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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.

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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modelling type 1 quasar colours in the era of Rubin and Euclid

We construct a parametric SED model which is able to reproduce the average observed SDSS–UKIDSS–WISE quasar colours to within one-tenth of a magnitude across a wide range of redshift (0 < z < 5) and luminosity (–22 > M i > –29). This model is shown to provide accurate predictions for the colours of known quasars which are less luminous than those used to calibrate the model parameters, and also those at higher redshifts z > 5. Using a single parameter, the model encapsulates an up-to-date understanding of the intra-population variance in the rest-frame ultraviolet and optical emission lines of luminous quasars. At fixed redshift, there are systematic changes in the average quasar colours with apparent i-band magnitude, which we find to be well explained by the contribution from the host galaxy and our parametrization of the emission-line properties. By including redshift as an additional free parameter, the model could be used to provide photometric redshifts for individual objects. For the population as a whole we find that the average emission line and host-galaxy contributions can be well described by simple functions of luminosity which account for the observed changes in the average quasar colours across 18.1 < i AB < 21.5. Finally, we use these trends to provide predictions for quasar colours at the luminosities and redshifts which will be probed by the Rubin Observatory LSST and ESA-Euclid wide survey. The model code is applicable to a wide range of upcoming photometric and spectroscopic surveys, and is made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2021

Investigation of grid-based vorticity-velocity large eddy simulation off-body solvers for application to overset CFD

Accurately predicting unsteady wakes and vortex-dominated flows is essential to a wide range of engineering applications, including aircraft, rotorcraft, shipboard operations, bio-inspired unsteady flight and propulsion, wind turbines, and urban flows. While current CFD software can model the complete flow field and wake system, the computational costs incurred in high Reynolds number unsteady turbulent flow simulations often remain prohibitive for routine engineering use, particularly for applications involving moving components. Prior work has demonstrated that by adopting a vorticity-velocity formulation in a grid-based off-body flow solver (VorTran-M and VorTran-M2) one can lower these costs by several orders of magnitude when compared to conventional approaches. This paper describes the extensions made to VorTran-M2 to support turbulent flows, and associated benchmarking activity to assess its performance for problems involving strong stretching and diffusion processes, whose competing contributions to the vorticity field are core drivers of turbulent flow evolution. Predictions are presented for: (i) the Kida-Pelz problem whose inviscid form is of mathematical interest due to its apparent formation of singular flow in finite time; and (ii) the Taylor Green vortex arrangement, which has been extensively studied as a fundamental simulation challenge in the turbulent modeling community. Here, the results are used to evaluate the overall predictive ability and performance of two sub-grid scale models incorporated into VorTran-M2. Results indicate that the computational cost savings seen previously for inviscid and convection dominated problems extend to turbulent flow simulations supporting the viability of VorTran-M2 as a low cost means for accurately modeling the far-field and background flow, particularly when long duration vorticity evolution is of interest.

42 ENGINEERING↗
DOE OSTI2021

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

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

A multiscale approach to analyze the effect of radial void fraction distributions on BWR neutronic lattice parameters

The goal of this study is to analyze the effect of radial void fraction distribution, considered at three degrees of fidelity, on lattice and 2-group homogenized cross sections. The novelty of this work lies in its use of experimentally-determined radial void fraction distributions which allows for fine-scale coolant density data with no predictive uncertainty and which could be prohibitively expensive to simulate over the range of operational parameters used in the experiments. The experimentally-determined radial void fraction distributions are also characterized using a statistical tool called a semi-variogram in order to determine the scale of continuity. Void fraction distributions are averaged at the lattice and subchannel level for comparison with the fine-scale model. Models averaged at the subchannel and lattice level show a difference in about 68% of the time. Also, by comparing 2-group homogenized cross-sections, the differences between the subchannel and homogeneous cases are measurable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Equilibrium carbon isotope fractionation factors of hydrocarbons: Semi-empirical force-field method

Here, we have calculated the reduced partition function ratios for carbon isotopes (β-factor) of 67 hydrocarbons (alkanes, alkenes, alkynes, cycloalkanes, and aromatics), including their 267 single-substituted isotopomers. The calculations were performed using the harmonic oscillator – rigid rotator model and the Urey / Bigeleisen-Mayer approach. Normal frequencies of molecular vibrations of the isotopologues were calculated from the molecular structures data and valence field force constants stored in the Light-handled Elucidation of Vibrations (LEV) database elaborated by Dr. Gribov and his colleagues in Russia. The LEV database is constructed from experimental spectroscopic and structural data by solving inverse problems of molecular vibrations. The LEV is internally consistent and the most comprehensive database available to date. The β-factor were calculated in the temperature range of 200–800 K with a 10 K step. Our calculations predict that the β-factors increase with an increasing number of C atoms within the same groups of hydrocarbons (e.g., alkanes). Our calculations also show a general descending order of 13 C enrichments among the different groups of hydrocarbons: cycloalkanes, aromatics, alkenes (double bonds) and isoalkanes, alkanes, alkynes (triple bonds). Position-specific, intramolecular isotope effects within hydrocarbons are determined by the β-factors of C in the different functional groups in the order: quaternary (C), tertiary (methine, CH), secondary (methylene, CH 2 ), primary (methyl, CH 3 ) and double bond (C=C), saturated bond (C - C), triple bond (HC≡). Our calculations on bulk and position-specific carbon isotope β-factors of the hydrocarbons, which are generally consistent with very limited ab initio calculations in the literature, are internally consistent and the most comprehensive to date for future applications to position-specific isotope geochemistry of hydrocarbons.

58 GEOSCIENCES↗
DOE OSTI2024

Stairway to discovery: A report on the CMS programme of cross section measurements from millibarns to femtobarns

The Large Hadron Collider at CERN, delivering proton-proton collisions at much higher energies and far higher luminosities than previous machines, has enabled a comprehensive programme of measurements of the standard model (SM) processes by the CMS experiment. These unprecedented capabilities facilitate precise measurements of the properties of a wide array of processes, the most fundamental being cross sections. The discovery of the Higgs boson and the measurement of its mass became the keystone of the SM. Knowledge of the mass of the Higgs boson allows precision comparisons of the predictions of the SM with the corresponding measurements. These measurements span the range from one of the most copious SM processes, the total inelastic cross section for proton-proton interactions, to the rarest ones, such as Higgs boson pair production. They cover the production of Higgs bosons, top quarks, single and multibosons, and hadronic jets. Associated parameters, such as coupling constants, are also measured. These cross section measurements can be pictured as a descending stairway, on which the lowest steps represent the rarest processes allowed by the SM, some never seen before.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Water level changes in Lake Erie drive 21st century CO 2 and CH 4 fluxes from a coastal temperate wetland

Wetland water depth influences microbial and plant communities, which can alter the above- and below-ground carbon cycling of a wetland. Wetland water depths are likely to change due to shifting precipitation patterns, which will affect projections of greenhouse gas emissions; however, these effects are rarely incorporated into wetland greenhouse gas models. Seeking to address this gap, we used a mechanistic model, ecosys, to simulate a range of water depth scenarios in a temperate wetland, and analyzed simulated predictions of carbon dioxide (CO 2 ) andmethane (CH 4 ) fluxes over the 21st century. We tested our model using eddy covariance measurements of CO 2 and CH 4 fluxes collected at the Old Woman Creek National Estuarine Research Reserve (OWC) during 2015 and 2016. OWC is a lacustrine, estuarine, freshwater, mineral-soil marsh. An empirical model found that the wetland water depth is highly dependent on the water depth of the nearby Lake Erie. Future wetland surface water depths were modeled based on projection of Lake Erie's water depth using four separate NOAA projections, resulting in four wetland water-depth scenarios. Two of the four 21st century projections for Lake Erie water depths used in this study indicated that the water depth of the wetland would remain nearly steady; however, the other two indicated decreases in the wetland water depth. In our scenario where the wetland dries out, we project the wetland's climatological warming effect will decrease due to smaller CH 4 fluxes to the atmosphere and larger CO 2 uptake by the wetland. We also found that increased water level can lower emissions by shifting the site towards more open water areas, which have lower CH 4 emissions. We found that decreased water depths would cause more widespread colonization of the wetland by macrophyte vegetation. Using an empirical relationship, we also found that further drying could result in other, non-wetland vegetation to emerge, dramatically altering soil carbon cycling. In three of our four projections, we found that in general the magnitude of CO 2 and CH 4 fluxes steadily increase over the next 100 years in response to higher temperatures. However, in our driest simulations, we projected a different response due to increased oxidation of soil carbon, with CH 4 emissions decreasing substantially from an annual cumulative peak of 224.6 to a minimum of 104.7 gC m –2 year –1 . In that same simulation, net cumulative flux of CO 2 changed from being a sink of 56.5 gC m –2 year –1 to a source of 369.6 gCm –2 year –1 over the same period, despite a temperature increase from 13.7 °C to 14.2 °C. This temperature shift in our other three cases with greater water depths increased the source strength of CH 4 and the sink strength of CO 2 . Here, we conclude that the magnitude of wetland greenhouse-gas fluxes depended on the water depth primarily as it affected the areal percentage of the wetland available for plant colonization, but dramatic decreases in water depths could cause significant reductions in the wetland CH 4 fluxes, while simultaneously altering the wetland vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Photodissociation and Infrared Spectroscopy of U + (CO 2 ) n , UO + (CO 2 ) n , and UO 2+ (CO 2 ) n Cation-Molecular Complexes

Laser vaporization of uranium in a pulsed supersonic expansion of carbon dioxide is used to produce complexes of the form U + (CO 2 ) n , UO + (CO 2 ) n , and UO 2+ (CO 2 ) n . These ions are selected in a reflectron time-offlight mass spectrometer and studied with visible laser photodissociation and tunable infrared laser photodissociation spectroscopy in the region of the CO 2 antisymmetric stretch. The dissociation patterns and spectroscopy of these ions indicate that CO 2 ligands are intact molecules. Although reaction products that form oxide-carbonyl or oxalate species are predicted to be stable, there is no direct evidence in the frequency range studied for the formation of these species. There is no clear indication for the coordination numbers for singly charged uranium and its oxide complexes with CO 2 . However, there is strong support in the vibrational patterns for an eight-coordinate complex of the doubly charged UO 2+ species, i.e., UO 2+ (CO 2 ) 8 .

Cluster chemistry↗
DOE OSTI2024

Catalytic Reduction of Esters over Zirconia-Supported Metal Catalysts

Esters are often produced as unwanted byproducts during the catalytic upgrading of ethanol to diesel fuel precursors through Guerbet coupling. Removal of esters from the product stream is important to prevent the loss of downstream catalyst activity from ester-derived carboxylic acids. In this work, we studied ester hydrogenolysis to the parent alcohols as a viable route for enhanced diesel fuel production. Specifically, we investigated the reduction of hexyl acetate in butanol over ZrO 2 -supported Ni, Co, Cu, Rh, Pd, and Pt catalysts, where Cu/ZrO 2 was the most selective catalyst for the hydrogenolysis of hexyl acetate into hexanol and ethanol. Thermodynamic analysis reveals that a 90% alcohol yield can be obtained at 200 °C, 30 bar, and a relatively high H 2 :hexyl acetate molar ratio of 480:1. Experimentally, an alcohol yield of 88% yield was obtained with a 10 wt % Cu/ZrO 2 catalyst at these conditions with a residence time of 5.4 h kg cat kmol gas –1 . Catalytic tests on the support revealed that ZrO 2 catalyzes the transesterification reaction between hexyl acetate and butanol. However, only the Cu sites can catalyze the hydrogenolysis of the esters into the final alcohols. We developed a kinetic model for our experimental results, which shows that the transesterification and hydrogenolysis reactions run at two different timescales, the former being 10 times faster than the latter. Data regression has been used to develop a model to predict the mole fraction distribution of ester hydrogenolysis products over a wide range of contact times. Cu/ZrO 2 loses half its catalytic activity after 80 h of time on stream. Modeling of deactivation data reveals that the ZrO 2 support conserves a residual activity due to external active sites, while active sites over the Cu surface deactivate at different rates. Furthermore, the catalytic conversion of esters into their parent alcohols is relevant to the production of surrogate liquid fuels since alcohols can be bimolecularly dehydrated to produce a blend of ethers with diesel fuel-like properties.

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

A Monte Carlo Approach to Approximating the Effects of Pore Geometry on the Phase Behavior of Soil Freezing

Abstract Freezing in porous media is associated with a host of dynamic phenomena that stem from the presence and mobility of premelted liquid at subzero temperatures. Accurate assessments of the progressive liquid‐ice phase transition is required for predictive models of frost damage, glacier‐till coupling, and many other cold regions processes, as well as for evaluating the capacity for water storage in near‐surface extraterrestrial environments. We use a Monte Carlo approach to sample the pore space in a synthetic 3D packing of poly‐dispersed spherical particles and evaluate local geometrical constraints that allow us to assess changes in the relative proportions of pore fluid and ice. By approximating the phase boundary geometry in fine‐grained pores while considering both the curvature of the liquid‐ice interface and wetting interactions with matrix particles, our model predicts changes in phase equilibrium in granular media over a broad temperature range, where present accounting for the colligative effects of chloride and perchlorate solutes. In addition to formulating the constitutive behavior needed to better understand properties and processes in frozen soils, our results also provide insight into other aspects of phase equilibria in porous media, including the formation of methane hydrates in permafrost and marine sediments, and the partitioning between liquid water and vapor in the vadose zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Weakened Orographic Influence on Cool-Season Precipitation in Simulations of Future Warming Over the Western US

High-resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain-shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high-resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predicting the Evolution of Shallow Cumulus Clouds With a Lotka‐Volterra Like Model

Abstract In numerical weather prediction and climate models, boundary‐layer clouds are controlled by a wide range of subgrid‐scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large‐eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka‐Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale‐aware parameterizations of shallow‐convective clouds in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Real-space observation of ergodicity transitions in artificial spin ice

Abstract Ever since its introduction by Ludwig Boltzmann, the ergodic hypothesis became a cornerstone analytical concept of equilibrium thermodynamics and complex dynamic processes. Examples of its relevance range from modeling decision-making processes in brain science to economic predictions. In condensed matter physics, ergodicity remains a concept largely investigated via theoretical and computational models. Here, we demonstrate the direct real-space observation of ergodicity transitions in a vertex-frustrated artificial spin ice. Using synchrotron-based photoemission electron microscopy we record thermally-driven moment fluctuations as a function of temperature, allowing us to directly observe transitions between ergodicity-breaking dynamics to system freezing, standing in contrast to simple trends observed for the temperature-dependent vertex populations, all while the entropy features arise as a function of temperature. These results highlight how a geometrically frustrated system, with thermodynamics strictly adhering to local ice-rule constraints, runs back-and-forth through periods of ergodicity-breaking dynamics. Ergodicity breaking and the emergence of memory is important for emergent computation, particularly in physical reservoir computing. Our work serves as further evidence of how fundamental laws of thermodynamics can be experimentally explored via real-space imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

First-principles study of structural, elastic, electronic, transport properties, and dielectric breakdown of Cs 2 Te photocathode

The pursuit to operate photocathodes at high accelerating gradients to increase brightness of electron beams is gaining interests within the accelerator community, particularly for applications such as free electron lasers (FEL) and compact accelerators. Cesium telluride (Cs 2 Te) is a widely used photocathode material and it is presumed to offer resilience to higher gradients because of its wider band gap compared to other semiconductors. Despite its advantages, crucial material properties of Cs 2 Te remain largely unknown both in theory and experiments. In this study, we employ first-principles calculations to provide detailed structural, elastic, electronic and transport properties of Cs 2 Te. It is found that Cs 2 Te has an intrinsic mobility of 20 cm 2 /Vs for electrons and 2.0 cm 2 /Vs for holes at room temperature. The low mobility is primarily limited by the strong polar optical phonon scattering. Cs 2 Te also exhibits ultralow lattice thermal conductivity of 0.2 W/(m*K) at room temperature. Based on the energy gain/loss balance under external field and electron–phonon scattering, we predict that Cs 2 Te has a dielectric breakdown field in the range from ~ 60 to ~ 132 MV/m at room temperature dependent on the doping level of Cs 2 Te. Our results are crucial to advance the understanding of applicability of Cs 2 Te photocathodes for high-gradient operation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Using the Stix finite element RF code to investigate operation optimization of the ICRF antenna on Alcator C-Mod

Abstract As the Ion Cyclotron Radio Frequency range (ICRF) heating becomes more favorable in fusion devices, the urgency of predicting and mitigating impurity generation that arises from it becomes more pressing. In the ICRF regime, rectified Radio Frequency (RF) sheaths are known to form at antenna and material edges that influence negative effects like sputtering and a decrease in heating efficiency. Methods to mitigate the formation of these RF sheaths through RF image currents cancellation have been experimentally studied. A power-phasing scan done on Alcator C-Mod in which the amount of power on the two inner straps ( P in ) versus the total 4 straps ( P tot ) was varied showed a minimization of enhanced potentials between P in / P tot ∼ 0.7–0.9 while impurities were minimized for P in / P tot ∼ 0.5–0.8. New capabilities in the realm of representing the RF sheath numerically now allow for these experiments to be simulated. Given the size of the sheath relative to the scale of the device, it can be approximated as a Boundary Condition (BC). A new parallelized cold-plasma wave equation solver called Stix implements a non-linear sheath impedance model BC formulated by Myra et al (2015 Phys. Plasmas 22 062507) through the method of finite elements using the MFEM library [ http://mfem.org ]. It is seen that Stix shows qualitative agreement with the measured C-Mod enhanced potentials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Rotational excitation and de-excitation of magnesium mono-sulphide (MgS) by He collisions

ABSTRACT Magnesium mono-sulphide (MgS) plays a crucial role in astrochemical processes, particularly in the interstellar medium (ISM), where metal-sulphur chemistry influences molecular formation and evolution. This work presents a detailed study of the rotational excitation and de-excitation of MgS through collisions with helium (He) atoms, the second most abundant species in the ISM after hydrogen, which includes both atomic (H) and molecular forms (H2). The focus on MgS–He collisions arises from He's high abundance, chemical inertness, and simpler electronic structure, which make it well suited for quantum scattering calculations. These characteristics establish He as an ideal candidate for initial studies, providing fundamental data for future investigations involving H2. The study uses quantum scattering methods to calculate the collisional rate coefficients over a broad temperature range. These rates are critical for interpreting observational data on MgS and predicting its abundance in space. The interaction potential between MgS and He is calculated using the rigid rotor approximation and the Jacobi coordinate system, employing the CCSD(T)-F12a/aug-cc-pVTZ method for accurate two-dimensional potential energy surface. The study explores the anisotropic nature of the MgS–He interaction, which favours odd Δj rotational transitions at low collision energies. The inelastic cross-sections for rotational transitions involving up to 16 rotational levels of MgS were computed up to 1000 cm−1, enabling the calculation of rate coefficients up to 150 K for Δj = ±1, ±2, and ± 3 rotational transitions. The results show that Δj = 1 transitions dominate at low temperatures, while Δj = 2 transitions become more significant at higher temperatures. This study provides valuable data for interpreting future astrophysical observations of MgS. The findings also propose new rotational transitions for MgS detection in space, enhancing our ability to track and study this molecule in various cosmic environments.

Hendaoui, Hamza (ORCID:0000000218641872)↗