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Abstract:
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The explosive growth of digital data collection and processing demands a new
approach to the historical engineering methods of data correlation and model creation. A
new prediction methodology based on high dimensional data has been developed. Since
most high dimensional data resides on a low dimensional manifold, the new prediction
methodology is one of dimensional reduction with embedding into a diffusion space that
allows optimal distribution along the manifold. The resulting data manifold space is then
used to produce a probability density function which uses spatial weighting to influence
predictions i.e. data nearer the query have greater importance than data further away.
The methodology also allows data of differing phenomenology e.g. color, shape,
temperature, etc to be handled by regression or clustering classification.
The new methodology is first developed, validated, then applied to common
engineering situations, such as critical heat flux prediction and shuttle pitch angle
determination. A number of illustrative examples are given with a significant focus
placed on the objective identification of two-phase flow regimes. It is shown that the
new methodology is robust through accurate predictions with even a small number of data points in the diffusion space as well as flexible in the ability to handle a wide range
of engineering problems. |
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