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Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.

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.

Three closely related polynomial‐based group key pre‐distribution schemes have recently been proposed, aimed specifically at wireless sensor networks. The schemes enable any subset of a predefined set of sensor nodes to establish a shared secret key without any communications overhead. It is claimed that these schemes are both secure and lightweight, that is, making them particularly appropriate for network scenarios where nodes have limited computational and storage capabilities. Further studies have built on these schemes, for example, to propose secure routing protocols for wireless sensor networks. Unfortunately, as shown by the author, all three schemes are completely insecure; whilst the details of their operation vary, they share common weaknesses. In two cases, we show that an attacker equipped with the information built into just one sensor node can compute all possible group keys, including those for which the attacked node is not a member; this breaks a fundamental design objective. In the other case, an attacker equipped with the information built into at most two sensor nodes can compute all possible group keys. In the latter case, the attack can also be achieved by an attacker armed with the information from a single node together with a single group key to which this sensor node is not entitled. Repairing the schemes appears difficult, if not impossible. The existence of major flaws is not surprising given the complete absence of any rigorous proofs of security for the proposed schemes. A further recent work proposes a group membership authentication and key establishment scheme based on one of the three key pre‐distribution schemes analysed here; as the author demonstrates, this scheme is also insecure, as the attack we describe on the corresponding pre‐distribution scheme enables the authentication process to be compromised.

Through extenics-based case reasoning, this research puts forward a method to configure the modules of complex machine products. Firstly, the knowledge on the process of product design was represented, and the convergence modes of design plans were analyzed, with the aid of product semantics and the knowledge representation model of the primitives. Next, the semantic correlation function was constructed, and a priority degree evaluation method for product family was created, in order to obtain the optimal configuration plan. In this way, the excellent design genes in product plans with relatively low correlations will be preserved. Finally, the proposed method was verified on the design of computer numerically controlled (CNC) roll grinder. Specifically, the quantitative and qualitative user needs were converted into the design objectives, producing the set of primitives (design objectives). Based on sematic correlations and priority degrees, the product/product family that best match the conceptual product was searched for in the case library, and the optimal configuration plan was prepared, in the light of semantic correlation network and local correlation evaluation. The results show that the local correlations of the bed and workbench module, headstock module, grinding system, measuring system, and support of MK8420 with design objectives were 0.298, 0.450, 0.600, 0.500, and 0.298, respectively, indicating that the configuration plan is in line with user needs and suitable for case-based reasoning. The proposed method has been successfully applied in actual production, laying a good basis for the intelligent design of products.

The recent advances in the development of coding metasurfaces created new opportunities in realization of radar cross section (RCS) reduction. Metasurfaces, composed of optimized geometries of meta-atoms arranged as periodic lattices, are devised to obtain desired electromagnetic (EM) scattering characteristics. Despite potential benefits, their rigorous design methodologies are still lacking, especially in the context of controlling the EM wavefront through parameter tuning of meta-atoms. One of the practical obstacles hindering efficient design of metasurfaces is implicit handling of RCS performance. To achieve essential RCS reduction, the design task is normally formulated in terms of phase reflection characteristics of the meta-atoms, whereas their reflection amplitudes—although contributing to the overall performance of the structure—is largely ignored. As a result, the conventional approaches are unable to determine truly optimum solutions. This article proposes a novel formulation of the metasurface design task with explicit handling of RCS reduction at the level of meta-atoms. Our methodology accounts for both the phase and reflection amplitudes of the unit cells. The design objective is defined to directly optimize the RCS reduction bandwidth at the specified level (e.g., 10 dB) w.r.t. the metallic surface. The benefits of the presented scheme are twofold: (i) it provides a reliable insight into the metasurface properties even though the design process is carried out at the level of meta-atoms, (ii) the obtained design requires minimum amount of tuning at the level of the entire metasurface. None of these is possible for phase-response-based approach fostered in the literature. For practical purposes, the design is conducted using a surrogate-assisted procedure involving kriging metamodels, which enables global optimization at a low computational cost. To corroborate the utility of our formulation, a high-performance metasurface incorporating crusader-cross-shaped meta-atoms has been developed. The obtained results indicate that the system characteristics predicted at the design stage are well aligned with those of the EM-simulated structure (which is not the case for the traditional design approach). The metasurface features 10-dB RCS reduction in the frequency range of 16.5 GHz to 34.6 GHz, as validated both numerically and experimentally.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Welding flux design involves optimising multiple quality attributes which are often conflicting and are of varying degree of concern to the stakeholders. The attributes are often selected as design criteria and are sometimes at different levels in the hierarchy such as primary, secondary and tertiary attributes. The articulation and incorporation of the opinions of stakeholders concerning the intensity of importance attached to each attribute at all the levels in the hierarchy in the optimisation process has remained a challenge. In this study, a framework was proposed for the articulation and incorporation of the preferences of stakeholders concerning all levels of attributes and illustrated with a lifecycle welding flux design case from the literature involving 5, 21 and 25 primary, secondary and tertiary criteria, respectively. Fifteen out of the 21 secondary criteria could not be further unbundled and were added to the tertiary criteria to make 40 lowest level criteria. The subjective judgements of stakeholders were converted to weighting coefficients using the analytical hierarchy process. The coefficients for the lowest level criteria were converted to global weights(WGC)which were the preference indices for use in the optimisation models. Based on the values of WGC, moisture pick-up, extrudability and slag detachability were ranked 1st, 2nd, and 3rdrespectively and the respective 38th, 39th and 40th lowest level criteria were fume generation, dilution and charpy impart strength.

Reliability has been considered as an important design measure in industry. The design of a system involves in general numerous discrete choices among available component types based on reliability, cost, performance, weight, etc. If the design objective is to maximize reliability for a certain cost requirement, then a strategy is required to identify the optimal combination of components and/or design configuration. This leads to combinatorial optimization problems which are NP-hard. For large industrial problems, exact methods are lacking since they require a very large amount of computation time to obtain the solution of the problem. This chapter will focus on the use of ant colonies to solve three optimal design problems which are among the most important in practice: 1. The reliability optimization of series systems with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. This is a nonlinear binary integer programming problem and characterized as an NP-hard problem. 2. The redundancy allocation problem (RAP) of binary series-parallel systems. This is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. 3. Buffers and machines selections in unreliable series-parallel production lines: we consider a series-parallel manufacturing production line, where redundant machines and in-process buffers are included to achieve a greater production rate. The objective is to maximize production rate subject to a total cost constraint. Machines and buffers are chosen from a list of products available in the market. The buffers are characterized by their cost and size. The machines are characterized by their cost, failure rate, repair rate and processing time. To estimate series-parallel production line performance, an analytical decomposition-type approximation is proposed. Simulation results show that this approximate technique is very accurate. The optimal design problem is formulated

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

The demand for fast charging is increasing owing to the rapid expansion of the market for electric vehicles. In addition, the power generation technology for distributed photovoltaic has matured. This paper presents a design scheme for a fast charging station for electric vehicles equipped with distributed photovoltaic power generation system taking the area with certain conditions in Beijing as an example construction site. The technical indexes and equipment lectotype covering the general framework and subsystems of the charging station are determined by analyzing the charging service demand of fast charging stations. In this study, the layout of the station is developed and the operation benefits of the station is analyzed. The design scheme realizes the design objective of “rationalization, modularization and intelligentization” of the fast charging station and can be used as reference for the construction of a fast charging network in urban area. Keywords: Electric vehicle, Fast charging station, Charging demand, Design scheme, Distributed photovoltaic

[EN] A set of tuning rules for Linear Active Disturbance Rejection Controller (LADRC) with three different levels of compromise between disturbance rejection and robustness is presented. The tuning rules are the result of a Multiobjective Optimization Design (MOOD) procedure followed by curve fitting and are intended as a tool for designers who seek to implement LADRC by considering the load disturbance response of processes whose behavior is approximated by a general first-order system with delay. The validation of the proposed tuning rules is done through illustrative examples and the control of a nonlinear thermal process. Compared to classical PID (Proportional-Integral-Derivative) and other LADRC tuning methods, the derived functions offer an improvement in either disturbance rejection, robustness or both design objectives., This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00.

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

A suite of genetic components and computational models enables the predictive design of mammalian cellular functions., Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations., Lecture Notes in Computer Science, 12881, ISSN:0302-9743, ISSN:1611-3349, Computational Methods in Systems Biology. CMSB 2021, ISBN:978-3-030-85632-8, ISBN:978-3-030-85633-5

Catalysts are of paramount importance as most chemical processes would be uneconomical without suitable catalysts. Consequently, the identification of appropriate catalysts is a key step in chemical process design. However, the number of potential catalysts is usually vast. To suggest promising candidates for experimental testing, in silico catalyst design methods are highly desirable. Still, such computational methods are in their infancy. Moreover, simple performance indicators are commonly employed as design objective instead of evaluating the actual process performance enabled by considered catalysts. Here, we present the CAT-COSMO-CAMPD method for integrated in silico design of homogeneous molecular catalysts and processes. CAT-COSMO-CAMPD integrates design of molecular catalysts with process optimization, enabling a process-based evaluation of every designed candidate catalyst. Reaction kinetics of catalytic reactions are predicted by advanced quantum chemical methods. We demonstrate for a catalytic carbamate-cleavage process that CAT-COSMO-CAMPD successfully identifies catalyst molecules maximizing the predicted process performance., Computers & Chemical Engineering, 153, ISSN:0098-1354, ISSN:1873-4375

The use of standard designs in construction is a topical subject of research within the frame of project management. Construction projects, especially industrial construction ones, are characterized by a great diversity of design and other solutions. Just about any new project produces new ways of resolving issues and involves looking for optimum constructive, planning, and other solutions. There arises the need to coordinate all parts of the work with the client, although essentially there is nothing particularly novel about the project, as many of the issues were already resolved in past activity. Quite naturally, many construction companies will look for tried-and-true solutions and strive for the unification and standardization of design work. This paper describes a set of tools for implementing a system of standard designs and proposes a set of ways to achieve one’s key design objectives.

The integration of renewable energy systems in buildings has become a common practice. The design of net Zero Energy Buildings promotes the energy transition from fossil fuels based technologies by coupling renewable systems as photovoltaic (PV) plants with heat pumps for heating and cooling. In this framework, the correct sizing of PV plants plays a central role in the perspective of the definition of self-consumption schemes. Considering the problems related to small-scale photovoltaic plants, the present paper proposes an analysis of different sizing strategies on the energy streams between the building and the electrical grid to reduce the bidirectional energy flows with the electrical grid. The design objective concern the minimization of both the energy exported and imported through an optimized size of the PV plant and the integration of electrochemical energy storage. The investigation is based on the experimental data under real operating conditions obtained from two different residential buildings, a conventional residential house, and a nearly Zero Energy Building (nZEB). The paper aims to discuss the results of the monitoring activity and define criteria and guidelines for the design of this kind of systems based on real collected data. The use of two different self-consumption indexes helps to determine how close the system is to a zero-energy configuration showing that the use of a small to medium size storage system is fundamental for maximizing the self-consumption share of the energy produced.

Component feeding is an important process in assembly and packaging lines, affecting the throughput. It is therefore important to optimize the feeding process with the aim of reducing the takt time of the production line. In kitting lines, the feeding devices are typically used to create little groups of components, preparing the batches for final grouping into a kit. In the paper, a dynamic model is developed and validated, which describes the interaction of the components to be fed with the blades of a rotating feeding unit. Then, an optimization method is proposed based on the dynamic model using a weighted cost function, which allows to define the shape of the blade according to a set of design objectives.

Many recent research studies focused on the development of innovative seismic resilient structures by chasing the objectives of minimising both seismic damage and repair time, hence allowing the definition of structures able to go back to the undamaged, fully functional condition, in a short time. In this context, the present study investigates an innovative type of self-centring damage-free steel column base (CB) connection and its beneficial effects when used within steel moment-resisting frames (MRFs). The proposed connection consists of a rocking column equipped with a combination of friction devices, providing energy dissipation capacity, and post-tensioned bars with disk springs, introducing restoring forces in the joint. Contrary to conventional steel CBs, the proposed connection exhibits moment–rotation behaviours that can be described by simple analytical equations, allowing the definition of an easy-to-apply design procedure. Numerical models of the connection, developed in OpenSees, are validated against experimental results and successively implemented within a four-storey case study steel MRF. Incremental Dynamic Analyses are performed to derive the samples of the demand for the engineering demand parameters of interest while accounting for the record-to-record variability. Fragility Curves show the effectiveness of the proposed solution in reducing the residual storey drifts and in protecting the first-storey columns from damage, hence providing significant advantages in terms of repairability, and hence resilience of the structure with a negligible increase on the overall cost. The results show that the damage-free behaviour of the CBs is a key requirement when self-centring of MRFs is a design objective.

Forschung im Ingenieurwesen = Engineering research (2021). doi:10.1007/s10010-021-00445-2, Published by Springer, Berlin

In the perspective of sustainable cities, urbanist and planners have to deal with a constantly increasing penetration of renewable energy systems (RES) in the urban structure: in tertiary sector, retail and supermarket stores as particularly energy-intensive compounds play a core role in this scenario, as they are often characterized by similar kinds of structures mostly, large surfaces and a single floor, and analogous composition of energy loads. Most of the current research focuses on energy efficiency, but these buildings are particularly interesting for a systematic application of PhotoVoltaic (PV) generation technology as the energy uses associated with them are mainly electrical, in connection with air conditioning and food cooling. This article analyses the energy consumption in the supermarket sector for the sale of food. After an analysis of the main energy consumption parameters, standard solutions for the sizing of PV systems are proposed based on different design objectives, highlighting the potential of each proposed solution. Results show that a high percentage of self-consumption can be achieved, and that a battery storage set at a mean daily PV potential production level (4 kWh/kW in the case) perfectly suits to reach a self-sufficiency between 50-70%. Retail and food stores have proven to be a perfect promoter for PV diffusion either in a high self-consumption configuration, or turning them into energy hub for mobility to building or energy sharing policies.

The Holos-Quad micro-reactor concept is proposed by HolosGen LLC to generate 22 MWt with a lifetime of approximately 8 effective full power years (EFPYs) for civilian applications. The design is based on a very innovative high-temperature gas-cooled reactor concept using four Subcritical Power Modules (SPMs) that fit into one commercial 40-foot transport ISO container. A rigorous design approach was employed in order to ensure that all input parameters were fully investigated and the best solutions possible were considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the design objectives, the operational constraints, and the input parameters. In the second step, a sensitivity analysis was performed to enable a preliminary investigation of the input parameters to identify the correlations among input and output variables. Finally, the design optimization was performed in the third step, employing a genetic algorithm to effectively explore the highly constrained input parameters and find global optimal solutions. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications. For applications where economics matters less and the ease of transportation matters more, a core weight of ~15 tons could achieve a lifetime of ~3.5 EFPYs. For applications where economics matters more and the ease of transportation matters less, a lifetime of 8.3 EFPYs could be achieved with a total core weight of ~26.7 tons.

Over the last decades, finite element analysis (FEA) has become a standard tool in industrial product development, allowing for virtual analysis of designs, quick turnaround times and prompt implementation of results. Although academic research also provides numerous approaches for evaluating a product’s robustness towards geometrical, material and load variations based on FEA analyses, this, however, stands in striking contrast to the limited use of these FEA-based variation simulations in industry. In order to bridge the existing gap between academic research and industrial application, this paper explores the barriers that limit the adoption of FEA-based variation simulation. The investigation is based on interviews with five lead engineers, followed by a case study that details the underlying technical challenges and allows for some initial suggestions for future solutions. The case study involves a sterile canister with seven geometrical variables. The design objective is to ensure sufficient sealing within the range of expected probabilistic variation. The combined study details the identified main barriers for a wider application, that is, the lack of robust CAD, practical guidelines to select an efficient design of experiments for design purposes, and the complexity of the automated processes. From a technical perspective, the case study results in estimations for main and interaction effects, an accurate metamodel and Monte Carlo simulations of 100,000 samples providing the design engineer with more detailed and actionable insights on the performance of the product compared with the traditional nominal or best/worst case simulations.

In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning approach, where a network is trained to approximate the mapping from observation data to regularization parameters. Once the network is trained, regularization parameters for newly obtained data can be computed by efficient forward propagation of the DNN. We show that a wide variety of regularization functionals, forward models, and noise models may be considered. The network-obtained regularization parameters can be computed more efficiently and may even lead to more accurate solutions compared to existing regularization parameter selection methods. We emphasize that the key advantage of using DNNs for learning regularization parameters, compared to previous works on learning via optimal experimental design or empirical Bayes risk minimization, is greater generalizability. That is, rather than computing one set of parameters that is optimal with respect to one particular design objective, DNN-computed regularization parameters are tailored to the specific features or properties of the newly observed data. Thus, our approach may better handle cases where the observation is not a close representation of the training set. Furthermore, we avoid the need for expensive and challenging bilevel optimization methods as utilized in other existing training approaches. Numerical results demonstrate the potential of using DNNs to learn regularization parameters., 27 pages, 16 figures

The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian methodology. The chosen design objectives were to maximise pressure recovery, and minimise wall-frictional losses along the geometry. The design variables were chosen to explore potential new designs, using a series of subdivision-curves and splines on the inflow cone, outer-heel, and diffuser. The optimisation run was performed under part-load for the Kaplan turbine. The design with the lowest energy-loss identified on the Pareto-front was found to have a straight tapered diffuser, chamfered heel, and a convex inflow cone. Analysis of the performance quantities showed the typically used energy-loss factor and pressure recovery were highly correlated in cases of constant outflow cross-sections, and therefore unsuitable for use of multi-objective optimisation. Finally, a number of designs were tested over a range of discharges. From this it was found that reducing the heel size increased the efficiency over a wider operating range.

In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidently that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even when the control policy is publicly known. In this paper, we propose a new approach for enforcing opacity using non-deterministic supervisors. A non-deterministic supervisor is a decision mechanism that provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set to actually control the plant. Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy. We provide a sound and complete algorithm for synthesizing a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors are strictly more powerful than deterministic supervisors in the sense that there may exist a non-deterministic opacity-enforcing supervisor even when deterministic supervisors cannot enforce opacity., 16 Pages. This paper has been accepted by IEEE transactions on automatic control

This paper analyzes the optimal sizing of a particular solution for renewable energy residential building integration. The solution combines a photovoltaic (PV) plant with a heat pump (HP). The idea is to develop a system that permits the maximum level of self-consumption of renewable energy generated by using a small-scale solar array installed on the same building. The problem is analyzed using data obtained from an experimental system installed in a building in Pisa, Italy. The residential house was equipped with a PV plant (about 3.7 kW of peak power), assisting a HP of similar electrical power (3.8 kW). The system was monitored for eight years of continuous operation. With reference to the data acquired from the long-term experimental analysis and considering a more general perspective, we discuss criteria and guidelines for the design of such a system. We focus on the possibility of exporting energy to the electrical grid, from the perspective of obtaining self-consumption schemes. Considering that one of the problems with small-scale PV plants is represented by the bidirectional energy flows from and to the grid, possible alternative solutions for the design are outlined, with both a size reduction in the plant and utilization of a storage system considered. Different design objectives are considered in the analysis.

In this paper, sensitivity analysis and optimization of a high altitude long endurance (HALE) solar aircraft was implemented. Zephyr S was referred to for the aircraft conference configuration, and OpenVSP and XFLR5 were employed to create configuration and perform aerodynamic analysis. In the conceptual design stage of the HALE solar aircraft, technology identification, evaluation, and selection (TIES) methodology was employed. According to the design requirements, problem definition was established, and design goal, variations, and targeted values were set up to implement independent design variables to meet the design requirements. Based on the design of experiments (DOE), modeling of the relationship between design objective parameters and independent design values was implemented. The independent design variables with the largest influence were selected in the screening test. By employing the selected independent design variables, regression equations and sensitivity profiles were produced through response surface method. Inter-factor relationship was easily analyzed through the sensitivity profile. Regression equations were employed in the Monte Carlo simulation to draw design objective parameter values for 10,000 combinations of independent design variables. As a result of the Monte Carlo simulation, the design feasibility of design objective parameters was assessed. Optimization was performed using the desirability function of JMP software, and constraints were applied to each design objective parameter to derive the optimum values of independent design variables. Then, the values of optimized design independent variables were applied to the solar aircraft design framework and analyzed for the endurance flight performance. By comparing the endurance of the optimized configuration with the reference configuration, it was confirmed that the endurance could be improved by using the methodology proposed in this study.

Traditional rigid column base connections are damaged to different degrees after an earthquake and the damage is generally nonrecoverable. Thus, the cost of repairing or dismantling is quite high. A new type of slip-friction column base connection is proposed in this paper, which aims to replace the yielding energy dissipation of the traditional column base connection by the sliding friction energy dissipation between the arc endplates, thus achieving the design objective of damage-free energy dissipation. Finite element simulation was conducted to study the hysteretic performance of the proposed connections considering different axial compression ratios. The research indicates that both kinds of the proposed connections show good energy dissipation behavior and the increase of axial compression force can increase the energy dissipation ability. It also shows that the two kinds of connections can achieve the objective of damage-free energy dissipation. For the proposed connection, future research is still needed such as corresponding tests in the laboratory, the effect of the connection on the whole structure, and the re-centering systems for the connections.

As modern embedded systems are becoming more and more ubiquitous and interconnected, they attract a world-wide attention of attackers and the security aspect is more important than ever during the design of those systems. Moreover, given the ever-increasing complexity of the applications that run on these systems, it becomes increasingly difficult to meet all security criteria. While extra-functional design objectives such as performance and power/energy consumption are typically taken into account already during the very early stages of embedded systems design, system security is still mostly considered as an afterthought. That is, security is usually not regarded in the process of (early) design-space exploration of embedded systems, which is the critical process of multi-objective optimization that aims at optimizing the extra-functional behavior of a design. This position paper argues for the development of techniques for quantifying the ’degree of secureness’ of embedded system design instances such that these can be incorporated in a multi-objective optimization process. Such technology would allow for the optimization of security aspects of embedded systems during the earliest design phases as well as for studying the trade-offs between security and the other design objectives such as performance, power consumption and cost.

Designing a mechatronic system is a complex task since it deals with a high number of system components with multidisciplinary nature in the presence of interacting design objectives. Currently, the sequential design is widely used by designers in industries that deal with different domains and their corresponding design objectives separately leading to a functional but not necessarily an optimal result. Consequently, the need for a systematic and multiobjective design methodology arises. A new conceptual design approach based on a multicriteria profile for mechatronic systems has been previously presented by the authors, which uses a series of nonlinear fuzzy-based aggregation functions to facilitate decision-making for design evaluation in the presence of interacting criteria. Choquet fuzzy integrals are one of the most expressive and reliable preference models used in decision theory for multicriteria decision-making. They perform a weighted aggregation by the means of fuzzy measures assigning a weight to any coalition of criteria. This enables the designers to model importance and also interactions among criteria, thus covering an important range of possible decision outcomes. However, specification of the fuzzy measures involves many parameters and is very difficult when only relying on the designer's intuition. In this paper, we discuss three different methods of fuzzy measure identification tailored for a mechatronic design process and exemplified by a case study of designing a vision-guided quadrotor drone. The results obtained from each method are discussed in the end.

The objective of this work is to identify artifacts that occur during the lesson planning activity and their meanings for the participants. This activity takes a lot of time from public primary school teachers. We adopt a situated approach that allows analysis of the meanings of artifacts for the teacher in his planning activities. With the application of digital visual ethnography techniques in order to identify patterns in the planning activity. The results show knowledge that seems to the teacher in his practice. This evidence guides the specification of an information system whose design objective is to increase the collective capacity for lesson planning by teachers.

Today’s manufacturing facilities and processes offer the potential to collect data on an unprecedented scale. However, conventional Programmable Logic Controllers are often proprietary systems with closed-source hardware and software and not designed to also take over the seamless acquisition and processing of enormous amounts of data. Furthermore, their major focus on simple control tasks and a rigid number of static built-in I/O connectors make them not well suited for the big data challenge and an industrial environment that is changing at a high pace. This paper, advocates emerging hardware- and I/O reconfigurable Programmable System-on-Chip (PSoC) solutions based on Field-Programmable Gate Arrays to provide flexible and adaptable capabilities for both data acquisition and control right at the edge. Still, the design and implementation of applications on such heterogeneous PSoC platforms demands a comprehensive expertise in hardware/software co-design. To bridge this gap, a model-based design automation approach is presented to generate automatically optimized HW/SW configurations for a given PSoC. As a case study, a metal forming process is considered and the design automation of an industrial closed-loop control algorithm with the design objectives performance and resource costs is investigated to show the benefits of the approach.

International audience; Deep artificial Neural Networks (DNNs) are currently one of the most intensively and widely used predictive models in the field of machine learning. However, the computational workload involved in DNNs is typically out of reach for lowpower embedded devices. The approximate computing paradigm can be exploited to reduce the DNN complexity. It improves performance and energy-efficiency by relaxing the need for fully accurate operations. There are a large number of implementation options leveraging many approximation techniques (e.g., pruning, quantization, weight-sharing, low-rank factorization, knowledge distillation, etc.). However, to the best of our knowledge, a few or no automated approach exists to explore, select and generate the best approximate version of a given DNN according to design objectives. The goal of this paper is to demonstrate that the design space exploration phase can enable significant network compression without noticeable accuracy loss. We demonstrate this via an example based on weight sharing and show that our direct conversion method can obtain a 4.85x compression rate with 0.14% accuracy loss in ResNet18 and 4.91x compression rate with 0.44% accuracy loss in SqueezeNet without involving retraining steps.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.