Results for “loss factor” — Page 14

DOE OSTI2024

Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning

Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.

EELS↗

DOE OSTI2022

Broccoli seed extract rich in polysaccharides and glucoraphanin ameliorates DSS ‐induced colitis via intestinal barrier protection and gut microbiota modulation in mice

Abstract BACKGROUND Broccoli has received widespread attention because of its anti‐inflammatory and antioxidant effects. The present study aimed to explore the composition of broccoli seed extract (BSE) and its effect on colitis induced by dextran sulfate sodium (DSS). RESULTS BSE mainly comprises glucoraphanin and polysaccharides composed of arabinose, galactose, glucose and mannose. Animal experiments suggested that BSE intervention effectively reversed body weight loss, suppressed the levels of proinflammatory interleukin‐6, tumor necrosis factor‐α and interleukin‐1β, and elevated the levels of anti‐inflammatory interleukin‐10 and the activities of superoxide dismutase and glutathione in DSS‐induced colitis mice. According to histopathologic and immunohistochemical analysis of colon tissue, BSE intervention may repair the intestinal barrier by upregulating mRNA levels and the expression of tight junction proteins (claudin‐1, occludin and zonula occludens‐1). Gas chromatography–mass spectrometry (MS) analysis demonstrated that cecal short‐chain fatty acids in mice with BSE administration were significantly increased compared with the model group. Sulforaphane and sulforaphane‐ N ‐acetylcysteine were only detected in BSE group mice by ultra‐performance liquid chromatography‐MS analysis. In addition, BSE intervention evidently increased the abundance of Alistipeds , Coriobacteriaceae UCG‐002 and Bifidobacterium and decreased the abundance of Escheichia‐Shinella , Lachnospiraceae others , Parabacteroides , Ruminococcaceae others and Turicibacter , which possibly promoted carbohydrate metabolism and short‐chain fatty acid production. CONCLUSION The present study aimed to elucidate the effect of BSE on colitis and found that BSE, as a novel food ingredient, has great potential for the improvement of colitis. © 2022 Society of Chemical Industry.

Wu, Jiaying↗

DOE OSTI2022

Fast and flexible analysis of direct dark matter search data with machine learning

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. Furthermore, this technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Full-Cycle Simulations of the Fermilab Booster

The Proton Improvement Plan phase II (PIP-II) project currently under construction at FNAL will replace the existing 400 MeV normal conducting linac with a new 800 MeV superconducting linac. The beam power in the downstream rapid-cycling Booster synchrotron will be doubled by raising the machine cycle frequency from 15 to 20 Hz and by increasing the injected beam intensity by a factor 1.5. This has to be accomplished without raising uncontrolled losses beyond the administrative limit of 500 W. In addition, slip-stacking efficiency in the Recycler, the next machine in the accelerator chain, sets an upper limit on the longitudinal emittance of the beam delivered by the Booster. As part of an effort to better understand potential losses and emittance blow-up in the Booster, we have been conducting full cycle 6D simulations using the code PyORBIT. The simulations include space charge, wall impedance effects and transition crossing. In this paper, we discuss our experience with the code and present representative results for possible operational scenarios.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Modeling and Measuring the Stress Distribution in Soft Magnetic Cores due to Epoxy Impregnation and Curing

Understanding the effects of viscoelastic relaxation of stresses is critical in electric motor and inductor applications, where the magnetic material is fully or partially comprised of an amorphous phase and impregnated in an epoxy. Magnetic Amorphous Nanocomposites (MANCs) are a class of recently developed high-frequency magnetic materials that exhibit low magnetic hysteresis, high saturation flux density and very low AC magnetic losses. MANCs are formed as long, thin ribbons, a shape factor that minimizes eddy current-induced self-heating. This feature, in addition to their 1 GPa strength, and makes them attractive candidates for high-speed, high-power density, electric motors. MANCs are used in the form of tape-wound cores (TWCs). During the manufacturing process, the cores are impregnated with liquid epoxy between each layer of ribbon. Validated knowledge of the stresses imposed by the manufacturing process and during operation is necessary to understand motor performance limitations with respect to strength. Magnetostriction, the strain imposed by reversal of the magnetic field, also affects performance. When the epoxy cures, it contracts volumetrically by 8%, imposing radial and circumferential stresses that vary along the radial direction. To date, these stresses have received little attention in the literature. Here, we model the stress distribution throughout a TWC due to epoxy curing. Strain gauge measurements reported here indicate substantially lower tangential strain than model values, which is beneficial and which we attribute to viscoelastic relaxation of the epoxy layers. We will incorporate a constitutive model of this viscoelastic relaxation, and conduct more detailed strain measurements to understand how this takes

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Magnetics, Inc. 0077894A7 (Datasheet)

Description of Core Under Test (CUT). Magnetics, Inc. 0077894A7 is a powder core material having low loss and high saturation fields. Known for the marketing name Kool Mu, was transformed by taking the known magnetic material, Sendust, and altered the manufacturing process to lower core losses. Possessing these specific properties makes it ideal for power factor correction circuits as well Pulse and Flyback Transformers. In-Line Noise Filters and Inductors are also common due to the near zero magnetostriction. Exhibits a potential saturation induction of 1 T.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Full Cycle Simulations of The Fermilab Booster

The Proton Improvement Plan phase II (PIP-II) project currently under construction at FNAL will replace the existing 400 MeV normal conducting linac with a new 800 MeV superconducting linac. The beam power in the downstream rapid-cycling Booster synchrotron will be doubled by raising the machine cycle frequency from 15 to 20 Hz and by increasing the injected beam intensity by a factor 1.5. This has to be accomplished without raising uncontrolled losses beyond the administrative limit of 500 W. In addition, slip-stacking efficiency in the Recycler, the next machine in the accelerator chain, sets an upper limit on the longitudinal emittance of the beam delivered by the Booster. As part of an effort to better understand potential losses and emittance blow-up in the Booster, we have been conducting full cycle 6D simulations using the code PyORBIT. The simulations include space charge, wall impedance effects and transition crossing. In this paper, we discuss our experience with the code and present representative results for possible operational scenarios.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Technical Potential for Hydropower Capacity at Non-powered Dams

In the last decade, retrofits of existing nonpowered dams (NPDs) have made up the largest share of capacity increases for US hydropower. Accurate estimates of potential capacity and generation at NPDs help identify sites that may be worth investing in detailed feasibility analyses and design exploration. This dataset contains estimates of technical potential capacity and generation at 2,616 NPDs in the conterminous US. This is based on a subset of dams that were found by earlier resource assessments to have at least 100kW of theoretical potential. Historical daily streamflow (modeled or from USGS gauge records) and hydraulic head (based on historical observations or primary purpose and dam height) are the main inputs to the HydroGenerate model, which determines design flow, turbine efficiencies, and assumed friction losses and then calculates nominal capacity, daily generation, and capacity factor. These estimates represent the conditions over the historical period of 1980-2015, and are summarized on a monthly basis (i.e., averaged for each month of the year) and overall (i.e., nominal capacity, average annual generation (MWh), and average annual capacity factor). A total of nearly 4 GW capacity is estimated across all 2,616 NPDs included in the dataset.

Hansen, Carly [Oak Ridge National Laboratory (ORNL↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗

DOE OSTI2023

Quantitative Analysis of Origin of Lithium Inventory Loss and Interface Evolution over Extended Fast Charge Aging in Li Ion Batteries

During the extreme fast charging (XFC) of lithium-ion batteries, lithium inventory loss (LLI) and reaction mechanisms at the anode/electrolyte interface are crucial factors in performance and safety. Determining the causes of LLI and quantifying them remain an essential challenge. We present mechanistic research on the evolution and interactions of aging mechanisms at the anode/electrolyte interface. We used NMC 532 /graphite pouch cells charged at rates of 1, 6, and 9 C up to 1000 cycles for our investigation. The cell components were characterized after cycling using electrochemical measurements, inductively coupled plasma optical emission spectroscopy, 7 Li solid-state nuclear magnetic resonance spectroscopy, and high-performance liquid chromatography/mass spectrometry. The results indicate that cells charged at 1 C exhibit no Li plating, and the increase of SEI thickness is the dominant source of the Li loss. In contrast, Li loss in cells charged at 9 C is related to the formation of the metallic plating layers (42%) the SEI layer (38.1%) and irreversible intercalation into the bulk graphite (19%). XPS analysis suggests that the charging rate has little influence on the evolution of SEI composition. The interactions between competing aging mechanisms were evaluated by a correlation analysis. In conclusion, the quantitative method established in this work provides a comprehensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, forecasting SEI failure scenarios, and assessing the XFC lithium-ion battery capacity fade.

25 ENERGY STORAGE↗

DOE OSTI2026

Measuring Loss Tangents of Substrates for Superconducting Qubits with Part-per-Billion Precision

We report precision measurements of dielectric loss tangents in substrates for superconducting qubits using an ultra-high quality factor niobium SRF cavity operating at millikelvin temperatures and low electric fields. Multiple substrate types and surface treatments are compared to assess how processing impacts microwave dissipation. The RF results are correlated with materials analysis, including ToF-SIMS and XPS, to identify dominant loss mechanisms. This combined study links microscopic surface chemistry to macroscopic performance and provides a framework for materials-driven improvements in qubit coherence and device design.

Bafia, Daniel [Fermilab]↗

DOE OSTI2026

Measuring Loss Tangents of Substrates for Superconducting Qubits with Part-per-Billion Precision

We report precision measurements of dielectric loss tangents in substrates for superconducting qubits using an ultra-high quality factor niobium SRF cavity operating at millikelvin temperatures and low electric fields. Multiple substrate types and surface treatments are compared to assess how processing impacts microwave dissipation. The RF results are correlated with materials analysis, including ToF-SIMS and XPS, to identify dominant loss mechanisms. This combined study links microscopic surface chemistry to macroscopic performance and provides a framework for materials-driven improvements in qubit coherence and device design.

Bafia, Daniel [Fermilab]↗

DOE OSTI2023

An Investigation on the Pollen-Induced Soiling Losses in Utility-Scale PV Plants

Soiling, the accumulation of dust and other contaminants on the surface of photovoltaic (PV) modules, is a common factor that can negatively impact the performance of PV systems. In this study, the authors aim to analyze the impact of pollen on soiling losses in PV systems located in North Carolina, USA, particularly during the spring season. The performance data of two utility-scale PV plants was collected and analyzed using the two soiling extraction methods. Environmental data, including croplands and vegetation was also collected and analyzed to identify correlations with soiling losses. The results of the study may help improve understanding of necessary operation and maintenance activities for PV plants and provide new insights into the phenomenon of pollen deposition on PV systems.

correlation↗

DOE OSTI2020

Understanding Hailstone Temporal Variability and Contributing Factors over the U.S. Southern Great Plains

Abstract Hailstones are a natural hazard that pose a significant threat to property and are responsible for significant economic losses each year in the United States. Detailed understanding of their characteristics is essential to mitigate their impact. Identifying the dynamic and physical factors contributing to hail formation and hailstone sizes is of great importance to weather and climate prediction and policymakers. In this study, we have analyzed the temporal and spatial variabilities of severe hail occurrences over the U.S. southern Great Plains (SGP) states from 2004 to 2016 using two hail datasets: hail reports from the Storm Prediction Center and the newly developed radar-retrieved maximum expected size of hail (MESH). It is found that severe and significant severe hail occurrences have a considerable year-to-year temporal variability in the SGP region. The interannual variabilities have a strong correspondence with sea surface temperature anomalies over the northern Gulf of Mexico and there is no outlier. The year 2016 is identified as an outlier for the correlations with both El Niño–Southern Oscillation (ENSO) and aerosol loading. The correlations with ENSO and aerosol loading are not statistically robust to inclusion of the outlier 2016. Statistical analysis without the outlier 2016 shows that 1) aerosols that may be mainly from northern Mexico have the largest correlation with hail interannual variability among the three factors and 2) meteorological covariation does not significantly contribute to the high correlation. These analyses warrant further investigations of aerosol impacts on hail occurrence.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Characterization of the indoor near-field aerosol transmission in a model commercial office building

To evaluate the exposure potential of infectious aerosols containing SARS-CoV-2 in an office building setting, synthetic test aerosols were used to experimental study airborne particle transmission in a multizone small office test building at the Oak Ridge National Laboratory. Nine measurement points in a single zone using active aerosol impactors report that the coefficient of variation of the time-averaged concentration is <10% in two campaigns and < 15% in one campaign, so a nearly well-mixed condition was noted. To understand the effect of HVAC system operation on the dynamic concentration of aerosols in office spaces, an aerosol transport model that includes factors such as outside air (OA) ratio, filtration, return air fraction, transport loss in air ducts, and particle deposition has been developed. The results of model fitting demonstrate strong agreement with experimental data. Overall, our investigation finds the return air fraction effects outweigh other mechanisms for the aerosol recirculation in this study, and the impact of air change rate (ACR) is more important than the small particle deposition for aerosol removal. Because ACR dominates the aerosol transport, the full model can be simplified to just one factor, the ACR, while maintaining an acceptable representation of the experimental data.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Performance Enhancement of the Transcritical CO2 Cycle with a Near-Isothermal Liquid Piston Compression by an Ejector

This study investigates the use of an ejector in a transcritical CO2 cycle employing a near-isothermal liquid piston compressor, with the objective of mitigating refrigerant degassing in mineral oil. While the liquid piston compressor demonstrated high isothermal efficiency (˜90%), overall cycle performance was limited by CO2 degassing during chamber pressure fluctuations. A thermodynamic model was developed to compare baseline and ejector-assisted cycles, incorporating solubility correlations, entrainment ratio characteristics, and a degassing correction factor. Results indicate that the ejector increased suction pressure and reduced CO2–oil solubility variation, which improved refrigerant intake and reduced degassing losses. Despite these benefits, the ejector-assisted cycle showed a lower coefficient of performance (COP) than the baseline. Possible contributing factors include the limited marginal benefit of pressure reduction in a highly efficient isothermal compressor, reduced effectiveness of the separator due to the use of a suction-line heat exchanger in both cycles, and uncertainties in the solubility data under transient conditions. These findings suggest that degassing mitigation is essential for realizing the potential of liquid piston compression, but ejector integration alone is unlikely to enhance COP. Future work should focus on ejector optimization, improved oil–refrigerant management, introducing less-soluble working liquids, and experimental validation under realistic operating conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Equation of motion coupled-cluster cumulant approach for intrinsic losses in x-ray spectra

We present an equation of motion coupled cluster approach for calculating and understanding intrinsic inelastic losses in core level x-ray absorption spectra (XAS). The method is based on a factorization of the transition amplitude in the time-domain, which leads to a convolution of an effective one-body spectrum and the core-hole spectral function. The spectral function characterizes these losses in terms of shake-up excitations and satellites, and is calculated using a cumulant representation of the core-hole Green’s function that includes non-linear corrections. The one-body spectrum also includes orthogonality corrections that enhance the XAS at the edge.

Rehr, John J.↗