Angkor Wat is one of the most important archaeological sites in the world. However, due to its changes in natural environment and human activities, the Angkor Wat and its surrounding environment are facing great chall...
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The thermal hydraulic characteristics and heat transfer of two-phase fluid in helical coil once-through steam generator under ocean conditions like rolling and heaving motions are studied numerically. A CFD model is e...
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The thermal hydraulic characteristics and heat transfer of two-phase fluid in helical coil once-through steam generator under ocean conditions like rolling and heaving motions are studied numerically. A CFD model is established to analyze the effects of motion parameters like amplitude, period and rolling radius and the heat flux distribution are discussed. The analysis shows that the overall heat transfer decreases under ocean conditions, but the overall deterioration is limited, indicting the helical coil pipe has the stability for ocean conditions. The additional forces like Coriolis, centrifugal and inertial forces introduced by rolling motion are analyzed theoretically and the result shows it detorts the original two main vortexes responsible for high heat transfer due to its spiral geometry, making heat transfer fluctuates up and down periodically near the steady state value. As rolling height increases, the heat transfer is nonlinear due to the competition between Coriolis force and centrifugal and inertial forces. As rolling amplitude increases, the heat transfer variation increases and the local heat transfer deterioration is severely aggravated, indicating high rolling amplitude should be avoided as possible. Increasing rolling period makes heat transfer behavior more approximate to static case as gravity and centrifugal force due to spiral geometry is dominate. Under heaving motion, the overall heat transfer decreases, and increasing heaving amplitude and decreasing period make the fluctuation more severe.
This paper presents the development and validation of numerical transient critical heat flux (CHF) prediction model based on the coupling of dynamic bubble simulation by Monte Carlo (MC) method and two-dimensional hea...
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This paper presents the development and validation of numerical transient critical heat flux (CHF) prediction model based on the coupling of dynamic bubble simulation by Monte Carlo (MC) method and two-dimensional heat conduction model of the substrate by finite volume method. The model aims to predict the transient CHF value, estimate the dynamic heat flux and temperature distribution and evaluate various phenomena of bubble behaviors during transient boiling heat transfer process under exponentially escalating heat power input. The dynamic bubble simulation is performed by modeling and tracking bubble nucleation, bubble growth, bubble departure and bubble coalescence procedures of individual bubbles in real time based on simplified fundamental bubble parameters including nucleation site density (NSD), bubble departure diameter, bubble growth and waiting time. The heat flux partitioning model was used to evaluate different heat fluxes based on the classification of microlayer and dry spot regions. According to the dynamic heat flux boundary due to dynamic bubble behavior and exponentially escalating heat input, the thermal conductivity model was coupled to feed back the wall temperature distribution for further updates and simulation of dynamic bubble behavior. Numerical simulation results showed good agreement with transient flow boiling experimental results with escalating periods ranging from 500 ms to 5 ms, and the results showed the trend that the transient CHF value increases with decreasing escalating period as observed in experiments. Sensitivity analysis of input bubble parameters was performed and showed that the model is more sensitive to bubble departure diameter and bubble waiting time.
Interfacial mass and heat transfer are crucial factors in phase-change models. In this study, based on the open source toolbox OpenFOAM, an extended volume-of-fluid (VOF) solver with phase transition inserted is utili...
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Interfacial mass and heat transfer are crucial factors in phase-change models. In this study, based on the open source toolbox OpenFOAM, an extended volume-of-fluid (VOF) solver with phase transition inserted is utilized to study three typical phase-change models (Lee model, energy balance model, and Rattner model). Through simulations and analysis of phase-change problems, we found that these three models exhibit different sensitivities to gas thermal conductivity, particularly through the interfacial heat conduction equation. The temperature and heat source distributions at the vapor-liquid interface in each model were analyzed using analytical problems, such as the one-dimensional Stefan problem and the Nusselt film condensation problem. Based on the analysis, we proposed a new energy balance model that considers the ratio of vapor-liquid thermal conductivity to correct the impact of vapor thermal conductivity. Furthermore, we introduced a temperature correction term to stabilize the interfacial temperature at the saturation temperature. The simulation results of the proposed model demonstrated excellent agreement with the analytical solutions of quasi-static condensation processes, including the one-dimensional Stefan problem and the Nusselt film condensation problem. Throughout the dynamic condensation process with rising bubbles, different phase-change models exhibited significant differences in condensing power distribution over space and time.
In large-scale group decision-making (LSGDM) problems, clustering and feedback mechanisms play crucial roles in the consensus-building process. This study focuses on LSGDM problems that incorporate historical data in ...
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In large-scale group decision-making (LSGDM) problems, clustering and feedback mechanisms play crucial roles in the consensus-building process. This study focuses on LSGDM problems that incorporate historical data in dynamic social networks and introduces a novel consensus decision-making method. First, we define a picture fuzzy linguistic Euclidean distance metric, which effectively quantifies the distance between picture fuzzy linguistic information, providing a robust quantitative tool for related research. Second, we constructed a hybrid trust-based dynamic social network that enables decision-makers (DMs) to interact closely based on both trust relationships and preference similarities. This approach overcomes the limitations of existing studies that consider only trust relationships when constructing hybrid trust social networks. Additionally, we propose a novel clustering method that integrates hybrid trust networks and historical data. Furthermore, we address the identification and management of noncooperative behavior within the consensus feedback mechanism to enhance the efficiency of group consensus. Finally, a high-speed rail construction site selection problem is presented as a case study to demonstrate the effectiveness and superiority of the proposed method.
We propose a semiparametric Bayesian discriminant analysis method. Statistical simulation shows that when the hypothesis of normal distribution is satisfied, our method is comparable to and even slightly better than t...
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We propose a semiparametric Bayesian discriminant analysis method. Statistical simulation shows that when the hypothesis of normal distribution is satisfied, our method is comparable to and even slightly better than the traditional method;when the hypothesis of normal distribution is not satisfied, our method is obviously better than the traditional one. We also apply our method to analyze a real data set and find that our method is better than the traditional one. Finally, we point out that implementation of our method is easy since the usual polytomous logistic regression procedures in many statistical softwares can be employed.
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