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DOE OSTI2020

Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers

Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.

Liu, Jeremy (ORCID:0000000172299417)↗

DOE OSTI2020

Feature Engineering and Process Mining to Enable Hazard Detection in Health Information Technology

Motivation for this work originates from a recognized need for developing data-driven approaches through analysis of complex electronic health records. Specifically, we aim to enhance the reliability of health information technology (HIT) systems by detection of plausible HIT hazards in clinical order transactions. In the absence of well-defined event logs in corporate data warehouses, our proposed approach identifies relevant timestamped data fields that could indicate transactions in the clinical order life cycle generating raw event sequences. Subsequently, we adopt state transitions of the OASIS Human Task standard to map the raw event sequences and simplify the complex process that clinical radiology orders go through. We describe how the current approach provides the potential to investigate areas of improvement and potential hazards in HIT systems. The discussion concludes with a use case and opportunities for future applications.

Ozmen, Ozgur↗

DOE OSTI2022

CTF Improved Drag Model and Flow Regime Transition Criteria

The demand for accurate prediction of two-phase flow behavior in a boiling water reactor (BWR) requires a comprehensive understanding of flow regime, void fraction, heat transfer, and pressure drop. The CTF subchannel code, which is used for the Thermal/Hydraulic (T/H) solution in the Consortium for Advanced Simulation of Light Water Reactors (CASL)-developed Virtual Environment for Reactor Application (VERA) core simulator, is being further developed for BWR applications. In support of this goal, the present work highlights some of the two-phase closure model developments towards improving the CTF void fraction prediction, especially for subcooled boiling. The drift-flux approach has been well-developed for upward dispersed two-phase flows and proven to be accurate in predicting void fraction in bubbly and slug flow regimes. In this work, these kinematic constitutive relations for the drift-flux velocity have been implemented into CTF to describe the interfacial drag of bubbly flow as an alternative to the existing model for better void fraction prediction. The success of these constitutive relations also relies on a good flow regime map that accounts for flow conditions and channel geometry. A more reliable flow regime transition criteria that account for the flow condition has also been implemented in this study for modeling the flow regime transition criteria. The newly implemented models are shown to give improved void fraction predictions in comparison to experimental data.

Hizoum, Belgacem↗

DOE OSTI2023

Additive-Mixing Fuel Injection (AMFI): A device for robust control and improved performance of high-efficiency LTGC engines

Low-temperature gasoline combustion (LTGC) engines can provide high efficiencies with very low NOx and particulate emissions. Despite these major advantages, methods for controlling LTGC combustion timing, achieving robust autoignition, and good low-load performance have challenged the development of practical LTGC engines. A key reason is that the ideal reactivity of the fuel varies with operating conditions, with low-reactivity fuels working well at high loads and higher-reactivity fuels working better at low loads. This article introduces a new technique called Additive Mixing Fuel Injection (AMFI) that adjusts the reactivity of the gasoline to obtain good performance over the operating map. Moreover, AMFI can provide robust combustion-timing control because it precisely meters very small amounts (0.01–0.6 mm 3 ) of an ignition-enhancing additive into the fuel each engine cycle. The additive used was 2-ethylhexyl nitrate (EHN), a common, inexpensive diesel-fuel ignition improver, but other additives could be used. Because additive amounts are so small, a 2-gal reservoir would be sufficient for medium-duty applications with refilling only at service intervals of about 8000 mi. The AMFI system has been installed on a single-cylinder LTGC research engine and demonstrated to provide robust combustion-timing control over wide ranges of combustion phasing, fueling rate, intake boost, engine speed, and intake temperature. AMFI also increases the fuel’s reactivity sufficiently to greatly reduce or eliminate the need for charge heating, simplifying engine design and significantly increasing thermal efficiency and the maximum load at low-boost conditions. The nitrogen in the EHN produced a modest increase in NOx, but it was mitigated by lower intake temperatures, and NOx emissions remained very low. Finally, the additive increases the sensitivity of autoignition reactions to variations in the local fuel/air mixture within the charge. This allows the use of controlled fuel stratification for improved LTGC-engine performance, and its potential to improve low-load operation was demonstrated.

Dec, John E.↗

DOE OSTI2026

Flow pattern and void fraction characterization in nitrogen/water flows through a diamond-type triply periodic minimal surface lattice

Here, this study presents, to the authors knowledge, the first experimental investigation on the void fraction and flow patterns in two-phase flows through a diamond-type Triply Periodic Minimal Surface (TPMS) lattice. An additively manufactured TPMS structure was tested under upward co-current flow of a water/nitrogen mixture. Superficial velocities varied for a total of 42 test conditions (gas: 0.01–2.4 m/s; liquid: 0.01–2.4 m/s; mass flux: 20–2370 kg/m 2 ·s). High-speed video and X-ray imaging enabled time-averaged void fraction measurements and identified six distinct flow regimes which were used to develop a flow pattern map. Comparison of the void fraction data with correlations from literature demonstrated the Rouhani and Axelsson (1970) [49] correlation modified by Steiner (1993) [52] provided the best agreement, which was improved with empirically fit coefficients. This approach predicted the void fraction with errors < ±20% for 64% of the data, with a mean absolute percent deviation (MAPD) of 23%. The measured frictional pressure drop was compared to correlations from literature which captured the observed trends but did not provide good accuracy. The best agreement was found after optimizing the empirical coefficients of the Muller-Steinhagen & Heck (1986) [56] correlation; this approach captured 33% of the data within ±20% with a MAPD of 46% over the full range and captured 71% of the data within ±20% a MAPD of 13.3% at mass fluxes >1100 kg/m 2 -s. These results provide foundational insight into TPMS two-phase flow behavior and inform modeling and design of advanced heat exchange components incorporating TPMS geometries.

42 - ENGINEERING↗

DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure–function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1–3 GPa, which is consistent with crystalline proteins. Altogether, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Chemical structure↗

DOE OSTI2022

Characterization and repair of core gap manufacturing defects for wind turbine blades

Various wind turbine blade components, such as shear webs and skins, commonly use fiber-reinforced composite sandwich structures with a core material like balsa or foam. During manufacturing, core gap defects may result from the misalignment of adjacent foam or balsa core sheets in the blade mold. It is important to understand the influence that core gaps have on the structural integrity of wind turbine blades and how to mitigate their influence. This research characterized the effects of core gap defects at the manufacturing and mechanical levels for both epoxy and next-generation thermoplastic composites. Common repair methods were assessed and compared. Multiple defect sizes were compared using temperature data gathered with thermocouples embedded during manufacturing to core gap defect characteristics obtained using image-mapping techniques, optical microscopy, and mechanical characterization by long beam flexure. Results showed that peak exothermic temperatures during curing were closely related to core gap size. The long beam flexure tests determined that transverse core gaps under pure bending loads can have a substantial effect on the ultimate facesheet strength of both epoxy and thermoplastic composite sandwich structures (up to 25% strength reduction), although the size of the defect itself had less of an influence on the magnitude of the strength reduction. The supporting image-mapping techniques indicated that the distortion of the composite facesheets by the core gaps contributed to the premature failures. The repair methods used in this study did very little to improve the ultimate strength of the sandwich panels that previously had core gap defects. The repair of the thermoplastic panel resulted in a further loss in ultimate facesheet strength. This research demonstrated that there is a vital need for the development of a compatible thermoplastic polymer repair resin system and appropriate resin specific repair procedures for the next generation of recyclable thermoplastic wind blades.

17 WIND ENERGY↗

DOE OSTI2025

Multiscale maps of Active Layer Depth for Teller site Mile Marker 27 and Kougarok Mile Marker 80, Seward Peninsula, AK

Remote sensing maps of active layer depth derived from Unmanned Areal System (UAS) data. The UAS datasets were stepwise scaled until matching the AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer - Next Generation) and Sentinel-2 spatial resolutions. Using the field observed Active Layer Depth (ALD) measurement in combination with spectral and topographic predictors derivatives from DJI UAS imagery, we used a spatially explicit RF regression model to predict and map ALD across our study landscapes. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, and Kougarok MM80 (aka Mile 80) watersheds. The field, map data, and metadata are provided as geoTIF and text (*.csv) formats. These datasets are provided in support of Hantson et al., 2024 (accepted) “Scaling Arctic landscape and permafrost features improves active layer depth modeling”

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Improved Performance of Cu(InGa)(SeS) 2 PV Modules Using the Reaction of Metal Precursors. Final Report

This project “Improved Performance of Cu(InGa)(SeS) 2 PV Modules using the Reaction of Metal Precursors” was a partnership led by the Institute of Energy Conversion (IEC) at the University of Delaware with Columbia University and the Molecular Foundry at the Lawrence Berkeley National Laboratory. The aim was to develop pathways to improve Cu(InGa)(SeS) 2 (CIGSS) thin film photovoltaic modules using processes compatible with low manufacturing cost. The CIGSS approach investigated was a two-step process including deposition of metal precursor films following by reaction in hydride gases utilizing IEC’s novel reactor. The process was similar to that under commercial development by the project’s industry partner Stion. When Stion went out of business mid-project the focus changed to a rapid thermal process considered more commercially viable. Approaches to improve the performance of solar cells using the reacted films focused on two material innovations. First, the overall Ga content was increased to increase the operating voltage, which is desirable for scale-up to commercial modules. Second, the processing and performance advantages arising from Ag alloying were investigated. Advanced characterization guided process and material development including control of relative composition gradients. Research on the formation of Cu-Ga-In metal precursors utilized sputtering deposition which is normally used in commercial applications. The work resulted in processes for deposition of precursor stacks with increased relative Ga content and effects of deposition parameters on morphology and phase composition were established. It was shown that the metal precursor films have comparable phase composition and morphology so subsequent reaction follows from the same starting point. The addition of Ag to the metal precursors gave more uniform morphology and improved adhesion of reacted films which enable higher reaction temperature for faster processing. A novel outcome was the discovery of a previously undocumented material phase in sputter-deposited and evaporated Ag-Cu-In-Ga thin films. Hydride gas reaction processes including time-temperature-concentration profiles were developed for different precursor compositions. This enables control of composition profiles to engineer through-film gradients for solar cell optimization with characterization and simulations used to correlate measured film composition profiles to measurements of devices. In particular, the gradient of sulfur at the front of the CIGSS film was found to be critical. The simulations guided process development leading to improved reproducibility of devices improved performance with higher Ga content and higher voltage. With Ag-alloyed precursors, the reaction pathways leading were determined. A significant finding was that Ag-alloying increases the reaction rate to completely convert precursor films to the final chalcopyrite which could enable reduced reaction time to benefit manufacturability. To maintain potential commercial viability, the process under investigation was refocused to a rapid thermal process that could potentially be incorporated into an in-line process for manufacturing. Precursors with different composition were capped with an extra selenium layer and reacted in hydrogen sulfide 5-15 minutes, compared to typically 2 hours in the previous multi-step batch process. Critical RTP parameters were identified to control the reaction. Further optimization would be needed for high efficiency solar cells but pathways to high quality devices with further optimization and improved heating uniformity were developed. The project also developed new optoelectronic characterization approaches with a focus on development and application of spatial- and time-resolved photoluminescence and a custom mapping photoluminescence microscope built. It was shown how critical electronic transport properties strongly depend on the chemical composition of the material and that a wide range of samples show inhomogeneity on a length scale larger than the grains in the films. Additionally, two-photon excitation capability was developed to distinguish bulk vs surface losses. The project advances the state-of-the -art for precursor reaction processes in several ways that could impact manufacturing. This includes validation of approaches to increase voltage and establishment of model-guided control to form optimal composition profiles. The application of process control approaches with knowledge of phase formation and reaction pathways can be critically valuable in designing a large-scale process.

14 SOLAR ENERGY↗

DOE OSTI2025

Laboratory Testing and Performance Characterization of the High Efficiency Dehumidification System (HEDS) Technology

The high efficiency dehumidification system (HEDS) is a unique air handling unit (AHU) design with two water coils. Connected in series, these water coils can sensibly reheat the airstream after it is cooled and dehumidified, thereby mitigating (or sometimes eliminating) the need for conventional sensible reheat with additional energy from hot water, electric resistance or steam coils. Another unique design feature is the size of the HEDS primary cooling coil. While typical AHU cooling coils have a 6-row geometry, the HED's cooling coil has a deeper 10-row geometry enabling discharge air dew point temperatures at or below 55 degrees F despite warmer chilled water supply temperatures approaching (sometimes exceeding) 50 degrees F. The ability to meet acceptable discharge air dew points with warmer chilled water supply temperatures improves both operational efficiency and system resilience against chilled water plant operational or control issues. This report summarizes the 59 steady-state test results that form a comprehensive performance map of the HEDS technology including its reheat capability. The breadth of outdoor air conditions and flow rates over which the HEDS technology was tested and characterized encompasses the unit 1) operating as a dedicated outdoor air system or mixed air system and 2) controlled with constant air volume or variable air volume fan control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Ameri, Kimia (ORCID:0000000328791871)↗

DOE OSTI2020

Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls

Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I ( V )-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er 0.99 ,Zr 0.01 )MnO 3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er 0.99 ,Zr 0.01 )MnO 3 . Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Biofilm Interaction Mapping and Analysis (BIMA) of Interspecific Interactions in Pseudomonas Co-culture Biofilms

Pseudomonas species are ubiquitous in nature and include numerous medically, agriculturally and technologically beneficial strains of which the interspecific interactions are of great interest for biotechnologies. Specifically, co-cultures containing Pseudomonas stutzeri have been used for bioremediation, biocontrol, aquaculture management and wastewater denitrification. Furthermore, the use of P. stutzeri biofilms, in combination with consortia-based approaches, may offer advantages for these processes. Understanding the interspecific interaction within biofilm co-cultures or consortia provides a means for improvement of current technologies. However, the investigation of biofilm-based consortia has been limited. We present an adaptable and scalable method for the analysis of macroscopic interactions (colony morphology, inhibition, and invasion) between colony-forming bacterial strains using an automated printing method followed by analysis of the genes and metabolites involved in the interactions. Using Biofilm Interaction Mapping and Analysis (BIMA), these interactions were investigated between P. stutzeri strain RCH2, a denitrifier isolated from chromium (VI) contaminated soil, and 13 other species of pseudomonas isolated from non-contaminated soil. One interaction partner, Pseudomonas fluorescens N1B4 was selected for mutant fitness profiling of a DNA-barcoded mutant library; with this approach four genes of importance were identified and the effects on interactions were evaluated with deletion mutants and mass spectrometry based metabolomics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB’s sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Magnetic Fusion Energy↗

DOE OSTI2024

TEAMER: Pitching Foil Crossflow Turbine Efficiency Data

This dataset documents the efficiency testing of a pitching foil crossflow turbine, conducted at the University of New Hampshire's (UNH) Chase Ocean Engineering Laboratory tow tank facility. The tests explored various pitch phases and amplitudes, ranging from 0 to 18 degrees, across different flow speeds and turbine RPMs. Specifically, testing was performed at pitch amplitudes of 0,3,6,9,12,15 and 18 degrees. The results indicate that turbine efficiency improves with increased pitch amplitude, reaching optimal performance at 12 degrees. Modifications were made to the test frame by UNH to enhance the stiffness of the support structure. The testing specifically mapped the efficiency of a single quadrant of the Ocean Renewable Power Company (ORPC) Autonomous Turbine Generator Unit (ATGU), covering the full range of allowable pitching amplitudes and phases. The dataset provides both raw and processed data, including drag and torque measurements, and contains Python scripts used for data processing and visualization. This research was supported by funding from TEAMER RFTS 1 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Validation of an Erythema-Weighted UV Model Using Broadband Solar Irradiance Measurements From Eleven U.S. Sites: Preprint

Erythema-weighted UV solar irradiance (UV-E) has a potential impact on human health if the recommended maximum exposure times are exceeded. In spite of this, it is not measured at most sites that measure Global Horizontal Irradiance (GHI). However, since UV-E is highly correlated with GHI and total ozone content it can be estimated from this information with sufficient accuracy to assisst in public health recommendations. The Power Model (PM) provides a simple method to estimate the erythema-weighted UV irradiance (UV-E) from measured GHI, total ozone column and air mass. In this work, the performance of the PM method is assessed using high-quality data from 11 sites in the continental U.S. (part of SURFRAD and SOLRAD networks) and total ozone estimates publicly available from the MERRA-2 re-analysis database. A three year period (2021- 2023) at 1-minute frequency is considered. The results (for time aggregations of 5 and 60 minutes) show a high Pearson's correlations (> 0.99), consistently positive mean bias deviations (below 13%) and dispersions in the 8-19% range at all sites. Relative values are expressed in terms of the corresponding measurement mean. The performance indicators remain consistent across time resolutions (5 or 60 minutes), suggesting that the model's performance is robust and not significantly affected by short-term variability (which is captured by GHI). Spatial patterns reveal higher biases and RMSD in northern and eastern locations. These values represent a significant improvement over widely used satellite-based global UV-E estimates and open the possibility of using the PM with satellite-based GHI estimates for operational UV-E mapping over the contiguous U.S. territory.

14 SOLAR ENERGY↗