Thank you for reading numerical heat transfer and fluid flow. Maybe you have knowledge that, people have search numerous times for their favorite books like this numerical heat transfer and fluid flow, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their computer. numerical heat transfer and fluid flow is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the numerical heat transfer and fluid flow is universally compatible with any devices to read.

Numerical Heat Transfer And Fluid Flow

Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

Thank you very much for reading principles of enhanced heat transfer. As you may know, people have look numerous times for their chosen books like this principles of enhanced heat transfer, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. principles of enhanced heat transfer is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the principles of enhanced heat transfer is universally compatible with any devices to read.

Principles Of Enhanced Heat Transfer

Thermal conductivity of hybrid nanofluids: A critical review

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

An improved grey wolf optimizer for solving engineering problems

Study on the Risk Factors during Component Integration in Component Based System Development

In this research, five machine learning methods are employed to create the formula and make the model for communication between the melting and solidification processes to find the latent heat and communication between the thermal conductivity and the thermal diffusivity. Paraffin wax was the PCM, while SiO2, ZnO, Fe2O3, and Al2O3 nanoparticles were utilized as additives to enhance the thermal conductivity by considering four concentrations between 2 and 8 wt.%. The K-Nearest Neighbours (KNN), the decision tree, the stochastic gradient descent (SGD) regressor, the Support Vector Machines (SVM), and the Huber regression are the algorithms that are used in this study. Using the Huber regression method, the formula for the latent heat of the solidification-process is created by the melting-process (by latent heat and wt.) with the polynomial form by degree 2, and the R-Squared value is 0.998, and MSE is 0.971172, and by the KNN (K = 3) is made the model with the linear form. The R2 value is 0.999, and MSE is 0.41809, and for the latent heat of the melting-process by the solidification-process (by latent heat and wt.) with the same methods reached to best the R2 for creating the formula and making the model, respectively the R2 values are 0.996 and 0.999, and the MSEs are 1.482458 and 0.608658. Using the Huber regression method, reach the best result for creating the formula in the polynomial form by degree 2 for the thermal conductivity by the thermal diffusivity and wt. and create the formula with the R2 value of 0.998 and MSE is 0.000033, and make the model by the SVM with the R2 value of 0.999 in the linear form, and MSE is 0.000016. The same methods are used for thermal diffusivity by the thermal conductivity and wt.; the R2 values for the formula and the model, respectively, are 0.997 in polynomial form (degree = 2) and 0.999 in linear form. MSEs, respectively, are 0.000020 and 0.000006. 

Predicting thermophysical properties enhancement of metal-based phase change materials using various machine learning algorithms

Gridshells are commonly known as structures with the shape and rigidity of a double curvature shell consisting of a grid, not a continuous surface. In recent decades, these structures have attracted significant attention. The impact of various geometric patterns on free form gridshell structures is investigated here to demonstrate the necessity of collaboration between structural and architectural characteristics in enhancing structure efficiency. To that goal, a framework is proposed where three shells are first designed, and then six geometric patterns are formed on them. The main factors for evaluation of gridshells are decreasing the steel weight as an economic index and decreasing the displacement as a structural index, also, finite element method is used for structurally analyzing the gridshells, and the generated gridshells are compared to each other based on the mentioned indices. For the optimization process, an approach is suggested to find the most optimum gridshell, then numerical results show the efficiency of the proposed alternative approach.

Marjan Goodarzi

Papers

Study on the Risk Factors during Component Integration in Component Based System Development

Predicting thermophysical properties enhancement of metal-based phase change materials using various machine learning algorithms

Effects of different geometric patterns on free form gridshell structures