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Optimization in engineering is a significant tool for selecting the best fit when several design variables are present. It helps in determining the optimum through a combination of a set of design variables with objective functions subject to certain constraints. In the design of heat exchangers too, where tremendous research is going on to optimize its effectiveness, certain efforts are being done to improve the quality of the inner tube, shell, or plate design. In this respect, surface enhancement has been actively researched in recent decades. This sort of augmentation is usually dominant on the tube side. It has been seen that the study is greatly conducted in the past experimentally, but numerical studies are limited to determine friction factor or Nusselt number. Only a few discussed an important factor called entropy generation minimization. In this paper, with the optimization in view, the design is based on multiple disciplines. That is, first a numerical study is performed on the helically grooved tubes to examine the thermal enhancement factor. Numerical results are initially validated with published experimental results. The optimized tube is then selected based on the D-optimal design for the thermal enhancement factor and finally, the entropy minimization study concerning the Reynolds number is conducted on the optimized tube. It is observed that the tube with the greatest number of grooves, the maximum depth, and the least pitch performs the best. However, the optimum Reynolds number is at the point where the tube has the least entropy generated as compared to the smooth tube.

Surface enhancement in heat exchangers has been actively researched, but enhancements have been mainly on the tube side, involving optimization of the geometry. The effect of surface enhancement has been studied in great detail experimentally, but numerical studies are scarce and involve performance comparisons based on the friction factor and Nusselt number. However, true performance can be assessed based on entropy generation minimization. We performed a numerical study on tubes with helical groove formation to assess entropy generation minimization. The tube data were initially validated experimentally and tube performance was then examined via thermal enhancement factors and entropy generation minimization. The Reynolds number was taken as 5000 to 10000 in accordance with an experimental study. All the tubes had an enhancement factor greater than unity, meaning that they were thermally efficient. However, with regard to irreversibility, the tube with the minimum pitch-0.051 m-(GT02) produced the lowest value and hence the minimum entropy generation rate, and thus represented the optimal choice for heat exchangers.

This paper deals with the heat transfer enhancement due to groove formation in a metallic tube. A detailed computational fluid dynamics (CFD) analysis was performed on helical groove tubes with three geometries (obtained from a published article) to validate the results, and three other geometries, randomly selected, with variable pitch length apart from experiment. The range of Reynolds number was from 4000 to 10,000. Performance criterion through CFD was based on the heat transfer via Nusselt number as well as the friction factor. The friction factor and Nusselt number comparison with experimental data was found to be in good agreement with the experimental data, with an average deviation of 2 and 7%, respectively. Numerical experimentation was extended by examining the performance of three more tubes of varying pitch lengths. The new tubes had pitch length of 51 mm (GT02), 102 mm (GT04) and 152 mm (GT06). The performance was evaluated in terms of thermal enhancement factor. It was found that the all tubes have enhancement factor greater than unity which means that tubes are efficient in terms of heat transfer. Among the tubes studied, the maximum thermal enhancement factor was also obtained for GT02 (51 mm pitch length).

Aerodynamic coefficients play a key role to determine the behavior of a flying vehicle along its trajectory for a particular mission. Prediction of these aerodynamics coefficients can be made through experiments, analytical solution or Computational Fluid Dynamics (CFD). These coefficients are determined for a wide range of Mach numbers and angles of attack at different altitudes. During flight, Mach numbers may be similar at two (or more) different altitudes. However, Reynolds number can be different at each altitude due to the variation of density and temperature with altitude. In present work, the dependency of aerodynamic coefficients on Reynolds number is studied for subsonic flow conditions. For this purpose, simulations are performed on 3D half body of HB-1 test case with 2nd order coupled steady state solver and k-ω SST turbulence model. Simulations are performed for Mach numbers 0.6 and 0.8 for altitudes (sea level to 70 km) and angles of attack. Axial force, normal force and pitching moment coefficients are determined. Results show that axial force coefficient varies significantly with Reynolds number for a fixed Mach number. Variation in normal force and pitching moment coefficient is insignificant for smaller values of angle of attack. However, at larger values variations are significant. Results show that low subsonic regimes are more affected by Reynolds number. For validation purpose numerical results are also compared with experimental data of HB-1 test case.

Hammerhead nose cone configurations are essential in accommodating larger payloads. However, during transonic flight regime, they experience high-level of pressure fluctuations due to flow-induced turbulence, wake effects, flow separation and shock oscillations which can lead to severe buffet phenomenon. Buffet loads can cause severe structural damage to the payload; and can ultimately destroy the whole mission. Therefore, it is extremely necessary to determine unsteady pressure fluctuations on a hammerhead configuration and consider them in the overall design load analysis to ensure that the configuration is safe from the severity of buffet loads. In the present work, time-accurate CFD simulation of flow over a hammerhead nose cone configuration (NASA Model IV) has been performed for a Mach number of 0.79. The objectives of the present work are to compute the unsteady surface pressure fluctuations, analyze the time-accurate aerodynamic behaviour of the flow, and determine the validity and accuracy of the computational methodology using ANSYS Fluent®. For validation, the rms (root mean square) value of the computed instantaneous pressure is compared with the experimental results and the time-average solution is compared with the steady state solution. In the present work, the separated shear layer off the boat-tail edge is successfully captured and the turbulence region downstream is found responsible for unsteady loadings.

Rapid growth in the capability of single-processor computers has slowed in recent years. It is now evident that further increases in speed require multiple processors. The advantage of high-performance computing over classical vector supercomputers is scalability. The computers also use standard chips and are therefore cheaper to produce. Commercially available parallel computers may have thousands of processors, terabytes of memory, and computing power able to perform one quadrillion floating point operations per second. However, it is a fact that computational fluid dynamic (CFD) algorithms designed for traditional serial machines may not run efficiently on parallel computers. This chapter focuses on how CFD can be used in parallel and on parallel architectures.

This chapter introduces high-performance computing (HPC). High-performance computing is used in every field of science and engineering and cannot be taken for granted. The pioneers of HPC and their contribution are discussed and the world's top five computers are mentioned and discussed in detail. Sparse matrices, libraries necessary to run an HPC environment, such as LAPACK and BLAS, are also discussed in detail.

This chapter is about the use of high-performance computing in computational flow dynamics, a real devourer of resources. Various types of comparative studies are analyzed in detail and commented on in terms of interconnectivity, storage, and memory requirements. The ANSYS ® Fluent ® , CFX ® , and OpenFOAM benchmarks provide an overview of the nature of problems run on the various types of hardware.

This chapter describes networking in some detail, with special attention to remote simulation and access. Remote simulation has high and frequent use in high-performance computing (HPC) and it is the beauty of HPC that you can access your work from anywhere in the world. The concept of networking with transmission control protocol/Internet protocol is also discussed and presented in a simplified manner that should help a computational flow dynamics engineer to work well when dealing with cluster issues.

This chapter discusses the classification of clusters. Many types of clusters are used in the world. It all depends on the needs and budget of the user and the solution the user wants. This chapter gives an idea of the different types of distributed clusters and their advantages. The qualities and different components of clusters are also discussed in detail.

This chapter is an introduction to Computational Fluid Dynamics (CFD). Many organizations implement CFD in the computer-aided engineering phase. However, most of the time, higher management is not interested, perhaps because of the lengthy simulations or uncertainty regarding results. These issues are discussed and various misconceptions about CFD are explored and cleared up. The basics of CFD with governing equations are also discussed.

This chapter is about problems that really need High Performance Computing (HPC) to run. Twenty-first century computational flow dynamics is about Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) and almost all real-world flows are turbulent in nature; therefore, to predict flow physics accurately, computer algorithms must be capable of fully resolving all structures to the Kolmogorov scale. This chapter will discuss why DNS and LES are important in turbulence and why HPC is the answer to these large problems.

Graphics chips started as fixed-function graphics pipelines. Over the years, these graphic chips became programmable, which led Nvidia Corporationto introduce the first graphics processing unit (GPU) at the end of the last century. Nvidia realized the potential in bringing this performance to the largest scientific and research community and decided to invest in modifying the GPU to make it fully programmable for scientific applications and support higher-level languages such as C and C++. This led to the emergence of Compute Unified Device Architecture. GPUs are now extensively being used in high-performance computing. Because they are involved in every field of sciences and technology, computational flow dynamics (CFD) also uses them. This chapter focuses on GPUs, their architecture, and how they are used in CFD.

The viscous dissipation within boundary layers of high speed vehicle creates high skin temperatures. Designing of an appropriate thermal protection system requires computation of thermal loads which would be experienced by vehicle during its flight trajectory. The objective of the present work is to develop a methodology for transient aerothermal analysis of high speed vehicle. The most appropriate method for predicting aerodynamic heating is computational fluid dynamics solution (CFD). Solid-Fluid coupling and transient boundary condition capabilities of CFD software FLUENT are used to develop required methodology. The available X-15 flight data is used for its validation. Temperature transients are calculated for complete flight trajectory of X-15 at wing mid-span chord location and compared with available flight data at stagnation, 4%, 20%, and 46% chord locations. The results obtained for skin temperatures at different locations are found both qualitatively and quantitatively in good agreement with in-flight data. This validates the methodology utilized in modeling the transient aero-thermal analysis of high speed vehicles. This method could be very useful in predicting the aerodynamic heating loads of high speed vehicles.