Gas Particle Research Articles

Numerical simulation on adsorption of CO2 using K2CO3 particles in the bubbling fluidized bed

AbstractWith the increase of greenhouse gas emissions, global warming has become an urgent problem, and the use of solid adsorbents to capture CO2 gas in flue gas has attracted more and more attention. In this study, the process of CO2 capture by K2CO3 particles in the bubbling fluidized bed (BFB) is numerically simulated with Eulerian–Eulerian(E–E) two fluid model incorporating with the kinetic theory of granular flows (KTGF). The results are verified through a detailed comparison with experimental data from Ayobi et al. Furthermore, Regarding the fundamental factors influencing CO2 adsorption rate is revealed, diminishing the inlet gas superficial velocity and augmenting the particle size of the solid adsorbent both contribute to improve adsorption performance. Specifically, the adsorption rate increases from 76.7% to 81.7% at the gas superficial velocity reducing from 1.10 to 0.71 m/s, while the adsorption rate from 77.6% to 79.7% with the particle size ranging from 400 to 600 μm. Additionally, the study delves into an exploration of fluid dynamic characteristics pertaining to gas particles within the bubbling fluidized bed while systematically considering varied inlet gas superficial velocities and particle sizes.

Adiabatic invariance and its application to Wien's complete displacement law of blackbody radiation

We derive the “complete” or “strong” version of Wien's displacement law from two adiabatic invariants: one of a thermodynamic system composed of a finite-sized segment of frequencies taken from the spectrum of blackbody radiation and one of the individual electromagnetic waves that compose this system. By exploiting the algebra of these invariants, we shift the calculational burden of deriving Wien's displacement law toward the methods of classical thermodynamics. These methods also produce a class of displacement laws that constrain both the particles of a classical ideal gas and the acoustic waves of the Debye model of a solid.

Direct numerical simulation of supersonic nanoparticles flow in free-molecule regime using the angular coefficient method

We employ molecular flow methods to numerically simulate the supersonic nanoparticles flow in free-molecule regime. To streamline the computational complexity, interaction forces between the gas and solid particles are disregarded. We first develop a discrete phase model (DPM) method that integrates the non-rigid body collision model, enabling an accurate simulation of nanoparticle diffusion under the influence of the drag force and Brownian motion force. The nanoparticles considered in this study have sizes below 10 nm, and the accuracy of the DPM method is verified by comparing its results with experimental data. Subsequently, we theoretically and numerically investigate the transmission probability and number density of N2 molecules flowing through two-dimensional (2D) channels and three-dimensional (3D) tubes by using the angular coefficient (AC) method and the direct simulation Monte Carlo (DSMC) method. The findings indicate that as the diameter of the nanoparticle (dp) decreases to 1 nm, the diffusion coefficient (D) and the root mean square displacement (x) of nanoparticles approach the N2 molecules. The microscopic velocity of most N2 molecules falls within the range of 62–1400 m/s, and the macroscopic velocity of N2 flow falls within the range of Ma = 1.28–1.35. In contrast to the DSMC method, the AC method exhibits enhanced accuracy even with a reduced number of grids and obviates the process for large-scale sampling. Additionally, the solution time required by the AC method is approximately 1/10 and 1/13–1/32 of the DSMC method in 3D cylindrical tubes and 2D channels, respectively. Moreover, the AC method demonstrates superior adaptability when dealing with complex geometries.

Kinetic Theory for the Low-Density Lorentz Gas

The Lorentz gas is one of the simplest and most widely-studied models for particle transport in matter. It describes a cloud of non-interacting gas particles in an infinitely extended array of identical spherical scatterers. The model was introduced by Lorentz in 1905 who, following the pioneering ideas of Maxwell and Boltzmann, postulated that in the limit of low scatterer density, the macroscopic transport properties of the model should be governed by a linear Boltzmann equation. The linear Boltzmann equation has since proved a useful tool in the description of various phenomena, including semiconductor physics and radiative transfer. A rigorous derivation of the linear Boltzmann equation from the underlying particle dynamics was given, for random scatterer configurations, in three seminal papers by Gallavotti, Spohn and Boldrighini-Bunimovich-Sinai. The objective of the present study is to develop an approach for a large class of deterministic scatterer configurations, including various types of quasicrystals. We prove the convergence of the particle dynamics to transport processes that are in general (depending on the scatterer configuration) not described by the linear Boltzmann equation. This was previously understood only in the case of the periodic Lorentz gas through work of Caglioti-Golse and Marklof-Strömbergsson. Our results extend beyond the classical Lorentz gas with hard sphere scatterers, and in particular hold for general classes of spherically symmetric finite-range potentials. We employ a rescaling technique that randomises the point configuration given by the scatterers’ centers. The limiting transport process is then expressed in terms of a point process that arises as the limit of the randomised point configuration under a certain volume-preserving one-parameter linear group action.

Probing Polaron Clouds by Rydberg Atom Spectroscopy.

In recent years, Rydberg excitations in atomic quantum gases have become a successful platform to explore quantum impurity problems. A single impurity immersed in a Fermi gas leads to the formation of a polaron, a quasiparticle consisting of the impurity being dressed by the surrounding medium. With a radius of about the Fermi wavelength, the density profile of a polaron cannot be explored using insitu optical imaging techniques. In this Letter, we propose a new experimental measurement technique that enables the insitu imaging of the polaron cloud in ultracold quantum gases. The impurity atom induces the formation of a polaron cloud and is then excited to a Rydberg state. Because of the mesoscopic interaction range of Rydberg excitations, which can be tuned by the principal numbers of the Rydberg state, atoms extracted from the polaron cloud form dimers with the impurity. By performing first principle calculations of the absorption spectrum based on a functional determinant approach, we show how the occupation of the dimer state can be directly observed in spectroscopy experiments and can be mapped onto the density profile of the gas particles, hence providing a direct, real-time, and insitu measure of the polaron cloud.

Quality of Mixedness Using Information Entropy in a Counter-Current Three-Phase Bubble Column

Knowledge of mixing phenomena is of great value in the mineral and other chemical and biochemical industries. This work aims to analyze the quality of mixedness (QM), the intrinsic mass transfer (MT) number, and the MT efficiency based on information entropy theory in the counter-current microstructured slurry bubble column. A thorough analysis is conducted to assess the effects of particle loading, gas and slurry velocity, and axial variation on the QM. The range of gas velocity, slurry velocity, particle size, and particle loading was 0.011–0.075 m/s, 0.018–0.058 m/s, 242.72–408.31 μm, and 15.54–88.94 kg/m3, respectively. QM is a time-dependent parameter, and the concept of contact time has been used for scale-up purposes. The maximum QM was achieved at dimensionless times of 0.40 × 10−3, 0.15 × 10−3, and 0.85 × 10−3 for the maximum superficial gas velocity, particle loading, and axial height, respectively. The gas velocity positively influenced both the intrinsic MT number and its efficiency. In contrast, the slurry velocity and particle loading had a negative effect. The present theoretical analysis will pave the path for industrial process intensification in counter-current flow systems.

Gas Morphology of Milky Way–like Galaxies in the TNG50 Simulation: Signals of Twisting and Stretching

We present an in-depth analysis of gas morphologies for a sample of 25 Milky Way–like galaxies from the IllustrisTNG TNG50 simulation. We constrain the morphology of cold, warm, hot gas, and gas particles as a whole using a local shell iterative method and explore its observational implications by computing the hard-to-soft X-ray ratio, which ranges between 10−3 and 10−2 in the inner ∼50 kpc of the distribution and 10−5–10−4 at the outer portion of the hot gas distribution. We group galaxies into three main categories: simple, stretched, and twisted. These categories are based on the radial reorientation of the principal axes of the reduced inertia tensor. We find that a vast majority (77%) of the galaxies in our sample exhibit twisting patterns in their radial profiles. Additionally, we present detailed comparisons between (i) the gaseous distributions belonging to individual temperature regimes, (ii) the cold gas distributions and stellar distributions, and (iii) the gaseous distributions and dark matter (DM) halos. We find a strong correlation between the morphological properties of the cold gas and stellar distributions. Furthermore, we find a correlation between gaseous distributions with a DM halo that increases with gas temperature, implying that we may use the warm–hot gaseous morphology as a tracer to probe the DM morphology. Finally, we show gaseous distributions exhibit significantly more prolate morphologies than the stellar distributions and DM halos, which we hypothesize is due to stellar and active galactic nucleus feedback.

Viscosity-modulated clustering of heated bidispersed particles in a turbulent gas

Clustering of externally and evenly heated particles is enhanced by the increased viscosity of heated fluid in the vicinity of these clusters – a phenomenon known as viscous capturing (VC). Herein we study, via direct numerical simulations of decaying turbulence, the effect of temperature-driven viscosity on clustering with different particle loading densities. We employ a two-way momentum and energy coupling, and gas viscosity is modelled by a power law to understand the role of the increased drag and particle back-reaction force on the clustering intensity. For the continuum and dispersed phases, Eulerian and Lagrangian point particle schemes have been used, neglecting inter-particle collisions. We found that the enhanced viscosity-driven clustering is a strong function of particle loading density, as the increase in particle number density enables the formation of large uneven clusters before heating, which is the main condition for VC to take effect. Higher number density should result in greater turbulence modulation and negate local temperature-based viscous effects leading to VC. However, due to higher local particle number density in the clusters and interphase heat transfer, increased drag force prevails in such cases and delivers excessive clustering. By sampling conditionally the particle velocity and temperature inside the clusters, it is found that the thermodynamic and kinematic properties of the particles in the clusters are highly correlated, and this correlation increases with the particle loading density. Therefore, based on the particle number density, temperature-based viscosity can enhance considerably the clustering of heated particles and alter the effect of particles on the underlying turbulence.

Study on the Inhibition Effect of C6F12O on Charged Particles in the Flame of Transformer Oil Discharge Gas

ABSTRACT The accidental combustion of insulating oil poses a significant risk in the event of transformer failure. The charged particles generated through the combustion ionization reaction exert considerable influence on the combustion speed and stability of the flame. Therefore, the detailed chemical reaction mechanism including neutral matter and charged particle is simplified. The two-dimensional finite rate reaction model is established. The theoretical calculation investigates the suppressive effect of the novel fire extinguishing agent C6F12O on charged particles in the flame resulting from transformer oil decomposition. The analysis encompasses changes in flame temperature, free radicals, and charged particles in the decomposition gas of transformer oil under C6F12O concentrations ranging from 0% to 4%. The findings reveal that the addition of C6F12O exerts a discernible inhibitory effect on the flame temperature of the transformer oil exhaust gas. The large reduction of charged particles in the flame caused by C6F12O is achieved by its consumption of large amounts of O and OH radicals in the flame. It hampers key elementary reactions responsible for charged particle generation. In conclusion, C6F12O demonstrates a pronounced inhibitory effect on charged particles in the flame resulting from transformer oil decomposition.

Study of operation of a polymer insulator under uniform and non-uniform contamination

RELEVANCE. Suspended insulators, the technical condition of which largely determines the reliability of power supply to consumers of different categories, are one of the most damaged elements of overhead power transmission lines [1-2]. This is primarily due to the fact that during their operation, insulators are exposed to various climatic conditions that have a direct impact on their insulating properties. Moreover, precipitation, such as rain, fog or dew, has the greatest impact precisely in combination with various solid, liquid and gaseous particles deposited on the insulator surface from the air, and forming a layer of surface contamination. Wetting this layer increases the electrical conductivity of the entire overhead line insulation structure and reduces its insulating capacity. Study of influence of dampening of contaminated surface of suspended insulation on their discharge characteristics is an urgent scientific task. The solution of this task enables to develop the existing understanding of the mechanisms of formation and development of discharges on the contaminated and wetted surface of insulation and to formulate the appropriate diagnostic attributes applicable to control the condition of the overhead power line insulators. OBJECTIVE. To carry out laboratory research aimed at studying the behavior of suspended high-voltage polymer insulators of overhead power lines when they are moistened. To formulate appropriate diagnostic attributes applicable for control of condition of suspended insulators of overhead power lines during their operation. METHODS. At the solution of the posed problem experimental methods of research, consisting in simulation of work of the contaminated polymeric insulator in the atmosphere of a pure fog with application of the special experimental installation developed by the authors of this article were applied. RESULTS. As a result of laboratory research, by continuous registration of leakage current values, as well as signals coming from the remote sensor, characteristic features of contaminated insulator humidification were revealed, which can be used as diagnostic signs in the process of their operation.

Measuring solid particles in sand-carrying gas flow using multiscale vibration response statistics and deep learning algorithms

A method to quantitate sand particles in turbulent gas flow that combines the multiscale triaxial vibration response and deep learning algorithms is proposed. First, an optimized adaptive wavelet-empirical mode decomposition (EMD) denoising method is proposed based on multifrequency coherent and statistical analysis. Second, complex gas–solid turbulent flow information under multiple gas–particle coupling is characterized based on Hilbert-Huang transform (HHT), Hurst analysis, EMD entropy, etc. In addition, a deep learning algorithm that integrates multiscale flow information to determine sand content that includes two independent branches, a pure deep convolutional neural network (CNN) model driven by microscale triaxial response and a shallow long short-term memory (LSTM) network with regularization driven by mesoscale triaxial response, is proposed. Finally, a quantitative model to characterize sand-carrying turbulent gas flow based on the entropy weight effect of the triaxial vibration response is constructed as follows: Csand=A·p.∑i=xzSiQi. Experimental validation indicates that the proposed deep learning algorithm has recognition and prediction accuracy of 97.8% and 96.97% for sand particle size and the power spectrum, respectively, which are higher than those of the existing intelligent models to characterize sand information. Moreover, the quantitative sand content model based on the multiscale response and deep learning algorithm has a maximum error of only 1.56% under strong turbulence.

Optimization of Technological Parameters for Cold Spraying Using the Response Surface Method

Cold spray technology can obtain coatings in a solid state, which is suitable for deposition protective and restorative coatings. Currently, most of the research in cold spraying is based on a single-factor analysis to explore the law. However, the interaction effect of multiple factors is more scientific. In this study, the response surface method (RSM) was used to optimize the technological parameters of cold spraying. A multi-factor and multi-level quadratic regression model was established for gas temperature, pressure, and particle diameter of outlet velocity, and the process parameters were optimized. The results showed that the gas temperature, particle diameter, and gas pressure have significant effects under a single factor. Also, under the interaction of multiple factors, the P-value of the quadratic regression model was less than 1·10–4, and the R2 of the model was 0.9626, indicating that the curve fitting is good and the model has good credibility. The interaction between gas pressure and gas temperature is significant, while the interaction between gas temperature and powder diameter, gas pressure, and powder diameter are insignificant. Moreover, the parameter error between the optimized parameters through response surface analysis and the actual numerical simulation is 0.76 %, indicating high accuracy.

Surface phase diagrams from nested sampling.

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy. We apply NS to the adsorption of Lennard-Jones (LJ) gas particles on low-index and vicinal LJ solid surfaces and construct the canonical partition function from these recorded energies to calculate ensemble averages of thermodynamic properties, such as the constant-volume heat capacity and order parameters that characterize the structure of adsorbate phases. Key results include determining the nature of phase transitions of adsorbed LJ particles on flat and stepped LJ surfaces, which typically feature an enthalpy-driven condensation at higher temperatures and an entropy-driven reordering process at lower temperatures, and the effect of surface geometry on the presence of triple points in the phase diagrams. Overall, we demonstrate the ability and potential of NS for surface modeling.

Analog Astronaut Mission Habitat Bio Contamination Test

The presence of viable particles in materials, devices, people, surfaces, liquids, gases, or air is referred to as bio-contamination. Mold can grow anywhere there is enough moisture or a water problem. Most molds emit musty odors, which are the first indication of a problem. Mold can manifest itself as slightly fuzzy, discolored or slimy patches that expand in size. Mold growth can be significant in hermetically sealed and environmentally controlled environments. The enclosed habitats are well-exemplified by Analog Astronaut Mission Habitats (AAMH). A bio-contamination test was conducted at the AAMH in Poland from November 1 to 10, 2023. Mold was discovered growing in a bio-contamination experiment mixture of wheat flour, tomato powder and sugar. AAMHs should develop a bio-contamination control strategy as well as an internal system to protect the crew and citizens from diseases that the crew may transmit.

Optimization of Cold Spray Nozzles Based on the Response Surface Methodology

Spraying technical parameters are important factors that affect spraying efficiency. Most studies on spraying technical parameters use single-factor methods to study the speed of spray particles, and few scholars have studied the joint influence of multiple factors. This article uses gas temperature, particle size, and gas pressure as independent variables, and the independent variables interact. The design-expert method was used to establish a linear regression equation model of the velocity of sprayed Al and Cu particles at the Laval exit and the velocity before deposition with the substrate, and the response surface analysis method was used to predict the optimal spraying parameters of Al and Cu particles. The study found the contribution rate of three factors to particle velocity: the prediction of particle velocity at the exit of the Laval nozzle and before deposition with the substrate was realized; the error between the predicted value of particle velocity and the actual value obtained by simulation is less than 1.6 %, indicating that the speed linear regression equation established is effective and reliable in predicting the simulation results; the optimal spraying parameters and particle speeds of Al and Cu particles were obtained through response surface analysis.

DESIGN AND VERIFICATION OF THE MODEL OF STRUCTURE FORMATION AND HEAT TRANSFER IN A FLUIDIZED BED APPARATUS WITH A HEAT JACKET

A one-dimensional mathematical model of the formation of a fluidized bed and the evolution of its thermal state during the heating of an ensemble of particles is proposed based on the mathematical apparatus of the Markov chain theory. When describing the thermal state of the system, the processes of interfacial heat exchange between the gas flow and particles, as well as between the wall of the apparatus and the gas suspension, were taken into account. The results of previous studies and known empirical regularities for the material constants of the process were used to identify the parameters of the model. The parametric identification made it possible to adapt and apply the mathematical model for calculating the process of heat treatment of the suspension of silicate sand particles in an apparatus with a fluidized bed of a periodic principle of operation equipped with a heating jacket. Verification of the predictive capabilities of the physicomathematical model adapted in this way was carried out by comparing the forecasts obtained by calculation with the results of the full-scale experiment conducted during the study. To conduct the full-scale experiment, a sample of sand particles was placed in the apparatus, which was previously output to the established thermal regime of operation. After loading the material, a set of thermocouples equidistant from each other was placed in the apparatus and temperature readings were taken at different heights from the level of the gas distributor. The temperature distributions obtained in this way along the bed height were in good agreement with the calculated forecasts and show a significant heterogeneity of the gas phase temperature fields. The temperature of the particles was estimated only as an integral characteristic by measurements of the heat content of the bulk medium after unloading from the apparatus. The integral temperature of the particles also turned out to be close to the predicted values. In addition, during the computational and experimental study, the kinetic characteristics of the heating of the suspension of the material were clarified. Thus, the proposed mathematical model has sufficient predictive efficiency for engineering tasks and can be considered as a basis for constructing a computer method for calculating heat exchangers with a heating jacket using the fluidization technique of bulk media.

The impact of low-velocity shock waves on the dynamic behaviour characteristics of nanobubbles.

Low-velocity shock wave-induced contraction and expansion of nanobubbles can be applied to biocarriers and microfluidic systems. Although experiments have been conducted to study the application effects, the dynamic behavior characteristics of nanobubbles remain unexplored. In this work, we utilize molecular dynamics (MD) simulations to investigate the dynamic behavior characteristics of nanobubbles influenced by low-velocity shock waves in a liquid argon system. The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) machine learning method is used to calculate the equivalent radius of nanobubbles. Two statistical methods are then utilized to predict the time series changes in the equivalent radius of nanobubbles without rebound shock waves. The piston velocity is analyzed using the bisection method to obtain the critical impact states of the nanobubble. The results show that at the low velocity shock wave (piston velocity of 0.1 km s-1), the shock wave pressure is small, the non-vacuum nanobubbles contract and expand in a circular shape, and the gas particles inside the bubble are not dispersed. In contrast, the vacuum nanobubbles collapse directly. As the shock wave rebounds upon impact, it triggers periodic contraction and expansion of the nanobubbles. The predictions indicate that the equivalent radius will vary within a small range according to the pre-predicted values in the absence of the rebound shock wave. Nanobubbles are present in four critical impact states: dispersed gaps, multiple smaller bubbles, two split bubbles, and a concave bubble.

Residual gas noises in vacuum of optical interferometer for ground-based gravitational wave detection

Gravitational waves (GWs) are ripples in spacetime caused by most violent and energetic processes in the universe, such as the rapid motion of massive celestial bodies. The GWs carry energy when they propagate through the universe. The detection of GWs holds significance for advancing human understanding of the nature and driving scientific and technological progress. The continual upgrading and optimizing of GW detectors offer novel avenues for cosmic measurements. However, ground-based GW detectors based on a large interferometer necessitate addressing various noises which are harmful to the sensitivity of the GW detectors. Among these noises, the noise from residual gas in the light beam of the interferometer is a crucial factor to affect the sensitivity. Consequently, it is necessary to establish a vacuum system to shield the laser interferometer from the effects of gas flow. This paper focuses on China’s third-generation ground-based GWs detector, conducting theoretical analysis of the influence of residual gas noise on both a 20-meter arm-length prototype and a full-scale device with a 10-kilometer arm-length. In this paper, a theoretical model for the residual gas particles passing through the laser beam is established and the effect on the beam phase is analyzed. The theoretical simulations are performed to discover the relations between the residual gas noise and significant parameters such as gas pressure of the vacuum system, temperature, mass of residual gas particles, polarization rate of the residual gas, and the curvature radius of the test mass. The simulations indicate that when the residual gas pressure is below 2×10<sup>–6</sup> Pa, the GW detector can achieve the enough sensitivity, 10<sup>–24</sup> Hz<sup>–1/2</sup>, in a frequency range from 10 to 10<sup>3</sup> Hz. The findings of this research offer crucial theoretical insights for designing and constructing the vacuum systems in future third-generation GWs detector prototypes and full-scale devices.

Solution of the Carleman System Using Group Analysis

In this paper, we consider the discrete kinetic Carleman system. The Carleman system is the Boltzmann kinetic equation, and for this model momentum and energy are not conserved. Using group analysis methods, we obtain a solution representing the density of gas particles in a certain area. This limitation is due to the non-negativity of solution. Similarly, it is possible to find exact solutions for other kinetic models.

Establishing the parameters of the operation mode of the electric pulse automobile muffler

The purpose of the research in the article is to obtain analytical and experimental results on the purification of internal combustion engine exhaust gases by an electric pulse, allowing us to determine the main parameters of the muffler operation. The set goal was achieved by performing the following methodology. A mathematical model of the motion of a gas particle in a muffler has been developed and investigated, the relationship between the capacity of the engine combustion chamber, the number of revolutions of the crankshaft, the pulse intensity, and the dynamic viscosity of the medium has been established. The condition of the muffler operation is determined through the parameter - the distance between the electrodes. The optimality criterion is justified - the smokiness of the gas before and after purification. A nonlinear experimental plan has been compiled using the methods of similarity theory and dimension analysis. An experimental stand has been developed and results linking the process parameters have been obtained. The results obtained are the basis for the methodology for calculating the design of electric pulse mufflers with optimal smoke ratios.