MODELLING OF EMBEDDED MECHATRONIC SYSTEMS USING HYBRID PETRI NETS

Estonian Journal of Engineering, 2008, 14, 1, 3–16 doi: 10.3176/eng.2008.1.01A conceptual design method for the generalelectric vehicleRaivo Sell a, Mart Tamre a, Madis Lehtla b and Argo Rosin ba Department of Mechatronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia; {raivo,mart}@staff.ttu.eeb Department of Electrical Drives and Power Electronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia; {mlehtla,vagur}@cc.ttu.eeReceived 19 January 2007Abstract.The paper discusses conceptual design of mechatronic systems considering a mobile electrical vehicle platform as an application example. A set of design templates are developed and organized into libraries for the use in early stages of the system design. The advantages of retaining usability of component libraries, allowing verification of design alternatives on the conceptual level are demonstrated.Key words: mechatronics, mobile robotics, system design, conceptual modelling, simulation.1. INTRODUCTIONThe design of mechatronic systems differs considerably from the domains like mechanics, electronics, etc. Although mechatronics is often defined as a combina-tion of mechanics, electronics and control theory, design of a mechatronic product can not be divided into three separate parts. A mechatronic system needs to be designed as an integrated product from the very beginning. Domain-specific design plays also a certain role but the designer must always be aware of the interaction of design aspects of various components. Mechatronic system design is closely related to system engineering and therefore many tools and techniques used in system engineering are applicable also in the mechatronic system design.Decomposition of the general design cycle has been considered by several authors [1–3]. The design cycle starts with a conceptual stage, which consists of the specification of the requirements and situation analysis. According to French [4], the conceptual design stage puts greatest demands on the designer and in this stage the most important decisions are made. The result of this process is a candidate for the design solution and a clearly formulated set of desired measur-able properties of the future product, which introduces the quality measure into3the design process. This cross-domain design takes into account the overall system requirements and goals. The conceptual design as well as other design stages are implemented in many cycles. Complex mechatronic product design cycles (macrocycles) are described in greater detail in [5].In real design, many tools and techniques are used to carry out the whole design process. For the domain-specific stage, a large selection of most advanced tools exist. Conventional domains like mechanics, electronics and control engineering are well exploited. Computer-aided engineering (CAE) tools like computer-aided design (CAD), computer-aided manufacturing (CAM), finite element method (FEM), printed circuit board (PCB) routing & layout, etc. are probably known for every engineer. In software design, computer aided software engineering (CASE) and the unified modelling language (UML) are used [6,7].On the conceptual design stages fewer tools are available, although there is a great needed for CAE. Domain-independent techniques as Bond graphs [8], Petri nets [9] and hybrid automata [10] are not widely used in the design of mechatronic systems. However, recent research is focused on the conceptual design and automation of the generation of the candidate for the design solution. Several investigations [11–13] exploit the combination of artificial intelligence and domain-independent techniques. One of the reasons why the conceptual level lacks computer support is that the used methodology must support high-level conceptual design with the option to apply very specific constraints at the same time. The system design stage needs similar tools, but the system must be modelled on a more detailed level. The system components, subcomponents, behaviour and per-formance, etc., must be modelled in the frame of the whole system. The most used technique here is block diagrams of different modifications. In the recent years many efforts are put on the system design and mechatronics software development. Some software package examples are AMESim [14], Dymola [15], 20-sim [16] and also the well known MatLab/Simulink environment.Methodology of the mechatronic design process is presented in the mechatronic design guideline VDI 2206 [5], which proposes several tools and techniques for the design of mechatronic systems.The objective of the present work is to combine mechatronics design methods with the system modelling language and to develop practical tools for the design of a mobile electrical vehicle platform on conceptual level. We create a specific toolbox for a general electric vehicle, which can be utilized in the conceptual design process. Some practical examples are shown, based on current projects at Tallinn University of Technology.2. DESIGN APPROACHThe conceptual design method, considered in this paper, expands the approach [5] with the design templates and concept simulation aids. System modelling language (SysML) [17] is used as the basic modelling language. The general conceptual design modelling approach is described in Fig. 1.4Fig. 1. The SysML toolbox for mobile platform design.The approach consists of three integrated substages: requirements modelling, conceptual solution development and design candidate simulation. All stages are supported by specific template libraries. A template class from the template library provides a parameterized description of the model element, specifying its attributes and operations. By binding multiple elements to the template it is possible to generate new elements with the same characteristics as the template.SysML is used for requirement and concept modelling and Simulink for conceptual simulation, although other tools are not excluded. The proposed approach relies on the design methodology [18] and the SysML toolbox for the mobile platform design, which is taken as an example. The mobile platform is a generalization of different types of (mainly electrical) vehicles. Mobile robots, unmanned ground vehicles (UGV) and railway vehicles are used as examples for different diagrams later on.3. MODELLING OF THE REQUIREMENTSFormulation of the requirements is the foundation of a project. Every require-ment is tightly related to the cost and therefore the requirements modelling and analysis must be carried out with great care. Big changes in requirements in later stages of the design process may increase significally the cost of the whole project. Requirements arise from customer needs, regulations, legislation, organiza-tion environment, technology availability, etc.Definition of the requirements is a complex process and typically includes performance analysis, trade studies, constraint evaluation and cost analysis. Requirements modelling is not just a top-down process, but must be carried out with the interaction of the initial analysis and simulation of the concepts.Initial requirements model is completed usually on the system level. Oncearchived, it is necessary to allocate and flow the requirements down to lower56levels. According to [19], the requirements modelling process is iterative for each phase, with continuous feedback as the level of design specifications increases. The design of an electrical vehicle and mobile platform follows the system engineering concept. The general requirements model is shown in Fig. 2. The model is based on the SysML requirements diagram and describes general con-cept of the requirements model. A single requirement is described as a box with various parameters. The requirement can be decomposed into subrequirements and is linked with each of them as well as with analysis, design, implementation and testing elements. In a general requirement element the following parameters are used: ID (unique identifier across the model), priority, text (textual repre-sentation of the requirement or reference to a document), risk, weight, type, etc. According SysML specification, a requirement can be generated or deduced from another requirement using Derive relationship. A requirement can be ful-filled by another model element using Satisfy relationship. A requirement can be verified by various behaviours using the Verify relationship. Standard or specific test cases ()TestCase are developed for this purpose. All of these are specializa-tions of the UML Trace relationship; which is used to track requirements and changes across the model [17].Requirements template. Introducing new design tools to practising engineers is often related with various problems as people are used to work with habitual tools and methods. Therefore it is important to make the implementation of the new system as easy as possible. One way to do this is to use pre-defined modelFig. 2. Metamodel of the requirements.7templates. Templates are defined according to the specific product domain. In this paper we focus on the general electrical vehicle platform design. Templates are still general enough to be adopted to other subdomains.A requirement is defined by ID, textual representation and parameters. Default parameters are Weight , Risk , OptimizationDirection , and Source . Optional parameters are ConsistentStandard and MaxCost (Fig. 3). Every single requirement has to be verified on some level by one of the following methods: test, demonstration, analysis, inspection. Therefore every single requirement has a relationship with the activity TestCase . If a requirement needs multiple tests, the requirement should be decomposed into multiple subrequirements.When the process proceeds, the requirement can be connected with design elements as block, assembly, activity, etc. This relationship indicates specific design elements, which satisfy the particular requirement.Templates are intended to be used for effective and professional requirement modelling. The engineer can pick the best matching template according to the design scope and start to bind the predefined requirement parameters with real values. Requirement templates consists of activities like standardized TestCases . These activities are collected to the knowledge base library, where they can be extracted and redesigned if necessary.Fig. 3. Requirements metaclass and Activity relationship.4. CONCEPTUAL MODELS4.1. Conceptual designConceptual design is actually the first stage, where engineers start to develop the target system, corresponding in an optimal way to requirements. This stage is tightly related to the previous one, to the requirements modelling part. After starting to develop actual concepts of the system, often new aspects arise and very often they cause some changes in initial requirements. This is an interactive process and must be well coordinated.A frequent mistake in this interactive process, made by beginners, is that the very fist idea is taken as the best one and is developed into a product [3]. This may be a very costly approach. Correction of design mistakes and conceptual changes in a later, product development or integration stage, will cost much more than in the conceptual stage. Therefore it is very important to develop more than one candidate for the solution. Methodical comparison and initial simulation will ensure that the optimal solution is selected and the risk of project failure is reduced.4.2. Development of the conceptual solutionDevelopment of a candidate for a conceptual solution is the next step after the requirements model is established. Our concept is described by the static structure, interaction of components, behaviour and dynamic parameters. All these aspects are modelled with a corresponding diagram. At the same time, the diagrams can interact with each other and one diagram can consist of parts from other diagrams. In the concept design process, the requirements model must be kept in mind and relationships with the requirements diagram are allowed and strictly suggested through the <<satisfy>> relationship. Diagram template library incorporates categories for concept design shown in Table 1. Diagram templates are divided logically according to SysML diagram types.The interaction levels are as follows:Level I – System and subsystem hierarchy, subsystem general interactions are defined; main functionality and system states are indicated.Level II – Subsystems are opened and defined in general ‘black box’ components;parameters of subsystems are initiated, subsystem inner activities aredistinguished.Table 1. Diagram template libraryLevel(acronym)Name DiagramFor the whole conceptHierarchical structure Block Definition (bdd) ISystem usage Use Case (uc) IFor every solution candidateComponent interaction Internal Block diagram (ibd) IBehaviour Activity(act) II(par) II Dynamics Parametric8The first two diagrams (bdd, uc) are common for all solution candidates because in the context level they do not differ very much for various solution candidates.System usage at the context level is defined by the Use Case diagram. Use Case diagram defines the usage of the system under the development. This is particularly important in the early design stage. The diagram describes high-level behaviour and bounding of the system at the same time. This diagram can be compared with the context diagram in the data flow diagram (DFD). We use the original structure of the Use Case package. The syntax is analogous to UML. In that way the software engineers and mechanical engineers use the same syntax to describe the system usage and context. This ensures that the gap in understanding will be reduced to minimum.The concept level structure of the system is described by the Block Definition diagram (bdd). A Block is defined as a modular unit of the system and depending on the design detailization level the block describes different things. In the beginning of the conceptual stage, the hierarchy of the components is defined and disclosed to assembly level, e.g. vehicle drive. The Block encapsulates its contents, which include attributes, operations and constraints.Every system has interconnections between its blocks. These connections can be quite different, e.g. flow of energy or material, software operations, data exchange, analogue signals, etc. In the system hierarchy a model generalization relationship is used instead of interactions between the components. The system is decomposed and linked with parts or assemblies from the model library.Both the Block Definition diagram and the Use Case diagram are common for all solution candidates due to the reason that these diagrams describe a general view of the perspective system and are derived directly from the requirements. Solution-specific diagrams are more detailed and describe the specific solution.Three different diagrams are defined for the solution-specific development in the conceptual stage:• Internal Structure, describing the interaction between the components in terms of service and flow;• Parametric, describing the key parameters and system dynamics;• Activity, describing the behaviour.4.3. Conceptual design templatesIn the framework of the electrical vehicle profile, several design templates for the Block Definition diagram have been developed. The system hierarchy on the concept level has one main template with mostly common blocks. When starting to use the template, the engineer can select or not select these pre-defined blocks and their attributes. This enables to start quickly the system description without missing relevant components.Most commonly used components in electrical vehicle design are presented with common attributes and the generalization relationship. Attributes are in most cases optional and can be added or removed depending on the design characteristics. The Internal Block diagram template describes the interrelation-910ship between the movement components. Service and flow ports are defined but kept open for most cases.Activity diagram templates are composed with the Swim Lane technique, where activities are mapped with blocks on the basis of functions. An example of this is shown in Fig. 4. Templates based on functions and behaviour describe more specific activities, e.g. MotionTraction , Brake , Accelerate , etc., and TestCase for the satisfaction of the requirements.The general dynamic model [20] of the system or subsystem is shown schematically in Fig. 5. The system S is characterized by a set of state variables X that are influenced by a set of input variables, ,U representing the action of the system’s environment on the system. The set of output variables, ,Y are observable indicators of the system’s response.The general dynamic model can be applied for all subsystems or components. The dynamic model can be executed and by feeding different input parameters different system behaviour is achieved. The system and its model can be linear orFig. 4. Example of an Activity diagram.Fig. 5. Dynamic model of the system. ▪ ▪▪ ▪11non-linear. Linear systems generally can be described by a set of linear first-order differential equations and it is possible to obtain detailed solutions of the system response. If one of the components or a subsystem is non-linear, the overall system is non-linear and conventional analytical tools do not work any more [20]. Solving the non-linear system, the simulations according to time steps must be carried out. The real-world systems are in most cases non-linear and therefore it is important to have tools for early stage simulations, where even the dynamic model of the system is not fully defined yet. The early stage system dynamics is determined by the Parametric diagram. This defines how one value of the structural property affects other values. Parametric constraints are tightly connected with the system structure and are used in combination with Block diagrams.The Parametric diagram is the main input for the system simulation model. Different conceptual solutions can be obtained and simulation results used for the improvement of the design. For example this is used for the performance and reliability analysis as well as for meeting all the requirements, specified by the Requirement diagram.The Parametric diagram template for basic performance of an electrical vehicle is taken as an example (Fig. 6).Fig. 6. Parametric example.5. SIMULATION OF THE ELECTROMECHANICAL (PHYSICAL)MODELTo describe interaction, a pair of variables should be used. One variable is an input, which describes the effect and another is the result or feedback, showing the reaction (or vice versa). The instantaneous power can be calculated from this pair of variables. Equations of the mechanical behaviour of the motors and voltage–electromotive force equations are placed in different blocks [21] accord-ing to the structure of the energetic macromodels. For example, electrical sub-systems, described with transfer functions, have as an input the instantaneous value of voltage and the reaction is calculated as the instantaneous value of the current. Mechanical subsystems can have as input the linear or angular velocity and the calculated reaction is the force or torque.A simulation model should be simplified as much as possible because of energetical, technical and time restrictions. A model is always a simplification of the reality for a certain purpose. The simplified electromechanical model does not always describe electrical parameters of windings, supply network, controllers and converters. The main purpose of modelling of the control object and load is virtual testing of control methods for the simplification of the development process. Models allow the verification of control methods and software in different operation modes, mode-altering conditions and different control modes [22].For the verification of the model and comparison with the real system, it should be divided into subsystems using subsystem macromodels. These subsystem macromodels can be also divided into subsystem and component models. MatLab/ Simulink simulation model has a hierarchical structure and each block can be flexibly composed from configurable subblocks. The grouping of the blocks should take into account different configurations of the drive hardware, such as different motor–wheel configurations, different compositions of supply converters, motors, etc. This can be done via parametric (Fig. 6) and operation mode transition (Fig. 7) diagrams.Events that cause changes in the control structure can be defined as transitions in the operation-mode transition diagram shown in Fig. 7. Each state describes a different set of control structures and algorithms.Conceptual models, developed with design tools in Mobile Platform Toolbox, will be linked with the MatLab/Simulink object in the template model. Toolbox consists of several predefined models for multiple purposes. For example, the simulation models of electrical vehicle performance, current consumption, efficiency, etc., are stored in the simulation template library. A template is a general simulation model for a specific simulation target. Appropriate modification will be done and correct parameters assigned for the picked template model. The following example shows a simple simulation model template for electrical and performance simulation in different operation modes. The Simulink model shown in Fig. 8 uses torque reference values and state variables of wheels as arrays (multiple numbers) indicated with bold lines in the figure.1213Fig. 7. Operation mode transition diagram of the drive.Fig. 8. Simulation model of a multimotor drive describing axial weights, adhesion and wheel diameters.14The models of the mechanical part and control circuits together form the modelof the control object, including load and motor electromagnetic models and models of the electronic part of the converter, hardware of the control part and feedbacks. The model should be simple because of energetic, technical and time limitations and limitations of the available computer hardware. But it should include important properties and parameters needed for the control system design.Using the model of the mechanical part, shown in Fig. 8, and adding the detailed model of the electrical part, gives the opportunity to observe different operation modes and ranges of the system.These operation ranges, shown in Fig. 9, include the torque ramp, constant torque operation, motor field weakening in constant power operation and maximum speed limitation due to the end of field weakening. The torque ramp limits maximal available motor current and is needed for limiting the acceleration. The start of the motor field-weakening process depends on the maximum available supply voltage for drive systems. Control principles (methods) are an important part for system modelling. Most of modern systems are based on a microprocessor control system, thus a dynamic model should also contain descriptions of software based controllers, control algorithms, torque controllers, speed controllers or reference integrators, ramp-functions, anti-slip systems, control of the field weakening, control of the operation mode and control of the braking.Fig. 9. Acceleration process, calculated using a simulation model.6. CONCLUSIONSConceptual modelling for the design of mechatronic systems is described. The method utilizes new rapidly advancing SysML as the modelling language. The concept consists of models of the requirements, structure and behaviour. An important feature of the approach is instantaneous analysis and simulation link to get the fast response of strengths and weaknesses of the developed mechatronic system design candidate. The described method is bounded and specified for the mobile electric vehicle design. Some examples and metamodels for this case are presented. Design templates for modelling and simulation are required to start a fast product development process. Therefore the implemented methodology relies on predefined templates from the template library. The next step is to widen the template library for different design cases. Together with the develop-ment of the design templates, simulation templates (Simulink models) will be designed. Further work lies in the integration of the solution with an automatic mechatronics system development framework, where design concepts can be generated from the requirements diagram in a semiautomatic way.ACKNOWLEDGEMENTThe work is part of the robotics projects supported by the Estonian Ministry of Science and Education, grant No. 0142506s03.REFERENCES1. Hubka, V. and Eder, W. Design Science: Introduction to the Needs, Scope and Organization ofEngineering Design Knowledge. Springer, London, 1996.2. Pahl, G. and Beitz, W. Engineering Design – A Systematic Approach. Springer, Berlin, 1996.3. Ullman, D. G. Mechanical Design Process. McGraw–Hill, New York, 2002.4. French, M. Conceptual Design for Engineers. Springer, London, 1999.5. Design Methodology for Mechatronic System – VDI 2206. Beuth Verlag GmbH, DI, Düssel-dorf, 2004.6. Gurd, A. Using UMLTM 2.0 to Solve Systems Engineering Problems. Telelogic, 2003.7. Kukkala, P., Riihimäki, J., Hännikäinen, M., Hämäläinen, T. D. and Kronlöf, K. UML 2.0 Pro-file for Embedded System Design. In Proc. Design, Automation and Test in Europe Conference. Munich, 2005, 710–715.8. Gawthrop, P. Metamodelling: for Bond Graphs and Dynamic Systems. Prentice Hall, London,1996.9. Desel, J. and Juhas, G. What is a Petri net? – Informal answers for the informed reader. LectureNotes in Computer Science, Springer, Berlin/Heidelberg, 2001, 2128, 1–25.10. Davoren, J. M. and Nerode, A. Logics for hybrid systems. Proc. IEEE, 2000, 88, 985–1010.11. Rzevski, G. On conceptual design of intelligent mechatronic system. Mechatronics, 2003, 13,1029–1044.1512. Seo, K., Fan, Z., Hu, J., Goodman, E. D. and Rosenberg, R. C. Toward a unified and automateddesign methodology for multi-domain dynamic systems using bond graphs and genetic programming. Mechatronics, 2003, 13, 851–885.13. Granda, J. J. The role of bond graph modeling and simulation in mechatronics systems. Anintegrated software tool: CAMP-G, MATLAB–SIMULINK. Mechatronics, 2002, 12, 1271–1295.14. AMESim: Modeling & simulation environment for systems engineering. http://www.15. Dymola – dynamic modeling laboratory with Modelica (Dynasim AB). http://www.16. 20-sim, the dynamic modeling and simulation package for iconic diagram, bond graph, blockdiagram and equation models. 17. System modeling language (SysML) specification. Version 1.0, Draft. OMG documentad/2006-03-01, 2006. 18. Sell, R. and Tamre, M. Integration of V-model and SysML for advanced mechatronics systemdesign. In Proc. Research & Education on Mechatronics Conference REM05. Annecy, 2005, 276–280.19. Systems Engineering Handbook. INCOSE-TP-2003-016-02, version 2a. Technical Board ofInternational Council on Systems Engineering (INCOSE), 2004. 20. Karnopp, D. C., Margolis, D. L. and Rosenberg, R. C. System Dynamics – Modeling andSimulation of Mechatronic Systems. J. Wiley, New Jersey, 2006.21. Popa, I. S., Popescu, M. O. and Popescu, C. Energetic macroscopic representation applied to anelectrical urban transport system. In The Annals of “Dunarea de Jos”, University Of Galati Fascicle, 2002, III, 34–39.22. Lehtla, M. Microprocessor Control Systems of Light Rail Vehicle Traction Drives. TallinnUniversity of Technology Press, Tallinn, 2006.Mobiilse elektrisõiduki kontseptuaalse modelleerimise metoodika Raivo Sell, Mart Tamre, Madis Lehtla ja Argo Rosin On loodud elektrisõiduki modelleerimise metoodika, mis kasutab modellee-rimiskeelena uut, kiiresti arenevat keelt SysML. Metoodika on suunatud kontsep-tuaalse modelleerimise faasi kiiremale ja efektiivsemale projekteerimisele. Selleks on välja arendatud eeldefineeritud mudelite süsteem, mida arendaja saab andmebaasist valida sõltuvalt süsteemi spetsiifikast. Eeldefineeritud mudelid on nii süsteemi nõuete kui ka lahenduse kirjeldamiseks. Kirjeldatud metoodika abil saab modelleerida süsteemi struktuuri ja käitumist ning siduda erinevaid lahen-dusvariante simulatsioonimudelitega. Väljatöötatud lahendus võimaldab kiiresti ja efektiivselt alustada mehhatroonikasüsteemi arendusprotsessi ning juba kontsep-tuaalses faasis simuleerida erinevaid lahendusvariante. Töö on üks osa meh-hatroonikasüsteemide projekteerimisega seotud uuringust, mille eesmärk on auto-matiseerida tootearenduse kontseptuaalset faasi.16。

Mechatronics and Embedded Systems As a mechatronics and embedded systems engineer, I encounter various challenges in my field that require innovative solutions. One of the primary problems I face is the integration of mechanical and electrical components in a way that ensures seamless functionality. This involves designing and implementing control systems that can effectively manage the interaction between these components. It requires a deep understanding of both mechanical and electrical engineering principles, as well as proficiency in programming and software development. Another significant issue is the need to constantly adapt to new technologies and advancements in the field. With the rapid pace of technological development, it is essential to stay updated with the latest trends and tools. This requires continuous learning and skill development to ensure that I am equipped to tackle emerging challenges and opportunities. It can be overwhelming at times, but it is also incredibly rewarding to be at the forefront of technological innovation. One of the more frustrating aspects of my work is the complexity of debugging and troubleshooting embedded systems. When a system malfunctions, it can be challenging to pinpoint the exact cause of the problem and rectify it. This often requires a great deal of patience and persistence, as well as the ability to think critically and analytically. It can be a time-consuming and mentally taxing process, but successfully resolving such issues is incredibly satisfying. In addition to technical challenges, there are also practical considerations that need to be taken into account. For example, cost and time constraints often dictate the design and implementation of mechatronic and embedded systems. Balancing performance and functionality with budgetary and time limitations can be a delicate and demanding task. It requires careful planning and resource management to ensure that the end product meets the necessary requirements without exceeding the allocated resources. Furthermore,collaboration and communication are essential in my line of work. Mechatronics and embedded systems engineering often involves interdisciplinary teamwork, requiring effective communication and coordination with professionals from diverse backgrounds. This can present its own set of challenges, as different team members may have varying perspectives and approaches. Finding common ground and workingtowards a shared goal can be both rewarding and demanding, but it is a crucial aspect of successful project execution. Finally, ethical considerations are alsoa significant concern in mechatronics and embedded systems engineering. As technology continues to advance, it is important to consider the potential impact of our work on society and the environment. This involves taking into account issues such as safety, privacy, and sustainability in the design and implementation of mechatronic and embedded systems. It requires a conscientiousand thoughtful approach to engineering, ensuring that our innovations arebeneficial and responsible. In conclusion, mechatronics and embedded systems engineering present a myriad of challenges, ranging from technical complexities to practical and ethical considerations. While these challenges can be daunting at times, they also offer opportunities for growth and innovation. By approaching these problems with creativity, perseverance, and a commitment to ethical practice, I am able to navigate the complexities of my field and contribute to the development of cutting-edge technologies. It is a demanding yet immenselyrewarding profession, and I am grateful for the opportunity to be a part of it.。

MEWS评分在急诊留观患者护理决策中的作用分析一、MEWS评分的概念简化急诊患者危重度评估（Modified Early Warning Score，MEWS）是一种通过观察生命体征来评估患者病情变化的评分系统。

MEWS评分包括呼吸频率、心率、收缩压、体温和意识状态五个指标，通过对这些指标进行评分，并将评分结果相加，来评估患者的病情变化程度。

当评分结果高于一定阈值时，就需要及时采取相应的护理措施，以避免患者病情的进一步恶化。

MEWS评分系统简单易行、操作方便，因此在临床中得到了广泛的使用。

二、MEWS评分在急诊留观患者护理决策中的作用1. 及时发现患者病情变化在急诊留观患者的护理过程中，患者病情的变化可能随时发生，而且有些变化可能相当微弱，容易被忽略。

通过对患者进行定期的MEWS评分，可以及时监测患者的生命体征指标，并将评分结果及时记录在案。

一旦发现患者的MEWS评分升高，就可以及时采取护理措施，以防止患者病情的进一步恶化。

MEWS评分在急诊留观患者护理决策中可以起到及时发现患者病情变化的作用。

2. 提高护理质量MEWS评分可以帮助医护人员及时发现患者的病情变化，有利于提高护理质量。

通过对患者进行定期的MEWS评分，可以及时发现患者的病情变化，及时采取相应的护理措施，有利于减少医疗事故的发生，提高医疗质量和护理效果。

3. 促进医护人员间的交流在急诊留观患者的护理决策中，医护人员之间的交流配合是至关重要的。

通过对患者进行定期的MEWS评分，可以使医护人员更好地了解患者的病情变化情况，并及时进行交流，共同制定护理方案，有利于提高医护人员之间的沟通和配合，促进医护团队的协作效率。

三、MEWS评分在急诊留观患者护理决策中的局限性1. 评分标准不够客观MEWS评分系统主要通过对患者的生命体征指标进行评分，存在一定的主观性。

不同的医护人员可能会对患者的生命体征指标进行评判时存在主观性，因此可能会对评分结果产生一定的误差。

词向量embedding模型-概述说明以及解释1.引言1.1 概述概述：词向量embedding模型是自然语言处理领域中的重要技术之一，它将单词表示为高维空间中的向量，使得计算机可以更好地理解和处理文本信息。

通过将单词转化为实数向量，词向量embedding模型能够捕捉到单词之间的语义关系和语法结构，从而提高自然语言处理任务的效果。

本文将深入探讨词向量的概念、词向量embedding模型的原理，以及不同类型的词向量embedding模型。

通过对词向量embedding模型的应用、优缺点分析及未来发展趋势的探讨，希望能够全面了解词向量embedding模型在自然语言处理领域的重要性和前景。

1.2 文章结构：本文将分为三个主要部分：引言、正文和结论。

在引言部分，将介绍文章的背景和目的，为读者提供一个整体的认识。

在正文部分，将详细介绍词向量的概念、词向量embedding模型的原理以及不同类型的词向量embedding模型。

最后在结论部分，将探讨词向量embedding模型的应用、进行优缺点分析，并展望未来的发展趋势。

通过这三个部分的组织，读者将能够全面了解词向量embedding模型的作用、原理和发展方向。

1.3 目的词向量embedding模型作为自然语言处理领域的重要技术之一，其在文本数据处理、信息检索、情感分析等方面具有广泛的应用。

本文旨在深入探讨词向量embedding模型的原理和不同类型，分析其在实际应用中的优势和不足，以及未来的发展趋势。

通过对词向量embedding模型的深入研究，可以更好地理解自然语言处理技术的发展方向，为相关领域的研究和应用提供参考和借鉴。

同时，本文也旨在为读者提供关于词向量embedding模型的详尽介绍，帮助他们更好地理解和运用这一技术，促进自然语言处理领域的进步和发展。

2.正文2.1 词向量的概念词向量是自然语言处理中的一种重要技术，它将语言中的词语表示为向量形式，使得计算机能够更好地理解和处理文本数据。

南洋理工大学绿色电子（慕尼黑工业大学双学位）授课型研究生申请要求南洋理工大学简介学校名称南洋理工大学学校英文名称Nanyang Technological University学校位置新加坡2020 QS 世界排名11南洋理工大学概述洋理工大学（Nanyang TechnologicalUniversity），简称南大（NTU），是新加坡的一所世界著名研究型大学。

南大是环太平洋大学联盟成员，全球高校人工智能学术联盟创始成员、AACSB认证成员、国际事务专业学院协会（APSIA）成员，也是国际科技大学联盟的发起成员。

作为新加坡的一所科研密集型大学，其在纳米材料、生物材料、功能性陶瓷和高分子材料等许多领域的研究享有世界盛名，为工科和商科并重的综合性大学。

南洋理工大学前身为1955年由民间发动筹款运动而创办的南洋大学，南洋大学的倡办人是新马胶业钜子陈六使先生，云南园校址由新加坡福建会馆捐赠；1981年，新加坡政府在南洋大学校址成立南洋理工学院，为新加坡经济培育工程专才；1991年，南洋理工学院进行重组，将国立教育学院纳入旗下，更名为南洋理工大学，与快速发展的教育事业齐驱并进；2006年4月，南洋理工大学正式企业化。

绿色电子（慕尼黑工业大学双学位）专业简介绿色电子科学硕士是南洋理工大学与慕尼黑工业大学(慕尼黑工业大学)联合开设的一门高度专业化的课程。

本项目旨在培养下一代半导体研究人员和工程师从事新型电子/光电器件和系统的研究工作，特别关注能源、传感、监测和制造领域。

通过两年全日制课程，学生将全面深入了解微纳米制造技术和可再生能源、功率半导体以及有机半导体器件和系统的先进理论。

本课程的主题涉及最先进的研究和工业发展。

基本的非技术主题，如产品营销、国际管理、专利法以及文化和全球化方面也将在课程中涵盖。

这些非技术课程将主要由行业讲师讲授。

成功修毕课程后，学生将获南大与通大合办硕士学位。

绿色电子（慕尼黑工业大学双学位）专业相关信息专业名称绿色电子（慕尼黑工业大学双学位）专业英文名称M.Sc. (Green Electronics) - Joint Degree with Technosche Universitat Munchen (TUM), Germany.隶属学院工学院学制2年语言要求托福100 雅思6.5GMAT/GRE 要求不需要2019 Fall 申请时间10月15日开放申请2020 Fall 申请时间10月1日开放申请学费(当地货币)38520绿色电子（慕尼黑工业大学双学位）课程内容序号课程中文名称课程英文名称1半导体过程和器件仿真semiconductor process and device simulation2纳米器件的设计和建模design and modelling of nanodevices 3精密加工技术microfabrication technology 4光机电一体化测量系统optomechatronic measurement systems5电子设备材料meterials for electronic devices 6生物电子学bioelectronics7能源系统纳米技术nanotechnology for energy systems 8用于绿色电子的微结构设备和系统microstructured devices and systems for green electronics9电力系统简介introductions to power systems* 南洋理工大学绿色电子（慕尼黑工业大学双学位）研究生申请要求由 M astermate 收集并整理，如果发现疏漏，请以学校官网为准。

液压制动的终结－电子机械制动（EMB）技术1 EMB研究现状及发展趋势1. 1 EMB研究现状电控机械制动系统（Electromechanical Brake System,简称EMB）最早是应用在飞机上的，目前正处于向汽车领域转化的研究发展时期。

从20世纪90年代起，一些著名的汽车电子零配件生产厂商，如德国的Bosh（博世）、Siemens（西门子）和Continental Teves（大陆天合）等相继开始了对EMB的研究，并作过一些相应的系统仿真和装车试验[10]。

另外Eaton、Allied、Signal、Delphi、Varity Lucas、Hayes也参与了EMB的研发竞争之中。

而国内在此项目上的研究基本为空白，仅有清华大学研究过EMB的试验台、同济大学试制出了样机；其他高校也只是进行了一些相关的初步研究，一些核心技术仍未被突破。

由于鼓式制动效能恒定性差；制动鼓空间小，使EMB的电机和传动装置的布置受到限制。

现在各大公司均以浮钳盘式制动器为基体，进行EMB的研发。

EMB与汽车目前使图2 Continental Teves 公司第三代EMB样机用的普通盘式制动器结构类似，只不过其制动钳的促动力不是由液压产生，而是由电机经过传动装置直接驱动制动钳，来产生制动力。

如图2所示为Continental Teves（大陆天合）公司生产的EMB样机[4]。

另外一种采用楔块机构增力的EMB称为EWB(ElectromechanicalWedge Brake)，EWB是2006年法兰克福车展上电子和机械电子产品开图3 西门子EWB样机发商Siemens VDO（西门子VDO）推出的（如图3 所示）。

其原理是在支座和旋转的制动盘之间架起一对楔块，楔块相对运动时产生推动制动衬片压向制动盘方向的运动，从而产生制动力，同时利用伺服电机控制该楔块的运动，使之不至于锁死。

在智能控制下，楔块将车辆的动能直接转换为刹车能，由于其自增力作用，EWB 比现有的液压刹车更快，因此楔块式EMB电机的功率可大幅度下降。

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

embedding模型原理-回复embedding模型原理是一种常用的机器学习技术，它可以将高维度的离散数据转换成低维度的连续向量表示。

通过学习这些向量表示，模型能够更好地理解和处理复杂的自然语言和图像数据。

本文将深入探讨embedding模型的原理，逐步解释其工作流程和应用。

文章将从介绍嵌入模型的概念开始，然后详细探究Word2Vec和GloVe这两个经典的嵌入模型，并解释如何使用它们来处理自然语言数据。

接下来，我们将分析使用嵌入模型处理图像数据的方法，并讨论其在计算机视觉任务中的应用。

最后，我们将总结嵌入模型的优点和局限性，并展望其未来发展的方向。

嵌入模型的概念可以追溯到20世纪70年代的早期神经网络研究。

早期的嵌入模型被用于学习输入-输出映射，以及特征提取。

随着深度学习的兴起，特别是Word2Vec和GloVe模型的出现，嵌入模型开始逐渐应用于自然语言处理和计算机视觉领域。

Word2Vec模型是嵌入模型中最经典的一种，它通过学习词语在上下文中出现的频率来生成连续向量表示。

具体来说，Word2Vec模型使用了两种算法：CBOW（连续词袋模型）和Skip-gram（跳字模型）。

CBOW模型通过上下文中的词来预测目标词，而Skip-gram模型则相反，通过目标词来预测上下文中的词。

通过这种方式，Word2Vec模型可以将词语转换为高质量的向量表示，这些向量可以捕捉到词语之间的语义和关系。

Word2Vec模型在文本分类、文档聚类和语义搜索等自然语言处理任务中取得了很好的效果。

与之类似的，GloVe模型也是一种用于学习词语嵌入表示的模型。

与Word2Vec模型不同的是，GloVe模型使用了全局共现矩阵作为输入，而不是上下文窗口。

全局共现矩阵记录了每个词语在上下文中出现的频率，这个频率可以表示词与词之间的关系。

GloVe模型通过最小化每对词语的共现矩阵中的差异来学习词语的嵌入表示。

通过这种方式，GloVe模型可以更好地捕捉到词语之间的语义关系和语法规律。

Predict and reduce gear whine noise 5 times faster Generate transmission gearbox models automatically and boost vibro-acoustic performanceUnrestricted© Siemens AG 2019Realize innovation.Transmission Engineering ChallengesGuarantee Performance and DurabilityReduce Time for SimulationMinimize Vibration and Noise LevelsReduce Weight with Lightweight DesignsAnalysisResultsModellingPrototyping can cost up to 200k$ --per single gear80% of time for manual model creationMicrogeometry modificationscan reduce vibration level with 6dB (=half!)Transmission Error can increase 10x or more!Transmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairsMulti-Body Simulation of TransmissionsTransmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairs.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsMulti-Body SimulationScopePredicting, Analyzing, Improving the positions, velocities, accelerations and loads of a mechatronic system using an accurate and robust 3D multi-body simulation approachMechatronic Systems Flexible Bodies•Integration with tools for robust design of complex non-linear multi-physics systems:control systems, sensors, electric motors, etc •Predict mechanical system more accurately wrt displacements and loads•Gain insight in frequency response of a mechanism•Enable Noise, Vibration & Harshness (NVH) as well as Durability analysesSimcenter 3D Motion for Transmission Simulation Critical featuresMulti-Body Simulation Industry Modelling Practices•Joints •Constraints •Bearings•Linear Flexible Bodies•Nonlinearity (geometric & materials) by running FEcode•Deformations•Loads•Transmission Error•Time domain •Statics, dynamic,•Mechatronics / controlPost processing•Create gear geometry ✓CAE interface ✓Import CAD•Ext. Forces •Motor•Contacts, FrictionParametric Optimization loop Automation / CustomizationKinematicsDynamicsFlexible bodiesCADSolving1D -modelsControlsTEST dataA manual creation process can consume 80%of time!.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsNew ApproachTransmission Builder Vertical ApplicationProblem: Even experienced 3D-Multi Body Simulation experts can struggle to 1.Model complex parametric transmissions2.Capture all relevant effects correctly and efficiently3.Update and validate their modelsSolution: Transmission Builder Up to 5x faster Model creation processSimcenter TransmissionBuilderGear train specification based on Industry standardsMultibody simulation modelDemonstrationModel Creation and Updating1.Loading of pre-definedTransmission2.Geometry creation3.Creation of rigid bodies forgearwheels and shafts4.Positioning and Joint-definition5.Force element creation.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsNew Solver Methodologies Simulating and ValidatingValidation cases ensure resultsas accurate as non-linear Finite Elements simulationMeasured Transmission ErrorAnalytical MethodSiemens STS Advanced MethodExploiting intrinsic geometric properties of gears + Efficient-Only for gears, not for arbitrary shapes-No deformation includedBut, included as part of the Load CalculationFE based contact detection -“Brute force” Slow+ Any geometry+ Deformation effects includedDedicating Tooth ContactModeling –FE PreprocessorLocal Deformation –Analytic SolutionSlicing –Gear Force Distribution Along Line of Action •Includes Microgeometry Modifications and Misalignments in all DOF•Automatically takes in to account coupling between slices and between teeth•Accounts for actual gear body geometry with advanced stiffness formulation•Evaluates tip contact (approximation)Gear ContactMethodology HighlightsKey Features.Transmission BuilderSummaryNew Simulation Solution for GearsMulti-Body Simulation of TransmissionsMulti-Body Simulation of Transmissions SummaryValidated methodologySuperior insight in transmission vibrationsAutomated creation of transmission modelsGear simulation as accurate as FE whileextremely fast•Create CAD + MBD model•Connect and position housing•Add flexible modes (Autoflex)•Set up load casesSimcenter 3D Motion Simulate TransmissionDynamic bearing forcesSimulateAcoustic Simulation of TransmissionsTransmission Engineering ProcessTypical process for NVH analysisMore efficient process in Simcenter 3DTransmission Error or Stiffness, parametersAcoustics, NVH •Gear whine •Gear rattleEnd-to-end integrated process for transmission simulation from CAD to Loads to NoiseTransmission Builder →Motion →Motion-to-Acoustics →Acoustic Analysis•Automatic creation of multi-body simulation models •Accurate 3D simulation of gear forces•Semi-automatic link of gear forces to vibro-acoustics •Efficient and accurate acoustic simulationsPre-processing of loads orsurface vibrationsTransmission layout (stages, dimensions)Multi-body simulation •Simulation of forcesand dynamicsPositioning, dimensions…Gear-centric tool•Analysis of gear pairs.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic Process OverviewvvcvMulti-body simulation resultsD a t a p r o c e s s i n g a n d m a p p i n gLoad Recipe Time series Frequency spectraWaterfalls OrdersNoise PredictionMeasured dataORAcoustic Process OverviewFrom Motion to AcousticsInput Loads Time Data to Waterfallof Time DataFFT Post-Processing•Multi-body simulation results•Data selection (forces, vibrations)•Automatic mapping •Multiple RPM•RPM function•Frame size definition•Time range selection•Time segmentation•Fourier transform(windowing, frequencyrange, averaging)•Waterfalls•Functions•Order-cut analysis Benefits•Quick switch between Motion and Acoustics solutions•Efficient data processing (fast pre-solver)•Automatic data mapping•Pre-processing time reductionAcoustic Process Overview Acoustic SimulationGeometry Preparation Meshing andAssemblyStructural/AcousticPre-ProcessingSolver Post-Processing•Holes closing •Blends removal •Parts assembly •Mesh mating•Bolt pre-stress•Structural meshing•Acoustic meshing•Loading frommulti-body analysis•Fluid-StructureInterface•Output requests•Simcenter NastranVibro-Acoustics(FEM AML,FEMAO, ATV)•Structural results•Acoustic results•Contributionanalysis (modes,panels, grids) What-If, Optimization, Feedback to DesignerBenefits•Efficient model set-up•Efficient, accurate solutions•Quick solution update•Deep insight into results.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic SimulationModel Preparation –MeshesFrom multi-body analysis•CAD geometry•Structural mesh of body→Used to compute structural modes included in Motion model when accounting for flexibility of body Specific to acoustic analysis•Acoustic mesh around body for exterior noise radiation →Geometry cleaning (ribs removal, holes filling)→Surface and convex meshing →3D elements filling•Microphone mesh for acoustic responseAssembly of structural and acoustic meshesBenefits•Easy, fast, efficient model set-up•Quick switch between CAD and FEM environments •Quick update with associativity of meshes to CAD •Flexible modelling through assemblyAssociativityModel Preparation –Loads and Boundary Conditions Structural constraints and loads•Fixed constraints•Multi-body forces applied at center of bearings→Automatic mapping→Data processing (time to waterfall of time data, FFT) Acoustic boundary conditions•AML (Automatically Matched Layer)→Non-reflecting boundary condition to absorb outgoing acoustic wavesFluid-structure interface•Weak or strong couplingTime dataTo Waterfall of Frequency dataBenefits•Easy, fast, efficient model set-up•Quick switch between FEM and SIM environmentsρc AMLSize ~ 190k nodes ~ 14k nodes Timex s/freq.x/20s/freq.AML (Automatically Matched Layer)•Automatic creation of PML (Perfectly Matched Layer) at solver levelFull absorption of outwards-traveling waves•First, accurate results in “physical” (red) FEM domain •Then, accurate results outside the FEM domain (green), through post-processing •PML layer very close to radiatorBenefits•No manual creation of extra absorbing layer •Optimal absorption •Lean FEM model •Fast computationSolver Technologies –FEM AMLATV (Acoustic Transfer Vector)•Single computation of acoustic transfer vector between vibrating surface and microphones{p ω}=ATV ω×{v n (ω)}•Independence of ATV from load conditions (RPM, order)•For exterior radiation, smooth ATV functions in frequencyBenefits•Large frequency steps for ATV computation, and interpolation for acoustic response •Fast multi-RPM analysisSolver Technologies –ATV=+p ωv n (ω)304050607080901001003005007009001100130015001700S o u n d P r e s s u r e L e v e l (d B )f (Hz)FEMATV Response Frequency100-1700 Hz 100-1700 HzTime22 min3 minNo ATV ATVFEMAO (FEM Adaptive Order)•High-order FEM with adaptive order refinement •Hierarchical high-order shape functions•Auto-adapting fluid element order at each frequency (dependent on f, local c0, local ℎ), to maintain accuracy Benefits•Lean single coarse acoustic mesh •Optimal model size at each frequency •Huge gains vs standard FEM •Faster at lower frequencies•More efficient at higher frequencies • 2 to 10 x fasterAcoustic SimulationSolver Technologies –FEMAOStandard FEM →1 single model for all frequenciesStandard FEM →several modelsfor different frequency rangesFEMAO →1 single model for all frequenciesLess DOF required forFEMAO Optimal DOF size over all frequenciesEdge Shape Functions Face Shape FunctionsFEM FEMAO.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryRigid body vs Flexible body•No significant difference at low frequencies •Above 1400 Hz, more frequency content due to structural modes of flexible housing structurePlain gears vs Lightweight gears (flexible body)•Low harmonic at 200 Hz (6000 RPM), due to gear stiffness variation with holes in lightweight gear •Side band due to tooth stiffness variation (amplitude effect due to coupling with holes)Bearing Forces Frequency Domain Benefits•Deeper insight on input forces•Quick solution update for comparative studies involving design/modelling changesPlain gears vs Lightweight gears (flexible body)•Low RPM•Significant impact of lightweight gears •High RPM•Extra frequency content at low frequenciesRigid body vs Flexible body •Low frequencies•Reduced impact of flexibility •High frequencies•Larger impact of flexibilityRadiated Acoustic Power Functions300 RPM –Plain gears300 RPM –Lightweight gear 5900 RPM –Plain gears5900 RPM –Lightweight gears300 RPM –Rigid body 300 RPM –Flexible body 1500 RPM –Rigid body 1500 RPM –Flexible bodyBenefits•Efficient post-processing for results analysis •Quick solution update for comparative studiesinvolving design/modelling changesRigid Body vs Flexible Body Benefits•Efficient post-processing forresults analysis•Global overview oncorrespondencebetween source(dynamic forces)and receiver(acoustic power)Plain Gears vs Lightweight Gears Benefits•Efficient post-processing forresults analysis•Global overview oncorrespondencebetween source(dynamic forces)and receiver(acoustic power)Contribution AnalysisExamplesMultiple results types: structural displacements and modes, equivalent radiated power, acoustic pressure and power, panel contributions to pressure and power, grid contributions, etcBenefits•Efficient post-processing forresults analysis•Deepunderstanding ofmodel behaviorthrough multipleresults types Structural displacements Acoustic pressure Grid contributionsPanel contributions.Acoustic Simulation of TransmissionsAcoustic SimulationPost-ProcessingSummaryAcoustic Simulation of Transmissions SummaryEfficient model set-up with CAD associativity for quicksolution updateSuperior insight in vibro-acoustic responseFast and accurate solver technologiesMore efficient link of gear forces from Motion toAcoustics =+p ωv n (ω)Associativity•Transfer bearing forces into frequency domain•Set-up vibro-acoustic model•Map bearing forces onto vibro-acoustic modelSimcenter 3D Acoustics Simulate TransmissionSimulateAcoustic resultsConclusionUnrestricted © Siemens AG 20192019-05-08Page 42Siemens PLM SoftwarePredict and Reduce Gear Whine Noise 5 Times FasterGenerate transmission gearbox models automatically and boost vibro-acoustic performanceSimcenterTransmission Builder Motion Simulation Acoustic SimulationAutomation removes 80% of workload for transmission model generation New gear solver increases efficiencyand accuracy Automatic motion-to-acoustics linksimplifies pre-processing Fast acoustic solver gives superiorinsight to responseUnrestricted © Siemens AG 20192019-05-08Page 43Siemens PLM SoftwareEasy workflow from design specifications NVH gear whine analysisHyundai Motor CompanyGear Whine Analysis of Drivetrains Using Simcenter Simulation & Services•Predictive simulation for system level NVH and gear whine•Bring 3D simulation to the next level of usability, towards an holistic generative approach for drivetrain design and NVH“Simcenter Engineering and Consulting services helped us use the right analysistools to cover the entire gear transmission analysis […] The Simcenter 3D Transmission Builder software tool is well suited for our engineering purposes”Mr. Horim Yang, Senior Research Engineer•Simcenter 3D Motion and Transmission Builder for system level NVH in multibody •Simcenter Engineering and Consulting for solving complex engineering issues AutomaticCAD and multibody creationAccurateFE-based gear elementsMulti-disciplinaryCAD-FEMMultibody-Acoustichttps://youtu.be/bBM5TPP6iBg。