Gas Dispersion Process Research Articles

Study on hydrodynamics characteristics in a gas-liquid stirred tank with a self-similarity impeller based on CFD-PBM coupled model

BackgroundIn general, radial impeller has been the most widely employed for the gas-liquid mixing process in the stirred tank. However, gas cavities are easy to form behind the impeller blades, which results in the waste of power consumption, the limited gas handling capacity of impeller and the poor dispersion of gas. Based on self-similarity property of nonlinear theory, a type of self-similarity impeller was employed to strengthen gas-liquid dispersion process in this work. MethodsThe hydrodynamics of gas-liquid dispersion process in a stirred tank with Rushton turbine and self-similarity impeller were comparatively investigated by computational fluid dynamics (CFD) simulation combined with a population balance model (PBM). The gas holdup distribution, gas cavities, relative power demand (RPD), bubble size distribution and turbulent intensity analysis in the gas-liquid dispersion process were predicted. Significant findingsResults showed that self-similarity impeller could destroy the gas cavity structure, reduce the negative pressure zone and gas accumulation, enhance the relative power demand (RPD) and improve the uniformity of gas holdup distribution compared with Rushton turbine, and the gas-liquid mixing efficiency can be further improved with the increase of the self-similar iteration number of self-similarity impeller. The proper increase of impeller speed was beneficial to overcome gas flooding and improve the gas-liquid two phase dispersion uniformity. The increase of gas flow rate obviously increased the gas holdup in liquid phase. Meanwhile, self-similarity impeller could increase the uniformity of bubble size compared with Rushton turbine, and increasingly so with the increase of self-similar iteration number of self-similarity impeller. The uniformity of bubble size was improved with the increase of impeller speed and was decreased with the increase of gas flow rate. In addition, self-similarity impeller could significantly enhance the turbulent intensity and turbulent kinetic energy dissipation rate by the jet flows through the concave-convex edges, which was beneficial to the gas dispersion process.

Scenario Analysis of the Risk of Gas Pipeline Failures in the North

Low temperatures and abnormal meteoconditions of the North have significant effect both on the accident frequency, and on accident development. Abnormal weather conditions of the North are characterized by the powerful temperature inversions arising at an anti-cyclone due to the emission of permafrost soil at extremely low temperatures of air and conditions of air stagnation. These abnormal conditions influence process of gas dispersion in the atmosphere, promoting its deceleration and formation of explosive concentration of gas at the Earth’s surface. Based on the results of gas pipeline failures analysis occurred in the North, the main causes of gas pipeline accidents were identified, the gas leakage “fault tree” from the gas pipeline was developed with estimation of failures frequencies, and the frequency of emergency situations in case of gas leakage from the gas pipeline was estimated. The most dangerous scenarios of gas pipeline accident evolution have been identified and their risk levels in the zone of the damage factors have been assessed on the basis of acceptable risk criteria. It is justified to increase the action range of damage factors in an explosion of methane -air mixture cloud in open space at presence of temperature inversion.

Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks.

Emergency evacuation risk assessment and mitigation strategy for a toxic gas leak in an underground space: The case of a subway station in Guangzhou, China

In emergency system design, evacuating people from dangerous buildings has always been a significant yet challenging undertaking. This paper combines evacuation demand analysis with a toxic gas dispersion process to create an underground space risk assessment model for toxic gas leaks. The emergency evacuation capacity of a subway transfer station in Guangzhou has been evaluated and optimized based on computational fluid dynamics (CFD) and agent-based simulation. A strategy is proposed in which the subway ventilation system in the underground space is utilized to mitigate evacuation risks during gas leaks. The influence of the ventilation system on the gas diffusion process under different working conditions during simulations of toxic gas diffusion is also analyzed. A comparative analysis is conducted to discuss whether to adopt the ventilation system. Then, the effectiveness of the risk methods when the ventilation system is utilized in different working conditions is also verified. An empirical model of the impacts of the number of evacuees and individual risk perception levels on the evacuation risk has been obtained. Results indicate that the new risk assessment and risk mitigation methods are effective and can help guide the emergency evacuation and ventilation system design for underground buildings.

Quantifying leakage and dispersion behaviors for sub-sea natural gas pipelines

Accidental leakages in underwater natural gas pipelines pose significant risks for the environment. Therefore, it is essential to estimate the leakage rate and monitor the methane diffusion range outside the pipeline, which is challengeable due to the presence of water. The main contribution of this work is to establish the leakage rate calculation and gas dispersion models using the experimental and numerical methods. Firstly, the influences of the pressure, leakage orifice diameter and water depth on the leakage rate and gas dispersion process were revealed through experiments. Further, according to the established experimental facilities, the numerical models including the integral mathematical model and computational fluid dynamic model were established. According to the axial velocity, radius and spring height, the two model were compared and validated. In addition, a field simulation using computational fluid dynamic model was conducted to quantify the gas leakage rate and dispersion prediction, predicting the rising time and diffusion range. The results indicate that the quantifying estimation correlation can predict the leakage rate in the sea with errors less than 16.47%, and the computational fluid dynamic model could predict the dispersion process more accurately while the integral mathematical model could predict the dispersion process more quickly.

Study of Gas Turbulent Dispersion Process in the Alcântara Launch Center

The Alcântara Launch Center (ALC) is the Brazilian gate to the space located at the north coast of Maranhao State,close to the Equator. Topographical local characteristics modify the parameters of incident atmospheric winds and it can cause great influence on the gas dispersion process. In this work, detailed scale models of the ALC region was experimentally evaluated using a wind tunnel. The topographical scale models were built where mean and fluctuating flow characteristics were analysed in order to understand the real behaviour of ALC winds and then, simulations of the effluent dispersion process were made using these scale models. The wind velocity was measured by a hot wire anemometer and the concentration fields in the proximities of a gas emission source were analysed by an aspirating probe connected to the anemometer system. The results obtained show similarity with numerical outputs from previous study in the case of the emission at ground level. A coherent behaviour with the physic of the phenomena was observed for the case of emission downward.

Three dimensional gas dispersion modeling using cellular automata and artificial neural network in urban environment

The gas dispersion simulation in complex urban environment posts challenges on consequence analysis. Though computational fluid dynamics (CFD) are general approaches to provide building-resolving estimates, the time consuming calculation and complex process of modeling limit their application for emergency response. In this paper, a cellular automata dispersion model is prompted to simulate continuous point release of propane in 3-D domain with ground obstructions. An artificial neural network is employed to calculate the temporal state transition of cellular automata. To provide data for the neural network to train, fire dynamic simulator (FDS) code is adopted to simulate 100 scenarios of propane release from a fixed position in pre-specific domain with different combinations of meteorological conditions and source parameters. A proportion of the simulation results is selected to train the artificial neural network with different transition rules derived from the advection-diffusion equation. The dispersion processes are eventually replicated with the proposed approach on the remaining scenarios that the artificial neural network has never encountered. Provided with detailed meteorological field data, the cellular automata model could calculate the gas dispersion process about 1.5 times faster than FDS. As to the model performance, in the long term evolution, decreases in model accuracy are observed due to the nature of cellular automata in explicit evolution and the unavailability of error compensation methods. The transition rule that takes source terms into consideration outperforms in estimating the concentration distributions.

Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection.

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

Numerical simulation of gas dispersion during cold venting of natural gas pipelines

This article analyzes the effects of various factors on gas dispersion during cold venting based on the actual cold venting operations of long-distance natural gas pipelines in China. Under the circumstance that no obstacles existed, unified dispersion model and PHAST software were used to simulate the gas dispersion process of cold venting in order to study the changes of gas concentration along vertical and horizontal directions over time. FLUENT was used to make numerical simulations to analyze the effect of the existence of high obstacles on gas dispersion. The results indicated that (1) with an increase in wind speed, the concentration range in which the gas might explode (5%–15%) decreased along the vertical direction but slightly increased along the horizontal direction; (2) when the initial venting pressure gradually decreased with the proceeding of cold venting, the downwind distance that might trigger danger reduced significantly; and (3) with an increase in atmospheric stability, the dispersion height of vented gas first increased and then decreased continuously. High atmospheric stability could inhibit the gas dispersion height to a larger degree than the wind speed and aerostatic buoyancy could facilitate it.

Study of Wind Speed Effect on Laser Detection in Natural Gas Pipeline Leaking

Natural gas pipeline leakage and diffusion can lead to fire and explosion accidents. Laser technology is an important method of detecting natural gas pipeline leakage, but natural gas dispersion process has an obvious influence on laser detection. Dispersion model of buried natural gas pipeline leakage was established, and natural gas dispersion process with different wind speed was simulated by using Fluent software, then wind speed influence on laser detection was analyzed. The results show that in the detection distance, with the wind speed increasing, natural gas diffusion range deflects windward and are close to the ground, and the spectral value becomes small. Natural gas diffusion concentration becomes small with the increase of wind speed in leeward, and the spectral value becomes large.

CFD-Based Transient Ignition Probability Modeling of Gas Leaks in Enclosures

Risk management of loss of containment at facilities processing or storing liquid flammable fluids is crucial in order to ensure safe operations. To control the risk, an extensive set of safety functions are in general implemented in design, for example systems that minimize the occurrence for initiating events (e.g., spontaneous leak of flammable material due to fatigue) and measures that reduce explosion loads arising in case of delayed ignition of the dispersed fluid mixed with air. Effective ventilation of the released fluid that potentially generates an explosive atmosphere (gas and/or droplets generated from the liquid phase) is one of the crucial barrier elements to mitigate the explosion hazard. Hence, the gas explosion hazard in enclosed modules with poor ventilation is of particular concern as a flammable mixture may accumulate even for small release rates. This may result in both high likelihood of ignition and considerable explosion loads in case of ignition due to the big amount of chemical energy taking part in the combustion process relative to the size of the enclosure. Computational fluid dynamics (CFD) methods are increasingly being used to characterize the consequences of leaks of flammable fluid in complex geometries, both modeling of the initial gas dispersion process and the resulting explosion and fire loads following from the combustion process in case of ignition. This paper presents an advanced methodology based on the CFD tool OpenFOAM for detailed assessment of the transient gas dispersion process and the associated likelihood of ignition for leaks of flammable fluid inside enclosures. The objective is to understand how to optimize the design of safety functions that affect the fire and explosion risk picture. This custom made tool, denoted cloudIgnitionFoam, accounts for the transient gas leak behavior based on real-time gas detection, subsequent initiation of emergency shutdown (ESD) and blow down systems and computes ignition probability based on the transient history of the dispersed gas cloud. The consistent methodology based on the CFD technology available in OpenFOAM and its ability to present the results in detail leverages the risk-based decision process. Measures that can be assessed quantitatively includes number and types of gas detectors and their optimal positioning, ignition source isolation, gas detection system voting philosophy, capacity of depressurization system and structural integrity of explosion barriers.

Modeling and simulation of dense cloud dispersion in urban areas by means of computational fluid dynamics

The formation of toxic heavy clouds as a result of sudden accidental releases from mobile containers, such as road tankers or railway tank cars, may occur inside urban areas so the problem arises of their consequences evaluation.Due to the semi-confined nature of the dispersion site simplified models may often be inappropriate.As an alternative, computational fluid dynamics (CFD) has the potential to provide realistic simulations even for geometrically complex scenarios since the heavy gas dispersion process is described by basic conservation equations with a reduced number of approximations.In the present work a commercial general purpose CFD code (CFX 4.4 by Ansys®) is employed for the simulation of dense cloud dispersion in urban areas. The simulation strategy proposed involves a stationary pre-release flow field simulation followed by a dynamic after-release flow and concentration field simulations.In order to try a generalization of results, the computational domain is modeled as a simple network of straight roads with regularly distributed blocks mimicking the buildings. Results show that the presence of buildings lower concentration maxima and enlarge the side spread of the cloud. Dispersion dynamics is also found to be strongly affected by the quantity of heavy-gas released.

Oil and gas prospecting by ultra‐sensitive optical gas detection with inverse gas dispersion modelling

We report a new oil and gas prospecting technique based on measurements of sub‐part‐per‐billion ethane concentration in the atmosphere and local wind data. The sensitivity of our sensor enables detection, from a range of several km, of the naturally occurring ethane seepages that accompany hydrocarbon reservoirs. Using the concentration and wind data, the gas dispersion process is inverted to provide maps of surface ethane flux over several hundred km2. Within a desert environment, we demonstrate the technique is capable of locating a controlled release and corresponds with the results from a geochemical soil sampling survey conducted over the same oil field.

Radial gas mixing in a fluidized bed using response surface methodology

Radial gas mixing in a fluidized bed was studied using response surface methodology (RSM), which enables effect examinations of parameters with a moderate number of experiments. All experiments were conducted in a 0.29-m ID fluidized-bed cold model. The gas dispersion process within the bed is described using the dispersed plug flow model. Pure carbon dioxide was used as the tracer gas, continuously injected into the center of the bed by a point source. The downstream radial tracer concentration profile was measured using a gas chromatograph. The radial gas dispersion coefficient, D r, was well correlated with operating parameters and the particle and gas properties: ( U− U mf)/ U mf, H s/ d b, φ d, and Ar, with a determination coefficient R 2 of 0.966. Effect test indicates that the dimensionless characteristic velocity, ( U− U mf)/ U mf, has the most significant influence on D r, while the static bed height to bed diameter ratio, H s/ d b, is less remarkable. The interactions of ( U− U mf)/ U mf with the distributor open-area ratio, φ d, and with the Archimedes number, Ar, both play important roles. An evolutive response surface model was proposed to describe the radial gas mixing in the bubbling/slugging fluidization regimes.

Novel Gas Dispersion in Highly Viscous Fluids Using a Plasticating Extruder. II. Gas Dispersion Model

Abstract The process of gas dispersion in the mixing zone of an extruder employing on-line injection into a viscous polymeric melt, has been analyzed. In the model system considered, the gaseous component was injected into a non-Newtonian melt (LLDPE) via a permeable wall, during which a shear field was imposed at right angles to the direction of flow of the melt. The dispersion of the injectant has been quantified in terms of the spatial coordinate positions of the microvoids as they travel through the melt. Computer simulation was performed to ascertain the trajectories, radial velocities, and times of traverse of the particles migrating from the injection orifice under the imposed shear field. Numerical solutions have been obtained for system angular velocity from 6 to 10 rad/s, melt viscosity from 6 to 6.5 kPa . s, particle diameter/ annular gap ratio of 0.20, injectant/melt density ratio of 0.032, and power law index of 0.51. The computed trajectories were found to be consistent with previously recorded visual observations. The analytical results on the time of traverse (∼0.5–1.0 s) indicated that dispersion by on-line shear injection was viable. The latter results, however, need experimental verification.

Investigation of the kinetics of breakup of gas bubbles in liquid metals with digital simulation method

The method of digital system simulation can be effectively used to quantify the complex multiphase interactions within a gas injection process. Process simulation results yield a better understanding and a better aimed engineering of gas dispersion techniques in metallurgical processes. In this paper the breakup phenomenon of gas bubbles in stagnant liquids is simulated and the dependencies between breakup of bubbles and various parameters of a gas dispersion process such as operative parameters, system parameters and mass transfer rates are investigated.The bubble diameter after breakup is almost independent of the nozzle diameter and gas flow rate. The frequency of bubble breakup and critical bubble size depend on the rate of mass transfer into the bubble. An almost constant rising velocity is achieved only in those cases investigated where mass transfer and bubble breakup are considered. In all other cases no stationary rising velocity is obtained. The interplay between bubble size, rising velocity and the inertia of the surrounding liquid and the influence of mass transfer and breakup are investigated.Simulation results reveal that the behaviour of an ascending bubble is strongly influenced by the mass transfer rate, i. e. by the composition of the melt. Verification of the simulation results with empirical equations from literature shows a very good agreement in all dispersion systems investigated.