Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Coefficient of performance”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Local primordial non-Gaussian bias from time evolution

Primordial non-Gaussianity (PNG) is a signature of fundamental physics in the early Universe that is probed by cosmological observations. Here, it is well known that the local type of PNG generates a strong signal in the two-point function of large-scale structure tracers, such as galaxies. This signal, often termed “scale-dependent bias” is a generic feature of modulation of gravitational structure formation by a large-scale mode. It is less well appreciated that the coefficient controlling this signal, b ϕ , is closely connected to the time evolution of the tracer number density. This correspondence between time evolution and local PNG can be simply explained for a universal tracer whose mass function only depends on peak height and, more generally, for nonuniversal tracers in the separate universe picture, which we validate in simulations. We also describe how to recover the bias of tracers subject to a survey selection function and perform a simple demonstration on simulated galaxies. Since the local PNG amplitude in n-point statistics ($f$ NL ) is largely degenerate with the coefficient b ϕ , this proof of concept study demonstrates that Galaxy survey data can allow for more optimal and robust extraction of local PNG information from upcoming surveys.

Sullivan, James M. [University of California, Berk↗
DOE OSTI2024

Low temperature recovery of OFF-state stress induced degradation of AlGaN/GaN high electron mobility transistors

Thermal annealing is a widely used strategy to enhance semiconductor device performance. However, the process is complex for multi-material multi-layered semiconductor devices, where thermoelastic stresses from lattice constant and thermal expansion coefficient mismatch may create more defects than those annealed. We propose an alternate low temperature annealing technique, which utilizes the electron wind force (EWF) induced by small duty cycle high density pulsed current. To demonstrate its effectiveness, we intentionally degrade AlGaN/GaN high electron mobility transistors (HEMTs) with accelerated OFF-state stressing to increase ON-resistance ∼182.08% and reduce drain saturation current ∼85.82% of pristine condition at a gate voltage of 0 V. We then performed the EWF annealing to recover the corresponding values back to ∼122.21% and ∼93.10%, respectively. The peak transconductance, degraded to ∼76.58% of pristine at the drain voltage of 3 V, was also recovered back to ∼92.38%. This recovery of previously degraded transport properties is attributed to approximately 80% recovery of carrier mobility, which occurs during EWF annealing. We performed synchrotron differential aperture x-ray microscopy measurements to correlate these annealing effects with the lattice structural changes. We found a reduction of lattice plane spacing of (001) planes and stress within the GaN layer under the gate region after EWF annealing, suggesting a corresponding decrease in defect density. Application of this low-temperature annealing technique for in-operando recovery of degraded electronic devices is discussed.

Physics↗
DOE OSTI2022

Understanding the effect of irradiation and silicon carbide microstructure on silver diffusion in TRISO particles

The silicon carbide (SiC) layer in tristructural isotropic (TRISO) fuel particles serves as a barrier to prevent the escape of fission from the fuel kernel. The release of silver (Ag) is a concern due to the long half-life of the 110mAg isotope. In this study, a combination of atomistic and phase-field methods focuses on quantifying the effect of irradiation and SiC microstructure on Ag diffusion. Atomistic calculations provide the Ag diffusivity in SiC bulk and GBs, also providing an estimate of the effect of irradiation defects on Ag diffusivity in bulk SiC. These lower length scale efforts are leveraged to develop a mesoscale effective Ag diffusion coefficient (Deff) in SiC. Deff is defined as a function of temperature, microstructure variables, and fluence. Deff is implemented in the fuel performance code Bison to predict Ag release from AGR-1 and AGR-2 TRISO fuel particles. We hereby quantify the impact of SiC grain size and irradiation on Ag release and improve Bison's predictions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Versatile Test Reactor Conceptual Core Design

The VTR is a 300-MW(thermal) sodium-cooled fast reactor (SFR) designed for the specific purpose of delivering unique testing capabilities to enable the advancement of all reactor technologies. With its flux level, irradiation volume, and operational flexibility, the VTR will enable accelerated testing of materials, fuels, and various components needing irradiation testing. Proven SFR technologies and design approaches have been leveraged in designing the VTR core, ensuring the highest possible readiness level. This resulted in the VTR using ternary metallic fuel and delivering fast flux levels in excess of 4 x 10 15 n/cm 2 ∙ s over large useful volumes, corresponding to about 60 dpa/year in steel. As part of the design efforts, the VTR core performance has been determined for a representative configuration, ensuring that the reactivity control systems offer sufficient shutdown margins, that the core can be safely cooled in all situations, and that reactivity feedback coefficients are conducive to a favorable safety behavior. Furthermore, the incorporation of features such as fuel assembly storage in the shield region supports the flexible and reliable operation of the VTR. Additional design work has been ongoing as well. This includes thorough shielding performance evaluations to ensure safe operation of the VTR, verification and validation of the design tools used to achieve compliance with Nuclear Quality Assurance (NQA-1) requirements, early assessment of the impact of irradiation experiments on the core performance envelope and associated margins, and in-depth uncertainty quantification efforts to quantify the anticipated range of performance characteristics. An experimental program supporting the VTR core design has been set up, with the current focus being on thermal-hydraulic experiments. The purpose of this experimental program is to obtain confirmatory measurements to serve directly as part of the core design basis or as part of the validation cases supporting the simulation tools used.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Efficient Loads Surrogates for Waked Turbines in an Array

Accurately and efficiently predicting wind turbine structural loading is a crucial step in wind farm design. Without considering structural loading, wind farm optimization could negatively impact turbine fatigue and ultimate loads, especially for waked and partially waked turbines, which could result in higher maintenance costs and reduced turbine lifetime. However, predicting turbine loads throughout an array is a costly step, as these quantities require time-accurate results across long time histories, which is often intractable for large array optimization. Therefore, surrogate models that link array spacing to load outputs are often used, but the surrogates are then unique to the inflow conditions and array configurations in the training library. This work develops surrogate models for many wind turbine load outputs based solely on rotor plane velocity measurements, with no required input about array configuration or freestream inflow parameters. Surrogate models were constructed for many turbine quantities of interest (QoI), considering mean, standard deviation, ultimate, and fatigue loads. In general, most QoI statistics were accurately captured, as measured by predicted vs. actual correlation coefficient, confirming the suitability of the approach. Temporal mean values of the QoI required only temporal mean measurements of the rotor plane inflow velocity. However, accurate prediction of temporal standard deviation, ultimate, and fatigue values of QoI also required temporal standard deviations of the rotor plane velocity field. Poor surrogate performance was observed when too many correlated inputs were used, such as multiple velocity components. If the fewest inflow parameters are used to construct the surrogates, the average correlation coefficient value for all output QoI statistics is 0.89. Surrogates for standard deviations and damage equivalent loads (DELs) of turbine QoIs generally had lower accuracy and tower-base and shaft load channels posed the most difficult to capture accurately. The results suggest that these surrogates could be easily paired with analytic wake models, which are frequently used for pre-construction wind farm array optimization, to account for turbine loading in addition to power production. By including the optimal inflow conditions, the surrogate accuracy can improve to an average correlation coefficient value for all output QoI statistics of 0.92. This work has established the ability to build accurate surrogates for mean, standard deviation, ultimate load, and DEL turbine QoI values based on the rotor plane inflow velocity, and identified which inflow conditions lead to greater surrogate accuracy.

17 WIND ENERGY↗
DOE OSTI2023

Effective Lanthanide Diffusivity through U-Zr Metallic Fuel

Fuel-cladding chemical interaction (FCCI) is one of the main concerns for the performance of U-Zr metallic fuels. Although FCCI results in complex phase transformations and morphological changes in both the fuel and cladding sides of the interface, the formation of a brittle layer in the cladding side of the interface (sometimes referred to as wastage) has one of the most significant impacts on the cladding mechanical integrity. This brittle layer is associated with the formation of intermetallic compounds between the cladding constituents Fe, Cr and the lanthanide fission product species. The formation of this cladding wastage layer has been the focus of recent efforts to develop a mechanistic modeling framework, including production of lanthanides in the fuel, transport through the fuel to the fuel-cladding interface, and phase transformation to form intermetallic phases. To calculate the rate of transport of lanthanides through the fuel, a multi-scale computational approach has been used. Atomistic calculations have been performed to determine the diffusivities of Nd, the most prevalent lanthanide species, through the solid fuel matrix and along pore surfaces. These diffusivities have been used as input parameters for a mesoscale model to determine an effective diffusion coefficient that accounts for the formation of porosity, its interconnection, and infiltration with bond sodium from the fuel-cladding gap. The effective diffusivity has been used in engineering-scale simulations using the BISON fuel performance code, and BISON simulations have been compared to representative validation cases from the EBR-II reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Low-cost thermal/environmental barrier coatings: A first-principles study

Development of low-cost advanced thermal/environmental barrier coating (T/EBC) materials with acceptable thermal and mechanical properties is essential for safeguarding ceramic composites substrate against thermal and chemical degradation, thereby enhancing the efficiency of components in the high-temperature section of gas turbine engines. To this end, here we employed density functional theory-based approaches to predict the thermodynamic, mechanical, and thermal properties of rare earth disilicates based on abundant rare earth elements, namely La 2 Si 2 O 7 and Ce 2 Si 2 O 7 , as potential alternatives to the current-state-of-the-art ytterbium disilicate EBCs that uses expensive and scarce element Yb. The present study predicts that G-phase Ce 2 Si 2 O 7 has an ultralow thermal conductivity (0.26 W/m/K at 1500 K) and the apparent bulk coefficient of thermal expansion (ABCTE) (≈6.9x10 -6 K -1 ) slightly higher than SiC, demonstrating great potential as low-cost high-performance T/EBC. However, La 2 Si 2 O 7 and Ce 2 Si 2 O 7 undergo an A- to G-phase polymorphic transition at around 1470 K, resulting in significant changes to crystal structure and lattice parameters, and accordingly CTE and lattice thermal conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated reaction kinetics and network exploration (Arkane): A statistical mechanics, thermodynamics, transition state theory, and master equation software

The open-source statistical mechanics software described here, Arkane–Automated Reaction Kinetics and Network Exploration–facilitates computations of thermodynamic properties of chemical species, high-pressure limit reaction rate coefficients, and pressure-dependent rate coefficient over multi-well molecular potential energy surfaces (PES) including the effects of collisional energy transfer on phenomenological kinetics. Arkane can use estimates to fill in information for molecules or reactions where quantum chemistry information is missing. The software solves the internal energy master equation for complex unimolecular reaction systems. Inputs to the software include converged electronic structure computations performed by the user using a variety of supported software packages (Gaussian, Molpro, Orca, TeraChem, Q-Chem, Psi4). The software outputs high-pressure limit rate coefficients and pressure-dependent phenomenological rate coefficients, as well as computed thermodynamic properties (enthalpy, entropy, and constant pressure heat capacity) with added energy corrections. Some of the key features of Arkane include treatment of 1D, 2D or ND hindered internal rotation modes, treatment of free internal rotation modes, quantum tunneling effect consideration, transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) rate coefficient computations, master equation solution with four implemented methods, inverse-Laplace transform of high-pressure limit rate coefficients into the energy domain, energy corrections based on bond-additivity or isodesmic reactions, automated and efficient PES exploration, and PES sensitivity analysis. The present work describes the design of Arkane, how it should be used, and refers to the theory that it employs. Arkane is distributed via the RMG-Py software suite (https://github.com/ReactionMechanismGenerator/RMG-Py).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Iodine effective diffusion coefficients through volcanic rock: Influence of iodine speciation and rock geochemistry

Accurate prediction of the subsurface transport of iodine species is important for the assessment of long-term nuclear waste repository performance, as well as monitoring compliance with the Comprehensive Nuclear-Test-Ban Treaty, given that radioiodine decays into radioxenon. However, the transport of iodine through intact geologic media is not well understood, compromising our ability to assess risk associated with radioiodine migration. The current study's goal is to quantify the matrix diffusion of iodine species through saturated volcanic rock, with particular attention paid to the redox environment and potential speciation changes. Here, diffusion experiments were run for iodide through lithophysae-rich lava, lithophysae-poor lava, and welded tuff, whereas iodate diffusion was studied through welded tuff. Iodine transport was compared with a conservative tracer, HDO, and effective diffusion coefficients were calculated. Likely due to a combination of size and anion exclusion effects, iodine species diffused more slowly than the conservative tracer through all rock types tested. Furthermore, oxidation of iodide to iodate was observed in the lithophysae-poor lava, affecting transport. Results provide much needed data for subsurface transport models that predict radioiodine migration from underground sources, and indicate the pressing need for geochemical and redox interactions to be incorporated into these models.

58 GEOSCIENCES↗
DOE OSTI2025

Multi-scale modeling of wastage layer formation in metallic fuel cladding

Fuel-cladding chemical interaction (FCCI) is a major concern for U-Zr metallic fuels' performance, primarily due to the formation of a brittle layer (wastage) in the cladding. This brittle layer, resulting from intermetallic compounds between cladding constituents Fe, Cr, and lanthanide fission products, significantly impacts the cladding's mechanical integrity. Recent efforts focus on developing a mechanistic modeling framework to understand lanthanide production, transport to the fuel-cladding interface, and phase transformation to intermetallic phases. A multi-scale computational approach has been used to calculate lanthanide transport rates, with atomistic calculations determining Nd diffusivities through the solid fuel matrix and along pore surfaces. These diffusivities inform a mesoscale model to determine an effective diffusion coefficient, accounting for porosity and infiltration with bond sodium. This effective diffusivity is used in engineering-scale simulations via the BISON fuel performance code, which has been validated against EBR-II and FFTF reactor experiments.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵ m ≃ 7 x 10 -4 g 2 kg −2 s −1 for mixing ratio and ϵ θ ≃ 1.6 x 10 -3 K 2 s −1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃ 10 -2 m 2 s −3 . We also demonstrate that the vertical wind ACF coefficient k w ∝ $\overline{w'^{2}}$ and the TKE dissipation ϵ ∝ ($\overline{w'^{2}}$) 3/2 . For the molecular destruction rates, we show that ϵ m ∝ $\overline{m'^{2}}$ ($\overline{w'^{2}}$) 1/2 and ϵ θ ∝ $\overline{θ'^{2}}$ ($\overline{w'^{2}}$) 1/2 . These equations can be used for parameterizations of ϵ, ϵ m , and ϵ θ . All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

dissipation rate↗
DOE OSTI2022

Modeling of Cube Array Roughness: RANS, Large Eddy Simulation, and Direct Numerical Simulation

Abstract Flow over arrays of cubes is an extensively studied model problem for rough wall turbulent boundary layers. While considerable research has been performed in computationally investigating these topologies using direct numerical simulation (DNS) and large eddy simulation (LES), the ability of sublayer-resolved Reynolds-averaged Navier–Stokes (RANS) to predict the bulk flow phenomena of these systems is relatively unexplored, especially at low and high packing densities. Here, RANS simulations are conducted on six different packing densities of cubes in aligned and staggered configurations. The packing densities investigated span from what would classically be defined as isolated, up to those in the d-type roughness regime, filling in the gap in the present literature. Three different sublayer-resolved turbulence closure models were tested for each case: a low Reynolds number k–ϵ model, the Menter k–ω SST model, and a full Reynolds stress model. Comparisons of the velocity fields, secondary flow features, and drag coefficients are made between the RANS results and existing LES and DNS results. There is a significant degree of variability in the performance of the various RANS models across all comparison metrics. However, the Reynolds stress model demonstrated the best accuracy in terms of the mean velocity profile as well as drag partition across the range of packing densities.

Engineering↗
DOE OSTI2020

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Electro-chemo-mechanically Driven Ni Exsolution from (Pr,Ce,Ni)O 2−δ : Controlled Nucleation Density and Enhanced Electrode Kinetics

In situ exsolution of metal nanoparticles is a promising strategy to prepare electrocatalysts with enhanced activity and resistance to agglomeration for efficient chemical transformations and energy conversion. Achieving a high nucleation density of nanoparticles under mild conditions and understanding how to tailor the process is important for performance of these electrodes in electrochemical cells. In this work, we demonstrate facile exsolution of Ni nanoparticles using fluorite-structured (Pr,Ce)O 2−δ as the support oxide, driven by electrochemical potential and aided by the metastability of Ni in the solid solution (elastic driving force). We prepare single-phase oriented thin films of (Pr,Ce,Ni)O 2−δ (NPCO) on (Zr,Y)O 2−δ (YSZ) substrates by pulsed laser deposition. With the aid of a high-throughput electrochemical cell that provides a lateral gradient in Nernst voltage, we apply in situ near-ambient pressure synchrotron X-ray photoelectron spectroscopy and ex situ atomic force microscopy to investigate the impact of electrochemical potential on Ni nucleation density. We find that metallic Ni can be successfully exsolved at 550 °C upon cathodic biasing in 20 mTorr O 2 , and its nucleation density increases with increasing electrochemical driving force/decreasing oxygen chemical potential. We further evaluate the electrochemical performance under highly reducing (fuel electrode) conditions by electrochemical impedance spectroscopy. With the exsolved Ni nanoparticles, the surface exchange coefficient of the NPCO is found to be ∼4× higher than for PCO without exsolution. This work confirms mixed conducting fluorites as beneficial host lattices for facile transition-metal exsolution and suggests the possibility for constructing an all ceria-based electrochemical cell with PCO serving as both the cathode and the anode.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Residual stress analysis of aluminum nitride piezoelectric micromachined ultrasonic transducers using Raman spectroscopy

In this study, the Raman biaxial stress coefficients K II and strain-free phonon frequencies ω 0 have been determined for the E 2 (low), E 2 (high), and A 1 (LO) phonon modes of aluminum nitride, AlN, using both experimental and theoretical approaches. The E 2 (high) mode of AlN is recommended for the residual stress analysis of AlN due to its high sensitivity and the largest signal-to-noise ratio among the studied modes. The E 2 (high) Raman biaxial stress coefficient of ₋3.8 cm₋1/GPa and strain-free phonon frequency of 656.68 cm ₋1 were then applied to perform both macroscopic and microscopic stress mappings. For macroscopic stress evaluation, the spatial variation of residual stress was measured across an AlN-on-Si wafer prepared by sputter deposition. A cross-wafer variation in residual stress of ~150 MPa was observed regardless of the average stress state of the film. Microscopic stress evaluation was performed on AlN piezoelectric micromachined ultrasonic transducers (pMUTs) with submicrometer spatial resolution. These measurements were used to assess the effect of device fabrication on residual stress distribution in an individual pMUT and the effect of residual stress on the resonance frequency. At ~20 μm directly outside the outer edge of the pMUT electrode, a large lateral spatial variation in residual stress of ~100 MPa was measured, highlighting the impact of metallization structures on residual stress in the AlN film.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Determining best practices for using genetic algorithms in molecular discovery

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. Here, we show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Non-LTE modelling of cyanoacetylene: evidence for isomer-specific excitation

ABSTRACT Cyanoacetylene molecules are widespread in the interstellar medium (ISM) and several of its isomers have been detected in cold molecular clouds and circumstellar gas. Accurate estimates of the abundance ratio of cyanoacetylene isomers may provide deep insight into their environment. Such knowledge requires rigorous modelling of the emission spectra based on non-local thermodynamic equilibrium (LTE) radiative transfer calculations. To this end, we computed excitation cross-sections of HC2NC and HNC3 induced by collision with para- and ortho-H2, using a quantum mechanical close-coupling method. Then, by thermally averaging these data, we derived rate coefficients for the first 31 low-lying rotational levels of each isomer for temperatures up to 80 K. For the para-H2 collider, the propensity rules are in favour of rotational transitions involving Δj1 = 2 for both isomers, while for the ortho-H2 collider, Δj1 = 2 and Δj1 = 1 rotational transitions are favoured for HC2NC and HNC3, respectively. A comparison of rate coefficients for the HC3N isomers shows differences up to an order of magnitude, especially at low temperatures. Finally, we performed non-LTE radiative transfer calculations to assess the impact of such variations in the analysis of observations. Our simulation suggests that the lack of collisional data specific to each isomer could lead to errors up to a factor of 2–3 in the excitation temperatures. We expect that these data could help in better understanding the cyanoacetylene chemistry and constraining the nitrogen chemistry in the ISM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Simulation of the thermoelectric effect in a multi-metallic superconducting cavity

Superconducting radio-frequency accelerating cavities made with different material layers, such as copper, Nb or Nb₃Sn, are susceptible to thermoelectric effects due to differences in Seebeck coefficients between the metals. A temperature gradient across the surfaces can drive thermoelectric currents, which may impact the cavity performance. A layered Cu/Nb/Nb3Sn single-cell cavity was tested with cryocoolers in 2022. Three heaters were mounted on the cavity surface at different locations and three single-axis cryogenic fluxgate magnetometers were attached close to the cavity equator. A linear increase in the magnetic field was measured while increasing the heaters' power. The cavity setup was analyzed with COMSOL and the results showed a trend similar to that observed in the experiment. This contribution details the approach chosen for the simulation and some of the challenges encountered.

Accelerator Physics↗