An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model

dc.contributor.authorDomanski, Piotr A.
dc.contributor.authorYashar, David
dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.description.abstractOptimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For this reason, more intelligent search techniques are needed. This paper presents and evaluates a novel optimization system, called ISHED1 (Intelligent System for Heat Exchanger Design). This system uses a recently developed non-Darwinian evolutionary computation method to seek evaporator circuit designs that maximize the capacity of the evaporator under given technical and environmental constraints. Circuitries were developed for an evaporator with three depth rows of 12 tubes each, based on optimizing the performance with uniform and non-uniform airflow profiles. ISHED1 demonstrated the capability to design an optimized circuitry for a non-uniform air distribution so the capacity showed no degradation over the traditional balanced circuitry design working with a uniform airflow.
dc.format.extent2598 bytes
dc.format.extent299358 bytes
dc.identifier.citationDomanski, P. A., Yashar, D., Kaufman, K. and Michalski, R. S., "An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model," Reports of the Machine Learning and Inference Laboratory, MLI 04-1, George Mason University, Fairfax, VA, February, 2004.
dc.relation.ispartofseriesP 04-1
dc.relation.ispartofseriesMLI 04-1
dc.subjectMachine learning
dc.subjectEvolutionary computation
dc.subjectEngineering design
dc.subjectLearnable evolution model
dc.subjectMultistrategy learning
dc.titleAn Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model
dc.typeTechnical report


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