The analytical model [Moreno and Sun, Opt. Express 16, 1808-1819 (2008)] is applied to LED with attached secondary optics. It is shown that a slightly modified model using only three cosine power functions can be used for the cases with symmetric radiation pattern and that good fitting on realistic examples can be achieved with several standard optimization algorithms including local search heuristics and genetic algorithm.
When comparing various metaheuristics, even asking a fair and formally consistent question is often difficult. Having this in mind, we provide some further evidence that simple local search heuristics may be at least a very competitive choice. On a dataset from an industrial application, i.e., construction of an optical system, we compare local search and genetic algorithms. In our case, the best performance is obtained by a combination of both heuristics.
In the design of optical systems based on light emitting diode (LED) • The effects of a numerical method in conjunction with heuristics are studied.• Algorithms are developed with the use of C++ programing language.• The success of developed algorithms is tested on real and artificial datasets.• The results are statistically evaluated.• The numerical Newton's method prevails on both datasets, and provides substantial runtime shortening.
The rapid worldwide evolution of LEDs as light sources has brought new challenges, which means that new methods are needed and new algorithms have to be developed. Since the majority of LED luminaries are of the multi-source type, established methods for the design of light engines cannot be used in the design of LED light engines. This is because in the latter case what is involved is not just the design of a good reflector or projector lens, but the design of several lenses which have to work together in order to achieve satisfactory results. Since lenses can also be bought off the shelf from several manufacturers, it should be possible to combine together different off the shelf lenses in order to design a good light engine. However, with so many different lenses to choose from, it is almost impossible to find an optimal combination by hand, which means that some optimization algorithms need to be applied. In order for them to work properly, it is first necessary to describe the input data (i.e. spatial light distribution) in a functional form using as few as possible parameters. In this paper the focus is on the approximation of the input data, and the implementation of the well-known mathematical procedure for the separation of linear and nonlinear parameters, which can provide a substantial increase in performance.
-Integrated modeling (IM) of present experiments and future tokamak reactors requires the provision of computational resources and numerical tools capable of simulating multiscale spatial phenomena as well as fast transient events and relatively slow plasma evolution within a reasonably short computational time. Recent progress in the implementation of the new computational resources for fusion applications in Europe based on modern supercomputer technologies (supercomputer MARCONI-FUSION), in the optimization and speedup of the EU fusion-related first-principle codes, and in the *E-mail: Irina.Voitsekhovitch@ukaea.uk This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
FUSION SCIENCE AND TECHNOLOGY
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.