scholarly journals Efficient wave-based acoustic material design optimization

2016 ◽  
Vol 78 ◽  
pp. 83-92 ◽  
Author(s):  
Nicolas Morales ◽  
Dinesh Manocha
2019 ◽  
Vol 10 (6) ◽  
pp. 749-765
Author(s):  
Efstathios E. Theotokoglou ◽  
Georgios Balokas ◽  
Evgenia K. Savvaki

Purpose The purpose of this paper is to investigate the buckling behavior of the load-carrying support structure of a wind turbine blade. Design/methodology/approach Experimental experience has shown that local buckling is a major failure mode that dominantly influences the total collapse of the blade. Findings The results from parametric analyses offer a clear perspective about the buckling capacity but also about the post-buckling behavior and strength of the models. Research limitations/implications This makes possible to compare the response of the different fiber-reinforced polymers used in the computational model. Originality/value Furthermore, this investigation leads to useful conclusions for the material design optimization of the load-carrying box girder, as significant advantages derive not only from the combination of different fiber-reinforced polymers in hybrid material structures, but also from Kevlar-fiber blades.


Polymers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Victor J. Romero ◽  
Alberto Sanchez-Lite ◽  
Gerard Liraut

The plastic industry is undergoing drastic changes, due to the customer sustainability perception of plastics, and the eruption of new processes (such 3D printing) and materials (such as renewably sourced resins). To enable a fast transition to high-quality, sustainable plastic applications, a specific methodology could be a key competitive advantage. This novel methodology is focused on improving the objectivity and efficiency of plastic production and the design review process. It is applicable to discrete optimization events in any product lifecycle milestone, from concept design to serial production stages. The methodology includes a natural way to capture plastic-related knowledge and trends, oriented towards building a dynamic “interaction matrix”, with a list of potential optimizations and their positive or negative impacts in a comprehensive set of multi-criteria evaluations. With an innovative approach, the matrix allows the possibility to incorporate a business strategy, which could be different at every lifecycle stage. The business strategy is translated from the common “verbal” definition into a quantitative set of “Target and Restrictions”, making it possible to detect and prioritize the best potential design optimization changes according to the strategy. This methodology helps to model and compare design alternatives, verify impacts in every evaluation criteria, and make robust and objective information-based decisions. The application of the methodology in real cases of plastic material design optimization in the automotive industry has provided remarkable results, accelerating the detection of improvement methods aligned with the strategy and maximizing the improvement in product competitiveness and sustainability. In comparison with the simultaneous application of existing mono-criteria optimization methodologies (such as “Design to Cost” or “Eco Design”) and subjective expert-based reviews, the novel methodology has a reduced workload and risks, confirming its potential for future application and further development in other polymer-based products, such as consumer goods or packaging.


2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Darren J. Hartl ◽  
Edgar Galvan ◽  
Richard J. Malak ◽  
Jeffrey W. Baur

The success of model-based multifunctional material design efforts relies on the proper development of multiphysical models and advanced optimization algorithms. This paper addresses both in the context of a structure that includes a liquid metal (LM) circuit for integrated cooling. We demonstrate for the first time on a complex engineering problem the use of a parameterized approach to design optimization that solves a family of optimization problems as a function of parameters exogenous to the subsystem of interest. This results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. We solve this specialized problem using the predictive parameterized Pareto genetic algorithm (P3GA) and show that it efficiently produces results that are accurate and useful for design exploration and reasoning. A “population seeding” approach allows an efficient multifidelity approach that combines a computationally efficient reduced-fidelity algebraic model with a computationally intensive finite-element model. Using data output from P3GA, we explore different design scenarios for the LM thermal management concept and demonstrate how engineers can make a final design selection once the exogenous parameters are resolved.


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