Evaluation of Invariant Structural and Acoustic Loads Estimation Methods to be Used in Vehicle Platform Development Independent to the Boundary Conditions of Load Generation

<div class="section abstract"><div class="htmlview paragraph">Platform based vehicle development is standardized at John Deere. The challenges of frontloading the integration of individual components within different platforms using predictive methods is key to shortening the development cycle. Components are individually characterized on test benches and results cannot directly be used to evaluate system performance. Invariant characterization is needed instead, which is possible through techniques such as blocked loads estimation. To evaluate the applicability of such methods, the component-based loads and vehicle in-situ operational loads need to be compared. The confident use of these methods for obtaining structural and acoustic loads enables the use of hybrid system models, enhancing early NVH response predictions. The objective of this work was to enable the confident use of test stand measurements in predictive models across various vehicle platforms. This study compares a powertrain characterization in a vehicle against a test stand to assess the invariance of the operating mount structural loads as well as operating acoustic loads. The following milestones are discussed:<ol class="list nostyle"><li class="list-item"><span class="li-label">1</span><div class="htmlview paragraph">Perform in-situ Component Transfer Path Analysis (C-TPA) in an agricultural tractor to compute engine acoustic loads and engine mount structural invariant loads.</div></li><li class="list-item"><span class="li-label">2</span><div class="htmlview paragraph">Perform C-TPA on the same model engine in a dynamometer test cell to compute engine acoustic loads and engine mount structural invariant loads.</div></li><li class="list-item"><span class="li-label">3</span><div class="htmlview paragraph">Compare acoustic and structural loads in both test scenarios for their invariance and study sensitivity of variables in two test scenarios.</div></li><li class="list-item"><span class="li-label">4</span><div class="htmlview paragraph">Discuss lessons learned throughout the process for implementation in the development of complex machines.</div></li></ol></div></div>