Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle

The use of response surface models and kriging models are compared for approximating non-random, deterministic computer analyses. After discussing the traditional response surface approach for constructing polynomial models for approximation, kriging is presented as an alternative statistical-based approximation method for the design and analysis of computer experiments. Both approximation methods are applied to the multidisciplinary design and analysis of an aerospike nozzle which consists of a computational fluid dynamics model and a finite element analysis model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations. Four optimization problems are formulated and solved using both approximation models. While neither approximation technique consistently outperforms the other in this example, the kriging models using only a constant for the underlying global model and a Gaussian correlation function perform as well as the second order polynomial response surface models

Permanent Draft Genome Sequence of Frankia sp. Strain BR, a Nitrogen-Fixing Actinobacterium Isolated from the Root Nodules of Casuarina equisetifolia

Frankia sp. strain BR is a member of Frankia lineage Ic and is able to reinfect plants of the Casuarinaceae family. Here, we report a 5.2-Mbp draft genome sequence with a G+C content of 70.0% and 4,777 candidate protein-encoding genes

A Methodology to Support Product Family Redesign using Genetic Algorithm and Commonality Indices,"

ABSTRACT Many of today's manufacturing companies are using platform-based product development to realize families of products with sufficient variety to meet customers' demands while keeping costs relatively low. The challenge when designing or redesigning a product family is in resolving the tradeoff between product commonality and distinctiveness. Several methodologies have been proposed to redesign existing product families; however, a problem with most of these methods is that they require a considerable amount of information that is not often readily available, and hence their use has been limited. In this research, we propose a methodology to help designers during product family redesign. This methodology is based on the use of a genetic algorithm and commonality indices -metrics to assess the level of commonality within a product family. Unlike most other research in which the redesign of a product family is the result of many human computations, the proposed methodology reduces human intervention and improves accuracy, repeatability, and robustness of the results. Moreover, it is based on data that is relatively easy to acquire. As an example, a family of computer mice is analyzed using the Product Line Commonality Index. Recommendations are given at the product family level (assessment of the overall design of the product family), and at the component level (which components to redesign and how to redesign them). The methodology provides a systematic methodology for product family redesign

DETC2006-99538 ASSESSING AND INCREASING PRODUCT AND FAMILY DIFFERENTIATION IN THE MARKET

ABSTRACT 1 To help guarantee profit and stability in today's global market, companies must focus on the differentiation of their products. Successfully differentiated products will attract customers, generate revenue and benefit the brand image, whereas a banal product can lose money and leave a bad impression in the market. Many large companies have recently lost significant market share in part due to poor product differentiation. This paper introduces four indices to assess this differentiation at two levels-family and market-based on product function and function attributes. At the family level, the Product Differentiation Index (PDI) assesses the differentiation between a product and other products in the rest of the family and also the differentiation within the family. At the market level, the Family Differentiation Index (FDI), Family Coverage Index (FCI), and Family Un-coverage Index (FUI) assess the differentiation, the coverage, and the uncoverage of a family with another, and/or with the rest of the market, respectively. These indices help designers and marketers evaluate the positioning of their products and support product family planning. A case study involving two competitive single-use camera families is presented

Product Family Design Knowledge Representation, Aggregation, Reuse, and Analysis

A flexible information model for systematic development and deployment of product families during all phases of the product realization process is crucial for product-oriented organizations. In current practice, information captured while designing products in a family is often incomplete, unstructured, and is mostly proprietary in nature, making it difficult to index, search, refine, reuse, distribute, browse, aggregate, and analyze knowledge across heterogeneous organizational information systems. To this end, we propose a flexible knowledge management framework to capture, reorganize, and convert both linguistic and parametric product family design information into a unified network, which is called a networked bill of material (NBOM) using formal concept analysis (FCA); encode the NBOM as a cyclic, labeled graph using the Web Ontology Language (OWL) that designers can use to explore, search, and aggregate design information across different phases of product design as well as across multiple products in a product family; and analyze the set of products in a product family based on both linguistic and parametric information. As part of the knowledge management framework, a PostgreSQL database schema has been formulated to serve as a central design repository of product design knowledge, capable of housing the instances of the NBOM. Ontologies encoding the NBOM are utilized as a metalayer in the database schema to connect the design artifacts as part of a graph structure. Representing product families by preconceived common ontologies shows promise in promoting component sharing, and assisting designers search, explore, and analyze linguistic and parametric product family design information. An example involving a family of seven one-time-use cameras with different functions that satisfy a variety of customer needs is presented to demonstrate the implementation of the proposed framework

DETC2003/DAC-48759 ANALYSIS OF SUPPORT VECTOR REGRESSION FOR APPROXIMATION OF COMPLEX ENGINEERING ANALYSES

ABSTRACT A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a "model of a model" that provides a fast surrogate for a computationally expensive computer code. Common metamodeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we present Support Vector Regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is presented, and SVR approximations are compared against the aforementioned four metamodeling techniques using a testbed of 22 engineering analysis functions. SVR achieves more accurate and more robust function approximations than these four metamodeling techniques and shows great promise for future metamodeling applications

DETC2003/DAC-48759 ANALYSIS OF SUPPORT VECTOR REGRESSION FOR APPROXIMATION OF COMPLEX ENGINEERING ANALYSES

ABSTRACT A variety of metamodeling techniques have been developed in the past decade to reduce the computational expense of computer-based analysis and simulation codes. Metamodeling is the process of building a "model of a model" that provides a fast surrogate for a computationally expensive computer code. Common metamodeling techniques include response surface methodology, kriging, radial basis functions, and multivariate adaptive regression splines. In this paper, we present Support Vector Regression (SVR) as an alternative technique for approximating complex engineering analyses. The computationally efficient theory behind SVR is presented, and SVR approximations are compared against the aforementioned four metamodeling techniques using a testbed of 22 engineering analysis functions. SVR achieves more accurate and more robust function approximations than these four metamodeling techniques and shows great promise for future metamodeling applications