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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Predicting Future Soiling Losses Using Environmental Data: Preprint

Soiling consists of the accumulation of dust and dirt on the surfaces of photovoltaic (PV) modules and causes a reduction in the energy conversion. Investors generally consider an average yearly soiling loss to estimate the energy yield and the economic revenue of a site for a new PV installation. If soiling could be estimated from wide available parameters, describing the pollution and the rainfall pattern at a site, more accurate losses could be predicted, reducing the uncertainty in the estimation of the energy yield and therefore the finance rates offered for new PV systems. The present work investigates the correlations between present environmental parameters and future soiling losses.

14 SOLAR ENERGY↗
DOE OSTI2016

Scenario analysis for techno-economic model development of U.S. offshore wind support structures

Challenging bathymetry and soil conditions of future US offshore wind power plants might promote the use of multimember, fixed-bottom structures (or 'jackets') in place of monopiles. Support structures affect costs associated with the balance of system and operation and maintenance. Understanding the link between these costs and the main environmental design drivers is crucial in the quest for a lower levelized cost of energy, and it is the main rationale for this work. Actual cost and engineering data are still scarce; hence, we evaluated a simplified engineering approach to tie key site and turbine parameters (e.g. water depth, wave height, tower-head mass, hub height and generator rating) to the overall support weight. A jacket-and-tower sizing tool, part of the National Renewable Energy Laboratory's system engineering software suite, was utilized to achieve mass-optimized support structures for 81 different configurations. This tool set provides preliminary sizing of all jacket components. Results showed reasonable agreement with the available industry data, and that the jacket mass is mainly driven by water depth, but hub height and tower-head mass become more influential at greater turbine ratings. A larger sensitivity of the structural mass to wave height and target eigenfrequency was observed for the deepest water conditions (>40 m). Thus, techno-economic analyses using this model should be based on accurate estimates of actual metocean conditions and turbine parameters especially for deep waters. Finally, the relationships derived from this study will inform National Renewable Energy Laboratory's offshore balance of system cost model, and they will be used to evaluate the impact of changes in technology on offshore wind lower levelized cost of energy.

17 WIND ENERGY↗
DOE OSTI2019

Calculating Dose Release Factors to Obtain the Worst-Case Maximally Exposed Location by Sector

The aim of this project was to update the SRS Dose Release Factors (DRF's) for the Maximally Exposed Individual (MEI) for 20 area release points onsite. To update, potential MEIs now include onsite locations and modeled stack heights were updated to match onsite operations. The DRF's and MEI locations are used to ensure DOE and EPA compliance is met for dose standards. The Environmental Protection Agency (EPA) requires the use of the computer model CAP88 to estimate doses for demonstrating compliance with 40 CFR 61, the National Emission Standard for Hazardous Air Pollutants (NESHAP). The location of the maximally exposed individual (MEI) by sector and distance is required for execution of CAP88. The worst-case sector is the sector and distance where an offsite individual would receive the highest dose due to a potential release from a designated release location at a facility. The worst-case sector analysis addresses potential ground-level and stack-height releases from 20 areas of Savannah River Site (SRS). Weather patterns were analyzed at each of these areas relative to sixteen compass points. The analysis makes use of recently updated distance data and meteorological data. The computer model, CAP88 is EPA's software system used by the Department of Energy (DOE) to calculate the receptor dose due to routine atmospheric releases via a gaussian plume at the Savannah River Site (SRS). The dose-release factors (DRFs) are used to estimate the offsite and onsite dose from facility-specific releases and represent the dose to a receptor exposed to 1 Ci of a specified radionuclide being released into the atmosphere. The DRFs are simply a ratio of the TED to the release rate and, therefore, are directly scalable. The DRF values listed in the Appendix can be applied to expected release values from each area to estimate the potential dose to an individual adult at each location. NESHAP DRFs have been supplied to Environmental Compliance and Area Closure Projects (EC and ACP) for many years and are periodically updated to include changes in Cap88. estimate the potential dose to an individual adult at each location DRF in mrem Ci{sup -1} are estimated by modeling the total effective dose (TED) in mrem yr{sup -1} assuming an annual atmospheric release rate of 1 Ci yr-1 of the associated radionuclides. TED = Effective Dose (ED) + Committed Effective Dose (CED). TED = DRF/RR. Approach and Methods: Determine possible MEI locations Onsite/Offsite including buildings and farms; Determine the distances from the release area to the possible MEI location and the direction from the release to the MEI location; Using the most recent meteorological data (2007-2011) and parameters, run CAP88 for each release area for each compass sector and previously calculated distances to MEI locations using a 1 Ci release rate for tritium; Use the calculated the air concentration values to determine the MEI for each sector from CAP88. The relationship between air concentration and received dose is directly proportional and can be used to determine which locations to calculate DRF's and a dose for an MEI; The DRFs are found for each compass sector from 20 SRS release areas using CAP88-PC Version 4.0; Each location is calculated with ground level releases evaluated for each area for a list of radionuclides, some with and some without progenies; Compare the DRFs of the potential onsite MEIs to offsite MEIs. Based on the 2018 MEI Report, the Site worst case sector MEI is located 593 mW of B-Area. This MEI location represents a point along the main road that travels through a tract of land leased to the SC National Guard. While this location is estimated to have the highest exposure, minimal radioactivity is released from B-Area. In 2017, SRS requested and was approved by the EPA to change the location for modeling site releases from the Center of Site to H-Area, and to use six stack heights. H-Area emission account for approximately 90% of all radioactive operations onsite. The MEI for H-Area was found to be 11,458 m NNE which locates the Savannah River Ecology Lab Conference Center. Comparisons for 2018 data and 2014 shows the new methods of calculation are more conservative yet still remain well below the DOE 100 mrem limit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

A general moment expansion method for stochastic kinetic models

Moment approximation methods are gaining increasing attention for their use in the approximation of the stochastic kinetics of chemical reaction systems. In this paper we derive a general moment expansion method for any type of propensities and which allows expansion up to any number of moments. For some chemical reaction systems, more than two moments are necessary to describe the dynamic properties of the system, which the linear noise approximation is unable to provide. Moreover, also for systems for which the mean does not have a strong dependence on higher order moments, moment approximation methods give information about higher order moments of the underlying probability distribution. We demonstrate the method using a dimerisation reaction, Michaelis-Menten kinetics and a model of an oscillating p53 system. We show that for the dimerisation reaction and Michaelis-Menten enzyme kinetics system higher order moments have limited influence on the estimation of the mean, while for the p53 system, the solution for the mean can require several moments to converge to the average obtained from many stochastic simulations. We also find that agreement between lower order moments does not guarantee that higher moments will agree. Compared to stochastic simulations, our approach is numerically highly efficient at capturing the behaviour of stochastic systems in terms of the average and higher moments, and we provide expressions for the computational cost for different system sizes and orders of approximation. We show how the moment expansion method can be employed to efficiently quantify parameter sensitivity. Finally we investigate the effects of using too few moments on parameter estimation, and provide guidance on how to estimate if the distribution can be accurately approximated using only a few moments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Introducing the GeoRePORT Resource Size Tool: Reporting on Geothermal Resource Size Estimations Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT): Preprint

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. The assessment protocols used in GeoRePORT allow for comparison of project attributes across locations and geological settings to understand the feasibility of geothermal development. This work introduces the Resource Size Tool, a new feature within the GeoRePORT package that compiles two independent methods for estimating geothermal resource size in terms of energy capacity in MW. Energy production potential for twenty-three case studies was estimated with the Resource Size Tool in order to 1) generate a reasonable range of resource size estimates for a particular geothermal field; 2) illustrate the advantages and limitations of each methodology (such as data input requirements, estimate accuracy and precision, and the appropriate circumstances of use); and 3) test the ability of the resource size tool to provide useful and accurate information for geothermal stakeholders. The tool employs two methods widely used in the geothermal industry: (1) USGS Volumetric and (2) Power Density. Results from our case studies show general overlap between these two methods in terms of resource size estimates; however, they also reveal key differences between the two approaches that should be considered when using such estimates to drive development. First, the two methods rely on different input parameters and therefore one method may be more appropriate and/or accurate for a given project than the other. Second, the Power Density method was found to generate wider ranges of resource size predictions, more consistently aligning with actual power production of the field but with larger scales of error; whereas the USGS Volumetric method predicts narrower ranges but tends to overestimate when compared to current MW production. Future work will refine variables used in the methods with input data from other sections of GeoRePORT and modify uncertainty levels based on the particular datasets used for a given project.

geological↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

PROBING THE ISOTROPY OF COSMIC ACCELERATION TRACED BY TYPE Ia SUPERNOVAE

We present a method to test the isotropy of the magnitude–redshift relation of Type Ia Supernovae (SNe Ia) and single out the most discrepant direction (in terms of the signal-to-noise ratio) with respect to the all-sky data. Our technique accounts for possible directional variations of the corrections for SNe Ia and yields all-sky maps of the best-fit cosmological parameters with arbitrary angular resolution. To show its potential, we apply our method to the high-redshift SNe Ia from the recent Union2.1 compilation, building maps with three different angular resolutions. We use a Monte Carlo method to estimate the statistical significance with which we could reject the null hypothesis that the magnitude–redshift relation is isotropic based on the properties of the observed most discrepant directions. We find that, based on pure signal-to-noise arguments, the null hypothesis cannot be rejected at any meaningful confidence level. However, if we also consider that the strongest deviations in the Union2.1 sample closely align with the dipole temperature anisotropy of the cosmic microwave background, we find that the null hypothesis should be rejected at the 95%–99% confidence level, slightly depending on the angular resolution of the study. If this result is not due to a statistical fluke, it might either indicate that the SN data have not been cleaned from all possible systematics or even point toward new physics. We finally discuss future perspectives in the field for achieving larger and more uniform data sets that will vastly improve the quality of the results and optimally exploit our method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Autonomous monitoring of algal biomass: Success stories and lessons learned from long-term field deployment

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C a ) which enables near-real-time tracking of biomass accumulation and culture state at 2–5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to ~89% (2024–2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Katinas, Christopher Michael [Sandia National Labo↗
DOE OSTI2018

Using distributed temperature sensing to detect CO 2 leakage along the injection well casing

The objective of this study is to evaluate the sensitivity of Distributed Temperature Sensing (DTS) data to detect CO 2 leakage along an injection well casing. This paper describes the relationship between the CO 2 leakage rate and temperature response at DTS locations, and the method and numerical model used for understanding such a relationship. The uncertainties in the parameters are propagated to the interpretation of DTS measurements, which may lead to false positive and false negative identifications of CO 2 leakage. We propose to identify CO 2 leakage by analyzing both temperature history plots at selected vertical locations as well as the vertical temperature profiles at different times. The analysis should be combined with numerical simulations for the estimation of leakage rates. In addition, leakage needs to be confirmed by data from a warm-up test (temperature recovery after injection of cold CO 2 is stopped) to minimize the probability of false identifications.

42 ENGINEERING↗
DOE OSTI2013

Petroleum Refinery Jobs and Economic Development Impact (JEDI) Model User Reference Guide

The Jobs and Economic Development Impact (JEDI) models, developed through the National Renewable Energy Laboratory (NREL), are user-friendly tools utilized to estimate the economic impacts at the local level of constructing and operating fuel and power generation projects for a range of conventional and renewable energy technologies. The JEDI Petroleum Refinery Model User Reference Guide was developed to assist users in employing and understanding the model. This guide provides information on the model's underlying methodology, as well as the parameters and references used to develop the cost data utilized in the model. This guide also provides basic instruction on model add-in features, operation of the model, and a discussion of how the results should be interpreted. Based on project-specific inputs from the user, the model estimates job creation, earning and output (total economic activity) for a given petroleum refinery. This includes the direct, indirect and induced economic impacts to the local economy associated with the refinery's construction and operation phases. Project cost and job data used in the model are derived from the most current cost estimations available. Local direct and indirect economic impacts are estimated using economic multipliers derived from IMPLAN software. By determining the regional economic impacts and job creation for a proposed refinery, the JEDI Petroleum Refinery model can be used to field questions about the added value refineries may bring to the local community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science

Despite their wide and successful applications, deep learning (DL) models are prone to overfitting for small training datasets, produce a poor predictive performance for uncertain data, and provide point estimations without any indication of the accuracy and credibility. These limitations of the deterministic DL models hinder their effective application in Earth system science where the labelled data are sparse, noisy and incomplete with large uncertainty and where the predictive uncertainty quantification is needed for scientific understanding and policy decision making. Integration of Bayesian inference into DL models adds an estimate of uncertainty and regularization in the predictions. However, traditional Bayesian methods are computationally unaffordable and inflexible for high-dimensional problems, which limits their application to DL systems that typically have millions of model parameters. In this effort, we propose an efficient and general-purpose Bayesian inference method to advance DL model optimization and uncertainty quantification, so as to facilitate the adoption of DL in Earth sciences. In a demonstration, we integrate the proposed Bayesian method with a feedforward neural network (NN) to build a fast-to-evaluate surrogate of the complex Energy Exascale Earth System Land Model for efficient modeling. The formulated Bayesian NN, using a small number of training data, produces an accurate prediction with high credibility, whereas with the same small training size, the deterministic NN cannot yield a reasonable estimation and does not provide confidence information. The proposed Bayesian method is computationally efficient and flexible, capable of integration with diverse network variants such as convolutional NNs and recurrent NNs to advance the application of DL in Earth sciences.

Lu, Dan↗
DOE OSTI2025

Simplified Model and Approach to Transform Infrared Surface Temperature to Film Effectiveness in a Conjugate Heat Transfer Experiment

In the pursuit of more efficient gas turbines, film cooling is a critical technology. This article describes a simplified engineering model based on a one-dimensional thermal resistance network. The model is used to relate film-cooling effectiveness and heat transfer augmentation to local overall cooling effectiveness in a conjugate flat plate experiment. Here, this article presents experimental proof-of-concept data to demonstrate the potential for this model. In contrast to previous approaches, neither the wall heat flux nor the adiabatic wall temperature is required to estimate the local film-cooling performance parameters. The model predicts surface temperatures that are within the experimental uncertainties over the range for which the model is trained and to within five percent when the model is extrapolated to higher coolant channel Reynolds numbers. This article is relevant to conjugate test rigs that can measure the hot-surface temperature distribution with and without film cooling. This information may also be relevant to designers as a method to approximate surface temperatures or used as an approximate heat transfer model for optimization studies.

advanced turbines↗
DOE OSTI2025

Simplified Model and Approach to Transform Infrared Surface Temperature to Film Effectiveness in a Conjugate Heat Transfer Experiment

This paper describes a simplified engineering model based on a one-dimensional thermal resistance network. The model is used to develop a new method to relate film cooling effectiveness and heat transfer augmentation to local overall cooling effectiveness in a conjugate flat plate experiment. This paper presents experimental proof-of-concept data to demonstrate the potential for this model. In contrast to previous approaches, neither the wall heat flux nor the adiabatic wall temperature is required to estimate the local film cooling performance parameters. The model predicts surface temperatures that are within the experimental uncertainties over the range for which the model is trained, and to within five percent when the model is extrapolated to higher coolant channel Reynolds numbers. This paper is relevant to conjugate test rigs that can measure the hot surface temperature distribution with and without film cooling. This information may also be relevant to designers as a method to approximate surface temperatures or used as an approximate heat transfer model for optimization studies.

advanced gas turbines↗
DOE OSTI2016

A Poisson method application to the assessment of the earthquake hazard in the North Anatolian Fault Zone, Turkey

North Anatolian Fault (NAF) is one from the most important strike-slip fault zones in the world and located among regions in the highest seismic activity. The NAFZ observed very large earthquakes from the past to present. The aim of this study; the important parameters of Gutenberg-Richter relationship (a and b values) estimated and this parameters taking into account, earthquakes were examined in the between years 1900-2015 for 10 different seismic source regions in the NAFZ. After that estimated occurrence probabilities and return periods of occurring earthquakes in fault zone in the next years, and is being assessed with Poisson method the earthquake hazard of the NAFZ. The Region 2 were observed the largest earthquakes for the only historical period and hasn’t been observed large earthquake for the instrumental period in this region. Two historical earthquakes (1766, M{sub S}=7.3 and 1897, M{sub S}=7.0) are included for Region 2 (Marmara Region) where a large earthquake is expected in the next years. The 10 different seismic source regions are determined the relationships between the cumulative number-magnitude which estimated a and b parameters with the equation of LogN=a-bM in the Gutenberg-Richter. A homogenous earthquake catalog for M{sub S} magnitude which is equal or larger than 4.0 is used for the time period between 1900 and 2015. The database of catalog used in the study has been created from International Seismological Center (ISC) and Boğazici University Kandilli observation and earthquake research institute (KOERI). The earthquake data were obtained until from 1900 to 1974 from KOERI and ISC until from 1974 to 2015 from KOERI. The probabilities of the earthquake occurring are estimated for the next 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 years in the 10 different seismic source regions. The highest earthquake occur probabilities in 10 different seismic source regions in the next years estimated that the region Tokat-Erzincan (Region 9) %99 with an earthquake occur probability for magnitude 6.5 which the return period 24.7 year, %92 with an earthquake occur probability for magnitude 7 which the return period 39.1 year, %80 with an earthquake occur probability for magnitude 7.5 which the return period 62.1 year, %64 with an earthquake occur probability for magnitude 8 which the return period 98.5 year. For the Marmara Region (Region 2) in the next 100 year estimated that %89 with an earthquake occur probability for magnitude 6 which the return period 44.9 year, %45 with an earthquake occur probability for magnitude 6.5 which the return period 87 year, %45 with an earthquake occur probability for magnitude 7 which the return period 168.6 year.

58 GEOSCIENCES↗
DOE OSTI2018

Predicting Nanoparticle Suspension Viscoelasticity for Multimaterial 3D Printing of Silica–Titania Glass

A lack of predictive methodology is frequently a major bottleneck in materials development for additive manufacturing. Hence, exploration of new printable materials often relies on the serendipity of trial and error approaches, which is time-consuming, labor-intensive, and costly. In this paper, we present an approach to overcome these issues by quantifying and controlling the viscoelasticity of inks for multimaterial 3D printing of silica–titania glass using direct ink writing (DIW). We formulate simple silica and silica–titania inks from a suspension of fumed silica nanoparticles in an organic solvent with a dissolved molecular titania precursor. We use a small set of experimental rheological data and estimates of interaction potentials from colloidal theory to develop a predictive tool that allows us to design and obtain compatible inks that are matched both in desired rheological properties (viscosity profiles and elastic modulus) as well as solids loading. The model incorporates silica particle volume fraction, particle size, particle size distribution, and titania precursor concentration, and captures the effects of all formulation parameters on the measured viscoelasticity in a single curve. We validate the ink formulations predicted by the model and find that the materials can be very well matched in rheological properties as desired for 3D printing. Using the DIW and heat treatment methods we have reported previously, we use these inks to print and process a fully transparent glass with spatial change in dopant composition and refractive index. Finally, we believe that this approach can be extended to other colloidal systems and allow predictive ink formulation design for desired printability in direct ink write manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Beam-hardening (BH) artifacts are ubiquitous in X-ray CT scans of dense metal additively manufactured (AM) parts. While linearization approaches are useful for correcting beam-hardened data from single material objects, they either require a calibration scan or detailed system and material composition information. In this paper, we introduce a neural network-based, material-agnostic method to correct beam-hardening artifacts. We train a neural network to map the acquired beam-hardened projection values and the corresponding estimated thickness of the part based on an initial segmentation to beam-hardening related parameters, which can be used to compute the coefficients of a linearizing correction polynomial. A key strength of our approach is that, once the network is trained, it can be used for correcting beam hardening from a variety of materials without any calibration scans or detailed system and material composition information. Furthermore, our method is robust to errors in the estimated thickness due to the typical challenge of obtaining an accurate initial segmentation from reconstructions impacted by BH artifacts. We demonstrate the utility of our method to obtain high-quality CT reconstructions from a collection of AM parts -- suppressing cupping and streaking artifacts

Rahman, Obaid↗
DOE OSTI2025

Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs

Estimating actual evapotranspiration (ET) is particularly crucial for addressing how vegetation affects the water balance of ecosystems. ET estimation can be complex with empirical models due to their many parameters and reliance on aridity. In contrast, artificial neural networks (ANNs) could potentially estimate ET with fewer and more common meteorological parameters. In this study, we trained two ANNs, one using a feed-forward approach (FFN) and the other a nonlinear auto-regressive network (NARX), to predict ET and compared them to the commonly used empirical model Granger and Gray (GG). We trained our models on a nine-year eddy covariance (EC) dataset for Miscanthu s × giganteus ( M . × giganteus ) from Illinois (UIEF), then tested them using out-of-sample data from both UIEF and a different location in Iowa (SABR) to compare the accuracy of FFN, NARX, and GG models in estimating daily ET. A combination of air temperature (T a ) and solar radiation (R s ) was chosen as inputs due to the highest R 2 for FFN (R 2 = 0.79, 0.81, and 0.79 for training, testing, and validation, respectively) and only T a for NARX (R 2 = 0.70 for out-of-sample validation). The predictive power of the FFN model was superior to the NARX and GG models at the UIEF site (R 2 = 0.84, 0.70, and 0.83 for out-of-sample validation, respectively). Our analysis showed that ANN approaches are as accurate as empirical approaches for estimating ET but use fewer inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗