Cloud Size Research Articles

A Satellite Observational Study of Topographical Effects on Daytime Shallow Convective Clouds

Shallow convective clouds (SCCs) frequently occur over mountainous terrain. However, previous studies have mostly focused on SCCs over flat surfaces. Here, the effects of mountainous terrains on the cloud size distributions (CSDs) and spatial distributions of SCCs are investigated using data obtained from the Landsat-8 satellite. We find that the CSDs are well-described by double power laws separated by scale breaks. The CSDs are controlled by two parameters, i.e., the scale breaks and the number of clouds with sizes between 0.2 and 1 times the scale breaks. We also find that the number of clouds generally increases with the elevation. In particular, the number of clouds larger than the scale breaks increases faster than that of the smaller clouds. The sizes of the larger clouds (the 90th and 95th percentiles) increase with the elevation, while the sizes of the smaller clouds are not sensitive to the elevation. It is suggested that the variations of cloud numbers and sizes with elevation should be used together with the CSDs to describe the cloud fields over mountainous terrains.

Quantifying the dependence of drop spectrum width on cloud drop number concentration for cloud remote sensing

Abstract. In situ measurements of liquid cloud and precipitation drop size distributions from aircraft-mounted probes are used to examine the relationship of the width of drop size distributions to cloud drop number. The width of the size distribution is quantified in terms of the parameter k=(rv/re)3, where rv is the volume mean radius and re is the effective radius of the distributions. We find that on small spatial scales (∼100 m), k is positively correlated with cloud drop number. This correlation is robust across a variety of campaigns using different probe technology. A new parameterization of k versus cloud drop number is developed. This new parameterization of k is used in an algorithm to derive cloud drop number in liquid phase clouds using satellite measurements of cloud optical depth and effective radius from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor on Aqua. This algorithm is compared to the standard approach to derive drop number concentration that assumes a fixed value for k. The general tendency of the parameterization is to narrow the distribution of derived number concentration. The new parameterization generally increases the derived number concentration over ocean, where N is low, and decreases it over land, where N is high. Regional biases are as large as 20 % with the magnitude of the bias closely tracking the regional mean number concentration. Interestingly, biases are smallest in regions of frequent stratocumulus cloud cover, which are a regime of significant interest for study of the aerosol indirect effect on clouds.

Efficient Underground Tunnel Place Recognition Algorithm Based on Farthest Point Subsampling and Dual-Attention Transformer.

An autonomous place recognition system is essential for scenarios where GPS is useless, such as underground tunnels. However, it is difficult to use existing algorithms to fully utilize the small number of effective features in underground tunnel data, and recognition accuracy is difficult to guarantee. In order to solve this challenge, an efficient point cloud position recognition algorithm, named Dual-Attention Transformer Network (DAT-Net), is proposed in this paper. The algorithm firstly adopts the farthest point downsampling module to eliminate the invalid redundant points in the point cloud data and retain the basic shape of the point cloud, which reduces the size of the point cloud and, at the same time, reduces the influence of the invalid point cloud on the data analysis. After that, this paper proposes the dual-attention Transformer module to facilitate local information exchange by utilizing the multi-head self-attention mechanism. It extracts local descriptors and integrates highly discriminative global descriptors based on global context with the help of a feature fusion layer to obtain a more accurate and robust global feature representation. Experimental results show that the method proposed in this paper achieves an average F1 score of 0.841 on the SubT-Tunnel dataset and outperforms many existing state-of-the-art algorithms in recognition accuracy and robustness tests.

A Hybrid Algorithm with Swin Transformer and Convolution for Cloud Detection

Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network’s ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal).

Bubble cloud characteristics and ablation efficiency in dual-frequency intrinsic threshold histotripsy

Histotripsy is a non-thermal focused ultrasound ablation method that destroys tissue through the generation and activity of acoustic cavitation bubble clouds. Intrinsic threshold histotripsy uses single-cycle pulses to generate bubble clouds when the dominant negative pressure phase exceeds an intrinsic threshold of ∼25–30 MPa. The ablation efficiency is dependent upon the size and density of bubbles within the bubble cloud. This work investigates the effects of dual-frequency pulsing schemes on the bubble cloud behavior and ablation efficiency in intrinsic threshold histotripsy. A modular 500 kHz:3 MHz histotripsy transducer treated agarose phantoms using dual-frequency histotripsy pulses with a 1:1 pressure ratio from 500 kHz and 3 MHz frequency elements and varying arrival times for the 3 MHz pulse relative to the arrival of the 500 kHz pulse (−100 ns, 0 ns, and +100 ns). High-speed optical imaging captured cavitation effects to characterize bubble cloud and individual bubble dynamics. The effects of dual-frequency pulsing on lesion formation and ablation efficiency were also investigated in red blood cell (RBC) phantoms. Results showed that the single bubble and bubble cloud size for dual-frequency cases were intermediate to published results for the component single-frequencies of 500 kHz and 3 MHz. Additionally, bubble cloud size and dynamics were shown to be altered by the arrival time of the 3 MHz pulse with respect to the 500 kHz pulse, with more uniform cloud expansion and collapse observed for early (−100 ns) arrival. Finally, RBC phantom experiments showed that dual-frequency exposures were capable of generating precise lesions with smaller areas and higher ablation efficiencies than previously published results for 500 kHz or 3 MHz. Overall, results demonstrate dual-frequency histotripsy’s ability to modulate bubble cloud size and dynamics can be leveraged to produce precise lesions at higher ablation efficiencies than previously observed for single-frequency pulsing.

Cloud-Graph: A feature interaction graph convolutional network for remote sensing image cloud detection

Convolutional neural networks (CNNs) have made significant progress in the field of cloud detection in remote sensing images thanks to their powerful feature representation capabilities. Existing methods typically aggregate low-level features containing details and high-level features containing semantics to make full use of both features to accurately detect cloud regions. However, CNNs are still limited in their ability to reason about the relationships between features, while not being able to model context well. To overcome this problem, this paper designs a novel feature interaction graph convolutional network model that extends the feature fusion process of convolutional neural networks from Euclidean space to non-Euclidean space. The algorithm consists of three main components: remote sensing image feature extraction, feature interaction graph reasoning, and high-resolution feature recovery. The algorithm constructs a feature interaction graph reasoning (FIGR) module to fully interact with low-level and high-level features and then uses a residual graph convolutional network to infer feature higher-order relationships. The network model effectively alleviates the problem of a semantic divide in the feature fusion process, allowing the aggregated features to fuse valuable details and semantic information. The algorithm is designed to better detect clouds with complex cloud layers in remote sensing images with complex cloud shape, size, thickness, and cloud-snow coexistence. Validated on publicly available 38-Cloud and SPARCS datasets and the paper’s own Landsat-8 cloud detection dataset with higher spatial resolution, the proposed method achieves competitive performance under different evaluation metrics. Code is available at https://github.com/HaiLei-Fly/CloudGraph.

TransPCGC: Point Cloud Geometry Compression Based on Transformers

Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.

The Cool Circumgalactic Medium of Low-redshift Star-forming Galaxies. I. Empirical Model and Mean Properties

We present an analytic model for the cool, T ∼ 104 K, circumgalactic medium (CGM), describing the gas distribution, and thermal and ionization states. Our model assumes (total) pressure equilibrium with the ambient warm/hot CGM, photoionization by the metagalactic radiation, and allows for nonthermal pressure support, parameterized by the ratio of thermal pressures, η = P hot,th/P cool,th. We apply the model to the COS-Halos measurements and find that a nominal model with η = 3, gas distribution out to r ≈ 0.6R vir, and M cool = 3 × 109 M ⊙, corresponding to a volume filling fraction of f V,cool ≈ 1%, reproduces the H i and low/intermediate metal ions (C ii, C iii, Si ii, Si iii, and Mg ii) mean column densities. Variation of ±0.5 dex in η or M cool encompasses ∼2/3 of the scatter between objects. Our nominal model underproduces the measured C iv and Si iv columns, and these can be reproduced with (i) a cool phase with M cool ∼ 1010 M ⊙ and η ≈ 5, or (ii) cooling or mixing gas at intermediate temperatures, with M ∼ 1.5 × 1010 M ⊙ and occupying ∼1/2 of the total CGM volume. For cool gas with f V,cool ≈ 1%, we estimate an upper limit on the cloud sizes, R cl ≲ 0.5 kpc. Our results suggest that for the average galaxy CGM, the mass and nonthermal support in the cool phase are lower than previously estimated, and extreme scenarios are not necessary. We estimate the rates of cool gas depletion and replenishment, and find accretion onto the galaxy can be offset, allowing over long timescales.

Hydrogen column density variability in a sample of local Compton-thin AGN

We present the analysis of multiepoch observations of a set of 12 variable, Compton-thin, local (z < 0.1) active galactic nuclei (AGN) selected from the 100-month BAT catalog. We analyzed all available X-ray data from Chandra, XMM-Newton, and NuSTAR, adding up to a total of 53 individual observations. This corresponds to between three and seven observations per source, probing variability timescales between a few days and ∼20 yr. All sources have at least one NuSTAR observation, ensuring high-energy coverage, which allowed us to disentangle the line-of-sight and reflection components in the X-ray spectra. For each source, we modeled all available spectra simultaneously, using the physical torus models MYTorus, borus02, and UXCLUMPY. The simultaneous fitting, along with the high-energy coverage, allowed us to place tight constraints on torus parameters such as the torus covering factor, inclination angle, and torus average column density. We also estimated the line-of-sight column density (NH) for each individual observation. Within the 12 sources, we detected clear line-of-sight NH variability in five of them, non-variability in five of them, and for two of them it was not possible to fully disentangle intrinsic luminosity and NH variability. We observed large differences between the average values of line-of-sight NH (or NH of the obscurer) and the average NH of the torus (or NH of the reflector), for each source, by a factor between ∼2 to > 100. This behavior, which suggests a physical disconnect between the absorber and the reflector, is more extreme in sources that present NH variability. We note that NH-variable AGN also tend to present larger obscuration and broader cloud distributions than their non-variable counterparts. These trends however require a larger number of sources to confirm (or disprove) this. We observed that large changes in obscuration only occur at long timescales, and used this to place tentative lower limits on torus cloud sizes. Furthermore, we observed a median variation in NH between any two observations of the same source of ∼36%.

Numerical simulation of disperse process of fuel‐air explosive based on the computational fluid dynamics

AbstractThe formation process of fuel cloud is easily affected by the material properties of fuel mixture and the structural qualities of manufacture. The spread region of fuel cloud plays an important role in the damage performance of FAE bomb. However, there are not sufficient simulation study on the disperse process, lacking a useful model for calculating the fuel cloud size due to the physical complexity in disperse process. In this work, a FAE bomb was designated at first and an experiment was carried out to measure the fuel cloud diameters. And then a computational model was established to illustrate the formation process of fuel cloud. This computational model consisted of an explosion dynamics model in the near‐field stage and a fluid dynamics model in the far‐field stage. Furthermore, multiphase turbulence flow, discrete phase and species chemical transportation were incorporated in the fluid dynamics model. The results showed that good agreements were yielded by comparison of the simulation results and experimental data for the fuel cloud diameters. The fuel cloud diameter at 220 ms was 9.11 m for simulation and 9.37 m for experiment. Additionally, it was also discussed the influence of particle size distribution on the formation process of fuel cloud. Larger particles were more likely to aggregate at the outside region of fuel cloud due to individual inertial influence. This study can provide an effective computation model for evaluation of the fuel cloud region, which can be worthwhile in understanding the physical mechanism of disperse process.

Numerical Investigation of Hydrogen Jet Dispersion Below and Around a Car in a Tunnel

Accidental release from a hydrogen car tank in a confined space like a tunnel poses safety concerns. This Computational Fluid Dynamics (CFD) study focuses on the first seconds of such a release, which are the most critical. Hydrogen leaks through a Thermal Pressure Relief Device (TPRD), forms a high-speed jet that impinges on the street, spreads horizontally, recirculates under the chassis and fills the area below it in about one second. The “fresh-air entrainment effect” at the back of the car changes the concentrations under the chassis and results in the creation of two “tongues” of hydrogen at the rear corners of the car. Two other tongues are formed near the front sides of the vehicle. In general, after a few seconds, hydrogen starts moving upwards around the car mainly in the form of buoyant blister-like structures. The average hydrogen volume concentrations below the car have a maximum of 71%, which occurs at 2 s. The largest “equivalent stoichiometric flammable gas cloud size Q9” is 20.2 m3 at 2.7 s. Smaller TPRDs result in smaller hydrogen flow rates and smaller buoyant structures that are closer to the car. The investigation of the hydrogen dispersion during the initial stages of the leak and the identification of the physical phenomena that occur can be useful for the design of experiments, for the determination of the TPRD characteristics, for potential safety measures and for understanding the further distribution of the hydrogen cloud in the tunnel.

The Cosmic Ultraviolet Baryon Survey: Empirical Characterization of Turbulence in the Cool Circumgalactic Medium

This paper reports the first measurement of the relationship between turbulent velocity and cloud size in the diffuse circumgalactic medium (CGM) in typical galaxy halos at redshift z ≈ 0.4–1. Through spectrally resolved absorption profiles of a suite of ionic transitions paired with careful ionization analyses of individual components, cool clumps of size as small as l cl ∼ 1 pc and density lower than n H = 10−3 cm−3 are identified in galaxy halos. In addition, comparing the line widths between different elements for kinematically matched components provides robust empirical constraints on the thermal temperature T and the nonthermal motions b NT, independent of the ionization models. On average, b NT is found to increase with l cl following over three decades in spatial scale from l cl ≈ 1 pc to l cl ≈ 1 kpc. Attributing the observed b NT to turbulent motions internal to the clumps, the best-fit b NT–l cl relation shows that the turbulence is consistent with Kolmogorov at <1 kpc with a roughly constant energy transfer rate per unit mass of ϵ ≈ 0.003 cm2 s−3 and a dissipation timescale of ≲100 Myr. No significant difference is found between massive quiescent and star-forming halos in the sample on scales less than 1 kpc. While the inferred ϵ is comparable to what is found in C iv absorbers at high redshift, it is considerably smaller than observed in star-forming gas or in extended line-emitting nebulae around distant quasars. A brief discussion of possible sources to drive the observed turbulence in the cool CGM is presented.

Lagrangian and Eulerian Supersaturation Statistics in Turbulent Cloudy Rayleigh–Bénard Convection: Applications for LES Subgrid Modeling

Abstract Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed.

Experimental Determination of the Charge Carrier Transport Models for Improving the Simulation of the HR GaAs:Cr Detectors' Response.

The response of Timepix3 detectors with 300 µm and 500 µm thick HR GaAs:Cr sensors was studied with particle beams at the Danish Centre for Particle Therapy in Aarhus, Denmark. Therefore, the detectors were irradiated at different angles with protons of 240 MeV. The precise per-pixel time and energy measurements were exploited in order to determine the charge carrier transport properties. Using the tracks left by the penetrating charged particles hitting the sensor at the grazing angle, we were able to determine the charge collection efficiency, the charge carrier drift times across the sensor thickness, the dependency of the electron, and for the first time, the hole drift velocity on the electric field. Moreover, extracting the dependence of the charge cloud size on the interaction depth for different bias voltages, it was possible to determine the dependence of the diffusion coefficient on the applied bias voltage. A good agreement was found with the previously reported values for n-type GaAs. The measurements were conducted for different detector assemblies to estimate the systematic differences between them, and to generalize the results. The experimental findings were implemented into the Allpix Squared simulation framework and validated by a comparison of the measurement and simulation for the 241Am γ-ray source.

Pollution slightly enhances atmospheric cooling by low-level clouds in tropical West Africa

Abstract. Reflection of solar radiation by tropical low-level clouds has an important cooling effect on climate and leads to decreases in surface temperatures. Still, the effect of pollution on ubiquitous tropical continental low-level clouds and the investigation of the related impact on atmospheric cooling rates are poorly constrained by in situ observations and modeling, in particular during the West African summer monsoon season. Here, we present comprehensive in situ measurements of microphysical properties of low-level clouds over tropical West Africa, measured with the Deutsches Zentrum für Luft- und Raumfahrt (DLR) aircraft Falcon 20 during the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) campaign in June and July 2016. Clouds below 1800 m altitude, identified as boundary layer clouds, were classified according to their carbon monoxide (CO) pollution level into pristine and less polluted clouds (CO &lt; 135 ppbv) and polluted low-level clouds (CO &gt; 155 ppbv) as confirmed by the linear CO to accumulation aerosol number concentration correlation. Whereas slightly enhanced aerosol background levels from biomass burning were measured across the entire area, clouds with substantially enhanced aerosol levels were measured in the outflow of major coastal cities, as well as over rural conurbations in the hinterlands. Here we investigate the impact of pollution on cloud droplet number concentration and size during the West African monsoon season. Our results show that the cloud droplet number concentration (CDNC) measured in the size range from 3 to 50 µm around noon increases by 26 % in the elevated aerosol outflow of coastal cities and conurbations with elevated aerosol loadings from median CDNC of 240 cm−3 (52 to 501 cm−3 interquartile range) to 324 cm−3 (60 to 740 cm−3 interquartile range). Higher CDNC resulted in a 17 % decrease in effective cloud droplet diameter from a median deff of 14.8 µm to a deff of 12.4 µm in polluted clouds. Radiative transfer simulations show a non-negligible influence of higher droplet number concentrations and smaller particle sizes on the diurnally averaged (noon) net radiative forcing at the top of atmosphere of −3.9 W m−2 (−16.3 W m−2) of polluted with respect to less polluted clouds and lead to a change in instantaneous heating rates of −22.8 K d−1 (−17.7 K d−1) at the top of clouds. Thus, the atmospheric cooling by low-level clouds increases only slightly in the polluted case due to the already elevated background aerosol concentrations. Additionally, the occurrence of mid- and high-level cloud layers atop buffer this effect further, so that the net radiative forcing and instantaneous heating rate of low-level clouds turn out to be less sensitive towards projected future increases in anthropogenic pollution in West Africa.

Numerical analysis of the diffusion and explosion characteristics of hydrogen-air clouds in a plateau hydrogen refuelling station

The investigation of hydrogen diffusion and explosion characteristics after accidental hydrogen leaks in hydrogen refuelling stations can provide the theoretical reference of the safe application of hydrogen refuelling stations. The numerical study analyses the effects of plateau environment on the dispersion and explosion characteristics of combustible clouds, visualises the diffusion and explosion process of hydrogen-air combustible clouds and quantifies the hydrogen concentration and explosion pressure at monitoring points. Results show that combustible clouds may appear upstream of the leak hole due to the presence of obstacles. The low atmospheric pressure conditions in the plateau areas result in high hydrogen concentration near the blast wall and large combustible cloud size. The hydrogen concentration near the blast wall and combustible cloud volume are also shown a positive correlation with air temperature, which will be detrimental to fire protection in hydrogen refuelling stations. After the explosion of the hydrogen clouds, the mortality zone (explosion overpressure ≥0.69 bar) is observed near the blast wall and at high leak volumes, the hazardous zone (0.07 bar ≤ explosion overpressure ≤0.69 bar) may spread over the blast wall into the crowded area. However, the blast wall blocks the development of the high-temperature zone, which has a positive effect on preventing the spread of the explosion flame. An examination of the pressure monitoring point data reveals that there appears to be an opposite effect of atmospheric pressure on the positive and reverse overpressure. The maximum peak blast overpressure in calculated domain indicates that both higher air temperatures and atmospheric pressures promote the growth of explosion overpressure.

Convective Variabilities Leading to Different Pathways of Convective Self-Aggregation in Two Cloud-Resolving Models

Abstract Convective variability is used to diagnose different pathways toward convective self-aggregation (CSA) in radiative–convective equilibrium simulations with two cloud-resolving models, SCALE and VVM. The results show that convection undergoes gradual growth in SCALE and fast transition in VVM, which is associated with different mechanisms between the two models. In SCALE, strong radiative cooling associated with a dry environment drives the circulation from the dry region, and the dry environment results from strong subsidence and insufficient surface flux supply. The circulation driven by the radiative cooling then pushes convection aggregating, which is the dry-radiation pathway. In VVM, CSA develops due to the rapid strengthening of circulation driven by convective systems in the moist region, which is the convection-upscaling pathway. The different pathways of CSA development can be attributed to the upscale process of convective structures identified by the cloud size spectrum. The upscaling of large-size convective systems can enhance circulation from the moist region in VVM. In SCALE, the infrequent appearance of large convective systems is insufficient to generate circulation, as compensating subsidence can occur within the moist region even in the absence of convective systems. This study shows that the convective variabilities between models can lead to different pathways of CSA, and mechanism-denial experiments also support our analyses.

Using the motion of S2 to constrain scalar clouds around Sgr A*

ABSTRACT The motion of S2, one of the stars closest to the Galactic Centre, has been measured accurately and used to study the compact object at the centre of the Milky Way. It is commonly accepted that this object is a supermassive black hole, but the nature of its environment is open to discussion. Here, we investigate the possibility that dark matter in the form of an ultralight scalar field ‘cloud’ clusters around Sgr A*. We use the available data for S2 to perform a Markov Chain Monte Carlo analysis and find the best-fit estimates for a scalar cloud structure. Our results show no substantial evidence for such structures. When the cloud size is on the order of the size of the orbit of S2, we are able to constrain its mass to be smaller than 0.1 % of the central mass, setting a strong bound on the presence of new fields in the galactic centre.

Computational analysis of the hydrogen dispersion in semi-confined spaces

Although green hydrogen is an appropriate clean energy alternative, hydrogen leakage and dispersion are the challenges to establishing safe and reliable hydrogen infrastructure. Released hydrogen in confined or semi-confined space may build up flammable gas clouds, which can have severe consequences for the safety of human life and property. Proper ventilation systems will help in reducing hydrogen gas accumulation. This study presents a comprehensive study to analyse hydrogen dispersion behaviour and the influence of ventilation variations on hydrogen volumetric concentration and stratification. A computational model is developed for the leakage diffusion of hydrogen in an enclosed cuboid space with roof and door ventilation. A detailed analysis is conducted to shed light on the impact of door and roof ventilation positions on forming the flammable cloud. It is observed that the displacement flow and multi-layer stratification are established in the absence of wind or imperceptible air circulation. Single-vent configurations were ineffective, when installed at the top of the leakage point, but multiple-vent arrangements incorporating door and roof vents were found to be more efficient in extracting hydrogen. However, it should be noted that not all multiple-vent arrangements may meet safety requirements. The result indicated that the optimal arrangement for minimizing the size of a flammable cloud involves placing the door vent close to the ceiling while situating the roof vent near the source of leakage. These findings will aid in devising strategies for minimizing the occurrence of flammable cloud formation and preventing hydrogen fire and explosion incidents within partially enclosed facilities.

Corrections for Fluorescence and Charge-Sharing Effects to Bremsstrahlung Spectra for a Hyperspectral Pixelated CZT X-Ray Detector

A Monte-Carlo model was developed to simulate the response of a pixelated hyperspectral CZT X-ray detector. The first part of the simulation was carried out using Geant4 to obtain a list of energy depositions inside the CZT crystal. The second part of the simulation used charge transport equations to calculate the size of the electron charge cloud, as it drifts under the influence of an electric field to be read out. Experimentally acquired data from an Am-241 source with the HEXITEC detector were compared to simulated data, and good agreement was found. The model was used to investigate the energy dependence of fluorescence and charge sharing effects. The probability of producing an escaped fluorescence photon was quantified as a function of primary photon energy. As expected, at primary photon energies just above the K-edge of Cd, there was a greater chance of producing an escaped fluorescence photon, and this probability decreased as the primary photon energy increased. The probability of an event being shared across multiple pixels as a function of primary photon energy was quantified. It was found that as the primary photon energy was increased, there was a greater chance of producing an event shared across multiple pixels. The detector response to a Bremsstrahlung spectrum was simulated. Using previous results, fluorescence and charge sharing effects were corrected for, giving a corrected spectrum in good agreement with the input spectrum.