Abstract
A simple yet effective optimization technique is developed to solve nonlinear conjugate heat transfer. The proposed Nonlinear Optimization with Replacement Strategy (NORS) is a mutation of several existing optimization processes. With the improvements of 3D metal printing of turbine components, it is feasible to have film holes with unconventional diameters, as these holes are created while printing the component. This paper seeks to optimize each film hole diameter at the leading edge of a turbine vane to satisfy several optimum thermal design objectives with given design constraints. The design technique developed uses linear regression-based machine learning model and further optimizes with strategic improvement of the training dataset. Optimization needs cost and benefit criteria are used to base its decision of success, and cost is minimized with maximum benefit within given constraints. This study minimizes the coolant flow (cost) while satisfying the constraints on average metal temperature and metal temperature variations (benefits) that limit the useful life of turbine components. The proposed NORS methodology provides a scientific basis for selecting design parameters in a nonlinear design space. This model is also a potential academic tool to be used in thesis works without demanding extensive computing resources.
Highlights
The inspiration for this work came from the existing need to find the optimum film hole diameter with available correlations on flow and heat transfer
Without getting into too many mathematical formulations, the proposed Nonlinear Optimization with Replacement Strategy (NORS) can use existing optimization routines and switch between model-based and model-free machine learning domains to get a better design than what could be obtained with random selection of design parameters
We have developed a technique with linear regression and design of experiments to improve the conjugate heat transfer in a leading edge of a gas turbine airfoil
Summary
The inspiration for this work came from the existing need to find the optimum film hole diameter with available correlations on flow and heat transfer. The procedure is simple, does not require significant computing power, routines are available as open access, and it can be applied in any engineering or financial analysis, where the input and output have established correlations available. Significant proportion of thermal sciences masters’ students used nanofluid to graduate and some of the project topics on that were listed by Saidur et al (2011) [1]. Increase in thermal conductivity by adding nanoparticles in fluids is an interesting concept, has a catchy name, and easy to implement in labs. The process: take some nanopowder, mix with water or other fluid, and run convective heat transfer experiments
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
