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Design of engineering structures may benefit from reduction in assembly complexity through use of periodic components, in which uniform sub-structures combine to form a relatively simple topology. The benefits of periodic structures include lower manufacture costs as well as ease of assembly. Recent developments in periodic topology optimization have shown its efficacy for addressing a range of design objectives. However, constraints such as assembly conditions and the connection configuration of periodic sub-components present limiting factors in the application of periodic optimization to real-world engineering problems. This study addresses the current knowledge gap in periodic optimization assembly through inclusion of common interfacing connections between periodic components, such as screws, welds, or rivets, thus accounting for real assembly conditions. A bi-directional evolutionary structural optimization (BESO) method and solid isotropic material with penalization (SIMP) method are presented, for stiffness and frequency criteria, which simultaneously optimizes the topology of the periodic components and the joint configuration connecting components. Elemental sensitivities are derived and utilized to drive the design of both the periodic component and the connection layouts. Iterative updating of the topological design, guided by elemental sensitivities, allows for optimization of the periodic topology for given objective functions. To demonstrate the effectiveness of the proposed method, optimized structures are explored through different periodicities. Application of the methodology presented in this study will assist in providing new design capabilities to reduce the costs of manufacturing, transport, and assembly through optimized periodic components.

Hybrid electric powertrains in automotive applications aim to improve emissions and fuel economy with respect to conventional internal combustion engine vehicles. Variety of design scenarios need to be addressed in designing a hybrid electric vehicle to achieve desired design objectives such as fuel consumption and exhaust gas emissions. The work in this paper presents an analysis of the design objectives for an automobile powertrain with respect to different design scenarios, i. e. target drive cycle and degree of hybridization. Toward these ends, four powertrain configuration models (i. e. internal combustion engine, series, parallel and complex hybrid powertrain configurations) of a small vehicle (motorized three wheeler) are developed using Model Advisor software and simulated with varied drive cycles and degrees of hybridization. Firstly, the impact of vehicle power control strategy and operational characteristics of the different powertrain configurations are investigated with respect to exhaust gas emissions and fuel consumption. Secondly, the drive cycles are scaled according to kinetic intensity and the relationship between fuel consumption and drive cycles is assessed. Thirdly, three fuel consumption models are developed so that fuel consumption values for a real-world drive cycle may be predicted in regard to each powertrain configuration. The results show that when compared with a conventional powertrain fuel consumption is lower in hybrid vehicles. This work led to the surprisingly result showing higher CO emission levels with hybrid vehicles. Furthermore, fuel consumption of all four powertrains showed a strong correlation with kinetic intensity values of selected drive cycles. It was found that with varied drive cycles the average fuel advantage for each was: series 23 %, parallel 21 %, and complex hybrids 33 %, compared to an IC engine powertrain. The study reveals that performance of hybrid configurations vary significantly with drive cycle and degree of hybridization. The paper also suggests future areas of study.

Presently, the integration of renewable energy into existing power system to cater increasing power needs is a growing trend. Wind energy is one of the prominent utilized renewable sources. However, the generation level of integrated wind energy significantly affects the frequency characteristics of entire system. Further, if the control scheme of wind systems are not properly working then this situation may deteriorate the frequency characteristics of whole system. Therefore, it is indispensable to tune the parameters of controllers of existing and wind systems in proper coordination so as to improve the frequency characteristics of the system. Consequently, in this work, a coordinated tuning of parameters of controllers to improve frequency characteristics of a multi-area interconnected thermal system in presence of doubly-fed induction generator (DFIG) based wind generation is presented. A widely used two-area non-reheat thermal interconnected system is simulated having its one area integrated with DFIG based wind generation. The parameters of controllers of thermal and wind systems are coordinately tuned using symbiotic organisms search (SOS) algorithm. Different design objectives are framed to achieve improved frequency characteristics of entire system. The considered objectives are combined together by proper assignment of weights using analytic hierarchy process to form a single objective function. Several test cases with diverse disturbances under different wind penetration levels are conducted to test the performance of system with SOS based controllers. To further assess the effectiveness of proposed controllers, other controllers using differential evolution (DE), elephant herding optimization (EHO), particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO) are also designed and comparatively tested under the considered cases. Keywords: Automatic generation control, Doubly-fed induction generator, Multi-area power system, Proportional-integral-derivative controller, Symbiotic organisms search algorithm

A framework for optimal seismic design of structures, considering maximum value as the design objective, is introduced. In this framework, the value parameter incorporates a comprehensive set of pe...

The development of large-scale multi- and many-core platforms and the rising complexity of embedded applications have led to a significant increase in the number of implementation possibilities for a single application. Furthermore, rising demands on safe, energy-efficient, or real-time capable application execution make the problem of determining feasible implementations that are optimal with respect to such design objectives even more of a challenge. State-of-the-art Design Space Exploration (DSE) techniques for this problem demonstrably suffer from the vast and sparse search spaces posed by modern embedded systems, emphasizing the need for novel design methodologies in this field. Based on the idea of reducing problem complexity by a suitable decomposition of the system specification—in particular, by a reduction of target architecture or task mapping options—the work at hand proposes a portfolio of dynamic decomposition mechanisms that automatically decompose any system specification based on a short pre-exploration of the complete system. We present a two-phase approach consisting of (a) a set of novel data extraction and representation techniques combined with (b) a selection of filtering operations that automatically extract a decomposed system specification based on information gathered during pre-exploration. In particular, we employ heat map data structures and threshold as well as graph-partitioning filters to reduce problem complexity. The proposed decomposition procedure can seamlessly be integrated in any DSE flow, constituting a flexible extension for existing DSE approaches. Furthermore, it improves existing static decomposition techniques and other heuristics relying on information about the problem instance, since systems with irregular architectural topology or distribution of resource types can now be decomposed based on an automatic, problem-independent pre-exploration phase. We illustrate the efficiency of the proposed decomposition portfolio applied to state-of-the-art DSEs for many-core systems as well as networked embedded systems from the automotive domain. Experimental results show significant increases in optimization quality of up to 87% within constant DSE time compared to existing approaches.

Nonlinear transmission lines (NLTLs) have been increasingly studied to produce high power microwaves (HPM) at higher repetition rates than conventional HPM devices without requiring the same auxiliary systems. The ability to array NLTLs to produce higher power and achieve beam steering provides an additional capability. This review summarizes the various NLTL topologies designed to achieve these design objectives. Specifically, we summarize modeling and experimental studies for three primary topologies: the lumped element NLTL, the split ring resonator, and the traditional transmission line geometry using nonlinear materials. The lumped element NLTL and the traditional transmission line constructed with nonlinear materials lend themselves to higher power applications, while the split ring resonator is better suited for applications involving antennas. We provide a detailed summary of past studies for these topologies and conclude by exploring ongoing work and future opportunities for technological development.

The aspects of safety and occupational health have been included as part of the design criteria in many process and molecular design problems. However, the main design objectives of these works are the molecular properties and process variables that offer the optimized process objective. Safety and health criteria are not strongly emphasized in these problems, since they are often introduced as design constraints or are not evaluated in an extensive manner. In this research, a novel simultaneous process-molecular design framework by incorporating safety and occupational health aspects is presented to ensure the developed system does not pose much harm. The hazard level is evaluated with the utilization of safety and health indexes to examine chemical properties and process conditions that may bring about potential risks. The overall hazard level is quantified through the use of index scores. The proposed strategy is demonstrated on a gas-absorption case study to design a solvent with its corresponding process that has optimized property functionalities, process performance, and safety and health attributes. This framework allows user to identify the process or molecular factors that may contribute to high risk potential, so that the appropriate safety and health measures can be implemented in the designed process.

The first open invited track in multi-objective optimisation for control systems was organised in 2017 with the idea of exchanging ideas and research about how those techniques are valuable for control engineers. Given that control engineering problems are generally multi-objective problems, multi-objective optimisation offers an interesting approach via the simultaneous optimisation of all design objectives. Controller tuning is not except from this. In this paper we perform a review and analysis of the literature, limited to the IFAC environment, to appreciate and detect new tendencies in controller tuning applications via multi-objective optimisation. Time window under consideration is from 2015 to date, coinciding with a previous review on the topic, as well as the emigration of IFAC proceedings to Elsevier.

This paper studies a multi-objective optimization (MOO) problem in full-duplex (FD) networks with simultaneous wireless information and power transfer (SWIPT). In the considered networks, an FD base station (BS) communicates with multiple half-duplex (HD) uplink (UL) and downlink (DL) users simultaneously. The energy receivers (ERs) harvest energy from the ambient radio frequency (RF) signals and may act as potential eavesdroppers. Specifically, there exists two conflicting yet important system design objectives, i.e., secrecy energy efficiency (SEE) maximization and energy harvesting efficiency (EHE) maximization. An MOO design based on the weighted Tchebycheff approach is proposed to investigate the tradeoff between these two design objectives. The proposed design takes into account the quality-of-service (QoS) guarantees of secrecy rate and energy harvesting (EH) under the imperfect channel state information (CSI) of ERs. The formulated MOO problem is solved with a two-layer optimization algorithm, where the modified Dinkelbach method is applied to deal with the generalized fractional programming (FP) in the outer layer problem and semidefinite relaxation (SDR), successive convex approximation (SCA) as well as S-procedure methods are applied to transfer the inner layer problem into a convex iterative program. Moreover, we propose a suboptimal solution to the formulated problem and analyze the computational complexities. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithms and reveal a tradeoff region between SEE and EHE.

We present a framework for achieving efficient dynamic management of configurable reactive data-flow circuits subject to global design objectives such as mutual exclusion on shared resources and minimization of energy consumption. We propose a new symbolic controller synthesis algorithm that targets the optimization of a cost function summed over a sliding window of a given number of reactions of the system. We then present a technique for constructing symbolic models of configurable data-flow circuits that lends itself to the automatic computation of dynamic configuration controllers. We use these models to experimentally evaluate our control algorithm, and make the case for symbolic optimal discrete controller synthesis on such designs.

The development of high-frequency, wide-bandwidth, high-power extended interaction oscillators (EIOs) has always been the focus of researchers working on millimeter-wave and terahertz electronic devices. However, these design objectives are affected by many structural and operating parameters, and traditional manual optimization and local optimization are no longer suitable for solving these problems. In this paper, based on a one-dimensional, nonlinear, self-consistent program of EIOs, a multiobjective optimization method that employs the nondominated sorting genetic algorithm II (NSGA-II) is proposed to simultaneously optimize device output power, bandwidth, and structure length. By using this approach, the optimization process of a 95-GHz EIO is presented, and the corresponding Pareto solutions are obtained after 500 generations with a population size of 50. The results show that the beam-wave interaction and the coupling mechanism lead to synchronization of the structural parameters and the electrical parameters with each other, and the coexistence of multiple objectives guides the zonal distribution of the optimal solutions. That is, the oscillators with fewer gaps have shorter structure length and higher power, whereas those with more gaps are prone to start oscillation and have wider bandwidth. Several sets of optimization results obtained using the proposed method agree well with the results obtained in the CST-PIC solver, which proves that the proposed algorithm is effective for optimizing EIOs because it considers multiple design goals and can serve as a theoretical basis for engineering development.

This paper addresses the design of adaptive cruise controllers for guaranteed collision avoidance. The design problem is solved with distributed feedback controllers which work with locally measurable quantities and, hence, do not require a centralised coordinating unit or a communication system. The design objectives are achieved by placing the closed-loop eigenvalues in a proposed set so that the controlled vehicle is asymptotically stable and satisfies a sufficient condition on external positivity.

In this paper, we investigate the supervisory control problem for timed discrete-event systems (TDES) under partial observation. In the timed setting, the system consists of both standard logical events and time event, where the former can be disabled directly by the supervisor if it is controllable while the latter can only be preempted by forcing the occurrences of forcible events. We consider a general control mechanism where the supervisor can choose which events to force dynamically online at each instant. The design objective is to synthesize a maximally-permissive supervisor to restrict the behavior of the system such that the closed-loop language is within a safe specification language. Effective procedure is presented to synthesize such a supervisor. To our knowledge, how to synthesize a maximally-permissive partial-observation supervisor has not been solved for timed DES. We provide a solution to this problem under a general control mechanism.

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.

Self-healing materials, which are inspired by the repair functionality of biological systems, increasingly incorporate microvascular components as an approach to mimic the autonomic healing abilities of biological organisms. Synthetic microvasculature addresses the limitations in healing agent availability within microcapsule-based systems in order to achieve multiple healing events at a particular damage location, and to repair large-scale fracture damage. Biological microvascular systems are briefly reviewed with respect to key design elements and considerations for functional and reliable synthetic microvascular systems. Subtractive and additive manufacturing techniques are discussed and their relative advantages and disadvantages compared within the context of structural and nonstructural parts. The performance of fabricated specimens and structures are presented with an emphasis on mechanical restoration. The healing efficacy is discussed within broad categories of mechanical loading and damage conditions, and includes quasi-static, fatigue, impact, and macroscale material loss. The field of microvascular self-healing has significant potential for continued growth; further inspiration from biology may yield in situ damage sensing, active fluid rerouting, improved computational optimization of vascular networks for multifunctional design objectives, and novel material chemistries for improved manufacture of engineered networks within large-scale structures.

We study the design problem for the optimal classification of functional data. The goal is to select sampling time points so that functional data observed at these time points can be classified accurately. We propose optimal designs that are applicable to either dense or sparse functional data. Using linear discriminant analysis, we formulate our design objectives as explicit functions of the sampling points. We study the theoretical properties of the proposed design objectives and provide a practical implementation. The performance of the proposed design is evaluated through simulations and real data applications. The Canadian Journal of Statistics 48: 285–307; 2020 © 2019 Statistical Society of Canada

The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks.

In this paper, a novel distributed finite-time control scheme is proposed to enhance the resilience and frequency stability of smart grid by utilizing a distributed energy storage system (DESS). The proposed entire framework integrates the traditional governor-based control and excitation control with DESS-based control. The design objectives also address some practical challenges including communication delay, limited DESS capacity or absent DESS, and voltage dynamic and denial-of-service (DoS) attacks. The proposed excitation control can enhance the performance of other existing approaches for utilizing DESS. The IEEE 68-bus power system and East China Power Grid are used to validate the effectiveness of the proposed control framework in enhancing the frequency stability and resilience of smart grid.

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free Energy (EFE) minimisation, a core feature of the framework, does not lead to purposeful explorative behaviour in linear Gaussian dynamical systems. We provide a simple proof that, due to the specific construction used for the EFE, the terms responsible for the exploratory (epistemic) drive become constant in the case of linear Gaussian systems. This renders AIF equivalent to KL control. From a theoretical point of view this is an interesting result since it is generally assumed that EFE minimisation will always introduce an exploratory drive in AIF agents. While the full EFE objective does not lead to exploration in linear Gaussian dynamical systems, the principles of its construction can still be used to design objectives that include an epistemic drive. We provide an in-depth analysis of the mechanics behind the epistemic drive of AIF agents and show how to design objectives for linear Gaussian dynamical systems that do include an epistemic drive. Concretely, we show that focusing solely on epistemics and dispensing with goal-directed terms leads to a form of maximum entropy exploration that is heavily dependent on the type of control signals driving the system. Additive controls do not permit such exploration. From a practical point of view this is an important result since linear Gaussian dynamical systems with additive controls are an extensively used model class, encompassing for instance Linear Quadratic Gaussian controllers. On the other hand, linear Gaussian dynamical systems driven by multiplicative controls such as switching transition matrices do permit an exploratory drive.

We study a new design objective for "Internet in space" for terrestrial users: To align logical network topology, address, and route in the virtual cyberspace with movements of satellite mega-constellations and earth's rotations in the real physical world. We explain why this is particularly desirable by the recent low-earth-orbit (LEO) mega-constellations, and how it welcomes mobility to streamline the network design for stability, efficiency, and scalability in an unstable space-ground environment. We showcase its feasibility with the common fixed satellite sub-point trajectory in mega-constellations. We describe how it permits stable recursive topology, unifies cyber-physical locations in the address, and naturally embeds the geographical routing into the topological routing.

A cardiovascular stent design optimization method is proposed with application to a pediatric balloon-expandable prosthetic heart valve. The prosthetic valved conduit may be expanded to a larger permanent diameter in vivo via subsequent transcatheter balloon dilation procedures. While multiple expandable prosthetic heart valves are currently at different stages of development, this work is focused on one particular design in which a stent is situated inside of an expandable polymeric valved conduit. Since the valve and conduit must be joined with a robust manufacturing technique, a polymeric glue layer is inserted between the two, which results in radial retraction of the valved region after expansion. Design of an appropriate stent is proposed to counteract this phenomenon and maintain the desired permanent diameter throughout the device after a single non-compliant balloon dilation procedure. The finite element method is used to compute performance metrics related to the permanent expansion diameter and required radial force. Additionally, failure due not only to high cycle fatigue but also due to ductile fracture is incorporated into the design study through the use of an existing ductile fracture criterion for metals. Surrogate models are constructed with the results of the high fidelity simulations and are subsequently used to numerically obtain a set of Pareto-optimal stent designs. Finally, a single design is identified by optimizing a normalized aggregate objective function with equal weighting of all design objectives.

This paper studies the relevant design objectives guided by the concept of environmental perception and information means, finds the shortcomings of the urban environmental art design mode, and explores the solutions. This paper analyzes the information processing of urban environmental art design, and introduces the contents of the system with system functional requirements requirements.

The optimization process of compressors is usually regarded as a ‘black-box’ problem, in which the mathematical form underlying the relationship between design parameters and the design objective is impractical and costly to be obtained. To solve the ‘black-box’ problem, Bayesian optimization has been proven as an accurate and efficient method. However, the application of such a method in the design of compressors is rarely discussed, particularly no work has been reported in terms of the positive displacement type compressor. Therefore, this paper aims to introduce the Bayesian optimization to the design of positive displacement compressors through the optimization process of the novel limaçon compressor. In this paper, a two-stage optimization process is presented, in which the first stage optimizes the geometric parameters as per design requirements and the second stage focuses on revealing an optimum setting of port geometries that improves machine performance. A numerical illustration is offered to prove the validity of the presented approach.

In order to realize automatic detection of corrosion on the piston rod of hydraulic hoist of hydropower station, it is important to find the location of corrosion and prevent oil leakage of hydraulic hoist and the blocking of control valve or other adverse consequences. A quick and convenient inspection robot for piston rod of hydraulic hoist is developed. Firstly, according to the specific working requirements of the piston rod of the hydraulic hoist of hydropower station and the basic principle of the robot, the structure scheme of the design of the testing robot is put forward. Then the robot control system is designed for the detection robot, and the basic idea of image recognition is introduced. Next the human-computer interaction interface is designed. Finally, the performance of the detection robot was tested to verify whether the reliability of the robot and the accuracy of image recognition meet the design objectives, and to ensure that the performance of the piston rod detection robot meets the design requirements..

The design displacement, its corresponding acceleration performance, and the re-centering performance of bilinear hysteretic isolation systems are adopted as previously determined design objectives for equivalent linear analysis. To demonstrate the applicability and generalization of the analysis procedure, two sets of values for damping modification factors are employed in the analysis: those provided by ASCE/SEI 7-16, and those estimated for different ranges of the ratios of effective periods of seismic isolation systems to pulse periods of ground motions. To investigate a broad range of seismic responses of base-isolated structures, 15 pulse-like near-fault ground motions are used for numerical demonstration. The analysis procedure is numerically verified to be practically feasible. A numerical comparison also shows that the three design objectives previously determined in the analysis procedure are sufficiently conservative compared with analysis results from nonlinear dynamic response history, even when subjected to pulse-like near-fault ground motions. Regarding the approximation to maximum inelastic acceleration and displacement responses, it is particularly more conservative for the former when the design displacement is greater and when adopting values of the damping modification factors provided in ASCE/SEI 7-16. For the approximation to dynamic residual displacement responses, the influences of pulse-like near-fault ground motions and different design objectives on the re-centering performance of bilinear hysteretic isolation systems still need further study.

The rapid and massive development of technology is a challenge for Higher Education to continue adapting and innovating to maintain academic quality. Therefore, this study aims to analyze the diffusion of innovation models to increase the use of information technology so that every application developed in higher education can run optimally following the design objectives. The method used in this study is an innovation diffusion model, which includes an analysis of the essence of communication channel innovation, the reception process, and social systems. Technology acceptance is assessed based on the innovation-decision process stage, which includes the degree of knowledge, persuasion, decision-making process, implementation, and confirmation of the benefits of using technology. The results show that the process of innovation diffusion can run effectively when paying attention to the profile of human resources (brain-ware), technological conditions (software and hardware), organizational policies (orga-ware), documentation (info-ware), and environmental dynamics (social systems).

Purpose This paper aims to obtain a symmetrical step-down topology with lower equivalent capacity and wider step-down range under the condition of the same output. Two new symmetrical step-down topologies of star-connected autotransformers are proposed in this paper. Taking the equivalent capacity as the main parameter, the obtained topologies are modeled and analyzed in detail. Design/methodology/approach This paper adopts the research methods of design, modeling, analysis and simulation verification. First, the star-connected autotransformer is redesigned according to the design objective of symmetrical step-down topology. In addition, the mathematical model of two topologies is established and a detailed theoretical analysis is carried out. Finally, the theoretical results are verified by simulation. Findings Two symmetrical star-connected autotransformer step-down topologies are designed, the winding configurations of the corresponding topology are presented, the step-down ranges of these three topologies are calculated and the influence of step-down ratio on the equivalent capacity of autotransformer are analyzed. Through analysis, the target step-down topologies are obtained when the step-down ratio is [1.1, 5.4] and [1.1, 1.9] respectively. Research limitations/implications Because the selected research object is only a star-connected autotransformer, the research results may lack generality. Therefore, researchers are encouraged to further study the topologies of other autotransformers. Practical implications This paper includes the implications of the step-down ratio on the equivalent capacity of autotransformers and the configuration of transformer windings. Originality/value The topologies designed in this paper enable star-connected autotransformer in the 12-pulse rectifier to be applied in step-down circumstances rather than situations of harmonic reduction only. At the same time, this paper provides a way that can be used to redesign the autotransformer in other multi-pulse rectifier systems, so that those transformers can be used in voltage regulation.

The emergence of OEM and industry benchmarks [1-6] raised the expectation regarding automotive headlamps performance in the last decade, resulting in growing benefits regarding traffic safety. Concomitantly, designing headlamps has grown in complexity, especially for low beam and high beam applications. Fulfilling all requirements concurrently is challenging, not merely due to the large number of requirements to keep track off during development, but predominantly due to the simultaneous inclusion of lamp- (luminous intensity) and vehicle-based (illuminance) metrics in the design. In this paper we propose and demonstrate an alternative design methodology for the development of forward lighting systems with the goal of reducing development time, providing superior photometry, and providing more structure to the development process. Instead of iteratively adjusting optical surfaces and running subsequent simulations to validate each design step, checking whether the change could achieve the respective design objective, we are subdividing the overall design problem into multiple steps and solve multiple inverse problems instead. Specifically, we compile all requirements into a unified set of luminous intensity requirements. Subsequently, we create analytical light distributions that meet this total set of requirements. Once validated, the resulting target light distribution is fragmented into multiple light distributions, which are then used as individual design targets for the creation of freeform surfaces in support of each optical sub-component. The total light distribution is obtained by superposition of all partial light distributions.

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive, and time-consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free energy profiles of the ligand unbinding process, focusing on the free energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (IC) that form contacts between the protein and the ligand, it then iteratively updates these interactions during a series of biased molecular-dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite temperature string simulations to obtain the free energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for potential further development of these compounds. We compared the free energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates, and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

Reducing the power and energy consumption of Convolutional Neural Network (CNN) Accelerators is becoming an increasingly popular design objective for both cloud and edge-based settings. Aiming towards the design of more efficient accelerator systems, the accelerator architect must understand how different design choices impact both power and energy consumption. The purpose of this work is to enable CNN accelerator designers to explore how design choices affect the memory subsystem in particular, which is a significant contributing component. By considering high-level design parameters of CNN accelerators that affect the memory subsystem, the proposed tool returns power and energy consumption estimates for a range of networks and memory types. This allows for power and energy of the off-chip memory subsystem to be considered earlier within the design process, enabling greater optimisations at the beginning phases. Towards this, the paper introduces POMMEL, an off-chip memory subsystem modelling tool for CNN accelerators, and its evaluation across a range of accelerators, networks, and memory types is performed. Furthermore, using POMMEL, the impact of various state-of-the-art compression and activity reduction schemes on the power and energy consumption of current accelerations is also investigated.

Food stores and supermarkets are buildings, often with rather similar structures characterized by large surfaces and a single floor, that are particularly energy intensive. The energy uses associated with them are mainly electrical, in connection with air conditioning and food refrigeration. These buildings are particularly interesting for a systematic application of photovoltaic (PV) generation technology. After an analysis of the main energy consumption parameters and of the most common benchmarking approaches, standard solutions for the sizing of photovoltaic systems are proposed based on different design objectives, highlighting the potential of each solution proposed. Two specific indicators are defined for the sizing processes. The methodology is tested with reference to two different stores under the zero grid-injection restriction. The results showed how the degree of self-sufficiency for a supermarket obtained with a PV plant can be of the order of 20% in cases without storage system and can be increased over 50% and up to 70–75% but only using relevant battery storage dimensions.

In this paper, we seek to identify an effective management policy that could reduce supply-demand gaps at taxi queues serving high-density locations where demand surges frequently happen. Unlike current industry practice, which relies on broadcasting to attract taxis to come and serve the queue, we propose more proactive and adaptive approaches to handle demand surges. Our design objective is to reduce the cumulative supply-demand gaps as much as we could by sending notifications to individual taxis. To address this problem, we first propose a highly effective passenger demand prediction system that is based on the real-time flight arrival information. By monitoring cumulative passenger arrivals, and control for factors such as the flight's departure cities, we demonstrate that a simple linear regression model can accurately predict the number of passengers joining taxi queues. We then propose an optimal control strategy based on a Markov Decision Process to model the decisions of notifying individual taxis that are at different distances away from the airport. By using a real-world dataset, we demonstrate that an accurate passenger demand prediction system is crucial to the effectiveness of taxi queue management. In our numerical studies based on the real-world data, we observe that our proposed approach of optimal control with demand predictions outperforms the same control strategy, yet with Poisson demand assumption, by 43%. Against the status quo, which has no external control, we could reduce the gap by 23%. These results demonstrate that our proposed methodology has strong real-world potential.

Gearboxes are essential in mechanical drive trains for power transmission. A low noise emission and thus an optimized excitation behavior is a substantial design objective for many applications in terms of comfort and operational safety. There exist numerous processes for manufacturing gears, which all show different properties in relation to the process variables and, therefore, differences in the resulting accuracy and quality of the gear flank. In this paper, the influence of three different manufacturing processes for hard finishing—continuous generating grinding, polish grinding and gear skiving—on the surface structure of gear flanks and the excitation behavior is investigated experimentally and analyzed by the application force level. A tactile scanning of the gear flanks determines the flank surface structure and the deviations from the desired geometry. A torsional acceleration measurement during speed ramp-ups at different load levels is used to analyze the excitation of the gears. The results show only a minor influence of the surface structure on the application force level. The excitation behavior is dominated by the influence of the flank modification and its deviation from the design values.

Vibration control problem of structural systems considering uncertainties from both the model and the excitation is addressed using a reliability-based design methodology in this chapter. The control design objective is to achieve the optimal reliability of the structure or equivalently the least failure probability by the controller for the event involving important response quantities of the structure. Structural reliability is of great interest to structural engineers, the concept of reliability-based design to vibration control can be easily adopted in the field of civil engineering. The control performance is treated with a probabilistic perspective, which is now a common approach in dealing with uncertainties. Obtaining the probable robust performance of the control system adds an “extra dimension” to the robust analysis of uncertain systems. The first-passage problem is selected as the control performance for design. The reliability-based controller and performance analysis can be updated in conjunction with the Bayesian system identification techniques.

Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number of media components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design-of-experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48 h after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70 vs. 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48 h growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals.

Automated Storage and Retrieval Systems (AS/RS) are warehouses, specifically designed for material handling in modern manufacturing systems and are extensively utilized as distribution centers, due to the advantages of improved inventory control, cost-effective utilization of time, space and equipment. With the advent of smart manufacturing in the Industry 4.0 era, the significance of adopting automation technology in warehousing becomes increasingly imminent. Techniques and tools are being developed to manage, control, design, and optimize the AS/RS(s). Over the years, many researchers have focused on the design, analysis, and optimization of various AS/RS configurations. This paper aims to present a systematic literature review on the research in Configuration design and Optimization of AS/RS, by classifying the research according to the design objectives, configurations and optimization algorithms used, in order to highlight and expand the awareness on the current best practices, progress, and future research directions.

As with other engineering design tasks, mine design involves setting design objectives and constraints (the feasible solution space) and finding the optimal design alternative. Mine engineers often struggle to incorporate the preferences of local community members into their evaluation of mine design alternatives because the mining literature lacks tools to quantify such risks during mine planning. This paper presents an approach to evaluate community acceptance (i.e., community preferences for the alternatives) using discrete choice models and decision-based design during mine planning. Using discrete choice models and a rigorous framework, engineers can estimate the cost of social risks as a function of the probability that individuals in the host community will prefer a particular design alternative. They can then estimate the overall utility of a particular design alternative to the project proponents. This paper illustrates the proposed approach with a strategic mine planning exercise for a gold mine. The framework can be a useful tool for designing mines for sustainability, if combined with effective community engagement and management’s commitment to creating shared value.

Future shipboard electrical systems utilizing zonal Medium Voltage dc (MVdc) distribution, face conflicting design objectives. The system must be simultaneously power dense, cost effective, and survivable. DC zonal distribution with multiple generation sources offers survivability advantages. Shipboard electrical distribution systems can utilize a floating ground design to increase survivability in single line-to-ground (SLG) fault scenarios. However, this could decrease the survivability upon the incidence of a second line-to-ground (LG) fault or during multiple line-to-ground fault (MLG) scenarios. Particularly when it comes to critical loads that are cross-fed from opposite sides of the ship and MLG faults on opposite polarity buses or between ac and dc buses occurring in different zones. This work characterizes the most problematic MLG fault scenarios and analyzes mitigation anpproaches.

ICAO Annex 16 regulations are used to certify the acoustic performance of subsonic transport aircraft. Each aircraft is classified according to the measured EPNL levels at specific certification locations along the approach and departure. By simulating this certification process, it becomes possible to identify all relevant parameters and assess promising measures to reduce the noise certification levels in compliance with the underlying ICAO regulations, i.e., allowable operating conditions of the aircraft. Furthermore, simulation is the only way to enable an assessment of novel technology and non-existing vehicle concepts, which is the main motivation behind the presented research activities. Consequently, the ICAO Annex 16 regulations are integrated into an existing noise simulation framework at DLR, and the virtual noise certification of novel aircraft concepts is realized at the conceptual design phase. The predicted certification levels can be directly selected as design objectives in order to realize an advantageous ICAO noise category for a new aircraft design, i.e., simultaneously accounting for the design and the resulting flight performance. A detailed assessment and identification of operational limits and allowable flight procedures for each conceptual aircraft design under consideration is enabled. Sensitivity studies can be performed for the relevant input parameters that influence the predicted noise certification levels. Specific noise sources with a dominating impact on the certification noise levels can be identified, and promising additional low-noise measures can be applied within the conceptual design phase. The overall simulation process is applied to existing vehicles in order to assess the validity of the simulation resultsfcompared to published data. Thereafter, the process is applied to some DLR low-noise aircraft concepts to evaluate their noise certification levels. These results can then be compared to other standard noise metrics that are typically applied in order to describe aircraft noise, e.g., SEL isocontour areas. It can be demonstrated that certain technologies can significantly reduce the noise impact along most of an approach or departure flight track but have only a limited influence on the noise certification levels and vice versa. Finally, an outlook of the ongoing developments is provided, in order to apply the new simulation process to supersonic aircraft. Newly proposed regulations for such concepts are implemented into the process in order to evaluate these new regulations and enable direct comparison with existing regulations.

New design objectives as the digital twin encourage companies to replace the tradition document-based systems engineering approach by a model-centric one. All views of the system rely on different types of models that serve many objectives, especially to improve communication among stakeholders. However, the increasing number of heterogeneous models jeopardize communication at the end. Indeed, to get a holistic view of the virtual definition, engineers have no other alternative than to navigate through numerous models requiring domain-specific software and language. In this paper, we propose to use virtual reality to develop an immersive environment for a collaborative model-centric review of engineered systems. The virtual environment, which relies on a digital thread stored in a graph-oriented database, enables users to explore a model-centric design by navigating through the models in a unique virtual space. To illustrate our proposal, we use a model-centric design of a telescope and shows how our preliminary prototype supports the reviewing activity with data limited to the architecture and geometry. Future works will concentrate on the integration of data related to other perspectives on the system.

Plate shape is one of the key quality indices of steel plates after quenching. It is of great significance to realize the prediction and optimization of plate shape for obtaining high quality steel plates. This paper designs a prediction and optimization system for plate shape in roller quenching process. Firstly, the roller quenching process is described in detail, the design objectives are analyzed, and the architecture of the system is designed. Then, the system is designed from four parts: the prediction model of plate shape, the comprehensive evaluation model of plate shape, the intelligent optimization model of operating parameters and the case matching. Finally, the prediction and optimization system is applied to the industrial site. The results of preliminary tests show that the system improves the quality of plate shape.

International audience; Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

This paper presents design and development of an automated material handling and transfer system inside a containment structure of a nuclear facility. In the nuclear fuel cycle facilities, since, the feed material being handled is radiotoxic in nature all the process operations are carried out inside a mechanical containment structure known as a Glove Box. Main challenges for design are constraints arising due to the presence of highly radioactive material, confined space, limited access for maintenance and operation, maintaining vacuum inside the glove box and remote handling. To address the above-mentioned challenges, a conveyor system is designed for material transfer inside the shielded glove boxes. The system has been developed considering prime design objectives of modularity, ease of remote maintenance, standardization, provision for manual override, interchangeability and the absence of pneumatic devices inside the glove boxes. It is proposed to use the customized roller conveyor system. All the rollers are designed for the in situ maintenance. Three different types of power transmission systems shall be tested, viz. belt mechanism, gear mechanism and chain mechanism. The best out of three fulfilling the above-mentioned design objectives will be finally selected for the installation. It is proposed to initially develop a prototype and then, later on, expand it further to complete the entire fabrication line of glove boxes.

The IPv6 routing protocol for low-power and lossy networks (RPL) was standardized by the IETF to allow multiple resource-constrained devices to interconnect in an IPv6 multihop mesh network. One of its design objectives is the ability to customize the routing objectives according to different application requirements. This is achieved through the support of various routing metrics and constraints that are then used by objective functions to pick the best path. Another objective is the ability to scale to networks composed of hundreds to thousands of devices.In this paper, we focus on the specific topic of multi-instance, a feature discussed in the RPL standard but not investigated through much research, a situation we attribute to a lack of publicly available implementation. We detail the architecture of RPL when operating in a multi-instance environment, clarify parts of the standard related to forwarding packets along a specific instance and present an implementation based on ContikiRPL we developed during the last few years. We detail some of our design choices. Then, using our prototype, we present a first systematic performance evaluation of multi-instance RPL through experiments in a large scale testbed. Our results indeed show a tipping-point when the number of instances becomes excessive but more importantly confirm that a limited number of instances can efficiently cohabit on a common network without negatively impacting the packet delivery ratio and RPL convergence time.

Isolated renewable microgrids represent one promising solution to reach objectives of rural electricity access. Optimising cost and reliability of these systems is however crucial and there exists an inherent trade-off between environmental, economic and reliability objectives. Furthermore, investments are often fixed at the beginning of the project, although repowering can be made during the project life. The method presented in this paper allows for evolutive sizing of isolated microgrids and uses a genetic algorithm to return the set of optimal solutions under the three objectives of long-term cost, renewable integration, and reliability. As a case study, we consider a rural microgrid currently in the process of being deployed in Mali. The results show that there is an opportunity to improve system cost and reliability on renewable microgrids by considering evolutive sizing. The results enable us to observe the trade-off between all design objectives.

Traditional rugged enclosures utilizing forced-air cooling provide inadequate operating margins for modern, high-power modular electronics in typical military environments. VITA’s recent mechanical standard seeks to improve SWAP-C for 3U and 6U form factor plug-in modules by adopting air-flow-through (AFT) technology, namely ANSI/VITA 48.8-2017. By delivering coolant flow through the plug-in module heatframe, the conductive heat path is optimized, shifting the design objectives from maximizing convective heat transfer along a common coldwall towards obtaining acceptable flow distributions across all AFT modules with minimum supplied pressure head. Rugged chassis designs supporting AFT modules create a flow network of parallel channels which share common inlets and outlets, effectively operating as a manifold. An extensive matrix of operating conditions and compliant designs are numerically investigated with parametric design sweeps. Simulations adopt varying distributions of flow resistance that hypothetical AFT modules would produce. Z-type, U-type, and combining manifold configurations are investigated. Given the number of configurations, exclusive use of 2-dimensional steady RANS simulations is made within STAR-CCM+ commercial CFD software. Results confirm that the combining manifold avoids considerable losses associated with the delivery of flow through a header, yielding flow rates superior to U and Z-type manifolds. A positive correlation is observed between mean slot resistance and flow uniformity among slots. The adoption of ANSI/VITA 48.8-2017 is simplified by providing a thorough presentation of data, enabling critical insights into system performance for various design strategies.