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44 - Newsletter EnginSoft Year 6 n°4 muLab is a fundamental tool for a large variety of applications designed with FEA. Indeed, even in simple mechatronics applications, such as the temperature control of an air-conditioned railroad car (Figure 1), the algorithmic functionality implemented in a microcontroller may be quite complex. In general, it has to: read the temperature sensors and filter/compensate electrical disturbances and physical deficiencies of the sensor (nonlinearities, thermal inertia, condensed vapour, etc); infer the temperature at the user site; this is usually an indirect measurement, since the sensor can only be placed in hidden locations, which is performed by an algorithm that uses a numerical model of the process to predict the unknown quantity; the numerical model must typically have low computational cost and it is built by using emerging numerical methods in engineering, such as model order reduction, system identification, machine learning. implement the logical behaviour required by the machine design; this is usually a rather complex set of functions and procedures that covers machine initialization and configuration, manual or self-diagnosis, different operational modes, failure recognition and safe reaction, etc. Figure 1 control the actuators according to the specifications; this usually involves algorithms to safely operate, optimize and monitor the physical actions performed by actuators and related subsystems. These algorithmic functionalities are easy to implement and verify in muLab, especially when they require the support of a numerical model. In particular, system identification and machine learning, which are based on the comparison between model predictions and experimental data, will be algorithmically supported explicitly in the near future. MuLab: the software tool The main feature of the software tool muLab is the simulation-based prototyping and validation of algorithms that should run on the microcontrollers embedded in a variety of digital mechatronics systems. The approach followed by muLab is hardware software co-design. A main ingredient is the ability to monitor the functional Figure 2 behaviour of the system up to the finest scale detail. To do this efficiently, muLab offers to the user the possibility to write a multi-level ensemble of debug procedures (Figure 2) that renders this monitor activity fully automatic during the simulation. This is very important because the user typically wants the computer to do hundreds of simulations during the night. The language used to write the debug procedures is a slight customization of the simplest programming languages actually used in computer programming. Moreover, muLab is a collaborative design tool: the development of physical models becomes visible to the firmware designer and the firmware behaviour becomes visible to the mechanical engineer (Figure 3). As a consequence, the firmware development takes place in parallel with the hardware construction and fits to it. At the same time, the physical system structure and organization can be cheaply modified until the expected performance appears to be adequate. The environment includes also a source code debugger (Figure 4) that works both for the numerical models of the physical components and for the embedded software (firmware). The possibility to set a breakpoint during the simulation of a mechatronic system allows a deep understanding of the interactions between the firmware and its embedding physical system. It allows the numerical analysis of algorithms that run on embedded microcontrollers, i.e. running in a non-sequential mode. This is usually much more difficult than it is for FE numerical methods. In fact, their execution is intrinsically non-sequential and may actually involve several subtasks executed by routines which are activated by interrupts caused by non-deterministic (and sometimes only loosely predictable) events. Last but not least, muLab uses Standard and open languages and data formats: component model equations may be written in Python. model structures and user procedures are coded in XML.
Figure 3 Advantages: reduced experimentation costs and development time muLab enhances firmware debugging and simulation-based robust design. This is accomplished through the specification of detailed Test Sequences (Figure 5) by which the user can: specify complex test sequences; track and identify firmware fault conditions; Implement failure mode analysis (FMEA) of the hardware components. The automation of test sequences allows the user to verify the application functionality in a large variety of situations, much larger than what is physically possible. Moreover, the experimental test phase can take place selectively and on a relatively mature firmware, where a large number of bugs has already been removed; the quality and value of the firmware verification procedure increases, while the debugging time decreases. Thus, time, energy and money can be saved. Practical issues concerning the multi-level debugging of the firmware: About SimNumerica and EnginSoft SimNumerica was founded by a dedicated research team, all experts with broad experiences in numerical mathematics, electronics and software design, of University of Padua – Italy. SimNumerca’s industrial partner and co-founder EnginSoft is an international CAE Computer-Aided Engineering Consulting company with unique multidisciplinary competencies in virtual prototyping. SimNumerica’s joint expertise is focused on environments for the virtual prototyping of mechatronics systems based on micro-controllers. Newsletter EnginSoft Year 6 n°4 - 45 one advantage of the numerical simulation compared to a corresponding physical experiment is that the former is deterministic, and hence repeatable, while the latter is not; the methodology implemented in muLab supports a userdefined ensemble of debug procedures that monitor the numerical simulation: if something is suspect, a debug procedure can restart the simulation with increasing levels of diagnosis. In this way, following the diagnostic tree, the details of a wrong behaviour of the system can be traced at affordable time and computational cost. Future Development In a future release, additional parallel computing capabilities will be integrated in the software package. In particular, multi-core platforms and graphical processors (GPGPU) will be supported. The target is an efficient co-simulation of computational intensive models, such as large-scale dynamic FEM models, and of large firmware codes, in particular the ones involving the control of processes whose duration extends to relatively large time-scales. The combined use of multi-core CPUs and GPUs makes computational digital mechatronics affordable to small industrial engineering teams, even for quite complex applications. Contact Fabio Marcuzzi, PhD - Simone Buso, PhD SimNumerica s.r.l., Pordenone - Italy email: info@simnumerica.it Figure 4 Figure 5
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