Design Space Approach Research Articles

Design of solar thermal systems utilizing pressurized hot water storage for industrial applications

A large number of industrial processes demand thermal energy in the temperature range of 80–240 °C. In this temperature range, solar thermal systems have a great scope of application. However, the challenge lies in the integration of a periodic, dilute and variable solar input into a wide variety of industrial processes. Issues in the integration are selection of collectors, working fluid and sizing of components. Application specific configurations are required to be adopted and designed. Analysis presented in this paper lays an emphasis on the component sizing. The same is done by developing a design procedure for a specific configuration. The specific configuration consists of concentrating collectors, pressurized hot water storage and a load heat exchanger. The design procedure follows a methodology called design space approach. In the design space approach a mathematical model is built for generation of the design space. In the generation of the design space, design variables of concern are collector area, storage volume, solar fraction, storage mass flow rate and heat exchanger size. Design space comprises of constant solar fraction curves traced on a collector area versus storage volume diagram. Results of the design variables study demonstrate that a higher maximum storage mass flow rates and a larger heat exchanger size are desired while limiting storage temperature should be as low as possible. An economic optimization is carried out to design the overall system. In economic optimization, total annualized cost of the overall system has been minimized. The proposed methodology is demonstrated through an illustrative example. It has been shown that 23% reduction in the total system cost may be achieved as compared to the existing design. The proposed design tool offers flexibility to the designer in choosing a system configuration on the basis of desired performance and economy.

A configuration design based method for platform commonization for product families

Product families help companies reach customers in several different markets, lessen the time needed to develop new products, and reduce costs by sharing common components among many products. The product platform can be considered as a set of technologies, components, or functions, and their arrangements, that are utilized for more than one product. Configuration design focuses on the components in a product and their connections and relationships. Discrete, combinatorial design spaces are used to model design requirements regarding physical connections, module partitions, and assembly sequences for the product family. To ensure that products satisfy all design requirements, it is necessary to combine these design spaces into a common configuration space into which all requirements can be mapped. This paper presents computational methods for modeling and combining design spaces so those configurations can be identified that satisfy all constraints. A new representation of assembly sequences facilitates the development of an assembly design space, elements of which can be enumerated readily. Because the size of the combinatorial design spaces can become quite large, computational efficiency is an important consideration. A new designer guided method, called the partitioning method, is presented for decomposing configuration design problems in a hierarchical manner that enables significant reductions in design space sizes. An example of a family of automotive underbodies illustrates the application of the discrete design space approach to develop a common platform.

Reasonable Design Space Approach to Response Surface Approximation

Response surface approximations have become very popular as a tool for multidisciplinary optimization. However, the accuracy of these models is a major concern. One method to increase the accuracy of response surface approximations is to construct them only for the portion of the design space that represents reasonable designs. Our paper describes the methodology underlying this reasonable design space approach. This methodology is illustrated using two examples involving multidisciplinary optimization of a high-speed civil transport model. In these examples, low-fidelity analyses are used to estimate the boundary of the reasonable design space. Within the reasonable design space, response surface models are constructed, based on results from higher-fidelity analyses. The accuracy of the response surface models created with and without using the reasonable design space approach is compared, and the advantages of employing response surface models created via the reasonable design space approach are demonstrated.