Simulation Based Design

This paper presents a method for parameter design of mechanical products based on a set-based approach. Set-based concurrent engineering emphasises on designing in a multi-stakeholder environment with concurrent involvement of the stakeholders in the design process. It also encourages flexibility in design through communication in terms of ranges instead of fixed point values and subsequent alternative solutions resulting from intersection of these ranges. These alternative solutions can then be refined and selected according to the designers' preferences and clients' needs. This paper presents a model and tools for integrated flexible design that take into account the manufacturing variations as well as the design objectives for finding inherently robust solutions using QCSP transformation through interval analysis. In order to demonstrate the approach, an example of design of rigid flange coupling with a variable number of bolts and a choice of bolts from ISO M standard has been resolved and demonstrated.

Companies struggle with identifying relevant sustainability aspects strategically, assessing alternative solutions quantitatively, and making trade-offs. This paper reports results from a prescriptive study with an aerospace company, and presents the Sustainability Criteria And product life-cycle Data Simulation (SCADS) approach. Based on strategic integration of sustainability indicators, this approach aims to enable visualisation and comparison of the sustainability implications of different concepts in early design phases of product development.

Companies struggle with identifying relevant sustainability aspects strategically, assessing alternative solutions quantitatively, and making trade-offs. This paper reports results from a prescriptive study with an aerospace company, and presents the Sustainability Criteria And product life-cycle Data Simulation (SCADS) approach. Based on strategic integration of sustainability indicators, this approach aims to enable visualisation and comparison of the sustainability implications of different concepts in early design phases of product development.

Most engineering design processes rely on physical or numerical experiments to evaluate the candidate design performances. When the design space is large or when the model response is complex, the possibly high computational expense poses a challenge since the number of simulations is usually limited by time or financial cost. This is even more of a challenge when performing global optimization as many evaluations are required. Hydrofoil optimisation for naval applications is typically facing these limitations. One way to address this limitation is to build a simplified model of the actual model from responses of experiments. Meta-modelling is the process of generating such models of models or meta-models. This process is also referred in engineering as surrogate modelling. Strategies involving surrogate models are used in a wide range of engineering fields to efficiently address computational cost limitations. Evaluations of a surrogate model are cheap and can be used for visualiza...

Integrating design models with operational simulations allow engineers to obtain quantitative results about design performance and incorporate the results in the design process far in advance of implementation. To demonstrate the technological maturity of modeling and simulation-based product design, this pilot effort developed executable SysML design models for a 1553 avionics architecture. These models were integrated with behavioral/performance models of a production model aircraft operating in a digital environment. The 1553 model provides a link between a model of new avionics functionality (e.g., terrain following, obstacle avoidance) with a model of an existing avionics system. The 1553 design model represents the behavior of message flow between devices such as sensors and displays and specifies actual or desired performance such as message transmission times. The 1553 design model hosted actual terrain following software and the integrated simulations were used to determine...

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

An environment for simulation-based analysis, design, and product development is presented in this article. Specifically, we integrate the analytical target cascading (ATC) methodology for optimal design with the Active Mediator Object System (AMOS) II information management tool to represent and exchange engineering data in distributed and heterogeneous environments. The ATC process is applied to the design optimization problem of a vehicle model with many design variables. The vehicle model is ...

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

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-This work has been partially supported by the Ministero delle Infrastrutture e dei Trasporti in the framework of the research plan "

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

The curse of dimensionality represents a relevant issue in simulation-based shape optimization, especially when complex physics and high-fidelity computationally-expensive solvers are involved in the process and a global optimum is sought after. In order to have a deeper insight into this problem and indicate possible remedies, the present paper studies the effects of both design-space dimensionality reduction (DR) and optimization methods on the shape optimization efficiency. Linear and non-linear DR methods are used for the design-space DR, based on principal component analysis and deep autoencoders. Global and hybrid global/local deterministic derivative-free optimization algorithms (Deterministic Particle Swarm Optimization, DIviding RECTangles, Dolphin Pod Optimization, LSDFPSO, and DIRMIN-2) are applied to the original and the reduced-dimensionality design-spaces, investigating their efficiency and effectiveness. Example application is shown for the shape optimization of a destroyer-type vessel sailing in calm water at fixed speed.

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

The use of computational methods in design engineering is growing rapidly at all stages of the design process, with the final goal of a substantial reduction of the cost and time for the development of a design. Simulations and optimization algorithms can be combined together into what is known as Simulation-Based Design (SBD) techniques. Using these tools the designers may find the minimum of some user defined objective functions with constraints, under the general mathematical framework of a Non-Linear Programming problem. There are problems of course: computational complexity, noise, robustness and accuracy of the numerical simulations, flexibility in the use of these tools; all these issues will have to be solved before the SBD methodology can become more widespread. In the paper, some derivative-based algorithms and methods are initially described, including efficient ways to compute the gradient of the objective function. Derivative-free methods - such as genetic algorithms an...

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

A guideline for an effective and efficient use of a deterministic variant of the Particle Swarm Optimization (PSO) algorithm is presented and discussed, assuming limited computational resources. PSO was introduced in Kennedy and Eberhart (1995) and successfully applied in many fields of engineering optimization for its ease of use. Its performance depends on three main characteristics: the number of swarm particles used, their initialization in terms of initial location and speed, and the set of coefficients defining the behavior of the swarm. Original PSO makes use of random coefficients to sustain the variety of the swarm dynamics, and requires extensive numerical campaigns to achieve statistically convergent results. Such an approach can be too expensive in industrial applications, especially when CFD simulations are used, and for this reason, efficient deterministic approaches have been developed (Campana et al. 2009). Additionally, the availability of parallel architectures has offered the opportunity to develop and compare synchronous and asynchronous implementation of PSO. The objective of present work is the identification of the most promising implementation for deterministic PSO. A parametric analysis is conducted using 60 analytical test functions and three different performance criteria, varying the number of particles, the initialization of the swarm, and the set of coefficients. The most promising PSO setup is applied to a ship design optimization problem, namely the high-speed Delft catamaran advancing in calm water at fixed speed, using a potential-flow code.

An environment for simulation-based analysis, design, and product development is presented in this article. Specifically, we integrate the analytical target cascading (ATC) methodology for optimal design with the Active Mediator Object System (AMOS) II information management tool to represent and exchange engineering data in distributed and heterogeneous environments. The ATC process is applied to the design optimization problem of a vehicle model with many design variables. The vehicle model is decomposed into system models, and vehicle design targets are translated to system specifications. The ADAMS software is used to develop the necessary simulation models. AMOS II is used to enable the data exchange among the vehicle dynamics simulation software that is distributed on different computers connected by a network.

The authors describe a type of hardware-software codesign in which developers model a system specification independently of implementation and use simulation-based design to assess virtual prototypes before the system is built. In an experimental design, the authors produced a control unit directly from the model-based specification.

Experimental and numerical analysis of the interference effect for a fast catamaran is carried out. This work presents the status of an ongoing NICOP project, the focus is on the effect of the separation distance between the demihull on the performances as well as on the interference. To this aim, experiments and numerical simulations are performed for five different separation lengths (and for the monohull configuration) and for a wide range of Froude number (from 0.2 to 0.8).

The Southern Swedish hospital region is the home of nearly 2 million people, in
which 5,684 individuals were diagnosed by stroke during 2016, according to
statistics from the hospitals in the region. With this large number of
stroke-diagnosed patients across the region, an effective stroke transport policy is
inevitably important to provide fast treatment for these patients.

In this thesis, we developed an agent-based simulation model for evaluating the
performance of transport logistics policies. We followed the Design Science
Research methodology in order to design and develop the model. Using the model,
we assessed two transport logistics policies for the Southern Swedish hospital
region. We used a synthetic set of stroke patients, which we generated using Monte
carlo simulation, ​for the processes of developing the model and assessing our two
stroke transport logistics policies.

We argue that the assessment of transport logistics policies is important for the
ability to improve the planning process, for example, when choosing hospital for
diagnosis of patients showing stroke symptoms. The optimization of the stroke
logistics process aims to ensure the quality and operational efficiency of the hospital
sector as well as to increase the chance of survival of stroke patients.

This is a simulation-based paper that aims to understand the impact of adaptive long-jumps on innovation landscapes. In the context of a given technological space, the set of innovative outcomes is conceived as a landscape, with the various possible outcomes seen as dotting different fitness peaks. Organizations, in their pursuit of innovation, undertake adaptive walks over this landscape. It has been established that progress in scientific knowledge enables organizations to 'look beyond' local optima. We model this behaviour through the provision of long-jumps. We study the effect of organizations undertaking the best possible combination of long-jumps and 1-mutant walks towards their eventual landing forms. We expect to see 'basins of attraction' forming around global optima which lead to realization of concentrated structure. In effect, we find that this may lead to a reversal of the Schumpeterian hypothesis by showing that innovation strategies can influence market concentration.

Stroke is a life threatening medical condition that is caused either by a blood clot or a bleeding inside the brain. It is generally
agreed that immediate treatment of stroke patients is crucial for their ability to recover; however, before treatment can be initiated,
the stroke patient has to be diagnosed using, for example, a computed tomography scan of the brain. This, in turn, requires that the
patient is transported to a hospital for diagnosis, which is a procedure that consumes valuable time and resources. In the current
paper, we present an agent-based simulation model for assessment of logistical stroke patient triage policies. Using a population of
stroke patients as input, the model enables to simulate the activities and the main decisions involved in the logistical operations of
stroke patients. In an experimental study covering the southernmost part of Sweden, we used the simulation model in order to assess
two different policies regarding where to send patients for diagnosis; the nearest hospital policy and the nearest hospital towards
the stroke center policy. Our simulation results show that the patients in need of special treatment favor from being transported in
direction towards the stroke center, whereas the patients who are not in need of specialist treatment favor from being sent to the
closest hospital.

Simulation-based design optimization (SBDO) methods integrate computer simulations , design modification tools, and optimization algorithms. In hydrodynamic applications, often objective functions are computationally expensive and likely noisy, their derivatives are not directly provided, and the existence of local minima cannot be excluded a priori, which motivates the use of derivative-free global optimization algorithms. This type of algorithms (such as Particle Swarm Optimization, PSO) usually follow a stochastic formulation, requiring computationally expensive numerical experiments in order to provide statistically significant results. The objective of the present work is to investigate the effects of using (versus suppressing) random coefficients in PSO for ship hydrodynamics SBDO. A comparison is shown of 1,000 random PSO to deterministic PSO (DPSO) using 12 well-known scalable test problems, with dimensionality ranging from two to fifty. A total of 588 test functions is considered and more than 500,000 optimization runs are performed and evaluated. The results are discussed based on the probability of success of random PSO versus DPSO. Finally, a comparison of random PSO to DPSO is shown for the hull-form optimization of the DTMB 5415 model. In summary, test functions show the robustness of DPSO, which outperforms random PSO with odds of 30/1 for low-dimensional problems (indicatively N ≤ 30) and 5/1 for high-dimensional problems (N > 30). The hull-form SBDO (N = 11) shows how DPSO outperforms PSO with odds of 20/1. The use of DPSO in the SBDO context is therefore advised, especially if computationally expensive analyses are involved in the optimization.

The formulation of a CFD-based stochastic multi-objective optimization of a destroyer hull is presented for operational efficiency and effectiveness, considering variable sea state, speed, and ship/wave heading. A simplified problem is formulated and solved here using low-fidelity solvers and one design variable. The parent hull considered is the DTMB 5415 model, an early and open to public version of the DDG-51. Hull-form modifications are produced using an expansion of orthogonal functions. A stochastic radial basis function meta-model is used. A multi-objective particle swarm optimization algorithm is used to achieve a set of non-dominated design solutions. The results are promising and show the potentiality of the proposed formulation and methods. Ongoing and future activities include the use of higher-fidelity solvers and the extension of the design space by increasing the number of design variables.

This paper is a summary of the plenary presentation. The objectives of the presentation are threefold: a) to discuss conceptual foundations of cognitive computing, b) to demonstrate their impact on intelligent systems design, and c) to present a brief summary of relevant project experiences. An introduction to knowledgebased and cognitive systems, and the explanation of their origins and principles are given. Then, an agent metaphor is introduced as the basis for design of high autonomy, cognitive architectures. Examples of projects from both industry and research laboratories that leverage from the above concepts are discussed. Some recent work that focuses on decision making in complex, information rich environments, multi-agent gaming models, and implementation of symbolic representation techniques in highly flexible, reusable, object-oriented visualization systems is presented.

The paper deals with an innovative optimization scheme for the design of phononic crystals based on the coupling of a third-order Boundary Element Method (BEM) and a deterministic Particle Swarm Optimization (PSO) algorithm. The band-gap properties of a periodic scattering structure are of paramount interest in many engineering applications. The propagation of acoustic waves can be confined and guided through an appropriate design of the unit cells constituting the crystal, tailored to the specific application at hand. The main difficulty in this process resides in the fact that the topological layout of the periodic structure required to achieve the target cannot be obtained analytically in many practical applications. As a consequence, time-consuming simulation-based approaches must be adopted to obtain the appropriate design. The use of optimization schemes can be a viable and efficient strategy to address the problem from the engineering point of view, provided that accurate and efficient numerical schemes are used. In the present work, the problem is addressed through the coupling of two advanced algorithms for acoustic simulations and optimization. The first is addressed using a high-accuracy BEM solver, capable to capture the details of the acoustic propagation patterns with a relatively low numerical effort. The BEM solver is linked to an optimization scheme based on a cutting-edge original PSO scheme having remarkable properties of efficiency and robustness, especially for optimization problems pertaining to high-dimensional design spaces. The method is applied to the optimization of two-dimensional lattices with specific spectral requirement and/or spatial scattering patterns. Preliminary results show the capability of the method to design periodic structures capable to block the propagation in the prescribed frequency band and confine the scattering within predefined portions of the field.

A guideline for an effective and efficient use of a deterministic variant of the Particle Swarm Optimization (PSO) algorithm is presented and discussed, assuming limited computational resources. PSO was introduced in Kennedy and Eberhart (1995) and successfully applied in many fields of engineering optimization for its ease of use. Its performance depends on three main characteristics: the number of swarm particles used, their initialization in terms of initial location and speed, and the set of coefficients defining the behavior of the swarm. Original PSO makes use of random coefficients to sustain the variety of the swarm dynamics, and requires extensive numerical campaigns to achieve statistically convergent results. Such an approach can be too expensive in industrial applications, especially when CFD simulations are used, and for this reason, efficient deterministic approaches have been developed ). Additionally, the availability of parallel architectures has offered the opportunity to develop and compare synchronous and asynchronous implementation of PSO. The objective of present work is the identification of the most promising implementation for deterministic PSO. A parametric analysis is conducted using 60 analytical test functions and three different performance criteria, varying the number of particles, the initialization of the swarm, and the set of coefficients. The most promising PSO setup is applied to a ship design optimization problem, namely the high-speed Delft catamaran advancing in calm water at fixed speed, using a potential-flow code.

A derivative-free global design optimization of the DTMB 5415 model is presented, using local hybridizations of two global algorithms, DIRECT (DIviding RECTangles) and PSO (Particle Swarm Optimization). The optimization aims at the reduction of the calm-water resistance at Fr = 0.25, using six design variables modifying hull and sonar dome. Simulations are conducted using potential flow with a friction model. Hybrid algorithms show a faster convergence towards the global minimum than the original global methods and are a viable option for design optimization, especially when computationally expensive objective functions are involved. A resistance reduction of 16% was achieved.

The paper presents a global method for simulation-based design optimiza- tion (SBDO), which combines a dynamic radial basis function (DRBF) surrogate model with a sequential multi-criterion adaptive sampling (MCAS) technique. Start- ing from an initial training set, groups of new samples are sequentially selected aiming at both the improvement of the surrogate model global accuracy and the reduction of the objective function. The objective prediction and the associated un- certainty provided by the DRBF model are used by a multi-objective particle swarm optimization algorithm in order to define Pareto-optimal solutions. These are used by the MCAS technique, which selects new samples by down-sampling the Pareto front, allowing for a parallel infill of an arbitrary number of points at each iteration. The method is applied to a set of 28 unconstrained global optimization test prob- lems and a six-variable SBDO of the DTMB 5415 hull-form in calm water, based on potential flow simulations. Results show the effectiveness of the method in re- ducing the computational cost of the SBDO, providing the background for further developments and application to more complex ship hydrodynamic problems.

This paper is a summary of the plenary presentation. The objectives of the presentation are threefold: a) to discuss conceptual foundations of cognitive computing, b) to demonstrate their impact on intelligent systems design, and c) to present a brief summary of relevant project experiences. An introduction to knowledgebased and cognitive systems, and the explanation of their origins and principles are given. Then, an agent metaphor is introduced as the basis for design of high autonomy, cognitive architectures. Examples of projects from both industry and research laboratories that leverage from the above concepts are discussed. Some recent work that focuses on decision making in complex, information rich environments, multi-agent gaming models, and implementation of symbolic representation techniques in highly flexible, reusable, object-oriented visualization systems is presented.

A method that supports distribution of multibody dynamic analysis is proposed and developed. Ethnographic methods are used as a means for gaining a deeper understanding of the engineering analysis work practice, and the findings form the base for a cooperative design of the system. The concept of design rationale is applied in order to deal with current problems of engineering analysis, such as irrelevant input and output, as well as satisfying the need for useful, real-time feedback. The web based simulation environment, applied to vehicle system dynamics, contains the simulation packages ADAMS and MATLAB and incorporates database technology. The possibility to distribute simulation models and results, from simulation experts to design experts and engineers, as well as subcontractors, is created. The proposed method requires changes in the existing multibody dynamic simulation methodology regarding aspects of incorporation in product development theories.