Real Design Problems Research Articles

Enhancement of Systematic Design Processing by Diagrams

Diagrams play a significant role in most knowledge presentation areas. This is especially the case in disciplines concerned with conceptual design where data visualization, processing, documentation and presentation require graphic illustration to express their underlying tacit aspects. Despite their significance, the visual tools available in the toolbox of designers to help them apply generative processes are extremely limited. This paper introduces some conceptual and operative diagrams that aim to aid in the visualization of various stages of the design derivation and development processes. The structure underlying the proposed diagrams represents a basis for a diagrammatic framework for architectural designing that may help systematically induce form development, organize design sequence and externalize concept generative forces. The purpose of this framework is not to dictate a specific sequence of design, but rather to help designers process their designs in ways more structured than trial/error-based ones. The scope of the proposed framework includes all phases of designing that include the input, output, presentation and assessment activities, with a focus on the core design processing and form-making activities. To test the applicability of this framework, it was implemented to solve a real design problem in an architectural design studio. The feedback of the diagrammatic framework users was analyzed by a survey about its implementation in comparison to conventional design approaches. The results of this empirical study are reported and discussed. The results appear to support the role of diagrams in the enhancement of designing and learning processes. However, this contribution of diagrams to designing and learning needs further applications in various design settings to assess its limitations and strengths.

Multi-objective design optimisation: getting more for less

Multi-objective optimisation can help civil engineers achieve higher performance for lower costs in their designs. This is true whether ‘performance’ applies to structural strength or energy use, or whether ‘cost’ measures financial outlay or occupant satisfaction: if it can be quantified it can be optimised in some form. By exploring trade-offs between conflicting objectives and constraints, multi-objective optimisation enables informed decision-making. This paper outlines the principles and benefits of multi-objective optimisation and the means of implementation. The complementary aspects of parametric modelling and optimisation are discussed as an aid to the flexible design of buildings and structures. A range of real design problems are considered, including structural and environmental examples.

Multi-service multi-facility network design under uncertainty

The problem of designing high speed networks using different modules of link capacities, in the same model, in order to meet uncertain demands obtained from different probability distribution functions (PDF) is a very hard and challenging real network design problem. The novelty of the new model, compared to previous ones, is to allow installing more than one module per link having equal or different capacities. Moreover, the scenarios of traffic can be generated, according to practical observations, from the main classes of uncertain demands (multi-service) simulated from different PDFs, including heavy tailed ones. These classes of traffic are considered simultaneously for the scenario generation, different from related works in the literature that use only one probability distribution function to simulate the scenarios of traffic. In this work we present the problem formulation and report computational results using branch-and-bound and L-shaped decomposition solution approaches. We consider in the same model up to three different types of modular capacities (multi-facility), since it seems that using more than this can lead to an intractable model. The objective is to minimize penalty (in case of unmet demands) and investment costs. We obtain confidence intervals (with 95% of covering rate) on the expected optimal solution value for the resulting two-stage stochastic integer-modular problem and discuss when they are meaningful. Numerical experiments show that our model can handle up to medium real size instances.

Parameter Exploration in Science and Engineering Using Many-Task Computing

Robust scientific methods require the exploration of the parameter space of a system (some of which can be run in parallel on distributed resources), and may involve complete state space exploration, experimental design, or numerical optimization techniques. Many-Task Computing (MTC) provides a framework for performing robust design, because it supports the execution of a large number of otherwise independent processes. Further, scientific workflow engines facilitate the specification and execution of complex software pipelines, such as those found in real science and engineering design problems. However, most existing workflow engines do not support a wide range of experimentation techniques, nor do they support a large number of independent tasks. In this paper, we discuss Nimrod/K - a set of add in components and a new run time machine for a general workflow engine, Kepler. Nimrod/K provides an execution architecture based on the tagged dataflow concepts, developed in 1980s for highly parallel machines. This is embodied in a new Kepler Director” that supports many-task computing by orchestrating execution of tasks on on clusters, Grids, and Clouds. Further, Nimrod/K provides a set of Actors” that facilitate the various modes of parameter exploration discussed above. We demonstrate the power of Nimrod/K to solve real problems in cardiac science.

Supporting product architecture design using computational design synthesis with network structure constraints

A product’s architecture can affect many aspects of product and process quality, from technical performance to the design effort required, production costs and satisfaction of later lifecycle requirements. This paper explores how computational tools can augment creative methods in product architecture design. Based on an empirical study aiming to understand the context of product architecture design, a new computational method is proposed to support this activity. In the method, product architectures—networks of components linked by connections—can be synthesised using constraints on the structure of the network to define the set of ‘realisable’ architectures for a product. An example illustrates how the method might be used on a real design problem, including the construction of an appropriate set of network structure constraints and the identification of promising architectures from the synthesis results. Preliminary evaluation of the method’s usability, assessed through a laboratory experiment, and its utility, assessed through application to a real historical design problem, supported by initial validation by an engineer from the case study company, suggests that the method has value for engineering design practice.

Optimal Design of Regional Wastewater Pipelines and Treatment Plant Systems

This manuscript describes the application of a genetic algorithm model for the optimal design of regional wastewater systems comprised of transmission gravitational and pumping sewer pipelines, decentralized treatment plants, and end users of reclaimed wastewater. The algorithm seeks the diameter size of the designed pipelines and their flow distribution simultaneously, the number of treatment plants and their size and location, the pump power, and the required excavation work. The model capabilities are demonstrated through a simplified example application using base runs and sensitivity analyses. Scaling of the proposed methodology to real life wastewater collection and treatment plants design problems needs further testing and developments. The model is coded in MATLAB using the GATOOL toolbox and is available from the authors.

Automated Multigravity Assist Trajectory Planning with a Modified Ant Colony Algorithm

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems.

Engagement, Ecology, and Design Education: Outreach Work of the Tulane City Center

of Architecture, the Tulane City Center (TCC) supports teams of architecture faculty members and students, who work with local nonprofit groups to provide design and “visioning” help to underserved neighborhoods of the Greater New Orleans community. Since the center’s establishment less than two months after the devastating floods that followed Hurricane Katrina, Tulane School of Architecture students and faculty have collaborated on the design and construction of dozens of innovative and award-winning projects that support the city’s recovery. These projects include six ecologically sound single-family houses, several green markets and their related “urban agriculture” facilities, shade and watercollection pavilions that promote neighborhood communication and environmental education, and more than 50 other projects that range from neighborhood planning to the development of cooperative grocery stores in urban areas that lack access to healthful food. Tulane City Center projects are designed with extensive and diverse community input that is gathered through formal community meetings and informal neighborhood events, which often involve food and music (we are in New Orleans, after all). The design proposals are then published in “visioning booklets” that enable prospective supporters and constituents to read about the goals and ambitions. Finally, many of the projects are constructed by groups of students who gain hands-on experience in the execution of progressive design concepts and new construction technologies. This work gives students a unique educational experience—applying classroom lessons to real design problems and engaging a broad range of issues to connect the built and natural environments.

A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation

Recent advances in coupling novel optimization methods to large-scale computing problems have opened the door to tackling a diverse set of physically realistic engineering design problems. A large computational overhead is associated with computing the cost function for most practical problems involving complex physical phenomena. Such problems are also plagued with uncertainties in a diverse set of parameters. We present a novel stochastic derivative-free optimization approach for tackling such problems. Our method extends the previously developed surrogate management framework (SMF) to allow for uncertainties in both simulation parameters and design variables. The stochastic collocation scheme is employed for stochastic variables whereas Kriging based surrogate functions are employed for the cost function. This approach is tested on four numerical optimization problems and is shown to have significant improvement in efficiency over traditional Monte-Carlo schemes. Problems with multiple probabilistic constraints are also discussed.

Automating the conceptual design process: “From black box to component selection”

AbstractConceptual design is a vital part of the design process during which designers first envision new ideas and then synthesize them into physical configurations that meet certain design specifications. In this research, a suite of computational tools is developed that assists the designers in performing this nontrivial task of navigating the design space for creating conceptual design solutions. The methodology is based on automating the function-based synthesis paradigm by combining various computational methods. Accordingly, three nested search algorithms are developed and integrated to capture different design decisions at various stages of conceptual design. The implemented system provides a method for automatically generating novel alternative solutions to real design problems. The application of the approach to the design of an electromechanical device shows the method's range of capabilities and how it serves as a comparison to human conceptual design generation and as a tool suite to complement the skills of a designer.

English

The design of ultra supercritical and Advanced Ultra Supercritical steam turbines is posing challenges to design Engineers. Also retrofitting old blades of steam turbine with new blades to get additional power from the existing thermal station needs meticulous design of blades for optimum performance. One such realistic design problem has been chosen for the design of supercritical steam turbine blades for a given pressure drop. TCL/TK code has been used for the given input data to obtain airfoil data with the help of Baziers curve technique. Bezier knots are moved to change the camber of the airfoil and airfoil has been generated with the modified data used for steam turbine blade. The effect of stagger angle has been studied by using a commercial software package. Analysis has been performed by changing stagger angle from 0 to 550. Six different cases are studied and best one was selected to give optimum performance.

Real projects versus instructed protocols: a compromised profile for proposing architectural design teaching projects

In teaching architectural design, schools have various perspectives concerning the projects taught in design studios. Some argue that students should challenge real design problems: the real project (RP) approach. Others claim that applying hypothetical projects could be more viable if they fit within the teaching protocol: the instructed protocol (IP) approach. Based on observations and a field survey comparing the RP approach, applied in Kunstuniversitat-Linz, Austria, and the IP approach, applied in Assiut University, Egypt, it was argued that both approaches have advantages and disadvantages. To reach a compromise, a teaching profile called the instructed protocol real projects (IPRP) profile was proposed. The profile has two components: an instructed teaching protocol and four different scenarios for proposing educational design projects.

Structural analysis of Pareto-optimal solution sets for multi-objective optimization: An application to outer window design problems using Multiple Objective Genetic Algorithms

In single-objective optimization problems, with only one optimal design objective, the absolute optimal solution to maximize/minimize the objective function can be determined. However, in most real design problems, the optimization problems are multi-objective, where two or more independent design objectives must be optimized simultaneously, and no single absolute optimal solution necessarily exists. In these cases, it is helpful for designers to recognize the range of alternative solutions that exist in Pareto-optimal sets and choose an acceptable solution from among them. In this paper, the authors carried out multi-objective optimization using Multiple Objective Genetic Algorithms through a real case study involved in indoor environmental design – the design of outer windows. Then the authors analyzed structure of Pareto-optimal solution sets. Here we present the analysis process as well as the case study details, and show how the method proposed here is effective at finding an acceptable solution for multi-objective optimization problems.

A process-centric ship design management framework

As the concept of concurrent engineering has emerged along with support for optimization techniques, lots of endeavors have been made to apply optimization techniques to real design problems for holistic decision-making. Even if the range of design problems to which optimization is applicable has been extended, most ship designs use an iterative and manual approach due to the difficulties of seamless integration of all related design activities. This paper proposes a process-centric management framework for the preliminary ship design process depending on these approaches. Requirements for the framework are generated based on the features of the ship design process first. The proposed framework consists of both process scheduling and process management parts. Each of these modules is divided into submodules, and the modules and their interactions are elaborated to reflect actual design practice. The designed framework is embodied within a workflow system and its usefulness examined through a pilot project.

New global optimization methods for ship design problems

The aim of this paper is to solve optimal design problems for industrial applications when the objective function value requires the evaluation of expensive simulation codes and its first derivatives are not available. In order to achieve this goal we propose two new algorithms that draw inspiration from two existing approaches: a filled function based algorithm and a Particle Swarm Optimization method. In order to test the efficiency of the two proposed algorithms, we perform a numerical comparison both with the methods we drew inspiration from, and with some standard Global Optimization algorithms that are currently adopted in industrial design optimization. Finally, a realistic ship design problem, namely the reduction of the amplitude of the heave motion of a ship advancing in head seas (a problem connected to both safety and comfort), is solved using the new codes and other global and local derivative-free optimization methods. All the numerical results show the effectiveness of the two new algorithms.

Development of a multidisciplinary optimization process for designing optimal pharmaceutical formulations with constrained experimental regions

Pharmaceutical formulation design with the development of analytical technologies and methodologies is an integral part of a drug’s research and development. Conventional pharmaceutical formulation design problems are typically studied under the assumption of an unconstrained experimental region with a single quality characteristic. In real world pharmaceutical formulation design problems, however, we are often faced with multiple quality characteristics with a constrained experimental region due to physical limitations. In order to address these needs, we propose a multidisciplinary optimization process for the optimal design of pharmaceutical formulations by developing a response surface simplex design (RSSD) and a response surface D-optimal design (REDD) for experimental modeling and by developing a partitioned composite desirability function (PCDF) for multidisciplinary optimization. Two actual case studies are also presented here for model verification purposes.

Reliable System-Optimal Network Design

It is critical to account for uncertainty in the design of transportation networks. Various models assuming both user-optimal and system-optimal behavior (as a computationally viable proxy for the more realistic user-optimal design problem) have been proposed. Most of these models do not provide any form of probabilistic guarantee for the obtained capacity expansion decisions. However, for system reliability, it is often useful to know how likely it is that the total system travel time would deviate from a certain value if the prescribed solutions from a specific model are implemented. A new mean-variance type of system-optimal network design model with probabilistic guarantees on systemwide travel time is proposed. The proposed model has several unique features. First, uncertainty in the link performance function is considered. This uncertainty is a result of capacity uncertainty as well as fundamental uncertainty about the functional form of the link performance function itself. Second, instead of imposing an explicit chance constraint–which in general would lead to nonconvexity–probabilistic guarantees on the obtained system travel time are obtained implicitly. More specific, the model yields a one-sided confidence interval for the total systemwide travel time that has an a priori specified confidence level. Finally, it is not necessary to specify an explicit probability distribution to model the uncertainty. Instead, the proposed model is distribution free in that any symmetric probability distribution suffices. Numerical results are presented and discussed.

Optimizing Product Line Designs: Efficient Methods and Comparisons

We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration. We then use this guarantee of global optimality to benchmark the performance of more practical heuristic methods. We use two sources of data: (1) a conjoint study previously conducted for a real product line design problem, and (2) simulated problems of various sizes. For both data sources, several of the heuristic methods consistently find optimal or near-optimal solutions, including simulated annealing, divide-and-conquer, product-swapping, and genetic algorithms.

Computational fluid dynamics-based multiobjective optimization of a surface combatant using a global optimization method

The main objective of this article is to describe the development of two advanced multiobjective optimization methods based on derivative-free techniques and complex computational fluid dynamics (CFD) analysis. Alternatives for the geometry and mesh manipulation techniques are also described. Emphasis is on advanced strategies for the use of computer resource-intensive CFD solvers in the optimization process: indeed, two up-to-date free surface-fitting Reynolds-averaged Navier-Stokes equation solvers are used as analysis tools for the evaluation of the objective function and functional constraints. The two optimization methods are realized and demonstrated on a real design problem: the optimization of the entire hull form of a surface combatant, the David Taylor Model Basin—Model 5415. Realistic functional and geometrical constraints for preventing unfeasible results and to get a final meaningful design are enforced and discussed. Finally, a recently proposed verification and validation methodology is applied to assess uncertainties and errors in simulation-based optimization, based on the differences between the numerically predicted improvement of the objective function and the actual improvement measured in a dedicated experimental campaign. The optimized model demonstrates improved characteristics beyond the numerical and experimental uncertainty, confirming the validity of the simulation-based design frameworks.

Advances in computational contact mechanics

Great strides have recently been made in the application of computational mechanics to the design of highly complex engineering systems. It has now become abundantly clear that advanced modelling techniques are central to the competitiveness of the industrialised nations. Excellent examples of this assertion are the computer-integrated design of the recent Boeing 777 aircraft, the collapsible foam-filled structures for the car of the next century and new prosthetic implants for Rheumatoid Arthritis. It is with this in mind that the author focuses his attention to a class of problems where contact mechanics plays a major role in dictating the mechanical integrity of the component/system. Three aspects of the current study are accordingly examined. The first is concerned with the development of the appropriate dynamic variational inequalities expressions, which are capable of the accurate and consistent representation of contact problems. The second is concerned with the development of robust solution algorithms that guarantee the accurate imposition of the kinematic contact constraint and avoid interpenetration. The third is concerned with the application of the developed algorithms to realistic design problems involving intricate mechanical and biomechanical systems.