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CFD simulation of high-speed and highly reliable plate counterflow heat exchangers.

Webinar on June 24, 2021 (Thursday) at 11:00 PM! About creating meshes for plate counterflow heat exchangers, etc.

Predicting the performance of heat exchangers is often a challenging task. To use simplified methods such as the NTU (Number Transfer Units) method, a series of assumptions must first be made. By leveraging CFD, it is possible to analyze the flow details more accurately. With OMNIS, you can easily set up meshes and simulations to analyze this type of problem. See how Cadence's OMNIS provides fast and reliable results and contributes to the development of heat exchangers. In the webinar, you will learn about mesh creation and simulation for plate counterflow heat exchangers. [Topics covered in the webinar] - Multi-domain mesh creation, combining S2V and V2V approaches - Conjugate heat transfer simulation using OMNIS/Open-PBS *For more details, please refer to the related link page or feel free to contact us.

Company：日本ケイデンス・デザイン・システムズ社
Price：Other

Technical Seminar

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The importance of full-scale CFD simulation for ships.

Hybrid calculation method for reusing CFD information and metamodeling.

This paper demonstrates that the differences between model-scale flow and full-scale flow have significant impacts. It has been found that in various projects, the ranking of design candidates in optimization or the operating conditions in trim optimization differ between model-scale and full-scale calculations. The differences between model-scale and full-scale are particularly evident in the relatively different boundary layers and breaking waves behind the transom stern. Therefore, in simulation-based design projects, full-scale analysis is considered preferable. In rule-based optimization, the choice of approach becomes more complex due to time and computational resource constraints. The hybrid calculation method that reuses CFD information and metamodeling is seen as a technology that paves the way for using full-scale CFD as a standard option in optimization.