2 resultados para dynamic parameters identification
em Bucknell University Digital Commons - Pensilvania - USA
Resumo:
Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.
Resumo:
While beneficially decreasing the necessary incision size, arthroscopic hip surgery increases the surgical complexity due to loss of joint visibility. To ease such difficulty, a computer-aided mechanical navigation system was developed to present the location of the surgical tool relative to the patient¿s hip joint. A preliminary study reduced the position error of the tracking linkage with limited static testing trials. In this study, a correction method, including a rotational correction factor and a length correction function, was developed through more in-depth static testing. The developed correction method was then applied to additional static and dynamic testing trials to evaluate its effectiveness. For static testing, the position error decreased from an average of 0.384 inches to 0.153 inches, with an error reduction of 60.5%. Three parameters utilized to quantify error reduction of dynamic testing did not show consistent results. The vertex coordinates achieved 29.4% of error reduction, yet with large variation in the upper vertex. The triangular area error was reduced by 5.37%, however inconsistent among all five dynamic trials. Error of vertex angles increased, indicating a shape torsion using the developed correction method. While the established correction method effectively and consistently reduced position error in static testing, it did not present consistent results in dynamic trials. More dynamic paramters should be explored to quantify error reduction of dynamic testing, and more in-depth dynamic testing methodology should be conducted to further improve the accuracy of the computer-aided nagivation system.