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The mass and overall dimensions of massively downsized engines for very high bmep (up to 35 bar) cannot be estimated by scaling of designs already available. Simulation methods coupling different levels of method profoundness, as 1-D methods, e.g., GT Suite/GT Power with in-house codes for engine mechanical efficiency assessment and preliminary design of boosting devices (a virtual compressor and a turbine), were used together with optimization codes based on genetic algorithms. Simultaneously, the impact of optimum cycle on cranktrain components dimensions (especially cylinder bore spacing), mass and inertia force loads were estimated since the results were systematically stored and analyzed in Design Assistance System DASY, developed by the authors for purposes of early-stage conceptual design. General thermodynamic cycles were defined by limiting parameters (bmep, burning duration, engine speed and turbocharger efficiency only).

This article presents results of the Design Assistance System (DASY, will be referred to as a tool in this paper) development and examples of its application for engine concept modeling. The software tool for creating and maintaining knowledge database is being developed at the Czech Technical University in Prague. This tool is targeted to simplify and speed up the concept design process. The targets were met by providing the high level of flexibility along with a simple user interface. Two examples that show interaction of this tool with computer-aided design (CAD) and computer-aided engineering (CAE) software are presented. One example includes an optimization using implemented genetic algorithm. By using this tool, one can create templates for conserving the knowledge acquired during engine design in the past. It provides hints for the future design tasks by offering a data of similar designs, based on experiments and simulations at different levels of complexity and profoundness.

This article presents results of the Design Assistance System (DASY) development and examples of its application for engine concept modeling. The software (DASY) for creating and maintaining knowledge database was developed. This software is targeted to simplify and speed up the concept design process. The targets were met by providing the high level of flexibility along with a simple user interface. Two examples that show interaction of DASY with computer-aided design (CAD) software are presented. The DASY creates a template for conserving the knowledge acquired during engine design in the past. It provides hints for the future design tasks by offering a data of similar engines, based on experiments and simulations at different levels of complexity and profoundness.

This paper presents the development and applications of engine friction algorithms to quickly estimate performance, optimum geometry of critical engine components, and packaging for rapid engine concept assessments. The development and implementation of some knowledge-based design rules will also be presented to quickly estimate the critical geometry of engine components and component weight such as valve sizing, piston weight, crankshaft geometry, etc. Some examples of powertrain concept design, such as the estimation of friction and packaging will be presented. The simulation results of the friction algorithms will be compared to some of available experimental data and also other friction estimation methods.

PET (Powertrain Engineering Tool) [1, 2] is based on an object-oriented hierarchy system and therefore each component (parent) has its own sub-component (child) systems. Developing explicit forms of objective functions and constraints is simple due to the object-oriented component system of PET. This system automatically recognizes the geometry of components and related design functions in its sub-component levels. This paper discusses computational efficiency, solution accuracy and robustness of software when using closed-form representatives of the derivatives of objective functions and constraints in sequential quadratic programming. Examples of generating closed-form representatives of the derivatives of objective functions and constraints in C/FORTRAN language syntax by using a symbolic processor, Mathematica [3], and mass reduction of the piston-pin are also presented.