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Technical Paper

Multidisciplinary Design Method for Off-Road Vehicles Using Bayesian Active Learning

2024-04-09
2024-01-2595
When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.
Technical Paper

Time Series Modeling of Terrain Profiles

2005-11-01
2005-01-3561
Every time we measure the terrain profiles we would get a different set of data due to the measuring errors and due to the fact that the linear tracks on which the measuring vehicle travels can not be exactly the same every time. However the data collected at different times from the same terrain should share the similar intrinsic properties. Hence it is natural to consider statistical modeling of the terrain profiles. In this paper we shall use the time series models with time being the distance from the starting point. We receive data from the Belgian Block and the Perryman3 testing tracks. The Belgian Block data are shown to behave like a uniformly modulated process([7]), i.e. it is the product of a deterministic function and a stationary process. The modeling of the profiles can be done by estimating the deterministic function and fit the stationary process with a well-known ARMA model. The Perryman3 data are more irregular.
Technical Paper

Development of Portable Self Contained Phase Shifting Digital Shearography for Composite Material Testing

2005-04-11
2005-01-0590
The use of composite materials in the automotive industry has become increasingly widespread. With this increase in use, techniques for non-destructive testing (NDT) have become more and more important. Various optical NDT inspective methods such as holography, moiré techniques, and shearography have been used for material testing. Among these methods, shearography appears to be most practical. Shearography has a simple optical setup due to its “self-referencing” system, and it is relatively insensitive against rigid-body motions. Measurements of displacement derivatives, and thus strain directly, rather than the displacement itself is achieved through this method. Therefore shearography detects defects in objects by correlating anomalies of strain which are usually easier than correlating the anomalies of the displacement itself, as in holography. To date shearography has shown potential as a NDT tool for identifying defects in small structures.
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