Automatic Generation of Design Space Conversion Maps and Its Application for the Design of Compound Split Hybrid Powertrains

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Toumadher Barhoumi ◽  
Hyunjun Kim ◽  
Dongsuk Kum

Most of the prior design studies on compound split hybrids focused on the selection of optimal configurations through evaluating their performance within the physical design space, i.e., powertrain configurations. However, the authors revealed that using the compound lever for the performance analysis dramatically reduces the design space as redundant configurations exist for a single compound lever design, resulting in computational load reduction. Nevertheless, using the compound lever results in the loss of information required to realize the given configurations as these two configurations are represented by two different sets of variables. The powertrain configuration is defined by two physical design variables, i.e., gear ratios of the two planetary gears. However, the compound lever design is defined by two nonphysical design variables, α and β, which are the vertical bar lengths between the output node (vehicle) and the two motor/generators' (MG) nodes. Thus, if the compound lever is used as a design tool, the selected designs should be converted into powertrain configurations. This paper introduces an automatic methodology to generate feasible powertrain configurations for any given compound lever using generic conversion equations that express the relationship between the nonphysical design variables, α and β, and the physical design variables, gear ratios. Conversion maps relating the 252 powertrain configurations to the compound lever design space were generated, and the results confirmed that the compound lever removes the redundancy existing in the physical design space.

2018 ◽  
Vol 12 (2) ◽  
Author(s):  
Chi-Lun Lin ◽  
Dane Coffey ◽  
Daniel Keefe ◽  
Arthur Erdman

Design by Dragging (DBD) [1] is a virtual design tool, which displays three-dimensional (3D) visualizations of many simulation results obtained by sampling a large design space and ties this visual display together with a new user interface. The design space is explored through mouse-based interactions performed directly on top of the 3D data visualizations. Our previous study [1] introduced the realization of DBD with a simplistic example of biopsy needle design under a static bending force. This paper considers a realistic problem of designing a vacuum-assisted biopsy (VAB) needle that brings in more technical challenges to include dynamic tissue reaction forces, nonlinear tissue deformation, and progressive tissue damage in an integrated visualization with design suggestions. The emphasis is placed on the inverse design strategy in DBD, which involves clicking directly on a stress (or other output field parameter) contour and dragging it to a new (usually preferable) position on the contour. Subsequently, the software computes the best fit for the design variables for generating a new output stress field based on the user input. Three cases demonstrated how the inverse design can assist users in intuitively and interactively approaching desired design solutions. This paper illustrates how virtual prototyping may be used to replace (or reduce reliance on) purely experimental trial-and-error methods for achieving optimal designs.


Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 753
Author(s):  
Ivan Chajda ◽  
Helmut Länger

In order to be able to use methods of universal algebra for investigating posets, we assigned to every pseudocomplemented poset, to every relatively pseudocomplemented poset and to every sectionally pseudocomplemented poset, a certain algebra (based on a commutative directoid or on a λ-lattice) which satisfies certain identities and implications. We show that the assigned algebras fully characterize the given corresponding posets. A certain kind of symmetry can be seen in the relationship between the classes of mentioned posets and the classes of directoids and λ-lattices representing these relational structures. As we show in the paper, this relationship is fully symmetric. Our results show that the assigned algebras satisfy strong congruence properties which can be transferred back to the posets. We also mention applications of such posets in certain non-classical logics.


Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.


2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.


2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.


2021 ◽  
Vol 5 (2) ◽  
pp. 5
Author(s):  
Aatish Neupane ◽  
Derek Hansen ◽  
Jerry Alan Fails ◽  
Anud Sharma

This article reviews 103 gamified fitness tracker apps (Android and iOS) that incorporate step count data into gameplay. Games are labeled with a set of 13 game elements as well as meta-data from the app stores (e.g., avg rating, number of reviews). Network clustering and visualizations are used to identify the relationship between game elements that occur in the same games. A taxonomy of how steps are used as rewards is provided, along with example games. An existing taxonomy of how games use currency is also mapped to step-based games. We show that many games use the triad of Social Influence, Competition, and Challenges, with Social Influence being the most common game element. We also identify holes in the design space, such as games that include a Plot element (e.g., Collaboration and Plot only co-occur in one game). Games that use Real-Life Incentives (e.g., allow you to translate steps into dollars or discounts) were surprisingly common, but relatively simple in their gameplay. We differentiate between task-contingent rewards (including completion-contingent and engagement-contingent) and performance-contingent rewards, illustrating the differences with fitness apps. We also demonstrate the value of treating steps as currency by mapping an existing currency-based taxonomy onto step-based games and providing illustrations of nine different categories.


Author(s):  
Sudhakar Y. Reddy

Abstract This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.


2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.


2021 ◽  
pp. 1-32
Author(s):  
Branislav Mičko

Building on an original dataset, this article focuses on the interactions between NATO and its declared worldwide partners. It argues that the analysis of these interactions can reveal NATO’s strategic approach to partnerships, but it can also provide a tool for its classification as an organisation that is either exclusive – defined by the focus on defence of its members, or inclusive – emphasising the global protection of democracies and human rights. The relationship between types of interactions and NATO categorisation is estimated using an unconditional negative binomial regression with fixed effects as well as a within-between (hybrid) model. Furthermore, they are illustrated on two brief case studies of Sweden and Japan. The results of the study suggest that NATO engages primarily with countries that are powerful relative to their neighbourhood, even though they are not the most powerful among the partners. The given country’s level of democracy, integration into the international institutions, and stability, do not seem to play any overarching role here.


Psihologija ◽  
2017 ◽  
Vol 50 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Ana Genc

In most modern societies, nearly every realm of life involves some form of evaluation of our knowledge, abilities and skills. Given the potentially significant consequences of exams, it is not surprising that they are often very stressful. This study aimed to determine the existence and nature of the relationships between level of test anxiety, coping strategies, and achieved success on a mid-term test. As well as examining the direct relations between the given variables, our primary interest was to investigate the potential mediating role of coping mechanisms between the input and output variables of the examined stressful transaction. The study was conducted on a sample of 263 students from the Psychology and German Studies Departments of the Faculty of Philosophy at the University of Novi Sad. According to our results, only emotion-focused coping mechanisms were statistically significant mediators in the relationship between level of test anxiety and mid-term test achievement. The results indicate that students with high test anxiety who employ predominantly emotion-focused coping strategies score lower on a pre-exam knowledge test.


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