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工程最適化とエンジニアリング
Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design
Khurana and Winarto (2010) The Aeronautical Journal 114(1159):3503
A novel variant of the direct numerical optimization approach was developed, validated and applied in the design of a low-speed airfoil using evolutionary algorithms. The convergence of the established optimal to an acceptable solution is verified by an innovative approach using Viscovery SOMine.
Application of Swarm Approach and Artificial Neural Networks for Airfoil Shape Optimization
Khurana, Winarto and Sinha (2008), 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, British Columbia, Canada
The direct numerical optimization approach for airfoil shape design was presented, with a discussion of the required integration of the following modules: a geometrical shape function; computational flow solver and search model for shape optimization. The Particle Swarm Optimization (PSO) algorithm was introduced as the search agent. Viscovery SOMine was applied to illustrate trade-offs between PSO search variables.
Airfoil Geometry Parameterization through Shape Optimizer and Computational Fluid Dynamics
Khurana, Winarto and Sinha (2008), 46th AIAA Aerospace Sciences Meeting and Exhibit, Nevada, USA
Design of a Re-Configurable Multi Mission Unmanned Aerial Vehicle (RC-MM-UAV) based on intelligent airfoil optimization was presented. Viscovery SOMine was used to analyze the effect of Yup on PARSEC airfoil geometry and aerodynamics to optimize the shape parameters of airfoil geometry.
Application Methods for Self Organizing Map in Process Imaging for Dynamic Behavior of Aerated Agitation Vessel
Matsumoto, Masumoto and Kuroda (2007) Proceedings of the 10th International Congress on Engineering Applications of Neural Networks, Thessaloniki, Greece
Viscovery SOMine was adapted to process imaging for dynamic behavior of aerated agitation vessel, and the application methods were investigated. In the application, the direct imaging by CCD video camera and the PIV technology were adopted. It was shown that the generated map and clusters could give process engineers useful information about the degree of spatial dispersion of bubbles and about the determination of design parameters.
Making sense of sensor data
Cook (2007) IEEE Pervasive Computing 6:105-108
The tools and algorithms for analyzing sensor data were examined using SOMine clustering.
Design exploration of high-lift airfoil using Kriging model and data mining technique
Kanazaki, Tanaka, Jeong and Yamamoto (2006) in "European Conference on Computational Fluid Dynamics, ECCOMAS CFD 2006", eds. Wesseling, Oñate, and Périaux, TU Delft, The Netherlands
Viscovery SOMine was used for a multi-objective design exploration of a three-element airfoil consisting of a slat, a main wing and a flap. A total of 90 sample points were evaluated using the Reynolds averaged Navier-Stokes simulation for the construction of the Kriging model. Self-organizing map analysis was used to obtain qualitative information of the design space.
Data mining for aerodynamic design space
Jeong, Chiba and Obayashi (2005) Journal of Aerospace Computing, Information Communication 2:452-469
Self-organizing map analysis was applied to data mining for aerodynamic design space to identify the effect of each design variable on objective functions. Self-organizing maps can visualize the trade-offs among objective functions, and this information will be helpful for designers to determine the final design from non-dominated solutions of multi-objective problems. These methods were applied to two design results: a fly-back booster in reusable launch vehicle design, which has 4 objective functions and 71 design variables, and a transonic airfoil design performed with the adaptive search region method.
Visualization and data mining of pareto solutions using self-organizing map
Obayashi and Sasaki (2003) Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science 2632:796-809
Viscovery SOMine was used to visualize tradeoffs of Pareto solutions in the objective function space for engineering design obtained by Evolutionary Computation. Based on the codebook vectors of cluster-averaged values of respective design variables obtained from the self-organizing map, the design variable space is mapped onto another self-organizing map. The resulting self-organizing map generates clusters of design variables, which indicate roles of the design variables for design improvements and tradeoffs. These processes, data mining of the engineering design were applied to supersonic wing and supersonic wing-fuselage design.
Optimizing recombinant microbial fermentation processes: an integrated approach
Cserjan-Puschmann, Grabherr, Striedner, Clementschitsch and Bayer (2002) BioPharm 26-34
A new strategy for controlling recombinant gene expression for improving efficiency, maximizing host vector exploitation, reducing costs, improving product consistency and accelerating product development was described. Viscovery SOMine was used to create a model for plasmid copy number estimates. The integrated approach was useful for producing recombinant proteins on an industrial scale and for designing experiments for targeted process optimization.
Classification of metal ions according to their complexing properties: a data-driven approach
Pletnev and Zernov (2002) Analytica Chimica Acta 455(1):131-142
Factor, cluster and self-organizing map analyses were applied to the stability constants of complexes of metal ions and hydrogen with 3960 ligands. Both direct clustering and clustering on the basis of factor analysis established the existence of six different classes of similar cations. The self-organizing map created with Viscovery SOMine visually represents that similarity.
Optimized Data Exploration of Recombinant Fermentations using Neural Network Simulations
Dürrschmid, Spannbauer, Striedner, Clementschitsch and Bayer (1998), 7th International Conference on Computer Application in Biotechnology, Osaka, Japan
A combination of self-organizing maps and radial basis function networks was presented as a powerful tool applied to fermentation data, enabling rapid recognition of interdependencies and subsequent modeling. Viscovery SOMine was used to model the appearance and concentration of signal molecules in order to get a better understanding of the relations between metabolic load and recombinant protein production, to make use of the cell´s synthetic capacity.
