استفاده از زبان پرس و جو شی گرا توسعه پذیر در تجزیه و تحلیل سیستم چند تنه

Since modern software tools produce large amounts of engineering data, the demand for efficient data management may be met by integrating database technology with engineering applications. This approach is taken in MECHAMOS, which is a previously reported system for symbolic and numeric multibody system (MBS) analysis. This work focuses on the high level analysis performed with the available query language in MECHAMOS. The data management is considerably improved in this system compared to a traditional MBS analysis tool. For instance, MECHAMOS can easily combine and compare MBS data not only within the same MBS model but also over several MBS models, each governing different equations of motion. To avoid redundant computations in such analyses a simplified materialisation mechanism is implemented. Examples are given of combining and comparing both symbolic and numeric MBS data

The rapid development of computer technology and software tools has enabled engineers to build larger models and to perform more advanced analyses on these models. This generates large amounts of heterogeneous engineering data. Therefore sharing and combining this heterogeneous data as well as transforming it to a suitable form for further analysis have become important issues in the design process today. To meet these demands, full availability of data and efficient data access are important. One way to accomplish this is to access data through a query language, which is faster and requires less coding than using a conventional programming language [1]. This will add to the requirements on future generation engineering applications to supply database technology and a general query language for data management. In the field of multibody system (MBS) analysis, the development of computer technology has also opened up the possibility to perform the analysis in the symbolic domain and then move on to the numerical domain in a later stage of the analysis. A previously reported system for MBS analysis, based on object-relational database technology [2], shows how the availability of MBS data is increased and the data management facilitated to meet future requirements. In this system, named MECHAMOS, symbolic and numeric MBS data is fully available through a general query language and can be put into a suitable form for further analysis. A similar approach is taken in Ref. [3] where a system for finite element analysis (FEAMOS) is based on the same database technology as MECHAMOS. This work focuses on the MBS analysis in MECHAMOS where large amounts of MBS data can be generated, compared and searched for by taking advantage of the data management capabilities and the extensible query language integrated into the application.

This work demonstrates the benefits of integrating MBS analysis in a database environment. The available query language enables MBS analysis on a higher level and the database technology facilitates the MBS data management compared to traditional MBS analysis tools. The differences in retrieval of symbolic and numeric MBS data are discussed and from this discussion the importance of function materialisation emerges. The materialisation capabilities avoid redundant and unnecessary computations and the simplified implementation in MECHAMOS focuses on computational intensive operations and the most frequent operations in the system. The importance of this implementation is shown in some examples along with examples of combining and comparing of both symbolic and numeric MBS data. Some of the advantages of the available database technology in MBS analysis found in this work are: Several MBS models available to the analyst. In traditional MBS software each model is analysed separately, and usually each set of parameter values for the model are also analysed separately. The results of these separate analyses must then be stored and compared in a later stage of the analysis where the MBS models are no longer available to the analyst. This paper has presented and shown through examples how the MECHAMOS system handles MBS analysis and processes queries over several models. The examples have ranged over several sets of parameter values, within one and the same MBS model, but it is evident that extending the query processing to range over several MBS models is supported in MECHAMOS. Improved MBS data management. The large number of defined parameter sets (a total of 29) involved in the sliding pendulum example shows how the available database technology facilitates the data management of the MBS analysis in MECHAMOS. Through the available query language, the MBS analysis is performed on a higher level compared to the traditional MBS analysis software. Further, the MBS data is not generated in the beginning of the session as in traditional tools but generated along the analysis as the data is required to proceed with the analysis. Optimisation analysis extended to include several MBS models. In a traditional optimisation process, a given physical system is analysed in a parameter space in which each set of parameter values represents a variant of the system. The aim of the optimisation is to determine the parameter values giving a maximum or minimum value of some response. With the possibility to easily analyse several MBS models simultaneously, the optimisation can be performed over different physical concepts, i.e. different technical solutions to obtain a similar functional response. The different concepts yield different equations of motion and each of the concepts may also contain variants in terms of different parameter sets. This has not been shown in the examples of this work, but it is possible to perform such analyses in MECHAMOS. Combining data between engineering disciplines. This work has focused on MBS analysis but in a wider perspective where MBS analysis represents one discipline among others (e.g. CAD, FEA, CFD, etc.) the ability to combine engineering data can be of even greater importance. Finite element analysis (FEA) may need to combine geometrical data from a CAD model and dynamic loads from an MBS model to perform a structural analysis. This issue is further discussed in Ref. [2]. Possible future development directions for MECHAMOS have also been discussed in Ref. [2]. This work supports the idea that the available database capabilities are important factors for a successful implementation of the following suggestions: Automatic submodel assembly. Implementing a submodel representation and automatic assembly strategies will improve the MBS analysis in MECHAMOS considerably. Functional descriptions of physical components (e.g. motors, gearboxes, couplings, etc.) can then be stored in the database as submodels. MECHAMOS should then be able to combine these submodels and derive MBS data for the assembled system. This implies that MECHAMOS automatically can derive the different concepts discussed in the optimisation section above. MECHAMOS should then be able to select the assembled system that is optimal in some sense. Trajectory analysis. Grossman [12] and [13] presents a strategy for storing trajectories in a database and search among these trajectories to obtain information about the system described by these trajectories. In “path-planning problems”, the path closest to a given desired path of a given point in the mechanism can be selected. Further, given a trajectory that is a periodic orbit, other trajectories in the database can then be compared and those which also have a periodic orbit can be identified. The MECHAMOS system generates trajectories by deriving the equations of motion and solving these numerically. These trajectories can then be searched and the trajectories that fulfil certain criteria can be detected.