Plug-in Hybrid Electric Vehicles (PHEVs) offer a great opportunity to significantly reduce petroleum consumption. The fuel economy of PHEV is highly dependent on All-Electric-Range (AER) and control strategy. Previous studies have shown that in addition to parameters influencing Hybrid Electric Vehicles (HEVs), control strategies of PHEVs are also influenced by the trip distance. This additional parameter makes it even more difficult to manually tune the parameters that minimize fuel consumption. This study uses a pre-transmission parallel PHEV model developed with the Powertrain System Analysis Toolkit (PSAT). A non-derivative based algorithm called DIRECT (for DIvided RECTangles) is used to optimize the main control strategy parameters. The fuel economy and main performance criteria of the PHEVs are compared for the initial design and final optimal design. An optimal control solution resulting from an extensive of the entire design space can provide physical insight into the PHEV operation.

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To effectively reduce the specific fuel consumption of aeroengine and obtain the optimal geometric design parameters of compliant cylindrical intershaft gas film seal(CCIGFS) in the limited design space size, an optimal design method of CCIGFS based on the grasshopper swarm characteristics is established, and the optimal design parameters of CCIGFS for counter-rotating shafts are acquired by this method. In this paper, for the sake of leakage calculation, mechanical characteristics of the seal are analyzed and a fluid-structure coupling model of CCIGFS for counter-rotating shafts is built. This fluid-structure coupling model considers the influences of centrifugal expansion and rotor circular precession. Then, according to this method, the leakage rate with various seal widths and diameters is computed and the function relation of leakage rate with seal width and diameter is obtained by curve fitting method. Furthermore, by changing the design parameters of the seal width and diameter, the functional relation between the mass and the seal width and diameter is obtained by curve fitting method too. Thus, the multi-objective

design model about the design variables of the CCIGFS is developed. Finally, the state-of-the-art multi-objective grasshopper algorithm is adopted to obtain the corresponding Pareto frontier, and the geometric structure design parameters of the seal are selected according to the actual situation. This paper provides a complete and systematic method for the engineering design and analysis of CCIGFS in the counter-rotating shafts.

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Multi-objective

problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

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The present study is a technical report of the multi-objective design of the two-dimensional shielding effect for the reduction of aircraft fan noise. The design has been performed for the simplified cross section of aircraft with fuselage and plane wing. Two objective functions are considered for minimizing the sound pressure level at the side and the bottom locations relative to the fuselage, where standard measuring locations are defined by International Civil Aviation Organization. Those values are evaluated by using the linearized Euler equations. Since a wing, which is defined as a plate without thickness, is assumed as a V-tail wing, it is described by using the length and cant angle relative to the fuselage. The kriging-based response surface model is selected as an optimizer for the reduction of cost. As a result, it is revealed that fan noise described by a monopole sound source is reduced by the shielding effect. Moreover, there is no tradeoff between two objective functions, i.e., the sound pressure levels at the side and the bottom measuring locations can be simultaneously reduced. As it is better that the wing length is as long as possible, the cant angle is essential for the shielding effect to reduce fan noise.

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帯状疱疹に対するRo-8181の有効性, 安全性および有用性を検討するため, ヒト血清アルブミンを対照薬として, 二重盲検比較試験を行つた。皮膚症状の観察項目別の改善率では, 水疱·膿疱において投与開始後5日目でRo-8181群70%, placebo群50%で, 両群間に差(p<0.05)が認められた。皮膚症状改善度の推移を著明改善以上の症例についてみると, 投与開始後4日目の著明改善率はRo-8181群8%, placebo群0%であり, Fisherの直接計算法で両群間に差(p<0.05)が認められた。また, 投与開始後5日目の著明改善率はRo-8181群15%, placebo群3%であり, κ² 検定で両群間に差(p<0.05)が認められた。主治医による有効性の判定ではU検定でRo-8181群が優れている傾向が認められた。主治医による安全性の判定では副作用発現率がRo-8181群83%, placebo群14%であり, U検定で両群間に差(p<0.001)が認められた。主治医による有用性の判定ではU検定で両群間に差は認められなかつた。以上の結果より, 帯状疱疹に対し, Ro-8181は皮疹のうち水疱·膿疱に対して有効であり, 主治医判定でも有効性が認められたが, 安全性については本薬剤の耐薬性が示されたものの副作用の発現率が高かつたため, 有用性は高くないと考えられた。

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CTAの行政上の取り扱い、歴史、投資戦略等をまず簡単に説明する。CTAとは金融先物、オプション、スワップを対象とした取り引きに関する投資のアドバイスやサービスを個人、ファンドに提供する個人、または組織に対する金融行政上の用語で、正式名はCommodity trading advisorである。CTAという用語は投資信託、ETFを含むヘッジファンド、プライベートファンドへの投資アドバイスにも適応される。CTAs は米国連邦政府による規制対象であり Commodity Futures Trading Commission (CFTC) への登録と National Futures Association (NFA) のメンバーになることが義務付けられている。また、CTAはヘッジファンドの投資スタイルのひとつ (Global Macro) でもある。

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A new method of Adaptive Plan system with Genetic Algorithm called APGA is proposed to reduce a large amount of calculation cost and to improve a stability in convergence to an optimal solution for multi-peak problems with multi-dimensions. This is an approach that combines the global ability of Genetic Algorithm (GA) and the local ability of Adaptive Plan (AP). The APGA differs from GAs in handling design variable vectors (DVs). GAs generally encode DVs into genes and handle them through GA operators. However, the APGA encodes control variable vectors (CVs) of AP, which searches for local optimum, into its genes. CVs determine the global behavior of AP, and DVs are handled by AP in the process of APGA. In this paper, we introduce some strategies using APGA to solve a huge scale of problem and to improve the convergence towards the optimal solution. These methodologies are applied to several benchmark functions with multi-dimensions to evaluate its performance. We confirmed satisfactory performance through various benchmark tests.

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Plot plan drawings in the process and power industries are considered key decision-making documents for projects and are normally initiated in the pre-contract, conceptual, and developmental stages of a proposal. These drawings indicate the locations of the main equipment and building structures at a plant that contain pipes and cable connections between equipment units. This study proposes a multi-objective pipe and cable routing

method for a thermal power plant based on effective interactions between a designer and computer. We applied an interactive satisficing trade-off method and genetic algorithms to perform combinatorial with multiple objectives. To obtain pipe and cable route candidates for multiple-criteria decision-making, we applied an ant colony algorithm to a rule-based automatic layout of pipe racks. The features of this method can be optimized simultaneously not only for pipe and cable routes, but also for the placement of pipe racks among multiple conflicting evaluation functions. These functions may include the total cost of the pipes and cables, total length of the pipe racks, number of roads straddled by pipe racks, and total number of pipes and cables on a pipe rack. Finally, the validity and usefulness of this method is demonstrated by solving a layout design problem involving pipe and cable routing for a gas-turbine combined-cycle power plant.

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Film cooling design is essential for modern HPT vanes. In this paper, a semi-inverse design (SIDO) method for the film cooling arrangement of HPT vanes is introduced, which is based on a combinatorial algorithm, a 1D heat conduction model and CFD methods. The SIDO method can optimize the total coolant amount of the film cooling structure while ensuring an acceptable metal temperature distribution. The methodology was tested on a 2D vane derived from the 1st stage nozzle of a heavy–duty gas turbine, and the result was validated by the conjugate heat transfer (CHT) CFD simulations. In further study, the SIDO method is applied to predict the optimal (necessary) coolant amount of the vane at various design conditions with different inlet temperature, maximal metal temperature allowed, heat conductivity of TBC, and intensity of internal/film cooling structures. The quantitive results suggested that the inlet temperature and the maximal metal temperature allowed are arbitrary to the necessary coolant amount. Improving film cooling performance is more effective to save coolant compared with internal cooling, especially at higher inlet temperature level.

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The Japan Society for Occupational Health (JSOH) recommends the Occupational Exposure Limits (OELs) as reference values for preventing adverse health effects on workers caused by occupational exposure to chemical substances, continuous or intermittent noise, impulsive or impact noise, heat stress, cold stress, whole-body vibration, hand-arm vibration and time-varying electric, magnetic and electromagnetic fields and ultraviolet and ionizing radiation.

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In this paper, the Genetic Algorithm is used to optimize the room structure under the static and dynamic constraints. A penalty function method is proposed to handle the complicated constraint conditions which include stress, natural frequency, and allowable magnitude of design variables. The new operator called objective elitism as well as fitness elitism is introduced to improve the efficiency of the method. Varied probability of mutation is proposed to improve the convergence of the algorithm without the risk of premature convergence to a poor solution. To handle the discrete problems, we introduced the serial numbers of cross-sections whose shape data are stored in database beforehand. Successful application of the Genetic Algorithm in the room suggests its use to more complex engineering design problems.

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In this paper, we propose an system with parallel processing for reducing electromagnetic interference (EMI) on electronic control unit (ECU). We adopt simulated annealing (SA), genetic algorithm (GA) and taboo (TS) to seek optimal solutions, and a Spice-like circuit simulator to analyze common-mode current. Therefore, the proposed system can determine the adequate combinations of the parasitic inductance and capacitance values on printed circuit board (PCB) efficiently and practically, to reduce EMI caused by the common-mode current. Finally, we apply the proposed system to an example circuit to verify the validity and efficiency of the system.

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In the early stage of dynamic system development which has a multi-disciplinary and hierarchical structure, system requirements need to be cascaded down to target values of each component so that engineers can collaborate efficiently and concurrently. The purpose of this paper is to propose a novel set-based concurrent engineering method for a dynamic system by using machine learning. In the practice of the target setting study for concurrent engineering, both hierarchical simulations between system and component level and a solution to solve inverse problems using the simulation are required. The proposed method composes of two machine learning methods that satisfy these requirements. The first one is physics-informed long short-term memory (PI-LSTM) which enhances the mechanical modeling of component behavior. By applying the proposed PI-LSTM to where mechanical modeling is difficult, the adaptive range of mechanical modeling can be expanded. The PI-LSTM surrogate the dynamic behavior of the component model and can be used inside the system-level simulation. The other one is Bayesian active learning (BAL) applied to inverse problems to solve feasible regions where all system requirements are satisfied. In the proposed BAL, Gaussian process models are trained from the system-level simulation, and an acquisition function is evaluated and maximized to generate new sampling candidates. The set-based design using BAL has an advantage in the decoupling ability of design problems because feasible regions of each discipline sub-problem can be studied concurrently. To show the effectiveness of the proposed method, a numerical example of a vehicle design problem which has a hierarchical structure is demonstrated.

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Computational load is too much to solve a preference solution in the conventional interactive multiobjective methods. In this paper, theoretical results obtained using a new method which is improved so as to a preference solution effectively are shown, and its computational algorithm is proposed. Multiobjective methods are applied in the optimal design of diesel ; first, 1∼4 objective problems are formulated, and are solved by the conventional multiobjective method. Next, an improved interactive method is applied to the multiobjective optimal design problem in order to confirm its efficiency, the results being compared with those of the conventional methods.

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This paper discusses the Genetic Algorithms for Noisy Fitness functions (GANF), that is, the Genetic Algorithms (GAs) for of fitness function having random fluctuation. First, promising applications of the GANF are examined. Considering practical applicability of the GANF, requirements for the GANF are clarified. Several methods of the GANF proposed so far are overviewed, and the genetic algorithm with memory-based fitness evaluation (MFEGA) proposed by Sano and the author is described more in detail. Further, possible extensions of the GANF are also discussed.

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This article deals with the conceptual approach to optimize efficiency and power of a highly loaded gas turbine flow path design. Different problems were analyzed during low pressure turbine (LPT) preliminary design of high-bypass aircraft jet .

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Thermal cycles of a turbofan jet equipped with an intercooler are optimized for two different sizes to understand the characteristics. The tool consists of a thermodynamic cycle analysis module, a weight evaluation module, a heat exchanger model and an routine. It is confirmed that the tool searches for a reasonable design point within a ten-dimensional design space. The pressure ratio of a conventional turbofan is restricted by the compressor exit temperature. In contrast, intercooling enables higher pressure ratios and hence higher core thermal efficiency. The net decrease in fuel consumption is small however, because the thermal efficiency improvement, weight penalty and pressure loss are all at the same order of several percent. The minimum blade height at the compressor exit imposes another restriction on the pressure ratio increase for small intercooled engines, and net fuel consumption in small engines may increase by intercooling. The performance of an intercooled turbofan is determined by a balance between thermal efficiency improvement through the increase in pressure ratio and disadvantages resulting from additional weight and pressure losses. The development of a light, low-pressure-loss heat exchanger and of the thermodynamic cycle are important.

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Multi-Objective has been applied to a design problem of the twin concept for Silent Supersonic Business Jet (SSBJ). This problem aims to find main wing, body, tail wing and nacelle configurations, which can minimize both sonic boom and drag in a supersonic cruising flight. The multi-objective genetic algorithm (MOGA) coupled with the Kriging model has been used to globally and effectively for optimal design candidates in the multi-objective problem. The drag and the sonic boom have been evaluated by the computational fluid dynamics (CFD) simulation and the waveform parameter method. As a result, the present has successfully obtained low-boom and low-drag design candidates, which are better than the baseline design by more than 40% regarding each performance. Moreover, the structure of design space has been visualized by the self-organizing map (SOM).

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