagation enables an optimal synthesis of data accounting for observing conditions and data quality. Understanding data uncertainties is also important for principled hypothesis testing, information content assessment, and informed decision making by end users. We survey prior efforts in uncer- tainty quantification for imaging spectroscopy, and describe methods for validating the accuracy of uncertainty predictions. We conclude with a discussion of remaining challenges and promising avenues for future research.

Susiluoto, Jouni↗
NASA NTRS2002

Reliability Methods for Shield Design Process

Providing protection against the hazards of space radiation is a major challenge to the exploration and development of space. The great cost of added radiation shielding is a potential limiting factor in deep space operations. In this enabling technology, we have developed methods for optimized shield design over multi-segmented missions involving multiple work and living areas in the transport and duty phase of space missions. The total shield mass over all pieces of equipment and habitats is optimized subject to career dose and dose rate constraints. An important component of this technology is the estimation of two most commonly identified uncertainties in radiation shield design, the shielding properties of materials used and the understanding of the biological response of the astronaut to the radiation leaking through the materials into the living space. The largest uncertainty, of course, is in the biological response to especially high charge and energy (HZE) ions of the galactic cosmic rays. These uncertainties are blended with the optimization design procedure to formulate reliability-based methods for shield design processes. The details of the methods will be discussed.

Tripathi, R. K.↗
DOE OSTI2024

ETHOS: An automated framework to generate multi-fidelity constitutive data tables and propagate uncertainties to hydrodynamic simulations

Accurate constitutive data, such as equations of state and plasma transport coefficients, are necessary for reliable hydrodynamic simulations of plasma systems such as fusion targets, planets, and stars. Here, we develop a framework for automatically generating transport-coefficient tables using a parameterized model that incorporates data from both high-fidelity sources (e.g., density functional theory calculations and reference experiments) and lower-fidelity sources (e.g., average-atom and analytic models). The framework incorporates uncertainties from these multi-fidelity sources, generating ensembles of optimally diverse tables that are suitable for uncertainty quantification of hydrodynamic simulations. We illustrate the utility of the framework with magnetohydrodynamic simulations of magnetically launched flyer plates, which are used to measure material properties in pulsed-power experiments. We explore how changes in the uncertainties assigned to the multi-fidelity data sources propagate to changes in simulation outputs and find that our simulations are most sensitive to uncertainties near the melting transition. The presented framework enables computationally efficient uncertainty quantification that readily incorporates new high-fidelity measurements or calculations and identifies plasma regimes where additional data will have high impact.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Design Under Uncertainty with Design-Dependent Uncertain Variables

Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.

robust design↗
NASA NTRSDate not supplied

Design Under Uncertainty with Design-Dependent Uncertain Variables

Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.

Joanna N Schmidt↗
DOE OSTI2020

GIS-Based Modeling of Contaminated Soil Volumes at Multiple Sites in the Formerly Utilized Sites Remedial Action Program - 20149

The remediation of hazardous, toxic, and radioactive waste (HTRW) sites produces cost-related risks associated with the estimation of contaminated soil or debris volumes. Historical risk-management techniques include cost contingencies to cover volume uncertainties that affect project budgeting and decision-making. The Buffalo District teamed with project partners to lessen volume uncertainty and reduce project risks at multiple HTRW sites managed under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Historical remedial investigations under FUSRAP commonly identified the presence of radiological material in site media, the associated human health risk, and then areas of remediation. To manage remedial execution and reduce risk, pre-design or remediation-phase sampling essentially 'chased' contamination, which was not conducive to efficient predictive budgeting derived from Feasibility Study (FS) cost analyses. The Buffalo District first optimized their approach to better understand volume uncertainty by utilizing the Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software [1]. BAASS processed soft data (e.g., gamma walk-over data) and spatial sampling data to estimate the lateral extent of contaminated soil irrespective of depth (i.e., gross contamination extent) and define areas of contaminant uncertainty. The software performed a binary transformation of contaminant concentrations at all sampling points based upon remedial action goals or a sum of ratios approach (i.e., clean, impacted, or range of impacts in soil). The model produced two-dimensional (horizontal) contaminant probability contours and statistical uncertainty in the sampling coverage and resulting contaminant extents. This method was translated vertically by partitioning the sampling data into depth brackets that produced a stacked representation of contaminant extents and uncertainty in the subsurface (i.e., similar to construction lifts). The results commonly led to a better understanding of project uncertainty and the need for sampling strategies that produce high-confidence soil volumes, which control costs. The BAASS-based delineations were eventually replaced by Empirical Bayesian Kriging (EBK) methods available in ArcGIS Spatial or 3D Analysts [2]. The EBK method calculates contaminant probability zones derived from user-controlled semivariograms of the spatial datasets. The resulting probability zones (e.g., 50% or 80% of contaminant probability) represent the two-dimensional surface delineation of the overall horizontal remedial area, similarly to BAASS. However, unlike BAASS, the vertical sampling data within these probability zones became vertical control points to contour a subterranean surface that connects subsurface points to the land-surface delineations of contamination. The resulting representation of horizontal and vertical impacts within an enclosed envelop (volume) of soil included uncertainty distributions that are used to plan uncertainty-reduction sampling. These data-driven and math-based models of three-dimensional sampling results produced well-bounded remedial volumes for project planning and better uncertainty predictions during project budgeting. The EBK method was applied to several FUSRAP sites managed by the Buffalo District and compared to less rigorously modeled sites previously remediated by the District. The comparison of modeled to actual remediated volumes provide a basis for validating the volume-estimation method. This comparison is important to ensure modeled volumes match physical boundaries of site remediation. FUSRAP sites with denser investigative sampling and lesser volume uncertainty proved useful in remedial planning and contracting. The Buffalo District noted that historical sites with sparser sampling arrays had greater disparity between estimated volumes and final remedial volumes. The benefit achieved over the cost of detailed soil sampling appears positive for FUSRAP projects, especially where impacts vary widely and appear unbounded by investigation-phase sampling. The subsequent Empirical Bayesian Kriging of contamination coupled with vertical contouring for soil estimations reduces uncertainty in soil volumes or indicates where sampling is required to reduce uncertainty, which together optimize remedial planning and budgeting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1993

Physical model-set identification for robust control of flexible structures

An approach to dynamic system identification is presented taking into account the goal of enhancing robust control performance of flexible structures. Identification techniques are derived which take advantage of the physics of structural dynamics and can provide realistic bounds for all potential parameter uncertainties. The developed approach includes input optimization which distributes excitation energy in such a way that the influence of residual uncertainties on robust control performance is reduced.

Karlov, Valeri I.↗
DOE OSTI2013

PTV-based IMPT optimization incorporating planning risk volumes vs robust optimization

Purpose: Robust optimization leads to intensity-modulated proton therapy (IMPT) plans that are less sensitive to uncertainties and superior in terms of organs-at-risk (OARs) sparing, target dose coverage, and homogeneity compared to planning target volume (PTV)-based optimized plans. Robust optimization incorporates setup and range uncertainties, which implicitly adds margins to both targets and OARs and is also able to compensate for perturbations in dose distributions within targets and OARs caused by uncertainties. In contrast, the traditional PTV-based optimization considers only setup uncertainties and adds a margin only to targets but no margins to the OARs. It also ignores range uncertainty. The purpose of this work is to determine if robustly optimized plans are superior to PTV-based plans simply because the latter do not assign margins to OARs during optimization. Methods: The authors retrospectively selected from their institutional database five patients with head and neck (H and N) cancer and one with prostate cancer for this analysis. Using their original images and prescriptions, the authors created new IMPT plans using three methods: PTV-based optimization, optimization based on the PTV and planning risk volumes (PRVs) (i.e., 'PTV+PRV-based optimization'), and robust optimization using the 'worst-case' dose distribution. The PRVs were generated by uniformly expanding OARs by 3 mm for the H and N cases and 5 mm for the prostate case. The dose-volume histograms (DVHs) from the worst-case dose distributions were used to assess and compare plan quality. Families of DVHs for each uncertainty for all structures of interest were plotted along with the nominal DVHs. The width of the 'bands' of DVHs was used to quantify the plan sensitivity to uncertainty. Results: Compared with conventional PTV-based and PTV+PRV-based planning, robust optimization led to a smaller bandwidth for the targets in the face of uncertainties {l_brace}clinical target volume [CTV] bandwidth: 0.59 [robust], 3.53 [PTV+PRV], and 3.53 [PTV] Gy (RBE){r_brace}. It also resulted in higher doses to 95% of the CTV {l_brace}D{sub 95%}: 60.8 [robust] vs 59.3 [PTV+PRV] vs 59.6 [PTV] Gy (RBE){r_brace}, smaller D{sub 5%} (doses to 5% of the CTV) minus D{sub 95%} {l_brace}D{sub 5%}- D{sub 95%}: 13.2 [robust] vs 17.5 [PTV+PRV] vs 17.2 [PTV] Gy (RBE){r_brace}. At the same time, the robust optimization method irradiated OARs less {l_brace}maximum dose to 1 cm{sup 3} of the brainstem: 48.3 [robust] vs 48.8 [PTV+PRV] vs 51.2 [PTV] Gy (RBE); mean dose to the oral cavity: 22.3 [robust] vs 22.9 [PTV+PRV] vs 26.1 [PTV] Gy (RBE); maximum dose to 1% of the normal brain: 66.0 [robust] vs 68.0 [PTV+PRV] vs 69.3 [PTV] Gy (RBE){r_brace}. Conclusions: For H and N cases studied, OAR sparing in PTV+PRV-based optimization was inferior compared to robust optimization but was superior compared to PTV-based optimization; however, target dose robustness and homogeneity were comparable in the PTV+PRV-based and PTV-based optimizations. The same pattern held for the prostate case. The authors' data suggest that the superiority of robust optimization is not due simply to its inclusion of margins for OARs, but that this is due mainly to the ability of robust optimization to compensate for perturbations in dose distributions within target volumes and normal tissues caused by uncertainties.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty

This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first- and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming (SOCP) model with an adjustable coefficient. This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.

17 WIND ENERGY↗