When faced with a new challenge it is nice to be able to lean on the expertise of someone who has been there. When working on a new engine design or trying to get the most out of the one you have, there can be an almost limitless number of possible combinations in the design space. Just a few of the decisions to be made are valve sizes, intake and exhaust runner lengths, intake and exhaust runner diameters, bore, stroke, intake and exhaust valve lift and duration, and the list goes on and on. What variable has the biggest effect on power output? Which variables are most important and which are the least important? How can you increase your chances of success with the minimum amount of financial commitment? Engine Simulation can provide valuable information in a design space that is largely unfamiliar. Using Engine Simulation early in the design process can help ensure that the design will not only be a success but will form a solid foundation from which to improve through future development. Design studies can be conducted and analyzed to identify which variables are important and which are not. A Design of Experiment (DoE), called an Automated Engine Design (AED), can be performed to explore a sampling of the possible variable combinations and help guide the designer to an optimized solution. Literally, thousands of variable combinations can be explored in a few hours within a typical DoE.
A typical Engine Simulation project consists of the creation of a baseline model, correlating that baseline to measured data and then making small changes to the model and on the dyno to ensure the variable modifications are being modeled properly. Simple modifications such as header primary lengths and cam timing are good examples of changes that should be easy to predict for a well-built baseline. Once a solid baseline has been achieved, then the variable exploration and optimization process begins. Goals are specified and a strategy is defined based on a range of variable values that are within manufacturability. Many times the variables believed to be important turn out to have only a small effect on the outcome.
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