NEVADA - A Comprehensive and Proven Radiation Heat Transfer Computer Software Package

972440

07/01/1997

Event
International Conference On Environmental Systems
Authors Abstract
Content
The NEVADA 97 Software Package uses a new fast ray trace technique called ‘variable decomposition’. The software operating with this technique is up to 100 times faster than NEVADA 95. The Variable Decomposition Method (VDM) is a proprietary method of “de-composing” space (voxels) into sub-space (sub-voxels), ad infinitum. Currently, the user may choose between four (4) ray trace algorithms: Oct-Cell, Oct Tree, Cubic Decomposition Method (CDM), or User Defined. With CDM the program automatically assigns the number of voxels until each voxel or sub-voxel contains approximately N1/3 nodes, where N is the number of nodes in the model or parent voxel. CDM allows the use of higher order surface types, such as Bezier, NURBS, 3rd Order Quadrics, Bi-Cubic Patches, etc., without having to pay too much of an execution time penalty. Operation under VDM allows the user to optimize the ray trace algorithm for the type and size of the model to be executed. This altering of the voxel algorithm to match the model type allows the program to execute significantly faster than with any of the current methods used by other radiation programs such as ATRIUM, ESARAD, or TSS. A program ‘Voxel Wizard’ will be available in the near future that will do this optimization automatically. A new method of node assignment called ‘Master Nodes’ has also been developed that not only speeds up the program by another order of magnitude, but also allows for much faster and easier model building.
Meta TagsDetails
DOI
https://doi.org/10.4271/972440
Pages
13
Citation
Turner, R., Spencer, G., Baumeister, J., and Rigby, P., "NEVADA - A Comprehensive and Proven Radiation Heat Transfer Computer Software Package," SAE Technical Paper 972440, 1997, https://doi.org/10.4271/972440.
Additional Details
Publisher
Published
Jul 1, 1997
Product Code
972440
Content Type
Technical Paper
Language
English