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At least 469 records · Page 26

Computational and experimental investigation for new transition metal selenides and sulfides: The importance of experimental verification for stability

Expanding the library of known inorganic materials with functional electronic or magnetic behavior is a long-standing goal in condensed matter physics and materials science. Recently, the transition metal chalcogenides including selenium and sulfur have been of interest because of their correlated-electron properties, as seen in the iron-based superconductors and the transition metal dichalcogenides. However, the chalcogenide chemical space is less explored than that of oxides, and there is an open question of whether there may be new materials heretofore undiscovered. We perform a systematic combined theoretical and experimental search over ternary phase diagrams that are empty in the Inorganic Crystal Structure Database containing cations, transition metals, and one of selenium or sulfur. In these 27 ternary systems, we use a probabilistic model to reduce the likelihood of false negative predictions, which results in a list of 24 candidate materials. We then conduct a variety of synthesis experiments to check the candidate materials for stability. While the prediction method did obtain previously unknown compositions that are predicted stable within density functional theory, none of the candidate materials formed in our experiments. Finally, we come to the conclusion that these phase diagrams are “empty” in the case of bulk synthesis, but it remains a possibility that alternate synthesis routes may produce some of these phases.

36 MATERIALS SCIENCE↗

Three-loop Standard Model effective potential at leading order in strong and top Yukawa couplings

I find the three-loop contribution to the effective potential for the Standard Model Higgs field, in the approximation that the strong and top Yukawa couplings are large compared to all other couplings, using dimensional regularization with modified minimal subtraction. Checks follow from gauge invariance and renormalization group invariance. I also briefly comment on the special problems posed by Goldstone boson contributions to the effective potential, and on the numerical impact of the result on the relations between the Higgs vacuum expectation value, mass, and self-interaction coupling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Spontaneous generation of rotation in tokamak plasmas

Three different aspects of intrinsic rotation have been treated. i) A new, first principles model for intrinsic rotation [F.I. Parra, M. Barnes and P.J. Catto, Nucl. Fusion 51, 113001 (2011)] has been implemented in the gyrokinetic code GS2. The results obtained with the code are consistent with several experimental observations, namely the rotation peaking observed after an L-H transition, the rotation reversal observed in Ohmic plasmas, and the change in rotation that follows Lower Hybrid wave injection. ii) The model in [F.I. Parra, M. Barnes and P.J. Catto, Nucl. Fusion 51, 113001 (2011)] has several simplifying assumptions that seem to be satisfied in most tokamaks. To check the importance of these hypotheses, first principles equations that do not rely on these simplifying assumptions have been derived, and a version of these new equations has been implemented in GS2 as well. iii) A tokamak cross-section that drives large intrinsic rotation has been proposed for future large tokamaks. In large tokamaks, intrinsic rotation is expected to be very small unless some up-down asymmetry is introduced. The research conducted under this contract indicates that tilted ellipticity is the most efficient way to drive intrinsic rotation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Measurement of gravitational lensing of the cosmic microwave background using SPT-3G 2018 data

We present a measurement of gravitational lensing over 1500 deg 2 of the Southern sky using SPT-3G temperature data at 95 GHz and 150 GHz taken in 2018. The lensing amplitude relative to a fiducial Planck 2018 Lambda cold dark matter ( Λ CDM ) cosmology is found to be 1.020 ± 0.060 , excluding instrumental and astrophysical systematic uncertainties. We conduct extensive systematic and null tests to check the robustness of the lensing measurements, and report a minimum-variance combined lensing power spectrum over angular multipoles of 50 < L < 2000 , which we use to constrain cosmological models. When analyzed alone and jointly with primary cosmic microwave background (CMB) spectra within the Λ CDM model, our lensing amplitude measurements are consistent with measurements from SPT-SZ, SPTpol, ACT, and Planck. Incorporating loose priors on the baryon density and other parameters including uncertainties on a foreground bias template, we obtain a 1 σ constraint on σ 8 Ω m 0.25 = 0.595 ± 0.026 using the SPT-3G 2018 lensing data alone, where σ 8 is a common measure of the amplitude of structure today and Ω m is the matter density parameter. Combining SPT-3G 2018 lensing measurements with baryon acoustic oscillation (BAO) data, we derive parameter constraints of σ 8 = 0.810 ± 0.033 , S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.836 ± 0.039 , and Hubble constant H 0 = 68.8 - 1.6 + 1.3 km s - 1 Mpc - 1 . Our preferred S 8 value is higher by 1.6 to 1.8 σ compared to cosmic shear measurements from DES-Y3, HSC-Y3, and KiDS-1000 at lower redshift and smaller scales. We combine our lensing data with CMB anisotropy measurements from both SPT-3G and Planck to constrain extensions of Λ CDM . Using CMB anisotropy and lensing measurements from SPT-3G only, we provide independent constraints on the spatial curvature of Ω K = 0.01 4 - 0.026 + 0.023 (95% C.L.) and the dark energy density of Ω Λ = 0.72 2 - 0.026 + 0.031 (68% C.L.). When combining SPT-3G lensing data with SPT-3G CMB anisotropy and BAO data, we find an upper limit on the sum of the neutrino masses of ∑ m ν < 0.30 eV (95% C.L.). Due to the different combination of angular scales and sky area, this lensing analysis provides an independent check on lensing measurements by ACT and Planck.

79 ASTRONOMY AND ASTROPHYSICS↗

From models to reality: a systematic review on simulated and measured residential heat pump energy savings

High-performance HVAC solutions are central to residential energy management. A substantial share of these are electric, reversible-cycle systems, with heat pumps representing the largest portion of current and near-term adoption. This review synthesizes peer-reviewed and grey literature on residential space heating and cooling heat pumps. The academic literature is dominated by modeling (73.8%), with limited field measurement (13.1%). Grey literature from United States serve as a supplemental resource providing measured savings. Conversions from electric-resistance heating consistently show the largest site energy reductions, while oil/propane baselines yield moderate savings, and gas baseline scenario often deliver small and region-dependent savings. This study cross-checks the grey literature measured data with simulation data filtered from the ResStock dataset. The comparison indicates a discrepancy between simulations and measured data: simulated site EUIs are typically lower than measured EUIs, but percentage energy savings fall in similar ranges, implying simulations capture directional effects while underestimating energy use. Factors associated with variability and model–measurement differences include system characterization and control representation (e.g., backup heat engagement, thermostat/setpoint strategies, commissioning/installation quality), occupant behavior, weather normalization, metering scope, and envelope characterization. This paper also outlines the proposed methodology for comparing simulation and measured data for heat pumps. It emphasizes the metrics used for comparison and units harmonization, building characteristics matching, and compact metadata are needed for simulations to match measured data. The proposed methodology is expected to improve the credibility of simulated savings as measured evidence grows.

Yu, Lili↗

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗

Improved Weld Residual Stress Modeling System in BlackBear

This report presents enhancements to the MOOSE-based BlackBear application aimed at improving its capability to simulate welding and other thermo-mechanical manufacturing processes. Two primary avenues of improvement are pursued. First, to enhance user accessibility, we introduce a centralized default block restriction mechanism that ensures coverage checks are performed within user-specified default blocks. This default setting is applied consistently to all block-describable objects, such as variables, kernels, and more. In addition, we develop a modular action for moving heat source simulations, which integrates path file parsing, subdomain modification, and heat source kernel enforcement into a single, streamlined configuration. Second, to improve solver robustness, we implement an alternative method for assigning initial conditions to the updated active domain during the simulation, thereby enhancing convergence behavior. To validate the framework, we design and conduct several benchmark simulations, including heat conduction with progressive material addition, linear elasticity with time-dependent material deposition, and viscoplasticity model with isotropic hardening under similar conditions. Finally, we demonstrate the effectiveness of the proposed framework through large-scale thermo-mechanical welding simulations in both two and three dimensions.

42 ENGINEERING↗

Plasma-induced magnetic responses during nonlinear dynamics of magnetic islands due to resonant magnetic perturbations

Resonant magnetic perturbations (RMPs) produce magnetic islands in toroidal plasmas. Self-healing (annihilation) of RMP-induced magnetic islands has been observed in helical systems, where a possible mechanism of the self-healing is shielding of RMP penetration by plasma flows, which is well known in tokamaks. Thus, fundamental physics of RMP shielding is commonly investigated in both tokamaks and helical systems. In order to check this mechanism, detailed informations of magnetic island phases are necessary. In experiments, measurement of radial magnetic responses is relatively easy. In this study, based on a theoretical model of rotating magnetic islands, behavior of radial magnetic fields during the self-healing is investigated. It is confirmed that flips of radial magnetic fields are typically observed during the self-healing. Such behavior of radial magnetic responses is also observed in LHD experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

On the image in RHIC of AGS spin vector via the AtR

This note re-visits the transport of the spin nn0 vector, from its periodic orientation in the AGS, to the downstream end of the Blue and Yellow RHIC ring injection kickers via the AGS extraction system and the AtR line. The goal is to verify the optimal injection energy into RHIC, in matter of spin matching. This is done using newly computed OPERA 3-D field maps of the AGS cold snake, including a new setting of the cold snake solenoid as discussed in a companion Tech. Note (C-A/AP/485, July 2013), together with the machinery of the AGS and AtR models developed in the stepwise ray-tracing code Zgoubi. Computing tools and methods employed in these investigations are discussed as well, in order to facilitate possible further checks or investigations.

43 PARTICLE ACCELERATORS↗

TU-FG-201-07: Development of SRS Conical Collimator Collision Prediction Software for Radiation Treatment Safety

Purpose: Stereotactic radiosurgery (SRS) treatments using conical collimators can potentially result in gantry collision with treatment table due to limited collision-clear spaces. An in-house software was developed to help the SRS treatment planner mitigate potential SRS conical collimator (Varian Medical System, Palo Alto, CA) collisions with the treatment table. This software was designed to remove treatment re-planning secondary to unexpected collisions. Methods: A BrainLAB SRS ICT Frameless Extension used for SRS treatments in our clinic was mathematically modelled using surface points registered to the 3D co-ordinate space of the couch extension. The surface points are transformed based on the treatment isocenter point and potential collisions are determined in 3D space for couch and gantry angle combinations. The distance between the SRS conical collimators and LINAC isocenter is known. The collision detection model was programmed in MATLAB (Mathwork, Natick, MA) to display graphical plots of the calculations, and the plotted data is used to avoid the gantry and couch angle combinations that would likely result in a collision. We have utilized the cone collision tool for 23 SRS cone treatment plans (8 retrospective and 15 prospective for 10 patients). Results: Twenty one plans strongly agreed with the software tool prediction for collision. However, in two plans, a collision was observed with a 0.5 cm margin when the software predicted no collision. Therefore, additional margins were added to the clearance criteria in the program to achieve a lower risk of actual collisions. Conclusion: Our in-house developed collision check software successfully avoided SRS cone re-planning by 91.3% due to a reduction in cone collisions with the treatment table. Future developments to our software will include a CT image data set based collision prediction model as well as a beam angle optimization tool to avoid normal critical tissues as well as previously treated lesions.

60 APPLIED LIFE SCIENCES↗

SU-F-J-148: A Collapsed Cone Algorithm Can Be Used for Quality Assurance for Monaco Treatment Plans for the MR-Linac

Purpose: Treatment plans for the MR-linac, calculated in Monaco v5.19, include direct simulation of the effects of the 1.5T B{sub 0}-field. We tested the feasibility of using a collapsed-cone (CC) algorithm in Oncentra, which does not account for effects of the B{sub 0}-field, as a fast online, independent 3D check of dose calculations. Methods: Treatment plans for six patients were generated in Monaco with a 6 MV FFF beam and the B{sub 0}-field. All plans were recalculated with a CC model of the same beam. Plans for the same patients were also generated in Monaco without the B{sub 0}-field. The mean dose (Dmean) and doses to 10% (D10%) and 90% (D90%) of the volume were determined, as percentages of the prescribed dose, for target volumes and OARs in each calculated dose distribution. Student’s t-tests between paired parameters from Monaco plans and corresponding CC calculations were performed. Results: Figure 1 shows an example of the difference between dose distributions calculated in Monaco, with the B{sub 0}-field, and the CC algorithm. Figure 2 shows distributions of (absolute) difference between parameters for Monaco plans, with the B{sub 0}-field, and CC calculations. The Dmean and D90% values for the CTVs and PTVs were significantly different, but differences in dose distributions arose predominantly at the edges of the target volumes. Inclusion of the B{sub 0}-field had little effect on agreement of the Dmean values, as illustrated by Figure 3, nor on agreement of the D10% and D90% values. Conclusion: Dose distributions recalculated with a CC algorithm show good agreement with those calculated with Monaco, for plans both with and without the B{sub 0}-field, indicating that the CC algorithm could be used to check online treatment planning for the MRlinac. Agreement for a wider range of treatment sites, and the feasibility of using the γ-test as a simple pass/fail criterion, will be investigated.

60 APPLIED LIFE SCIENCES↗

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Implications of CMS analysis of photon-photon interactions for photon PDFs

As part of a recent analysis of exclusive two-photon production of W + W – pairs at the LHC, the CMS experiment used di-lepton data to obtain an “effective” photon-photon luminosity. We show how the CMS analysis on their 8 TeV data, along with some assumptions about the likelihood for events in which the proton breaks up to pass the selection criteria, can be used to significantly constrain the photon parton distribution functions, such as those from the CTEQ, MRST, and NNPDF collaborations. We compare the data with predictions using these photon distributions, as well as the new LUXqed photon distribution. We study the impact of including these data on the NNPDF2.3QED, NNPDF3.0QED and CT14QEDinc fits. We find that these data place a useful and complementary cross-check on the photon distribution, which is consistent with the LUXqed prediction while suggesting that the NNPDF photon error band should be significantly reduced. Additionally, we propose a simple model for describing the two-photon production of W + W – at the LHC. In conclusion, using this model, we constrain the number of inelastic photons that remain after the experimental cuts are applied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Characteristics Analysis of SGTR accidents under the Shutdown Conditions

The Steam Generator Tube Rupture (SGTR) accident is one of design basis accidents that could result in undesirable radiation release as it bypasses the containment. According to Ref. [1], SGTRs have been experienced in several nuclear power plants and Ulchin 4 of Korea is one of them. Basically, SGTR analysis assumes that it occurs at full power, the SGTR at Ulchin 4 in 2002, was in the shutdown condition. Therefore, the transient behavior was different from the cases in full power conditions and especially, the effectiveness of Emergency Operating Procedure (EOP) needs to be checked if it is also applicable to the shutdown conditions, because EOP is prepared for full power conditions. In this paper, we set up a MARS-KS code model for Ulchin 4 unit and simulated the SGTR accident for benchmarking the model to the actual plant behavior during the accident. And then, several case studies were done to investigate the consequence of SGTR and the effectiveness of the EOP, focusing on the amount of the primary to secondary discharge flow which was influenced by operator actions. At the end, we'll discuss about the EOP for the shutdown conditions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

The Role of the CEBAF Element Database in Commissioning the 12GeV Accelerator Upgrade

The CEBAF Element Database (CED) was first developed in 2010 as a resource to support model-driven configuration of the Jefferson Lab Continuous Electron Beam Accelerator (CEBAF). Since that time, its uniquely flexible schema design, robust programming interface, and support for multiple concurrent versions has permitted it to evolve into a more broadly useful operational and control system tool. The CED played a critical role before and during the 2013 startup and commissioning of CEBAF following its 18-month long shutdown and upgrade. Information in the CED about hardware components and their relations to one-another facilitated a thorough Hot Checkout process involving more than 18,000 system checks. New software relies on the CED to generate EDM screens for operators on-demand thereby ensuring that the information on those screens is correct and up-to-date. The CED also continues to fulfill its original mission of supporting model-driven accelerator setup. Using the new ced2elegant and eDT (elegant Download Tool), accelerator physicists have proven able to compute and apply energy-dependant set points with greater efficiency than ever before.

Larrieu, T.↗

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗

Technical Report: Reference photon dosimetry data for Varian accelerators based on IROC-Houston site visit data

Purpose: Accurate data regarding linear accelerator (Linac) radiation characteristics are important for treatment planning system modeling as well as regular quality assurance of the machine. The Imaging and Radiation Oncology Core-Houston (IROC-H) has measured the dosimetric characteristics of numerous machines through their on-site dosimetry review protocols. Photon data are presented and can be used as a secondary check of acquired values, as a means to verify commissioning a new machine, or in preparation for an IROC-H site visit. Methods: Photon data from IROC-H on-site reviews from 2000 to 2014 were compiled and analyzed. Specifically, data from approximately 500 Varian machines were analyzed. Each dataset consisted of point measurements of several dosimetric parameters at various locations in a water phantom to assess the percentage depth dose, jaw output factors, multileaf collimator small field output factors, off-axis factors, and wedge factors. The data were analyzed by energy and parameter, with similarly performing machine models being assimilated into classes. Common statistical metrics are presented for each machine class. Measurement data were compared against other reference data where applicable. Results: Distributions of the parameter data were shown to be robust and derive from a student’s t distribution. Based on statistical and clinical criteria, all machine models were able to be classified into two or three classes for each energy, except for 6 MV for which there were eight classes. Quantitative analysis of the measurements for 6, 10, 15, and 18 MV photon beams is presented for each parameter; supplementary material has also been made available which contains further statistical information. Conclusions: IROC-H has collected numerous data on Varian Linacs and the results of photon measurements from the past 15 years are presented. The data can be used as a comparison check of a physicist’s acquired values. Acquired values that are well outside the expected distribution should be verified by the physicist to identify whether the measurements are valid. Comparison of values to this reference data provides a redundant check to help prevent gross dosimetric treatment errors.

60 APPLIED LIFE SCIENCES↗

Equation of State for the Thermodynamic Properties of Trans-1,2-dichloroethene [R-1130(E)]

We present an empirical equation of state in terms of the Helmholtz energy for trans-1,2-dichloroethene [R-1130(E)]. The range of validity is from the triple-point temperature, 223.31 K to 525 K with pressures up to 30 MPa. It may be used to calculate all thermodynamic properties in the fluid phase, including liquid, gas, and supercritical regions. Comparisons are given with existing literature data and estimated uncertainties are provided. In addition, checks were made for correct extrapolation behavior so that the equation behaves in a physically realistic manner when used outside of its range of validity, enabling its use in mixture models. The estimated uncertainties (at a k = 2 or 95 % level of confidence) are based on comparisons with critically assessed data and are 0.25 % for vapor pressure for temperatures in the range 300 K < T < 454 K, rising to 1.5 % as the temperature decreases from 300 K to 265 K. For density in the liquid phase the estimated uncertainty is 0.14 % for temperatures 270 K < T < 410 K and for pressures up to 30 MPa. For the vapor phase the estimated uncertainty in density is 3 %. The uncertainty for liquid-phase heat capacity is 1 % at atmospheric pressure over the temperature range 268 K < T < 309 K, and the uncertainty for the speed of sound in the liquid phase is 0.25 % for temperatures 230 K < T < 420 K and for pressures up to 30 MPa. The uncertainties are larger outside of these specified ranges and in the critical region.

1,2-Dichloroethene↗