By Yu Shi
ISBN-10: 0857296183
ISBN-13: 9780857296184
Computational Optimization of inner Combustion Engines provides the cutting-edge of computational types and optimization equipment for inner combustion engine improvement utilizing multi-dimensional computational fluid dynamics (CFD) instruments and genetic algorithms.
Strategies to minimize computational rate and mesh dependency are mentioned, in addition to regression research equipment. a number of case reports are provided in a piece dedicated to purposes, together with tests of:
- spark-ignition engines,
- dual-fuel engines,
- heavy responsibility and lightweight accountability diesel engines.
Through regression research, optimization effects are used to give an explanation for complicated interactions among engine layout parameters, equivalent to nozzle layout, injection timing, swirl, exhaust gasoline recirculation, bore measurement, and piston bowl shape.
Computational Optimization of inner Combustion Engines demonstrates that the present multi-dimensional CFD instruments are mature adequate for useful improvement of inner combustion engines. it really is written for researchers and architects in mechanical engineering and the car industry.
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Extra resources for Computational Optimization of Internal Combustion Engines
Example text
M0kj and m00kj are integer numbers for elementary reactions and may be non-integers for non-elementary reactions. Each reaction fulfills element and mass conservation. The mass reaction rate of the k-th species is the sum of the reaction rates of all reactions involving this species: x_ k ¼ Nr X x_ kj ¼ Wk j¼1 Nr X ð2:19Þ mkj qj ; j¼1 where mkj ¼ m00kj À m0kj is the overall stoichiometric coefficient of the k-th species in j-th reaction. The rate of progress of the j-th reaction qj is written using the molar concentration ½Xk ¼ qYk =Wk qj ¼ Kfj Ns Y 0 ½Xk mkj À Krj k¼1 Ns Y 00 ½Xk mkj ; ð2:20Þ k¼1 where Kfj and Krj are the forward and reverse rates of the j-th reaction, respectively.
Gradient-based methods and gradient-free methods, or specifically, evolutionary methods. This section explores the advantages and limitations of these optimization algorithms with three mathematical problems. The commercial software modeFRONTIER (ESTECO 2008) was used to compare different optimization algorithms with model problems. The theoretical fundamentals of three multi-objective genetic algorithms (MOGA) are discussed in detail, while the assessment of these three MOGAs in computational engine optimization is the subject of Chap.
First, the development of mesh-independent spray models (Munnannur 2007; Abani et al. 2008a; Abani and Reitz 2010) enables engine CFD simulations using coarser meshes without losing accuracy compared to those of fine meshes (Abani et al. 2008b). Second, multi-zone or multi-grid methods (Babajimopoulos et al. 2005; Shi et al. 2009a; Goldin et al. 2009; Liang et al. 2009a) divide computational domains into subdomains by grouping thermodynamically-similar cells, which largely reduces the calling frequency to the chemistry solver in engine CFD simulations.
Computational Optimization of Internal Combustion Engines by Yu Shi
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