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In this work, we investigate an optimal assessment strategy to measure student learning in the first-year undergraduate engineering course at X-Department at X University. Specifically, we evaluate and compare challenge-based and competency-based assessment strategies. In the challenge-based approach, the students are required to design a C++-based application that meet the required design objectives. The competency-based assessment involves assessing learning by asking a variety of pointed questions pertaining to a single or a small group of concepts. After studying the performance of 207 students, we found that in the challenge-based assessment, due to the complex nature of the questions that assess numerous concepts simultaneously, students who are not very thorough with even one or two concepts fared very poorly since they were unable to finish the challenge and present a functional prototype of the program. On the other hand, the competency-based assessment allowed for a more balanced approach in which the students’ learning was reflected more accurately by their performance in the various assessments.

The feasibility of a solar-powered hybrid airship where lift is generated by both buoyancy and aerodynamics is investigated using aerodynamic shape optimization. The design objective is to minimize...

System level optimization is used to design an innovative ocean–wind energy converter to meet the “as good as it can be done” design objective. This general design procedure is then applied to the design of a 20 kW energy system and it is demonstrated that a combined energetic and economical design procedure is required for an optimal solution.

It has long been established that simulation is a powerful tool for aiding decision making. This is due to a number of factor, such as the inherent ability to evaluate complex systems with large number of variables and interactions for which there is no analytical solution. It is considered as a tool to answer “what if” questions, not a solution generator technique. This scenario could be changed with the aid of optimization procedures, and so simulation could answer not only “what if” questions but also answers “how to” questions, providing the best set of input variables and maximize or minimize some performance measure. For the purposes of optimization, the simulation is simply a “black box” which computes a realization of the (stochastic) function value for a given combination of the parameter values (Magoulas et al., 2002). Simulation optimization is the process of finding the best values of some decision variables for a system where the performance is evaluated based on the output of a simulation model of this system. The components of a typical optimization system are present in a simulation optimization system: decision variables, objective function and constraints. The decision variables must be contained in some feasible region defined by the constraints. The objective function is a real valued function defined on these variables, but due to the complexity and stochastic nature of the underlying system an analytical expression can not be determined and the value returned by the stochastic simulation must be used instead. The predator-prey game is a multi-agent system, which originally came from the field of Artificial Intelligence (AI). At first, this field was called Distributed Artificial Intelligence (DAI); instead of reproducing the knowledge and reasoning of one intelligent agent as in AI, the objective became to reproduce the knowledge and reasoning of several heterogeneous agents that need to jointly solve planning problems. Some researchers have focused more on the agent and its autonomy (for instance, the definition of an agent proposed by Wooldridge and Jennings (1999): “an agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives”), while others, engaged in the field of multi-agent systems, have focused more on the organization of multiple agent interactions (Huhns & Stephens, 1999). The aiming of this work is to propose a simulation optimization tool for solutions' synthesis to a problem involving a dynamic and complex system (to bring forth strategies for a

This paper presents a design optimization method for a spoke-type variable flux motor for electrified transportation application. The proposed optimization method is comprised of analytical and finite-element-based methods to reduce the magnetization current, torque ripple, and maintain a constant torque value. The optimization objectives and the problem are stated based on the characteristics of an existing variable flux motor. A novel analytical criterion is proposed to select the magnet dimensions based on a desired torque and magnetization current of the variable flux motor. Besides the mentioned objectives, the no-load operating point of the AlNiCo magnets is monitored. Due to the complex relationship between the design objectives, the optimization procedure has two levels. The optimized case is finally compared with the existing variable flux machine.

A general strategy for the multiobjective optimization of electric machines with respect to multiple operating conditions is proposed and applied to two 10-pole 12-slot fractional slot concentrated winding (FSCW) machines. To define an optimization problem, including the effects of multiple operating points, both sensitivity analysis and conflict analysis of the design objectives were incorporated into the proposed strategy. An objective-reduction algorithm was applied in order to make the optimization process affordable under a limited computational budget. The effects of incorporating multiple operating points on the optimization of 10-pole 12-slot FSCWs are presented. The proposed methodology can be applied to solve motor optimization problems with a high number of objectives, in general.

This paper presents a general procedure to optimize the design of a PCM storage tank, including the specification of design objectives, the identification of decision variables (for optimization), the construction of computer simulation platform and the final decision making. The general procedure was demonstrated by applying it to an open-air swimming pool, where the design objective was to minimize the volume of the PCM storage tank when it was required to enable heat storing to maintain the water temperature of an open-air swimming pool inside the thermal comfortable range during its open period in winter season. After identifying decision variables, a computer simulation platform was developed using TRNSYS and MATLAB to investigate the effects of the decision variables on the volume of the PCM storage tank. Case studies show that the proposed optimization is able to efficiently reduce the volume of the PCM storage tank without sacrificing the thermal storage capacity. Therefore, this study provides useful guidelines to optimize the design of a PCM storage tank.

A NetZero Energy Home (NZEH) is designed, modelled, and constructed to produce as much energy as it consumes on an annual basis, with the required energy generated from renewable resources. However, the actual performance of an NZEH may vary from the design objective, and the performance of a given NZEH may differ from other NZEHs. In order to examine the actual energy performance and identify discrepancies among multiple NZEHs, a research project has been initiated in which six NZEHs in Edmonton and Calgary, Canada, built by Landmark Group of Companies, are monitored, assessed, and compared in this research. Complete annual data has been collected for four of the NZEHs to date, and the results show the average energy balance, excluding the NZEH with a period of non-occupancy (listed for sale), reaches −1.4%, which is very near the targeted balance. The actual energy performance is analyzed and compared for the monitored NZEHs by category, including space heating, domestic hot water (DHW) heating, ventilator, and base loads (including major appliances). Overall, space heating and cooling are found to account for more than 50% of the total energy usage of the NZEHs, except for one listed for sale; all the base loads of the analyzed NZEHs are lower than the base load predicted in HOT2000 (19.5 kWh/day). The factors contributing to energy performance variations are also analyzed by category in this paper. This research thus addresses the actual energy performance and discrepancies among multiple NZEHs in cold regions; the research results can be referenced to improve building operation and future design of NZEHs.

This study intends to achieve simultaneous vibration suppression and energy harvesting using a variant form of nonlinear energy sink (NES). The proposed apparatus is not a true NES as its spring is not essentially nonlinear. In a previous study [22] (Journal of Sound and Vibration, 333 (20) (2014)), it has been shown that the apparatus demonstrates the transient behaviors similar to those of the NES. As a sequel, the present paper focuses on harmonically forced responses of the system. First, the approximate solutions of steady state responses are derived. Using the approximate solutions, the steady state behaviors are investigated by using the numerical continuation method. This is followed by an experimental study. The study has shown that under harmonic excitation, the proposed apparatus functions similarly to the NES with the typical behaviors such as strongly modulated responses, amplitude jumping, excitation level dependence, etc. Overall, the apparatus meets the design objectives: the vibration suppression and energy harvesting in a broadband manner.

Naturally evolved biological structures exhibit the optimal characteristics of light weight, high stiffness, and high strength. Based on the growth mechanism of biological branch systems in nature, an optimization method for internal stiffener plate distribution in box structures is suggested. Under the given load and support conditions, the internal stiffener plates of machine pedestal structures grow, bifurcate, and degenerate towards the direction of maximum overall structural stiffness in accordance with the adaptive growth law. The optimal and distinct distribution of internal stiffener plates with the most effective load path is thus obtained. Based on this, a size optimization for lightweight design is conducted, in which the self-weight of the structures is taken as the design objective, and the natural vibration frequency and static stiffness in the direction that is sensitive to machining accuracy are set as constraints. Finally, an optimized structure is obtained. The effectiveness of the proposed method is verified by using a precision grinder bed as an example. The results of numerical simulation and 3D–printed model experiment indicate that both the dynamic and the static performance of the optimized structure are improved, while the structural weight is reduced by compared with the initial structure. The suggested design method provides a new solution approach for the design optimization of machine pedestal structures.

© 2017, Springer-Verlag GmbH Germany. Multi-cell thin-walled structures exhibit significant advantages in maximizing energy absorption and minimizing mass during vehicle crashes. Since the topological distribution of wall members has an appreciable effect on the crashworthiness, their design signifies an important area of research. As a major energy absorber, multi-cell tubes are more commonly encounter oblique loading in real life. Thus, this study aimed to optimize multi-cell cross-sectional configuration of tubal structures for multiple oblique loading cases. An integer coded genetic algorithm (ICGA) is introduced here to optimize topological distribution of multi-celled web members for single/multiple oblique impacting conditions. Specifically, material distribution in a form of allocating web wall thickness, starting from zero, is considered as design variables and maximization of energy absorption (EA) as the design objective under the predefined peak crushing force and structural mass constraints. The optimization allows generating uniform or non-uniform thickness distribution in different web wall configurations to maximize usage efficiency of material. Compared with the baseline structure, the optimized configurations largely improved the energy absorption in both single and multiple load cases. The examples demonstrate that the proposed ICGA-based design method not only provides a useful approach to searching for novel crashworthy structures in a systematic fashion, but also develops a series of novel multi-cell topologies for multiple oblique loading cases.

A new lighting design procedure is proposed based on how lighting influences the appearance of indoor spaces and their contents. Exitance is the measure of the density of luminous flux (lm/m2) exiting, or emerging from, a surface, and provides an effective basis for relating the quantity and distribution of lighting to peoples' assessments of its visual effects. The procedure guides users from identifying selected lighting design objectives relating to how peoples' assessments of appearance are affected by lighting, to developing a specification for the direct flux distribution that will optimally achieve those objectives. Applications can range from basic to complex. A selection of previously published lighting concepts is reviewed in the light of recently published research, and based on these concepts, the procedure takes the form of a downloadable spreadsheet that enables users to prioritise between efficient flux utilisation and achievement of design-oriented objectives. While it is recognised that more research is needed before the procedure could be adopted for general application, the aim of this paper is to demonstrate the opportunities offered by a practical alternative to current lighting practice.

Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.

This paper describes the optimisation of a nano-positioning stage for a Transverse Dynamic Force Microscope (TDFM). The nano-precision stage is required to move a specimen dish within a horizontal region of 1 μm × 1 μm and with a resolution of 0.3 nm. The design objective was to maximise positional accuracy during high speed actuation. This was achieved by minimising out-of-plane distortions and vibrations during actuation. Optimal performance was achieved through maximising out-of-plane stiffness through shape and material selection as well optimisation of the anchoring system. Several shape parameters were optimised including the shape of flexural beams and the shape of the dish holder. Physical prototype testing was an essential part of the design process to confirm the accuracy of modelling and also to reveal issues with manufacturing tolerances. An overall resonant frequency of 6 kHz was achieved allowing for a closed loop-control frequency of 1.73 kHz for precise horizontal motion control. This resonance represented a 12-fold increase from the original 500 Hz of a commercially available positioning stage. Experimental maximum out-of-plane distortions below the first resonance frequency were reduced from 0.3 μm for the first prototype to less than 0.05 μm for the final practical prototype.

Recent studies have shown that new devices with improved performance can be successful achieved by using optimization methods In this study, an optimal design of a double-vibrator ultrasonic motor using combination method of finite element method, sensitivity analysis and adaptive genetic algorithm is presented. The finite element method is the reliable method for modal and harmonic response analysis. The sensitivity analysis is employed to determine the optimal analysis parameters having high sensitivity to optimization objectives, which can significantly improve the efficiency of the optimization. The adaptive genetic algorithm is adopted to seek the optimal values of structural parameters, which is scientific and efficient for multi-objective optimization problems. After optimization, all of the design objectives have achieved significant improvements. To verify the effectiveness of this method, a prototype motor is manufactured according to the optimization results and its performance is measured. The measured results show that this method is available and effective for the optimal design of ultrasonic motors.

With the increasing penetration of distributed renewable generation being connected, a number of solutions have been developed to avoid network overloads. A number of the already implemented schemes are based on generation curtailment during times of high generation and low demand. The ACCESS (Assisting Communities to Connect to Electric Sustainable Sources) system implements a more sophisticated predictive based solution. In the ACCESS system the generation resource and electrical demand is predicted and the storage heating resource is managed, providing a demand side management instead of a generation curtailment solution. The ACCESS system was installed, tested and demonstrated in the Power Network Demonstration Centre (PNDC), a research facility that enables realistic testing of novel hardware and network operation schemes in a controlled environment, in 2015. Following the testing at the PNDC, the ACCESS system was deployed in the Isle of Mull, in 2016. This paper reports on the outcome of the testing of the demand response features of the ACCESS system at the PNDC prior to its deployment on the Isle of Mull. This paper reports on: the different components of the system in detail (i.e. what it is designed to do and why); the test regime undertaken at the PNDC; the results of the testing (i.e. how effectively it achieves its design objectives); and the updates to the ACCESS system based on the testing with regards to the solutions employed and the benefits achieved.

In rehabilitation training, high-fidelity simulation environments are needed for reproducing the effects of slippery surfaces, in which potential balance failure conditions can be reproduced on demand. Motivated by these requirements, this article considers the design of variable-friction devices for use in the context of human walking on surfaces in which the coefficient of friction can be controlled dynamically. Various designs are described, aiming at rendering low-friction shoe-floor contact, associated with slippery surfaces such as ice, as well as higher-friction values more typical of surfaces such as pebbles, sand, or snow. These designs include an array of omnidirectional rolling elements, a combination of low- and high-friction coverings whose contact pressure distribution is controlled, and modulation of low-frequency vibration normal to the surface. Our experimentation investigated the static coefficient of friction attainable with each of these designs. Rolling elements were found to be the most slippery, providing a coefficient of friction as low as 0.03, but with significant drawbacks from the perspective of our design objectives. A controlled pressure distribution of low- and high-friction coverings allowed for a minimum coefficient of friction of 0.06. The effects of vibration amplitude and frequency on sliding velocity were also explored. Increases in amplitude resulted in higher velocities, but vibration frequencies greater than 25 Hz reduced sliding velocities. To meet our design objectives, a novel approach involving a friction-variation mechanism, embedded in a shoe sole, is proposed.

Multi-objective design and operation optimization of energy supply systems with respect to costs, energy efficiency and environmental indicators is a very important, yet difficult and challenging task. The assumption of cost-optimal operation may impose the necessity for bi-level problems. This paper presents a methodology to formulate and solve multi-objective bi-level optimization problems where upper-level decision makers decide on design and policy, while plant operation is defined at the lower level. It can be used to assess energy consumption or emission reduction potential corresponding to cost-optimal operation. The methodology applies conceptual decomposition to the outer design- and inner operation-optimization problems. The first is solved with the scatter search method and the second using mixed integer linear programming. Scatter search is a metaheuristic method convenient to integrate problem-specific heuristics into the process and systematically take advantage of the designer's knowledge. The fuzzy approach considers multiple design objectives and helps formulate heuristic rules based on the decision criteria satisfaction rates. The case study illustrates the design of a trigeneration system for an urban settlement. The design objectives are: total costs, primary energy and greenhouse gases emission. Cost-optimal operation is presumed. The optimal plant design and operation are realistic and mostly consistent with the literature. Compared to the cost-optimal solution, other objectives might lead to reduced primary energy consumption for 7.21% or lower emission for 91.85% with 43.35% and 83.1% larger costs. The results are compared with the ones obtained with customary methods. It is demonstrated that the methodology is applicable and convenient for the considered class of problems.

This paper discusses the development of a surrogate bicycle target for evaluating the effectiveness of vehicular autonomous emergency braking (AEB)/ pre-collision systems (PCSs) that uses 77-GHz radar systems in detecting bicyclists on the road. The design objective of the surrogate bicycle is to produce similar optical appearance and radar response in a 76–78 GHz band as a real bicycle selected based on its popularity (in 2014) in the United States. In addition, the surrogate bicyclist needs to be able to withstand the impact of the test vehicle traveling at 30 mph (or 48 kph) should it fail to detect the surrogate target. Our design approach differs from the only other surrogate bicyclist developed by a European research group in that our surrogate bicyclist produces similar radar response as a real bicyclist over the entire 360° of azimuth angles. Our design approach began with experimental characterizing the radar cross section (RCS) patterns of real bikes of different types as well as identifying RCS contributions of different parts on a bike via accurate simulations, which were also used for design optimizations. The RCS performance of the fabricated surrogate bicycle was verified via 360° azimuth pattern measurement comparison with a real bicycle. Its physical performance was tested via actual field testing on a test track with two commercial vehicles equipped with AEB and PCS systems.

In contrast to the single design objective associated with force-based design approaches of different seismic force resisting systems (SFRSs), performance-based seismic design (PBSD) allows the selection of more than one design/performance objective. Each performance objective is linked to a tolerable risk level associated with the considered SFRS experiencing a specific damage at a specified seismic hazard level (presented in the form of fragility curves). Similar to other SFRSs, damage of reinforced masonry structural wall (RMSW) SFRSs can be linked to their lateral displacements/drifts. As such, this study focuses on using a multivariate data analysis techniques, to develop a RMSW load-displacement (backbone) model. The backbone model is calibrated using a database of RMSW experimental test results, and the model is further utilized to investigate the influence of the wall geometrical and mechanical characteristics on altering the displacement of RMSW corresponding to different performance levels. These analysis results are subsequently used to develop RMSW seismic fragility bands to facilitate visualizing the risk (losses) associated with the resulting range of displacement (drift) demands, when the different RMSW ranges of characteristics are considered. The developed fragility bands are compared to the individual fragility curves currently adopted by the Federal Emergency Management Agency, FEMA P-58 pre-standards, Seismic Performance Assessment of Buildings. This comparison shows that the developed fragility bands significantly deviate from the individual fragility curves currently adopted by FEMA P-58 at each damage state. The developed backbone model and fragility-bands are expected to not only facilitate adoption of the RMSW SFRSs in the next generation of PBSD codes, but also to equip researchers and designers with a clear understanding of the different aspects governing RMSW systems seismic performance and the associated risk.

A new method of designing fractional-order predictive PID controller with similar features to model based predictive controllers (MPC) is considered. A general state space model of plant is assumed to be available and the model is augmented for prediction of future output. Thereafter, a structured cost function is defined which retains the design objective of fractional-order predictive PI controller. The resultant controller retains inherent benefits of model-based predictive control but with better performance. Simulations results are presented to show improved benefits of the proposed design method over dynamic matrix control (DMC) algorithm. One major contribution is that the new controller structure, which is a fractional-order predictive PI controller, retains combined benefits of conventional predictive control algorithm and robust features of fractional-order PID controller.

Variable stiffness actuators were introduced to decouple an otherwise stiff actuator from the load by an adjustable elasticity. This variable elastic element can be used as torque sensor, acts as an energy storage, decouples the actuator for exogenous high frequency excitation inputs and contributes towards shock resistance and safety in human-robot interaction scenarios. However, the variable element complicates the design of torque and impedance controllers which have to be synthesized by employing contradicting design objectives, such as minimisation of the output impedance, robust stability and performance. Moreover, the system to be controlled consists of an additional control-loop to set-up the stiffness of the elastic element in real-time. To overcome these synthesis problems, we present a new controller design procedure that imposes a positive-real constraint on the load output port function to guarantee a stable interaction with respect to a passive, yet otherwise unknown environments. Additional design requirements are subsequently cast into a generalised plant. Ultimately, a H∞ - nonsmooth design procedure is employed to design a torque controller under these constraints and is tested in in silico experiments with the Mechanical Rotational Impedance Actuator (MeRIA).

In this study, a symmetric waveguide bandpass filter is proposed using a systematic design process. The waveguide device is designed to control the operation frequency range by changing the metallic barrier width in the system and the finite element method is employed for numerical analysis. We used topology optimization based on the On/Off method for initial concept design with the design objective to maximize the transmitted electric intensity at the target frequency. Detail two-dimensional metallic structure for a tunable bandpass filter was determined through successive parametric studies using the shape primitively derived from the On/Off method. The performance of the proposed waveguide system was verified by numerical simulations and experimental measurements.

A method that couples artificial neural network (ANN), genetic algorithm (GA) and computational fluid mechanics (CFD) was proposed to inversely optimize the geometric configuration of exhaust hood and operation parameters. The optimal objectives of exhaust hood optimization are the limitation concentration of emission at the exit and the minimum deposition on the exhaust hood walls of cut tobacco. The design variables are air flow rate, valve open values and the pressures at the test points. Twelve different orthogonal cases based on the 3-D CFD model were chosen to train and validate the ANN in order to obtain the relationship between the optimal objectives and the design variables. The CFD cases and those of ANN were used by the genetic algorithm (GA) to find the optimal design variables satisfying the design objectives. Both CFD and experimental methods were used to verify the performance of the optimized exhaust hood. The results showed that the emission at the exit and the wall deposition of cut tobacco was significantly reduced by optimizing the pressure distribution in the exhaust hood. The optimal exhaust hood structure and operation conditions were obtained.

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations.

Nuclear medicine is a medical specialization that uses radioactive materials injected into the body to diagnose and treat human diseases. The use of different radionuclides and high amounts of radioactive materials makes it necessary for the facilities where these procedures are conducted to evaluate the corresponding shielding to comply with the design dose limits of a facility and avoid radiological accidents [1] as recommended and accepted in international publications, like the International Commission on Radiological Protection (ICRP) and the National Council on Radiation Protection and Measurements (NCRP). This work compares two methods to calculate the shielding necessary to guarantee que las medicine service zones be safe from ionizing radiations. The first method consists in calculating the transmission factor B to find the thickness of the material necessary to protect the zone of interest, this factor is calculated by bearing in mind the occupancy factors, workloads, use factor, and the design objective dose limit. Upon obtaining the transmission factor B, half value layer (HVL) or tenth value layer (TVL) tables are used for each construction material, obtaining the thickness of the material. The other method is the calculation of is the calculation of rates of exposure through the air Kerma rate constant, then the XCOM databases are used, which were developed by the National Institute of Standards and Technology (NITS) to obtain the attenuation coefficient, used in the law of exponential attenuation; finally, the necessary thickness of the material is obtained to reach the design objective. Finally, the principal differences between both methods are shown and an analysis is performed of the shielding optimization, seeking to set criteria to make recommendations to nuclear medicine services on optimal shielding..

An experimental study was conducted to prove the concept of a new protection system—a sprinkler system that uses Simultaneous Monitoring, Assessment and Response Technology (SMART). The present work focuses on the system design and function evaluation at the component level. The objective is to demonstrate the feasibility that the SMART sprinkler system can provide adequate protection to highly challenging fires. The new protection system has several key functions including multi-sensor detection, real-time fire location calculation, dynamic sprinkler activation and wireless communication coordinating the system components. A series of fire detection, sprinkler activation and fire suppression tests were carried out to evaluate these system functions. Results show that a combination of smoke and temperature sensors can detect the fire at a very early stage, with the fire size reduced by one order of magnitude, and provide sufficient data to locate the fire. A thermal centroid based algorithm can determine the fire location within less than 50% of the sprinkler spacing on average. The sprinkler activation (six units) concentrating water discharge in the vicinity of the fire can suppress and even extinguish the fire under certain experimental conditions. These results confirm that the design objectives have been achieved. The experimental data also provide crucial information to assess system performance in full-scale tests.

To enhance the measurement performance of tapping-mode atomic force microscopy (TM-AFM), it is highly desirable that the TM-AFM probe has high resonant frequency and large effective slope at its free end. However, the two design objectives may lead to conflict requirements on the geometry of probe. Conventional design approaches that involve a lot of trials and errors are not so effective to solve such a complex design problem. Therefore, in order to address this difficulty, a structural optimization problem is formulated. The objective of optimization is to ensure the mode shape of probe match a user-specified target shape and the corresponding resonant frequency is maximized. Similarity between the actual mode shape and the target is characterized by modal assurance criterion. Users can freely select which vibration mode is to be optimized. A three-layer geometry model is used to represent cantilever probes. The middle layer is not subject to optimization. The top and bottom layers have the same width profile, and it is iteratively changed during the optimization. The optimization problem is solved through a gradient based method. Examples of design optimization show that the proposed method is effective.

The purpose of this study is to propose a concept design process for an automotive body structure using technical information on the major joints and members of vehicles. First, in order to collect the technical information on major joints and members, 17 vehicles were selected using benchmark data. The collected technical information for the selected vehicles was the cross sectional shapes of each joint and member which were used for the analysis of joint stiffness, crashworthiness and static stiffness of the member to make a database along with cross section properties. This study applied a ‘What If Study’ technique to perform a concept design of an automotive body using the analyzed information and selected cross section meeting the design objectives. The criteria for the selection of the cross section were defined by subdividing the defined design objectives of an automotive body structure and constraints into members and joints. In order to configure an analysis model of an automotive body structure using the selected cross section, a shape parametric model was used and static stiffness, dynamic stiffness and crashworthiness were assessed to evaluate the configured automotive body structure. The evaluation result showed that the crashworthiness and static/dynamic stiffness were improved compared to an existing body structure. In addition, the weight of the body structure was reduced. Through this study, the process that can rapidly and effectively derive and evaluate the concept design of an automotive body structure was defined. It is expected that, henceforth, this process will be helpful for the study of automotive body structures.

The mission planning of GEO on-orbit refueling (OOR) in Mixed strategy is studied in this paper. Specifically, one SSc will be launched to an orbital slot near the depot when multiple GEO satellites are reaching their end of lives. The SSc replenishes fuel from the depot and then extends the lifespan of the target satellites via refueling. In the mixed scenario, only some of the target satellites could be served by the SSc, and the remaining ones will be fueled by Pseudo SScs (the target satellite which has already been refueled by the SSc and now has sufficient fuel for its operation as well as the fuel to refuel other target satellites is called Pseudo SSc here). The mission sequences and fuel mass of the SSc and Pseudo SScs, the dry mass of the SSc are used as design variables, whereas the economic benefit of the whole mission is used as design objective. The economic cost and benefit models are stated first, and then a mathematical optimization model is proposed. A comprehensive solution method involving enumeration, particle swarm optimization and modification is developed. Numerical examples are carried out to demonstrate the effectiveness of the model and solution method. Economic efficiencies of different OOR strategies are compared and discussed. The mixed strategy would perform better than the other strategies only when the target satellites satisfy some conditions. This paper presents an available mixed strategy scheme for users and analyzes its advantages and disadvantages by comparing with some other OOR strategies, providing helpful references to decision makers. The best strategy in practical applications depends on the specific demands and user preference.

With the rapid development of the vehicle industry, crashworthiness has become a crucial aspect in vehicle body design. In fact, crashworthiness is a multivariable optimization design problem for a vehicle body, regardless of structure or material. However, when crashworthiness involves a large number of design variables, including both material and structure variables, it is more difficult to deal with. In this paper, an integrated design technique for materials and structures of vehicle body under crash safety consideration is suggested. First, a finite element model of the vehicle body is established according to relevant vehicle safety standards. Then, the material parameters of the vehicle body are set as analytical factors for factor screening. Next, significant factors are obtained using a three-level saturated design integrated with multi-index comprehensive balance analysis and the MaxUr(3) method, with an improved evaluation method. These screened material parameters along with the corresponding continuous variables of the structure, are considered as the design variables of the integrated design of the vehicle body. Both the weight and the crashworthiness properties are set as the design objectives. Optimal Latin hypercube sampling and radius basis functions are utilized to construct highly accurate surrogate models. Furthermore, the non-dominated sorting genetic algorithm II is implemented to seek the optimal solutions. Finally, two cases considering the roof module and the frontal module of a vehicle body are analyzed to verify the proposed method.

In modern building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, majority of these optimization algorithms are focused on single objective optimization procedures and require large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near optimal solutions were however sensitive to the chosen value of the weighting factor. Aiming to develop a multi-objective optimization platform with minimal computational cost, this paper presents a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room. In addition, an adaptive sampling procedure is also proposed to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Significant computational cost could be reduced without sacrificing the accuracy of the optimal solution. The proposed methods are applied and assessed in a typical HVAC system and the results indicate that comparing to traditional methods, the presented approach can handle multi-objective optimization in ventilation system with up to 46.6% saving of computational time.

In this paper, an adaptive full order sliding mode (FOSM) controller is proposed for strict feedback nonlinear systems with mismatched uncertainties. The design objective of the controller is to track a specified trajectory in presence of significant mismatched uncertainties. In the first step the dynamic model for the first state is considered by the desired tracking signal. After the first step the desired dynamic model for each state is defined by the previous one. An adaptive tuning law is developed for the FOSM controller to deal with the bounded system uncertainty. The major advantages offered by this adaptive FOSM controller are that advanced knowledge about the upper bound of the system uncertainties is not a necessary requirement and the proposed method is an effective solution for the chattering elimination from the control signal. The controller is designed considering the full-order sliding surface. System robustness and the stability of the controller are proved by using the Lyapunov technique. A systematic adaptive step by step design method using the full order sliding surface for mismatched nonlinear systems is presented. Simulation results validate the effectiveness of the proposed control law.

A new alloy design methodology is presented for the identification of alloy compositions, which exhibit process windows (PWs) satisfying specific design objectives and optimized for overall performance. The methodology is applied to the design of medium-Mn steels containing Al and/or Ni. By implementing computational alloy thermodynamics, a large composition space was investigated systematically to map the fraction and stability of retained austenite as a function of intercritical annealing temperature. Alloys exhibiting PWs, i.e., an intercritical annealing range, which when applied satisfies the given design objectives, were identified. A multi-objective optimization method, involving Pareto optimality, was then applied to identify a list of optimum alloy compositions, which maximized retained austenite amount and stability, as well as intercritical annealing temperature, while minimized overall alloy content. A heuristic approach was finally employed in order to rank the optimum alloys. The methodology provided a final short list of alloy compositions and associated PWs ranked according to their overall performance. The proposed methodology could be the first step in the process of computational alloy design of medium-Mn steels or other alloy systems.

The objective of the present paper is to investigate the influence of the design objective on the total cost of buildings. A series of reinforced concrete buildings are designed for various design objectives, and the construction cost is calculated. Additionally, the earthquake losses for three different earthquake scenarios are estimated. The total cost of the buildings is calculated as the sum of the construction cost plus the earthquake losses. The whole investigation demonstrates that designing for elastic response against the design earthquake is both the safest and the most economical in long-term option in the case of strong seismic excitations.

The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

Micro-siting is an optimal way of placing turbines inside a wind farm while considering various design objectives and constraints. Using a well-established Jensen wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization based on a multi-objective trade-off between minimization of the noise propagation and maximization of the energy generation. A novel hybrid methodology is developed which is a combination of probabilistic real-binary coded multi-objective evolutionary algorithm and a newly proposed deterministic gradient based non-dominated normalized normal constraint method. Based on the Inverted Generational Distance metric, the performance of the proposed method is found to be better than the conventional normalized normal constraint method or the concerned evolutionary method alone. Moreover, in contrast to the previous studies, the generated non-dominated front is capable of providing a trade-off between various alternative energy-noise solutions, along with an additional information about the corresponding turbine numbers and their optimal location coordinates. As a result, the decision maker can choose from different competing wind turbine layouts based on existing noise and other standard regulations.

Semi-rigid steel frames are often considered a cost-effective alternative to conventional fully rigid steel frames, because they use the economical partially restrained (PR) connections instead of the costly moment connections while performing satisfactorily under seismic loads. So far, semi-rigid steel frames have typically been designed using the traditional force-based method that is meant for the design of fully rigid steel frames. With this method, the sizing of various structural components is firstly carried out per code strength requirements and then revised iteratively to satisfy the stiffness requirements on both individual structural components and the overall frame. As a result, a designer often encounters considerable difficulty in achieving a structural solution that meets the target displacement and attains a desired yield mechanism. To address this issue, we propose a facile displacement-based method for the seismic design of semi-rigid steel frame structures by directly and explicitly relating the target displacement and deformation to the appropriate sizing of various structural components in terms of their strength, stiffness, and ductility. The new method is based on a simplified analytical semi-rigid frame model assuming that the structural plastic deformation is mainly concentrated within the PR connections while beams and columns largely remain elastic. A step-by-step procedure for implementing the new method is described and illustrated with example semi-rigid steel frame designs. The usefulness of the new method is demonstrated by comparing the target displacement and deformation values specified as a design objective with the actual values predicted by a nonlinear static pushover analysis.

Over the past decade a large amount of research has been dedicated in academic literature to improving the technical capabilities of improved cookstoves; primarily the performance efficiency and reduction of emissions. Unfortunately, as published literature has highlighted, the trade-offs that result from placing such a concentrated emphasis on these technical objectives is that improved cookstoves lack the same level of usability as the traditional cookstoves. Thus, users often return to using their traditional stoves and the potential impact of the improved cookstoves is never fully realized. In order for improved cookstoves to have greater impact, there must be better balance between the two competing design objectives of technical capabilities and usability. This paper explores the challenges and benefits associated with achieving the appropriate balance, and demonstrates its application through the modification of a traditional cookstove used in the Tambogrande region of Peru. This modification includes an inexpensive set of pot skirts that integrate directly with the traditional stove. These pot skirts allow for varying sizes and number of pots, and the use of traditional fuels. Laboratory testing, using the Water Boiling Test (WBT), identified the skirts' technical improvements: 41.7% increased thermal efficiency, 32.7% decreased fuel consumption, 28.8% decreased time to boil. Field testing was performed to determine the pot skirts acceptance and compatibility with the traditional cookstoves, with over 75% of the participants recognizing some type of benefit. Although the technical improvements of these pot skirts are less than other cookstoves on the market, the higher levels of usability are likely to lead to a more positive end-user reaction, which could potentially lead to higher rates of adoption and impact. Though these results are primarily focused on the application of improved cookstoves, the need for more balance between technical and user objectives is applicable to nearly all products being designed for the developing world. Keywords: Improved cookstove, Design, Usability, Adoption

The development and accelerated use of optimization frameworks in aircraft design is a testament to their ability to identify optimal and often non-intuitive shapes as a result of multi-disciplinary design objectives. Airfoil design is a continuously revised multi-disciplinary problem, and is pivotal to illustrate the performance of optimization frameworks involving numerical simulation, flexible shape parametrization, and intelligent evolutionary algorithms. An often overlooked component of this classic problem is to consider the dynamic aeroelastic behavior under trim conditions, which can generate explicit boundaries to the flight envelope. Trim introduces a significantly strong coupling with objectives governing static performance, e.g. aerodynamic and/or structural, thereby resulting in a highly nonlinear and discontinuous design space. In this paper, a multi-objective particle swarm optimization framework for multi-disciplinary performance improvement is presented, pertaining to aerodynamic, structural and aeroelastic design criteria at trim conditions. The framework is assisted by the construction of adaptive Kriging surrogates, which is cooperatively used with the numerical solver to identify optimal solutions within a computational constraint. Designer preferences are introduced to reflect the optimal compromise between the objectives. Results of the optimization process indicate a large spread in design variable influence and interaction, and a subtle yet clear distinction between all objectives is illustrated through the catalog of final airfoil candidates obtained.

Rotor airfoil design is investigated in this paper. There are many difficulties for this high-dimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis (PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the high-dimensional multi-objective engineering problem.

A Pareto-based multi-objective optimization approach has been proposed to design and obtain Pareto-optimal set of solutions for performance and emission characteristics of a single cylinder, four stroke diesel engine using hydrogen in dual fuel mode. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing load (%) and hydrogen flow rate. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an exclusive non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution(TOPSIS). NSGA-II has been utilized to search for the candidate solutions in-terms of both objectives. The obtained results have been obtained as Pareto-front. Subsequently, the best compromise solution has been determined by the TOPSIS method from the Pareto-front according to the decision maker's preference. The design results shows that the proposed approach yields a remarkable reduction in the emission spectra with enhancement in performance of the engine under a single podium obtained by multi-objective optimization.

In the architectural design process, the conceptual design stage is to devise a creative alternative in response to the intent of the architects. In this paper, we propose an alternative evolutionary-based architectural design method by using the Implicit Redundant Representation Genetic Algorithm (IRRGA) that is highly suited to explore unstructured problem formulation such as conceptual design. Also, a new string representation for apartment building designs is proposed such that the size and the number of apartment units with stairs are not fixed and can be changed during the design evolution. The design objectives are selectively applied in terms of symmetry, structure, circulation, and facade. Each objective is used respectively as a fitness function to demonstrate the performance of IRRGA. Finally, a multi-objective fitness function is applied and the resulting apartment building designs show their own level of creativity.

Purpose Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time. Design/methodology/approach An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces. Findings It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces. Originality/value The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.

Active balancing architectures effectively increase the efficiency of large battery packs by equalizing charge between cells. For this purpose, a balancing circuit and appropriate control scheme have to be designed to enable the charge transfer via energy storage elements such as inductors. Using a manual approach to design balancing architectures can be tedious and error-prone, resulting in potentially suboptimal solutions. As a remedy, this paper presents an automatic synthesis of balancing circuits and their corresponding control, optimizing the number of required metal-oxide-semiconductor field-effect transistors, and necessary control signals. The proposed synthesis combines a satisfiability solver to explore the search space with a graph-based verification that iteratively excludes infeasible solutions until the optimal architectures are obtained. The experimental results are carried out for three given template circuits and two signal templates. The synthesis results in architectures that are superior in terms of all design objectives in comparison to solutions from literature that result from a manual design approach.

This paper presents a novel controller design of a thyristor controlled series capacitor based on an optimized adaptive neuro-fuzzy inference system. The modified states of matter search algorithm are implemented to fit the premise and consequent parameters of the neuro fuzzy system. The design objectives are to reduce the power system oscillations and find a minimum number of strongest fuzzy rules, trends toward building a low-size controller model. Therefore, fuzzy decision-making mechanism is employed to rank the Pareto-optimal set to extract the best compromise solution. The proposed controller design depends upon the expected wide range of operating conditions. The effectiveness of smart control strategy based controller is tested and compared on single machine infinite bus and multimachine power systems under small scale disturbance as well as large scale disturbances.

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational capacity is the underlying design consideration. This paper analyzes the MPC tuning problem with control performance and required computational capacity as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational capacity separately – often with the latter as a fixed constraint – which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient optimizer are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the tuning approach and importance of the developed conditions for an effective optimizer of the MOD-MPC problem.

Energy efficiency is a mandatory requirement and integral part of green and sustainable buildings. Energy efficient design optimization is both a design philosophy and a practical technique that has been proposed and used by architects and other professionals for several decades, especially in the past few years. In this review, a set of selection criteria are proposed and 116 works are identified as the core literature. Taking the perspective of architects, analysis is conducted to the core literature to reveal the state of the art of building energy efficient design optimization. The analyzed subjects include the general procedure, the origin and development, the classification, the design objectives and variables, the energy simulation engines, the optimization algorithms, and the applications. The review findings confirm that building energy efficient design optimization is a promising technique to design buildings with higher energy efficiency and better overall performance. However, obstacles still exist and future research is needed.

Adaptive management is a design approach which allows one to change support and/or construction procedures during excavation based on observed responses to meet specific design objectives. ...

This chapter outlines new conceptual frameworks and analytics for sustainable urban development goals and design objectives for precincts. The precinct represents a critical scale for urban planning, design and management across all three contemporary urban development arenas: greyfields, brownfields and greenfields. Currently there is lack of analytical and engagement platforms capable of assessing the performance of precinct scale design-led development in these arenas within a time frame and across key dimensions of city performance now considered fundamental to sustainable urban development in the twenty-first century: demonstrably regenerative, low carbon, resilient and smart—across all scales. A clear urban design assessment deficit exists and is particularly acute at precinct scale. A suite of tools developed in the CRC for Low Carbon Living capable of integrated precinct assessment are introduced.