24,575 research outputs found
Eulerian and modified Lagrangian approaches to multi-dimensional condensation and collection
Turbulence is argued to play a crucial role in cloud droplet growth. The
combined problem of turbulence and cloud droplet growth is numerically
challenging. Here, an Eulerian scheme based on the Smoluchowski equation is
compared with two Lagrangian superparticle (or su- perdroplet) schemes in the
presence of condensation and collection. The growth processes are studied
either separately or in combination using either two-dimensional turbulence, a
steady flow, or just gravitational acceleration without gas flow. Good
agreement between the differ- ent schemes for the time evolution of the size
spectra is observed in the presence of gravity or turbulence. Higher moments of
the size spectra are found to be a useful tool to characterize the growth of
the largest drops through collection. Remarkably, the tails of the size spectra
are reasonably well described by a gamma distribution in cases with gravity or
turbulence. The Lagrangian schemes are generally found to be superior over the
Eulerian one in terms of computational performance. However, it is shown that
the use of interpolation schemes such as the cloud-in-cell algorithm is
detrimental in connection with superparticle or superdroplet approaches.
Furthermore, the use of symmetric over asymmetric collection schemes is shown
to reduce the amount of scatter in the results.Comment: 36 pages, 17 figure
Hybridization of multi-objective deterministic particle swarm with derivative-free local searches
The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts
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