Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “prediction uncertainty”

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

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

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improving Predictions Under Uncertainty of Material Plasma Device Operations

Understanding the properties of materials when exposed to various plasma temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure eXperiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto-MPEX, the predecessor to MPEX, operated from 2014 to 2021, and was designed to understand the generation of plasma temperatures and fluxes at orders of magnitude below what will be obtained by MPEX. This work uses the recently developed stochastic neural network (SNN), a machine learning technique capable of operating under uncertainty to provide a surrogate model for the Proto-MPEX device. We demonstrate that SNN outperforms Bayesian neural network (BNN), a standard in the field of machine learning with uncertainty. The development of a robust surrogate of the Proto-MPEX will aid in the commissioning and operation of the MPEX device.

Archibald, Rick↗
NASA NTRS1989

Atmospheric drag model calibrations for spacecraft lifetime prediction

Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented.

Binebrink, A. L.↗
NASA NTRS1993

The detectability of brown dwarfs - Predictions and uncertainties

In order to determine the likelihood for the detection of isolated brown dwarfs in ground-based observations as well as in future spaced-based astronomy missions, and in order to evaluate the significance of any detections that might be made, we must first know the expected surface density of brown dwarfs on the celestial sphere as a function of limiting magnitude, wavelength band, and Galactic latitude. It is the purpose of this paper to provide theoretical estimates of this surface density, as well as the range of uncertainty in these estimates resulting from various theoretical uncertainties. We first present theoretical cooling curves for low-mass stars that we have computed with the latest version of our stellar evolution code. We use our evolutionary results to compute theoretical brown-dwarf luminosity functions for a wide range of assumed initial mass functions and stellar birth rate functions. The luminosity functions, in turn, are utilized to compute theoretical surface density functions for brown dwarfs on the celestial sphere. We find, in particular, that for reasonable theoretical assumptions, the currently available upper bounds on the brown-dwarf surface density are consistent with the possibility that brown dwarfs contribute a substantial fraction of the mass of the Galactic disk.

Nelson, L. A.↗
NASA NTRS1993

Planning: Management of predictability and uncertainty and keeping abreast of developments

The purpose of this study is to propose method to set up and control of a space mission plan such as that of the HERMES spaceplane. The interest of this subject, other than its complexity, is due to the need to manage imprecision and uncertainty during a mission, as well as changes in between missions. Under these conditions, the set up and control of a flight plan require certain special attention and this has led us to define a certain number of qualities: mastery of complexity in order to resolve conflicts between activities: configuration, resource and time management; consideration of various criteria such as risk minimization or the attainment of mission objectives; robustness and flexibility to allow for hazards and deviations from the norm during operation without having to draw up new plans; aptness for replanning by making changes to the plan without having to set up the whole plan again; and memorization and explanation facility in order to manage developments between missions.

Bastien-Thiry, Christophe↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗
NASA NTRS2007

The Road to Mach 10: A History of the X-43A Hypersonic Flight Test Program at NASA Dryden...Toward the Future

In terms of technology, the X-43A/Hyper-X represented a singular milestone. After nearly a half century of high hopes, studies, wind tunnel tests, proposals, and canceled projects, a scramjet-powered vehicle had flown. The performance of the engine qualified the scramjet design tools and scaling laws. In turn, the theoretical calculations and ground testing could be used to design more advanced engine concepts. Just as important, both the scramjet and vehicle systems had successfully operated in the variable temperatures and densities of the atmosphere. The X-43A systems were able to maintain the exact flight conditions necessary for the scramjet to operate properly. Control deflections to correct the engine-induced moments were close to pre-flight predictions. When the unexpected occurred, such as when the vehicle pitched up during the cowl opening on the second flight, the control system was sufficiently designed to correct the situation. The airframe and wing structure, the thermal protection material, and the internal conditions of the X-43A performed largely as predicted. The HXLV thermal anomaly during the ascent on the third flight and "the Mach 8 unpleasantness" during the descent indicated that the HXLV and X-43A were not as resilient to aerodynamic heating as expected. The X-43A 's airframe drag and lift both were slightly higher than predicted, but still within preflight uncertainty predictions. The stability and control were as predicted, as was the boundary layer transition. The biggest aerodynamic worry before the flight was the separation of the HXLV and the X- 43A. After all was said and done, this went exactly as predicted, proving that non-symmetrical/high-dynamic pressure stage separations could be performed. This in turn meant that two-stage-to-orbit vehicles employing this technology were feasible. The Hyper-X program also served as a training ground for a new generation of scramjet and hypersonic researchers. This included both NASA and contractor personnel, providing them with experience in ground testing and component development; vehicle design, construction, integration, system checkout, and, ultimately, flight testing and data analysis. Additionally, researchers learned the practical details of running a project within finite budget and time limits, about the ambiguousness of risk assessment, and about the need to spend a significant amount of time and effort dealing with engineering problems, such as those with the FAS, that have nothing to do with the project's research goals. Finally, all those who worked on the X-43A project now know what it is like to spend years transforming an idea into a functional vehicle, only for it to be lost in a matter of seconds. And then to go through years of work to correct the problems, to face the possibility that still more might exist, and finally to savor the triumph of two successful flights. For those who will work on the hypersonic projects that emerge in coming years, these experiences may prove to be the most valuable of all.

Peebles, Curtis↗
DOE OSTI2021

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Surface Figure Measurement at 20K: Silicon Carbide and Cesic Mirrors

This report presents the facility, methods, and results of testing cryogenically-cooled spherical mirrors, using standard phase-shifting interferometry, at the Goddard Space Flight Center (GSFC). Two mirrors were supplied to GSFC by the European Space Technology Center, so that GSFC could render a second, independent cryo-measurement of their surface figures at 20K. These mirrors, produced by Galileo Avionica and its partners, demonstrate the technology of silicon carbide and SiC-composite lightweighted mirrors, designed for high accuracy (10 nm rms surface figure error) at both room temperature and 20K. The two mirrors provided for testing at GSFC include one made of sintered silicon carbide (mirror blank by Bettini), and a second made of the C/Si/SiC composite known as Cesic (supplied by ECM). Both mirrors are f/2 spheres with 150 mm clear aperture, and with integral mounts. At GSFC, the mirrors will be measured first at at room temperature, making use of standard techniques, with a predicted uncertainty of 2 nm. Then each mirror will be tested in the cryostat, down to 20K: first freely supported and, second, mounted to a Cesic plate that models a Cesic optical bench. The uncertainty of the resultant surface figure error is predicted to be about 5 nm rms. Details of the uncertainty budget are discussed in a related poster. GSFC's techniques and results can be usefully compared to the measurements performed on the same two mirrors by and for Galileo Avionica, who used a somewhat different test configuration and alignment approach.

Blake, Peter↗
DOE OSTI2025

Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

Here, in this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) or the Vera C. Rubin Legacy Survey of Space and Time (LSST), using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029. We use deep imaging data from the Hyper Suprime Camera (HSC) and the Canada-France-Hawaii Telescope Large Area U-band Deep Survey (CLAUDS) on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the u,g,r,i and z bands to UNIONS depth. The Random Forest algorithm is trained with the u,g,r,i and z bands to classify LBGs in the 2.5 < z < 3.5 range. We find that fixing a target density budget of 1,100 deg -2 , the Random Forest approach gives a density of z > 2 targets of 873 deg -2 , and a density of 493 deg -2 of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg 2 . We predict uncertainties on Alcock-Paczynski parameters α ⊥ and α ∥ to be 0.7% and 1% for 2.6 < z < 3.2, resulting in a potential 2% measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict σ fNL ≈ 7, which would be comparable to the current best precision achieved by Planck. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models (σ fNL ≈ 1) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

cosmological parameters from LSS↗