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

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NASA NTRS2021

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b(bp)(555)/(m). We developed a simple empirical algorithm for deriving b(bp)(555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R(rs)(λ)/(sr), and the spectral particulate backscattering coefficients, b(bp)(λ). The LH metric used is defined as the magnitude of Rrs(555) relative to a linear baseline drawn between R(rs)(490) and R(rs)(670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

ocean color↗

NASA NTRSDate not supplied

Applicability of a Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations

As improvements are made to the accuracy and reliability of modeling and simulation techniques, certification by analysis becomes a more attractive alternative compared to traditional aircraft flight testing. Certification by analysis is especially cost-effective when one considers modifications to a previously certified aircraft. However, it is important that the models and methods used are applicable and accurate throughout the intended use domain. A framework for estimating the performance and associated uncertainty was introduced in an earlier paper. The factors and limitations of this framework for estimating the performance and associated model form uncertainty are explored to determine the range of applicability of the framework, particularly with respect to model form, process and sensor noise, and quality of available flight test data. This paper focuses on the general limitations and applicability of the framework and not the applicability of the individual methods to a range of modified configurations, which requires a large number of modified configurations and is an area for future work. The effects of these factors on the performance and uncertainty results are demonstrated using NASA’s Generic Transport Model aircraft.

uncertainty quantification↗

NASA NTRS2022

Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations

Flight testing has been the historical standard for determining aircraft airworthiness. However, increases in the cost of flight testing and the accuracy of inexpensive CFD encourage the adoption of certification by analysis to reduce or replace flight testing. A framework is introduced to predict the performance in the special case of a modification to an existing, previously certified aircraft. This framework uses a combination of existing flight tests or high fidelity data of the original aircraft as well as lower fidelity data from CFD or wind tunnel testing of the original and modified configurations to create 6-DOF flight dynamics models. Two methods are presented which generate an updated flight dynamics model and estimate the model form uncertainty for the modified aircraft configuration using knowledge of the original aircraft. This updated dynamics model and uncertainty estimate are then used to conduct non-deterministic simulations with wind turbulence included. The framework is applied to an example aircraft system to demonstrate the ability to predict the performance and associated model from the uncertainty of modified aircraft configurations.

uncertainty quantification↗

NASA NTRSDate not supplied

Application of Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations Using NASA's X-57 Maxwell

A framework to estimate the performance and associated uncertainty of modified configurations of certified aircraft is applied to the X-57 Maxwell aircraft. In previous theoretical studies, the framework was shown to predict performance and uncertainty bounds accurately. The X-57 Maxwell is an experimental aircraft designed to demonstrate the benefits of distributed electric propulsion through a series of four incremental modifications to a Tecnam P2006T aircraft. The available models and data are first shown to be within the application domain of the framework. We then apply the framework to two X-57 Maxwell modifications. We compare the estimated performance and associated uncertainties against the airworthiness criteria. The results indicate that the framework is a promising tool for the certification by analysis workflow. We expect the framework to reduce and supplement the flight testing required to show compliance to airworthiness certification criteria for a modified configuration.

Uncertainty Quantification↗

NASA NTRSDate not supplied

Application of Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations Using NASA's X-57 Maxwell

Uncertainty Quantification↗

NASA NTRSDate not supplied

Measurement Accuracy and Uncertainty Analysis of the X-59 Air Data Probe Calibration Test Entry One

The X-59 is being developed to demonstrate quiet sonic boom technology. Data was obtained via two test entries in the NASA Glenn Research Center’s 8- by 6-Foot Supersonic Wind Tunnel to calibrate the X-59’s nose air data probe. The first entry tested two theoretically identical probes with the intention of one of the probes becoming the primary X-59 nose air data probe flight hardware and the other becoming a backup. A measurement accuracy and uncertainty analysis was performed on data obtained from the first test entry. The analysis showed that the uncertainties in the probe pressures were nearly identical for the two probes, with an average difference of 2.51 x 10-5 for the non-dimensional total pressure and 2.88 x 10-5 for the non-dimensional static pressures. The analysis also showed that the uncertainty in the probe yaw and pitch angles were 0.00034° and 0.01416°, respectively. This gives confidence in the X-59’s flight air data system.

Air Data Probe↗

NASA NTRS2023

Measurement Accuracy and Uncertainty Analysis of the X-59 Air Data Probe Calibration Test Entry One

The X-59 is being developed to demonstrate quiet sonic boom technology. Data was obtained via two test entries in the NASA Glenn Research Center’s 8- by 6-Foot Supersonic Wind Tunnel to calibrate the X-59’s nose air data probe. The first entry tested two theoretically identical probes with the intention of one of the probes becoming the primary X-59 nose air data probe flight hardware and the other becoming a backup. A measurement accuracy and uncertainty analysis was performed on data obtained from the first test entry. The analysis showed that the uncertainties in the probe pressures were nearly identical for the two probes, with an average difference of 2.51 x 10 -5 for the non-dimensional total pressure and 2.88 x 10 -5 for the non-dimensional static pressures. The analysis also showed that the uncertainty in the probe yaw and pitch angles were 0.00034° and 0.01416°, respectively. This gives confidence in the X-59’s flight air data system.

Air Data Probe↗

NASA NTRSDate not supplied

Results of the NASA Prediction Uncertainty Reduction Tech Challenge

In January 2021 the Advanced Air Vehicles Program approved a new Tech Challenge (TC) to be run out of the Commercial Supersonic Technology (CST) Project. The objective of the TC was to bring uncertainties in the empirical noise modeling for civilian supersonic aircraft into the same range as that of conventional aircraft. The TC goal statement was to “Produce data and demonstrate tools that reduce the uncertainty in predicting Landing & Takeoff Noise levels of supersonic-relevant designs by 5 EPNdB and are ready for use in studies to inform FAA rule-making.” To make the required improvements in noise prediction methods for supersonic aircraft, NASA decided to validate and use high-fidelity numerical simulations to acquire the needed noise data on supersonic propulsion configurations. High-fidelity component designs were developed, such as a two-stage propulsor behind a supersonic inlet designed by General Electric Aviation and variable area exhaust systems using input from recent contracts with GE and Rolls Royce. A noise database was generated as a function of geometric and flow parameters, providing corrections to the empirical noise models and added new input variables to describe the complexities created by the supersonic-specific design features. Statistically, the new models were able to predict the acoustic impact of supersonic-specific features, the reduction in uncertainty being reduced from the baseline 7.8 EPNdB at the beginning of the TC to 2.0 EPNdB at the end.

noise prediction↗

DOE OSTI2023

Uncertainty Quantification of PV Annual Energy Estimates in the System Advisor Model

This work will detail a proposed uncertainty quantification method for PV annual energy estimates. Motivations behind the proposed methodology will be discussed, including the frequent underperformance of installed PV systems to calculated probability of exceedance values being seen in industry. A methodology in which uncertainty factors correlated to different parts of the PV annual energy model chain are applied to annual energy estimates in conjunction with uncertainty from inter-annual solar resource variability will be described. The application of this methodology in the System Advisor Model (SAM) will also be discussed with example cases and graphical outputs of the probability of exceedance data calculated for the PV annual energy estimates.

energy modeling↗

DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PVPHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2025

Role of Uncertainty Quantification in the Explainability of Large Language Models for the Nuclear Industry

The meteoric rise of generative artificial intelligence (AI) large language models (LLMs) has created an opportunity to utilize them to increase efficiencies in a multitude of industries. While LLMs carry great potential to revolutionize the manner in which work is performed, numerous known deficiencies limit their utility, including the black box nature of the models, the stochastic nature of the response (i.e., presenting the same prompt multiple times results in different responses), and the potential for hallucination. Widespread adoption of LLMs in safety-critical industries such as nuclear will require some form of explainability to assure end users that the LLM’s response to a given query is valid. Model uncertainty is inherently linked to the concepts of trust and explainability, and can be used to identify situations in which the model is insufficiently certain about its answer. Although uncertainty is not enough in and of itself to determine the suitability of an answer—a model can be very certain of an inaccurate answer—it still provides valuable supporting information. Practical methodologies for gauging or quantifying the uncertainty in LLM outputs are presented herein, along with examples based on nuclear-specific prompts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

A minimally invasive, efficient method for propagation of full-field uncertainty in solid dynamics

In this work, we present a minimally invasive method for forward propagation of material property uncertainty to full-field quantities of interest in solid dynamics. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known a priori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the evolution equations based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the driving term of the evolution equations. The implementation is model independent, storage and communication efficient, and straightforward. We demonstrate its efficiency, accuracy, scaling with dimension of the parameter space, and convergence in distribution with two problems: a quasi-one-dimensional bar impact, and a two material notched plate impact. For the bar impact problem, we provide an analytical solution to PDF of the solution fields for method validation. With the notched plate problem, we also demonstrate good parallel efficiency and scaling of the method.

42 ENGINEERING↗

DOE OSTI2022

Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model

In this work, we propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints

Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Shortest path network interdiction with asymmetric uncertainty

Abstract This paper considers an extension of the shortest path network interdiction problem that incorporates robustness to account for parameter uncertainty. The shortest path interdiction problem is a game of two players with conflicting agendas and capabilities: an evader, who traverses the arcs of a network from a source node to a sink node using a path of shortest length, and an interdictor, who maximizes the length of the evader's shortest path by interdicting arcs on the network. It is usually assumed that the parameters defining the network are known exactly by both players. We consider the situation where the evader assumes the nominal parameter values while the interdictor uses robust optimization techniques to account for parameter uncertainty or sensor degradation. We formulate this problem as a nonlinear mixed‐integer semi‐infinite bilevel program and show that it can be converted into a mixed‐integer linear program with a second order cone constraint. We use random geometric networks and transportation networks to perform computational studies and demonstrate the unique decision strategies that our variant produces. Solving the shortest path interdiction problem with asymmetric uncertainty protects the interdictor from investing in a strategy that hinges on key interdictions performing as promised. It also provides an alternate strategy that mitigates the risk of these worst‐case possibilities.

Punla‐Green, She'ifa Z.↗