Introduction
Nowadays there is a growing interest in Computational Fluid Dy-namics (CFD) because of its many application fields (i.e. Me-chanics, Aeronautics and Chemical Engineering, Physics and so on). Computational fluid dynamics is one of the branches of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems that involve fluid flows, heat transfer and related phenomena, such as chemical reactions. Computers are used to perform the millions of calculations required to simulate the complex interactions which characterize the engineering sys-tems of interest. Even with simplified equations and high-speed supercomputers, only approximate solutions can be achieved in many cases. Ongoing research, however, may yield software that improves the accuracy and speed of complex simulation scenar-ios, as turbulent reacting flows. Initial validation of such software is often performed using a wind tunnel. The interest in CFD codes and their application has continuosly growth during the last years. The so called Computer Aided Engineering (CAE) for “virtual prototyping” and “virtual testing” is continuously grow-ing, due to the improved reliability of computational tools and the high cost and time associated with laboratory testing of com-ponents and complete systems. However, users and developers of computational tools face today the critical issue of determining how the confidence in modeling and simulations should be criti-cally assessed, to effectively help decision making in new design and regulation (i.e. safety and environmental). Verification and Validation (V&V) of computational simulations provides a pri-mary method for building and identifying this confidence. When referring to the numerical simulations of engineering systems, each step in the modeling process can introduce uncertainty in
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the final results, thus ultimately affecting the validation of the computational model. Numerical error can be introduced at each of the following modeling steps:
• geometry definition; • domain discretization;
• definition of the physical models; • definition of the boundary conditions.
Procedures must be employed to estimate both the uncertainty in the experiments and numerical simulations, to actually vali-date the computational model. To this purpose, V&V can be effectively exploited.
In the present Thesis, the Sandia Piloted Flame D has been analyzed to verify the level of agreement between the results pro-vided by a commercial CFD code, FlUENT 6.3, and high fidelity experimental data from the Sandia National Laboratories; more-over, the the Reactor Network Analysis (RNA) methodology has been compared to the classic CFD approach. RNA is a software that has been applied to industrial furnaces. It works as a ki-netic post-processor of simplified CFD simulations to determine pollutant formation using very detailed kinetic mechanisms.
This work of thesis is subdivided into four chapters:
1. Verification & Validation: the methodology is decribed and validation metrics used for comparing simulation results are reported;
2. CFD and RNA: the theory behind these two softwares is explained;
3. Experimental data for model validation: the description of simulation cases is reported;
4. Results and discussions: results from simulations and dis-cussion about are reported.
