In this two-part series publication a mathematical model of the energy conversion process in a diesel engine based combined-cycle power plant has been developed and verified. The examined configuration consists of a turbocharged diesel engine (the topping cycle), a heat recovery steam generator (HRSG) and a steam turbine plant (the bottoming cycle). The model is then used to provide an analysis of performance characteristics of the combined-cycle power plant for steady-state operation. Numerous practical performance parameters of interest have been generated, such as the mean indicated pressure, specific fuel consumption, hourly fuel consumption, brake horsepower of diesel engine, mass flow rate, pressure, and temperature of gases and air, respectively, through the gas turbine and compressor (in the frame of a turbocharger), temperature of flue gases at boiler inlet and outlet, mass flow rate of exhaust gases through the convection coils, and mass flow rate, temperature, pressure, and enthalpy of superheated steam. The performance maps have been derived. The effect of change in the major operating variables (mutual operation of diesel engine, HRSG, and steam turbine) has been analyzed over a range of operating conditions, including the engine load and speed. The model is used as a desktop design tool for accurate predictions of cycle performance, as well as insight into design trends.

In this two-part series publication, a mathematical model of the energy conversion process in a diesel engine based combined-cycle power plant has been developed. The examined configuration consists of a turbocharged diesel engine (the topping cycle), a heat recovery steam generator (HRSG) and a steam turbine plant (the bottoming cycle). The mathematical model describes the processes that occur simultaneously in the diesel engine cylinders, turbocharger, air filter, air inlet pipes, exhaust pipes, HRSG, steam turbine, and the associated auxiliary equipment. The model includes nonlinear differential equations for modeling the energy conversion in the diesel engine cylinders, fuel combustion, gas exchange process, energy balance in the turbocharger, inlet pipes and exhaust system, heat balance in the HRSG, and steam turbine cycle. The fifth-order Kuta-Merson method has been applied for numerical solution of these simultaneous equations via an iterative computing procedure. The model is then used to provide an analysis of performance characteristics of the combined-cycle power plant for steady-state operation. The effect of change in the major operating variables (mutual operation of diesel engine, HRSG, and steam turbine) has been analyzed over a range of operating conditions, including the engine load and speed. The model validation and the applications of the model are presented in Part II (Results and Applications) of this two-part series publication.

A technique which uses trained neural nets to model the compressor in the context of a turbocharged diesel engine simulation is introduced. This technique replaces the usual interpolation of compressor maps with the evaluation of a smooth mathematical function. Following presentation of the methodology, the proposed neural net technique is validated against data from a truck type, 6-cylinder 14-liter diesel engine. Furthermore, with the introduction of an additional parameter, the proposed neural net can be trained to simulate an entire family of compressors. As a demonstration, a family of compressors of different sizes is represented with a single neural net model which is subsequently used for matching calculations with intercooled and nonintercooled engine configurations at different speeds. This novel approach readily allows for evaluation of various options within a wide range of possible compressor configurations prior to prototype production. It can also be used to represent the variable geometry machine regardless of the method used to vary compressor characteristics. Hence, it is a powerful design tool for selection of the best compressor for a given diesel engine system and for broader system optimization studies.