Chapter One - Trends, Tricks, and Try-ons in CFD/CHT

The current state-of-the-art in solid rocket motor (SRM) ignition transient prediction is reviewed. Although the foundation of the computational fluid dynamics (CFD) approach to SRM ignition transient predictions was laid more than a quarter century ago by Peretz, Kuo, Caveny, and Summerfield, rapid advances have been made only recently. Three-dimensional (3-D) prediction tools are being developed and may require the usage of parallel computing systems to alleviate displeasingly long computer run times. In this paper, a quasi-3D CFD approach is presented as alternative cost-saving method for 3-D ignition transient flow predictions. Justifications and limitations are discussed. The approach is applied to two generic SRMs to illustrate: (1) 3-D flow phenomena that can not be obtained by using a 1-D or 2-D approach and (2) easy route that leads to parallel CFD. Introduction Depending on the application objective, solid rocket motor (SRM) ignition transient phenomena can be analyzed using methods with different degrees of computational sophistication. Currently, the analytic tools available for practical applications range from the simple volume-filling method to the complicated multi-disciplinary axisymmetric computational fluid dynamics (CFD) codes. The volume-filling method is a simple procedure for obtaining a transient chamber pressure and can be used for motor casing preliminary design. The method had been considered appropriate only for motors of small length-todiameter (L/D) ratios. However, it was reported recently by Luke et al that the volume-filling method was applied to compute the chamber pressure of the Space Shuttle's Redesigned Solid Rocket Motor (RSRM), which has a very large L/D ratio. The RSRM was modeled as two inter-connected volumes with the volume-filling method applied accordingly. The predicted chamber pressure rise rate was in good agreement with that obtained by using a 1-D CFD code. If the details of flow physics are needed, such as in the Senior Engineering Specialist Copy right © 2001 by The Aerospace Corporation. Published by the American Institute of Aeronautics and Astronautics, Inc. with Permission (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. case of dealing with the effects of propellant cracks on the SRM performance, then a flowfield solution coupled with heat transfer and ignition is required. The foundation for CFD flowfield approaches to the SRM ignition transient prediction was laid by Peretz, Kuo, Caveny, and Summer-field more than a quarter century ago. However, rapid advances have been made only recently. In the 1970s and 1980s, the progress of analytical predictions of SRM ignition transients was hampered by the lack of a reliable computational method for solving compressible flow equations and the insufficiency of available computing power. As will be illustrated later in this paper, the SRM ignition transient flowfields are dominated by the compressible flow wave interactions. The singular nature of the strong compressions and shocks in the compressible flow equations has impeded the progress of CFD to a very large extent until the TVD (total-variation-diminishing) algorithms were developed in the late 1980s and early 1990s. As a result, accurate numerical methods were available to resolve the complex wave interactions in multi-dimensional flows. At the same time, giga-flop computing powers also became available for computational fluid dynamics (CFD) simulations. Therefore, many SRM ignition transient CFD simulations were published in the 1990s.' Prediction methodologies have been also extended to include the coupling of flowfield and propellant deformation. However, most of the developments in this field are still based on the work of Peretz, et al. with the heat fluxes being computed by using a variety of semiempirical formulas. It is fair to say that, at the present, progress is hindered by the lack of reliable physical models for heat transfer. Fortunately, as indicated by Wang et al and demonstrated later in this paper, the computation of heat transfer in a CFD code can be conducted separately in a subroutine which can be replaced or updated without the need to change other parts of the code. Due to the progress in the performance enhancement of space launch systems, starand fin-shape propellant grain designs are now being used frequently. Therefore, there is urgent need to replace 1-D and 2-D approaches by 3-D methodologies. Recently, funded by DOE's Accelerated Strategic Computing Initiative (ASCI) project, the University of Illinois has established a Center for Simulation of Advanced Rockets (CSAR). The goal of the center is to develop an integrated rocket simulation tool capable of detailed, whole-system simulation of solidpropellant rockets under both normal and abnormal operating conditions. A report on the Center's progress was presented by Alvilli et al. With today's computing power, enhanced by visualization software, 3-D simulations of the SRM ignition transient are tractable. However, depending on the size of the burning area to be simulated and the computational grid resolution used, it could be very time consuming. The reason is that the mass injection on the burning surface belongs to a subsonic inflow boundary condition in CFD. An iteration procedure is therefore required, which is time consuming due to the non-linear (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. coupling of the equations. For the simulations presented in this paper, a quasi-3D approach is applied. Each case takes 36 CPU-hours on a Cray-SVl. This running time is considerably less than what is expected for a full 3-D simulation. The computation can be sped up by using parallel computing systems. As example of speed-up, super parallel computers have been applied by Wang and Taylor 13 to simulate the transonic flowfield over a complete plume-on Delta II 7925, which has one core vehicle plus 9 strap-on SRM boosters. Using 506 nodes of Intel's Paragon, a converged solution can be obtained in 48 hours. It was estimated that it would have required at least three months for the simulation if it had been done on a vector machine, CrayYMP. There is no doubt that future 3-D SRM ignition transient simulations will rely on super parallel computers. As reported by Alvi et al, super parallel computers are being used in CSAR at the University of Illinois. However, at present, full 3-D CFD simulations using parallel computers are still not popular. Furthermore, to convert existing 3-D CFD code to run on a parallel computing system is not easy task. A quasi-3D approach may be considered as alternative cost-saving approach. In this paper, the physical models currently used in CFD for SRM ignition transient are reviewed. A quasi-3D flowfield simulation methodology for SRMs with large numbers of fins will be presented. The first objective is to show the 3-D phenomena in the flowfield that could not be simulated using a 1-D or 2-D CFD approach. The second objective is to illustrate easy procedure that could lead to parallel CFD implementation. In this quasi-3D approach, axi-symmetric system of equations is applied to the bore and a 2-D Cartesian system of equations to the fins. The fundamental assumption is that the circumferential, 0-component, velocity in the bore region, and the wcomponent velocity, which is normal to the fin surface, in the fin region are negligible. The justification is that, as the number of fins increases, the planes of symmetry increase and the momenta associated with these velocity components cancelled each other on the plane of symmetry. In this paper, the flow will be assumed inviscid and the simplest heat transfer and ignition models will be applied. Since the purpose of this paper is to illustrate the methodology and the flow physics that the present approach can bring forth, generic SRMs will be used.

Solving mazes is an important scientific and engineering topic with various applications in the areas of image processing, intelligent traffic control, pipe network flow assurance, etc. There are many existing algorithms for solving mazes, which are mostly in the field of computer science. In this paper, two engineering approaches using computational fluid dynamics (CFD) and computational heat transfer (CHT) are presented. The hypothesis is, if a maze is injected with water or heat from the entrance, the water and heat should find their way to the exit naturally. If we can visually track the movement of the water and heat, the maze pathways can be shown and the maze can be solved. Computer simulations using both CFD and CHT methods were conducted to test the hypothesis and to analyze the performance of the two methods. The results show that both flow and heat from the inlet of the maze successfully find the outlet and the movement of water and heat can be clearly shown by the visual tools from the simulation soft, therefore the maze is successfully solved by both methods. The performance and simulation time between CFD and CHT approaches were compared, and it can be concluded that using CHT is more efficient than CFD since the heat transfer equations are less expensive to solve. In addition, a maze-solving-related application was presented in the paper to analyze the flow distributions of pipes in a network arrangement.

The numerical solution of partial differential equations that govern fluid dynamics with turbulence and combustion is challenging due to the multiscale nature of the dynamical system and the need to resolve small-scale physical features. In addition, the uncertainties in the dynamical system, including those in the physical models and parameters, initial and boundary conditions and numerical methods, impact the computational fluid dynamics (CFD) prediction of turbulence and chemical reactions. To improve the CFD prediction, this study focuses on the development and application of a maximum likelihood ensemble filter (MLEF), an ensemble-based data assimilation (DA), for flows featuring combustion and/or turbulence. MLEF finds the optimal analysis and its uncertainty by maximizing the posterior probability density function. The novelty of the study lies in the combination of advanced DA and CFD methods for a new comprehensive application to predict engineering fluid dynamics. The study combines important aspects, including an ensemble-based DA with analysis and uncertainty estimation, an augmented control vector that simultaneously adjusts initial conditions and model empirical parameters and an application of DA to CFD modeling of combustion and flows with complex geometry. The DA performance is validated by a turbulent Couette flow. The new CFD–DA system is then applied to solve the time-evolving shear-layer mixing with methane-air combustion and the turbulent flow over a bluff-body geometry. Results demonstrate the improvement of estimates of model parameters and the uncertainty reduction in initial conditions (ICs) for CFD modeling of flames and flows by the MLEF method.

Hybrid computational fluid dynamic algorithms based on analytic and finite volume methods

Second-law analysis (SLA) is an important concept in thermodynamics, which basically assesses energy by its value in terms of its convertibility from one form to another.[...]

The present paper is focused on numerical investigations of the heat transfer and flow field development around a low pressure turbine blade (T106) with the use of computational fluid dynamics (CFD) tools and methods. The CFD computations were performed with OpenFoam open source CFD software with the use of the Shear Stress Transport (SST) low-Reynolds number turbulence model. At the first part of the present work, the CFD computations were validated in relation to available detailed isothermal experimental measurements and useful conclusions about the accuracy of the modelling approach and the flow field development were derived. At the next stage, additional CFD computations were performed for flow and thermal conditions adapted in order to reflect more closely the low-pressure turbine blade operation. In these CFD computations the interaction between the velocity and thermal boundary layers and their effect on the heat transfer was quantified through the derivation of the distribution of the local Nusselt number on the T106 surface, which was compared in relation to available correlations from open literature. Through the analysis of the CFD results it was possible to identify the regions on the low-pressure turbine surface in which decreased heat transfer performance was presented as a result of the non-optimum flow and thermal field development. Furthermore, through the CFD computations secondary flow effects resulting in operational efficiency decrease were identified. The elimination of these sources of operational decrease is planned to be the main target of future research efforts targeting the development of methodologies for the design of highly efficient turbomachinery components.

All operations in process metallurgy involve complex phenomena comprising momentum, heat, and/or mass transport; iron- and steelmaking is not an exception. Transport phenomena, i.e. fluid flows, heat transfer and mass transfer, play a dominant role in process metallurgy since their respective laws govern the kinetics of the various physical phenomena occurring in ironmaking and in steelmaking. These phenomena include such events as three-phase reactions, entrainment of slag and gas in liquid steel, vacuum degassing, alloy melting and mixing, the movements and flotation of inclusions, melt temperature losses, residence times in a metallurgical reactor, erosion of refractory linings, etc. In all these aspects, the evolution in our techniques and abilities to model single and multiphase flows and their attendant heat and mass transfer processes has contributed significantly to our understanding and effectively operating these processes, to designing improvements, and to developing new processes. To be ignorant of these matters can doom a processing operation to the scrap heap of metallurgical failures. Computational fluid dynamics (CFD) and computational heat and mass transfer has been a very effective tool over the last three decades, for modelling iron- and steelmaking processes, starting from the blast furnace up to continuous casting and beyond. With the advent of commercial CFD packages and ever increasing computational power through parallel processing, CFD has now become the dominant approach for predicting various aspects in iron- and steelmaking processes. In Part 1 of this review paper, the applications of CFD in ironmaking processes are thoroughly reviewed, discussed and critiqued. In Part 2, fluid flows and CFD in steelmaking and steel casting processes are similarly reviewed and critiqued.

ABSTRACTThe development of high-performance computing and computational fluid dynamics methods have evolved to the point where it is possible to simulate complete helicopter configurations with good accuracy. Computational fluid dynamics methods have also been applied to problems such as rotor/fuselage and main/tail rotor interactions, performance studies in hover and forward flight, rotor design, and so on. The GOAHEAD project is a good example of a coordinated effort to validate computational fluid dynamics for complex helicopter configurations. Nevertheless, current efforts are limited to steady flight and focus mainly on expanding the edges of the flight envelope. The present work tackles the problem of simulating manoeuvring flight in a computational fluid dynamics environment by integrating a moving grid method and the helicopter flight mechanics solver with computational fluid dynamics. After a discussion of previous works carried out on the subject and a description of the methods used, validation of the computational fluid dynamics for ship airwake flow and rotorcraft flight at low advance ratio are presented. Finally, the results obtained for manoeuvring flight cases are presented and discussed.

Chapter 29 - Educational Requirements for Parallel Computing and Computational Fluid Dynamics

Chapter 8 - Artificial intelligence-based computational fluid dynamics approaches

The petroleum and petrochemical industries continually seek mechanical methods to improve heat transfer in shell-and-tube heat exchangers. Tube bundle inserts are popular mechanical devices that help improve performance. The increase in the tubeside heat transfer coefficient by the insert allows for a decrease in required shellside flow length, assuming single tube pass. The flow length reduction allows for designing higher velocities and subsequent shellside shear rates, to help reduce crude oil fouling potential. This work presents some of HTRI’s ongoing experimental measurements and preliminary Computational Fluid Dynamics (CFD) simulations. CFD visualization of swirl flow dynamics and heat transfer inside the augmented tube provides insight on complex flow physics, which is misunderstood. Heat Transfer Research, Inc. (HTRI) collected experimental data for in-tube single-phase flow using twisted tape inserts in the Tubeside Single-Phase Unit (TSPU) situated in the Research and Technology Center (RTC). Our data will be used to calibrate ANSYS FLUENT CFD simulations of a tube with a twisted tape swirl insert. We first performed plain tube simulations and compared the heat transfer results with open literature measurements, for validation. We will modify the CFD tube model to have a swirl flow insert, and compare numerical results against open literature experimental data of diabatic single-phase swirl flow. In future, we will compute heat transfer (heating and cooling) and pressure drop for tube insert configurations at laminar and turbulent Reynolds numbers from 3000 to 500000. The range of tubeside Reynolds numbers required the use of the laminar, transition, and Realizable k-epsilon turbulence models with scalable wall functions. This study describes some of the mechanisms behind turbulent swirl flow augmentation inside a tube, as well as the limitations of conventional in-tube heat transfer correlations applied to swirl flow inserts.

The development, implementation, and evaluation of an effective curriculum for students to learn integrated computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) including ePIV and Flowcoach in introductory undergraduate level courses and laboratories is described. The CFD objective is to teach students from novice to expert users who are well prepared for engineering practice using a CFD Educational Interface for hands-on student experience, which mirrors actual engineering practice. The Educational Interface teaches CFD methodology and procedures through a step-by-step interactive implementation automating the CFD process. A hierarchical system of predefined active options facilitates use at introductory and intermediate levels, encouraging self-learning, and eases transition to using industrial CFD codes. The EFD objective is to teach students use of modern facilities, measurement systems, and uncertainty analysis (UA) following a step-bystep approach, which mirrors the “real-life” EFD process: setup facility; install model; setup equipment; setup data acquisition; perform calibrations; data acquisition, analysis and reduction; and UA, and comparison CFD and/or analytical fluid dynamics (AFD) results. Students conduct fluids engineering experiments using tabletop and modern facilities such as pipe stands and wind tunnels and modern measurement systems, including pressure transducers, pitot probes, load cells, ePIV, Flowcoach and computer data acquisition systems (Lab View) and data reduction. Students analyze and relate CFD and EFD results to fluid physics and classroom lectures, including teamwork and presentation of results in written and graphical form. Implementation is described based on results for an introductory level fluid mechanics course, which includes integrated CFD and EFD laboratories for the same geometries and conditions. The laboratories constitute one credit hour of a four credit hour one semester course and include tabletop kinematic viscosity experiment focusing on UA procedures and pipe and airfoil experiments focusing on integrated EFD and CFD. An independent evaluation investigates and reports the learning outcomes and the effectiveness of the CFD educational interface, ePIV, Flowcoach and CFD and EFD laboratories.

Computational Fluid Dynamics (CFD), a mature discipline now, can contribute considerably to the design, analysis and development of engineering systems involving fluid flows. Visualization of flow-field, surface load distribution and various aerodynamic forces and moments are the criteria for basic design of aerospace configurations. CFD complements experimental and theoretical fluid dynamics by providing an alternative and cost effective means to simulate real flow phenomena. The main advantage lies in its ability to cut down the number of wind-tunnel tests leading to reduction in the design cycle time and design cost. After a brief introduction to CFD, the role played by the modern CFD tools developed at the Computational and Theoretical Fluid Dynamics Division of National Aerospace Laboratories, Bangalore in the design and analysis of Aerospace configurations will be discussed here.

The correlation between computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) is crucial for the behavior prediction of aerodynamic bodies. This paper’s objective is twofold: (1) to develop a method that approaches commercial CFD codes and their link with EFD in a more efficient way, using a downscaled model, and (2) to investigate the effect of rain on the aerodynamic behavior of a wing. More specifically, we investigate the one-phase and two-phase flow over a typical wing section NACA 641-212 airfoil, in the commercial code Ansys Fluent. Two computational models were developed; the first model represents the original dimensions of the wing, while the second is downscaled to 23% of the original. The response of the models in air and air–water flow were primarily studied, as well as the impact on aerodynamic efficiency due to the existence of the second phase. For the computational fluid dynamics simulations, a pressure-based solver with a second-order upwind scheme for the spatial discretization and the Spalart–Allmaras (SA) turbulence model were utilized. Meanwhile, for the two-phase flow of air–water, the discrete phase model (DPM) with wall–film boundary conditions on the surface of the wing and two-way coupling between continuous and discrete phase was considered. The second phase was simulated as water droplets injected in the continuous phase, in a Euler–Lagrange approach. The experimental model was constructed in accordance with the downscaled model and tested in a subsonic wind tunnel, using 3D printing technology which reduced the experiment expenses. The presence of water in two-phase flow was proven to deteriorate the aerodynamic factors of the wing compared to one-phase flow, as expected. The three-stage comparison of CFD and EFD results showed a very good convergence, in both single and two-phase flow. This can lead to the conclusion that a rapid and low-cost study for the estimation of the aerodynamic performance of objects with high accuracy is feasible with the suggested method.

To numerically study biomass gasification in a three-dimensional bubbling fluidized bed, a CFD-DEM (computational fluid dynamics – discrete element method) model with heat transfer and homogeneous and heterogeneous chemical reactions is implemented. An ideal reactor model is used for the air-steam bubbling fluidized bed (BFB) gasification reactor assuming perfectly mixed solids and plug flow. A validated computational particle fluid dynamics (CPFD) model has been applied to investigate the sensitivity analysis of mesh grids as well as to find the optimum number of grids. The result shows that 7452 grid cells are the optimal number of cells for the existence BFB gasifier. The effects of key process operating parameters such as steam to biomass ratio (SB), as well as temperature shows that by enhancing the SB ratio or reactor temperature, gas yields increase. H2 and CO2 concentrations promote by increasing the steam to biomass ratio while CO and CH4 production drop. The optimal value of SB for the gasification process can be found in the range of 0.3 to 1.