By Yu Shi
Computational Optimization of inner Combustion Engines offers the cutting-edge of computational versions and optimization equipment for inner combustion engine improvement utilizing multi-dimensional computational fluid dynamics (CFD) instruments and genetic algorithms.
Strategies to lessen computational rate and mesh dependency are mentioned, in addition to regression research equipment. numerous case stories are offered in a bit dedicated to purposes, together with exams of:
- spark-ignition engines,
- dual-fuel engines,
- heavy responsibility and lightweight responsibility diesel engines.
Through regression research, optimization effects are used to provide an explanation for complicated interactions among engine layout parameters, equivalent to nozzle layout, injection timing, swirl, exhaust fuel recirculation, bore measurement, and piston bowl shape.
Computational Optimization of inner Combustion Engines demonstrates that the present multi-dimensional CFD instruments are mature sufficient for sensible improvement of inner combustion engines. it really is written for researchers and architects in mechanical engineering and the automobile industry.
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Additional resources for Computational Optimization of Internal Combustion Engines
They found that the GA optimization efficiently located optimal engine operating parameters that demonstrated low emissions and improved fuel consumption capabilities of a diesel engine. The predicted optimal injection timing was very advanced, which suggests that HCCI-like combustion is useful for low emissions diesel engines at the considered mid-load condition. The optimization showed that the resulting long ignition delay allowed enough time for mixing and reduced the extent of fuel rich regions.
For the first problem, since it has only one local and global optimal solution, the BFGS method started with a single random set of the two input parameters. 1 Optimization Algorithms 17 Fig. 2 Multiple peak values problem Fig. 3 Function value of the one-peak problem using BFGS method optimal solution of unity with only 31 evaluations, which is quite efficient, as expected. Technically, one can also start the BFGS method with a single set of input variables for the second problem. However, it is almost impossible to obtain the global optimal solution with such configuration as it is easy to see that the method has a very high chance of converging towards a local (non) optimal point.
This explains why the application of non-evolutionary optimization methods is less popular than evolutionary methods in engine research community. But with some special algorithm treatments, a few studies have revealed that non-evolutionary methods can also be efficient and effective for some specific engine optimization problems. For example, Naik and Ramadan (2004) studied the effects of equivalence ratio (mass of injected fuel), injection timing, ignition timing, engine speed, spray cone angle, and velocity of fuel injection on GDI engine performance and HC emissions.
Computational Optimization of Internal Combustion Engines by Yu Shi