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DOE OSTI2020

Measurement of crack length in width tapered beam experiments

The width tapered beam method for measuring fracture toughness has been contemplated for use in measuring the adhesion of photovoltaic materials, is promoted by it being a viable method for use directly on modules rather than to engineered test specimens. However, precise determination of crack length is often difficult as failure often involves tendril formation, cavitation voids, and other plastic deformation at the crack front in addition to obfuscation by opaque materials. Because fracture energy varies as the square of crack length, imprecise crack tip length results in large measurement uncertainties. In this work, we develop a method to estimate the crack tip length using the relationship between the applied force and the displacement of the load frame. The crack tip is determined for every data point and can be defined from a calibration curve related to the amount of energy put into the beam, or from a theoretically idealized estimation. Once a calibration curve is set up and the equations programmed, the fracture energy as a function of position on the newly formed crack interface can be accurately estimated. The test additionally provides much easier analysis and insight into the joint failure through more precise knowledge of fracture toughness at each de-bonded area.

42 ENGINEERING↗

DOE OSTI2023

BLAST-Lite (Battery Lifetime Analysis and Simulation Tool - Lite) [SWR-22-69] Related to: BLAST aka: BLAST-Py

Battery Lifetime Analysis and Simulation Toolsuite (BLAST) provides a library of battery lifetime and degradation models for various commercial lithium-ion batteries from recent years. Degradation models are identified from publicly available lab-based aging data using NREL's battery life model identification toolkit. The battery life models predicted the expected lifetime of batteries used in mobile or stationary applications as functions of their temperature and use (state-of-charge, depth-of-discharge, and charge/discharge rates). Model implementation is in both Python and MATLAB programming languages. The MATLAB code also provides example applications (stationary storage and EV), climate data, and simple thermal management options. For more information on battery health diagnostics, prediction, and optimization, see NREL's Battery Lifespan webpage.

Smith, Kandler↗

DOE OSTI2020

A Study on Modeled Wind Speed Errors Using the U.S. Department of Energy Buoys

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies Office. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of wind energy in the U.S. waters. The first deployment for one buoy was off the coast of Virginia from December 2014 to May 2016, and the first deployment for the other buoy was off the coast of New Jersey from November 2015 until February 2017. This report describes recent analysis of data collected during these first two deployments. Specifically, we compare hub-height wind speed estimates using Monin-Obukhov Similarity Theory (MOST) to the lidar measurements, and examine how those errors are affected by wind direction, atmospheric stability, wind-wave direction differences, and various measures of the wave-state. The comparisons are done using standard similarity functions based on MOST; including the Businger - Dyer, the Beljaars & Holtslag and the Vickers & Mahrt similarity functions. All models produce large errors over the range of atmospheric stabilities that were observed, with the largest errors occurring for stable flows. The Vickers & Mahrt function resulted in the largest overall bias and standard deviation, while Beljaars & Holtslag function gave the smallest bias and standard deviation due to its better performance under stable conditions. The models perform best under unstable conditions, but even in this regime there is a consistent overestimation of the wind speed of between roughly 0 to 1 ms -1 compared to the lidar measurements. We identify specific metocean conditions (i.e. stability and wind and wave directions) at each of the deployment locations that lead to large errors in MOST predictions. Finally, a coupled ocean-atmosphere model framework was investigated to simulate large errors in weather research forecasting (WRF).

17 WIND ENERGY↗

DOE OSTI2023

Integrated Process Optimization for Biochemical Conversion

This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.

09 BIOMASS FUELS↗

DOE OSTI2025

Electric Vehicle and Charging Infrastructure Assessment in Cold-Weather Climates: A Case Study of Fairbanks, Alaska

The purpose of this report is to determine the effects that extreme cold temperatures have on electric vehicles (EVs) and electric vehicle supply equipment (EVSE). The adoption of EVs in extreme cold weather presents challenges that require careful analysis of efficiency and charging infrastructure. This report, "Electric Vehicle and Charging Infrastructure Assessment in Cold-Weather Climates: A Case Study of Fairbanks, Alaska," explores how EVs and EVSE perform in temperatures as low as -40 degrees C (-40 degrees F), focusing on real-world data from Teslas in Alaskan winter conditions. The findings indicate that EVs can successfully function in extreme cold, though efficiency is significantly affected. Vehicles stored in heated environments outperformed those stored outdoors, with efficiency dropping by up to 69% for outdoor storage. Despite these challenges, none of the vehicles experienced failures that prevented travel. Storing EVs indoors led to benefits such as faster preconditioning and improved efficiency, while charging in extreme cold, though slower, remained functional. Ultimately, the report concludes that with proper precautions and best practices, EVs are viable transportation solutions in cold climates. Investing in enclosed, heated storage is recommended to maximize efficiency and minimize battery strain. As advancements in battery chemistry and thermal management progress, EV adoption in extreme climates is expected to become even more feasible.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Structural analysis and functional evaluation of the disordered ß–hexosyltransferase region from Hamamotoa (Sporobolomyces) singularis

Hamamotoa (Sporobolomyces) singularis codes for an industrially important membrane bound ß-hexosyltransferase (BHT), (BglA, UniprotKB: Q564N5) that has applications in the production of natural fibers such as galacto-oligosaccharides (GOS) and natural sugars found in human milk. When heterologously expressed by Komagataella phaffii GS115, BHT is found both membrane bound and soluble secreted into the culture medium. In silico structural predictions and crystal structures support a glycosylated homodimeric enzyme and the presence of an intrinsically disordered region (IDR) with membrane binding potential within its novel N-terminal region (1–110 amino acids). Additional in silico analysis showed that the IDR may not be essential for stable homodimerization. Thus, we performed progressive deletion analyses targeting segments within the suspected disordered region, to determine the N-terminal disorder region’s impact on the ratio of membrane-bound to secreted soluble enzyme and its contribution to enzyme activity. The ratio of the soluble secreted to membrane-bound enzyme shifted from 40% to 53% after the disordered N-terminal region was completely removed, while the specific activity was unaffected. Furthermore, functional analysis of each glycosylation site found within the C-terminal domain revealed reduced total secreted protein activity by 58%–97% in both the presence and absence of the IDR, indicating that glycosylation at all four locations is required by the host for the secretion of active enzyme and independent of the removed disordered N-terminal region. Overall, the data provides evidence that the disordered region only partially influences the secretion and membrane localization of BHT.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗

DOE OSTI2025

Identification of the Liquid Argon Scattering Experimental Discrepancies Using Simulation

Scintillation light analysis in liquid argon based neutrino detectors is restrained in capability due to uncertainty in fundamental constants critical to the analysis process. One such property is the Rayleigh scattering length of liquid argon. In the fall of 2023, the TallBo cryostat, located in the Proton Assembly Building at Fermilab, was used to study the scattering length of liquid argon in the Liquid Argon Scattering (LArS) experiment. Due to systematic errors unknown during measurement analysis, LArS’s measurements were quite uncertain. By simulating the LArS experiment, we found that the downturn in detector count rate as a function of liquid argon height at low heights was caused by a misplaced photo multiplier. With concentrated effort, we may be able to successfully correct this effect by understanding the relationship between the specified Rayleigh scattering length and the measured attenuation length. With this information and further progression in analysis, we may be able to extract corrected measurements from the LArS data and attain a tangible experimental measurement of the scattering length of liquid argon.

Breaux, Auto D. [Tulane U.; Fermilab]↗

DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗

DOE OSTI2022

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗

DOE OSTI2025

Hydrogen Bond Benchmark: Focal‐Point Analysis and Assessment of DFT Functionals

We performed a hierarchical, convergent ab initio benchmark study and systematically analyzed the performance of density functional approximations for describing hydrogen bonds in small neutral, cationic, and anionic complexes, as well as in larger systems involving amide, urea, deltamide, and squaramide moieties. Focal point analyses (FPA), extrapolating to the ab initio limit, were carried out using correlated wave function methods up to CCSDT(Q) for the small complexes and CCSD(T) for the larger systems, together with correlation-consistent Gaussian basis sets up to the complete basis set limit. Optimized geometries and vibrational frequencies were obtained at the CCSD(T) level. The resulting FPA hydrogen-bond energies converge within a few tenths of a kcal mol −1 . These reference data were used to evaluate 60 density functionals (including 12 dispersion-corrected), spanning the local-density approximation (LDA), generalized gradient approximations (GGAs), meta-GGAs, hybrids, meta-hybrids, double-hybrids, and range-separated hybrids. Overall, the meta-hybrid M06-2X provides the best performance for both hydrogen bond energies and geometries, while the dispersion-corrected GGAs BLYP-D3(BJ) and BLYP-D4 also yield accurate hydrogen-bond data and can serve as cost-effective options for studying large and complex systems.

coupled cluster theory↗

DOE OSTI2024

Structural characterization of high-protein system through ultra-small and small-angle X-ray scattering

High-protein systems exhibit a hierarchical structure consisting of interconnected multiscale length assemblies. Recent investigations are directed toward identifying these structural units under various processing and environmental conditions to establish structure–function correlations. Ultra-small and small-angle X-ray scattering (USAXS/SAXS) has become a crucial tool for characterizing the structures of proteins and their clusters/aggregates, ranging from nanometers to micrometers, with minimal disruption to their original state. Here, this review first describes the facilities and principles of X-ray scattering, followed by discussions on the analysis of scattering data, including the interpretation of fitting models. It then delves into the main applications of USAXS/SAXS in plant and dairy protein-rich systems. Future strategies to enhance the utilization of scattering techniques for elucidating the structure of high-protein systems are also included.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Accurate Modeling of Bromide and Iodide Hydration with Data-Driven Many-Body Potentials

Ion–water interactions play a central role in determining the properties of aqueous systems in a wide range of environments. However, a quantitative understanding of how the hydration properties of ions evolve from small aqueous clusters to bulk solutions and interfaces remains elusive. Here, we introduce the second generation of data-driven many-body energy (MBnrg) potential energy functions (PEFs) representing bromide–water and iodide–water interactions. The MB-nrg PEFs use permutationally invariant polynomials to reproduce two-body and three-body energies calculated at the coupled cluster level of theory, and implicitly represent all higher-body energies using classical many-body polarization. Further, a systematic analysis of the hydration structure of small Br - (H 2 O) n and I - (H 2 O) n clusters demonstrates that the MBnrg PEFs predict interaction energies in quantitative agreement with “gold standard” coupled cluster reference values. Importantly, when used in molecular dynamics simulations carried out in the isothermal-isobaric ensemble for single bromide and iodide ions in liquid water, the MB-nrg PEFs predict extended X-ray absorption fine structure (EXAFS) spectra that accurately reproduce the experimental spectra, which thus allows for characterizing the hydration structure of the two ions with high level of confidence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Robust Automatic EXAFS First-Shell Fits

Extended X-ray absorption fine structure (EXAFS) is a widely used technique for atomic structure determination. Fourier transformation connects EXAFS in k space and R space. However, determining the appropriate k-range for the transformation can be challenging, but critical for the first-shell fit. In this study, we present an automatic method to determine the k-range using the Larch package and a Python program. The first step is to estimate spectral noise across a series of k-ranges with a fixed minimum value and identify the optimal maximum value in the k-range (k max ). The k max is determined by an empirical noise threshold that marks the point where the noise level in the Fourier transformed spectrum changes dramatically. Using the obtained k max value, the first shell is modeled to determine the minimum k value (k min ) by optimizing the background function through alignment of the spectrum with theory. The optimal k min corresponds to the point of the minimum R-factor, which quantifies the difference between the experimental and fitted spectrum. Our method was tested on various typical datasets and yielded suitable k-ranges for Fourier transformation and accurate first-shell fits. This approach helps avoid unreliable, irreproducible data analysis, especially for noisy data from diluted samples, and enables robust automatic first-shell EXAFS fitting.

EXAFS analysis↗

DOE OSTI2025

Satellites, core hole excitations, and spin-resolved electronic structure in the spectroscopy of half-metallic CrO 2

Photoelectron satellites—the structures appearing on the low kinetic or high binding-energy side of the “main” or “elastic” photopeak—betray the complex many-body interactions set in motion by the sudden creation of the core hole. In this work, we demonstrate, using the technologically important ferromagnetic half-metal CrO 2 , how such satellites can manifest themselves in other core-level spectroscopies of the material and how they can reveal important details pertinent to its electronic structure. Specifically, we identify a fluorescence satellite in the Cr 𝐿 3 resonant x-ray-emission spectra that radiates at a constant emission energy across the Cr 𝐿 3 x-ray edge with energy ≈1.3 eV above the ordinary valence fluorescence. Here, we provide evidence that this feature arises from the valence recombination of the Cr 2⁢𝑝 core hole “dressed” by the same shakeup charge-transfer process present in both the Cr x-ray photoelectron and the Cr x-ray absorption spectra with its energy uniquely measuring the exchange splitting of the Cr 3⁢𝑑 level. Further analysis of the x-ray emission data reveals three additional features that radiate at constant loss energy that are attributed to combinations of Cr 3⁢𝑑⁢(𝑡 2⁢𝑔 ) → Cr 3⁢𝑑⁢(𝑡 2⁢𝑔 ), charge-transfer O 2⁢𝑝→Cr 3⁢𝑑, and crystal-field Cr⁢ 3⁢𝑑⁡(𝑡 2⁢𝑔 )→Cr⁢ 3⁢𝑑⁡(𝑒 𝑔 ) excitations. These assignments and their energies are supported by density-functional theory calculations, the accuracy of which we demonstrate by hard x-ray valence-photoemission measurements. Atomic multiplet calculations, which include crystal-field effects, help interpret x-ray photoelectron and x-ray absorption spectra of the covalently mixed Cr ion. Resonant Cr K-𝐿 2,3 ⁢𝐿 2,3 Auger-electron emission spectra support a ligand-to-metal nature of the charge-transfer process while highlighting the charge sensitivity differences between photon-in/electron-out and photon-in/photon-out spectroscopies.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Jet charge with global event shapes: Probing quark flavor dynamics

We propose measuring the jet electric charge of jet regions, defined within the framework of global event shapes, as a probe of quark flavor dynamics within the nucleon and the hadronization process. In particular, we consider a measurement of the jet region charge while simultaneously keeping track of the energy flow throughout the event, as characterized by the global event shape. As a concrete example, we focus on the measurement of the (Q), the jet charge of the jet region (𝐽) defined within the framework of the 1-Jettiness global event shape (𝜏 1 ) for the Deep Inelastic Scattering (DIS) process, 𝑒 − +𝑝→𝑒 − +𝐽+𝑋, with unpolarized or longitudinally polarized protons. The 1-Jettiness distribution, binned according to jet charge, allows for enhanced quark flavor separation of the initial state unpolarized or polarized PDFs. On the other hand, the jet charge distribution binned by 1-Jettiness can serve as a probe of quark flavor dynamics in the final state hadronization process. We derive a factorization theorem for simultaneous measurements of 𝜏 1 and Q in the resummation region, 𝜏 1 ≪𝑃 𝐽 𝑇 , where 𝑃 𝐽 𝑇 denotes the transverse momentum of the jet region. The correlation between the jet region charge and the charge of the struck quark is expected to be the strongest in the resummation region. The factorization theorem contains a new universal , generalizing the standard jet function to include a jet charge measurement. Therefore these universal functions can be extracted from a global analysis of N-jettiness and thrust at 𝑒 + ⁢𝑒 − colliders. We provide simulation studies to demonstrate the sensitivity of the 1-Jettiness jet charge observable to quark flavor dynamics in nucleon structure and explore the possibility of probing the final state hadronization process. This observable is well-suited for applications with existing HERA data and the future Electron-Ion Collider (EIC).

Mantry, Sonny [University of North Georgia, Dahlon↗

DOE OSTI2020

Evidence for pseudo–Jahn-Teller distortions in the charge density wave phase of 1$\textit{T}-\text{TiSe}_2$

Here, the charge density wave (CDW) instability in 1$\textit{T}-\text{TiSe}_2$ is revisited to investigate its electronic and structural origins using angle resolved photoemission spectroscopy (ARPES), neutron diffraction, and density functional theory (DFT) calculations. The evidence for Jahn-Teller (JT)-like distortions in the CDW state is provided from the local structure analysis that shows splitting of the Ti-Se bonds into short and long. The magnitude of the split is inconsistent with the commonly accepted Di Salvo model for the structural distortions in the CDW phase. From the APRES data, it is deduced that a nontrivial temperature-dependent energy shift of the Se $4\textit{p}$ valence bands occurs, but is absent for the Ti $3\textit{d}$ conduction bands. Collectively, these observations suggest that a JT-like mechanism is most likely central to the CDW instability in 1$\textit{T}-\text{TiSe}_2$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗