‘Learning while Earning’ short distance-learning courses for industry professionals
Data Acquisition Systems A Motorsport Knowledge Exchange Exchange programme programme
A distance learning course which will provide you with an excellent excellent understanding of the theoretical and practical issues of a wide range of Data Acquisition Systems, in the context of a racing r acing car. car.
Motorsport Engineering Centre
Who is the course for? The course is designed for engineers having a mechanical and/or automotive/motorsport background, probably with a BEng or BSc degree, although it would be applicable for those with practical racing experience and some mathematical knowledge. It is set in the context of motor racing data acquisition systems, but is applicable to all areas of engineering. Data Acquisition Systems is a postgraduate level course and will require on average 200 hours of study.
What is the course about? The course is about providing a good understanding of the theoretical and practical issues involved in using a wide range of data acquisition systems. It covers sensors and transducers, errors, conversion of signals to the digital domain, noise and interference reduction, digital signal processing, and ltering. The course arose in response to a real industry need, and the course team worked with a number of race teams and manufacturers during its development. Practical issues relevant to automotive sensors are also covered, together with the processing of the data in loggers, and the use of features such as ‘math channels’. The nal chapter looks at practical applications for data acquisition systems in a motor racing context. The course covers the use of MATLAB to process signals, linearise sensor outputs, lter noise and interference from signals, and other tasks. An optional practical course, where you will work on a Formula Renault racing car using state-of-the-art equipment, is also available. The content is organised into four ‘strands’: 1) The sensors – how they work, issues such as calibration and accuracy etc .; what we can measure with them – the performance itself and the associated characteristics. 2) How to t the sensors to a car and how the data are converted, stored, and read. This section is automotive-related, but most material is generally applicable. 3) How to interpret the data. How do we process the signals to get what we want, if it is not directly given? What do signals mean in the context of running the car and, ultimately, reducing lap times? 4) Doing something about it; strategy and actions by the team in terms of practical changes needed on the car to implement the required improvements. The course can be part of a Masters programme, and relevant theoretical topics are covered. The course also covers some general academic ground, including aspects of the use of the MATLAB program, which may be new to some students, though no prior knowledge of this is assumed.
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Really good. Made me want to be back at university.
Pat Symonds, former Executive Director of Engineering, RenaultF1
I am really enjoying this course so far and want to commend you guys on your effort. Wayne Gross, Race Support Engineer
http://motorsport.brookes.ac.uk
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How does is work? The course is studied part-time via distance-learning and should take about six months to complete although there are no formal start- and end-dates. You will be sent a course pack which will include a Study Guide, Course Workbook, DVD-ROM, and supporting material. The workbook provides the main content of the course, with the DVD-ROM providing background material in the form of a number of short lms/case studies showing theory being put into practice. A number of these lms were shot at the Renault F1 base in Oxfordshire, England. Although you will be expected to study autonomously, you will be able to contact a tutor. Your progress will be monitored, for your own feedback and for assessment, through a series of short computer-marked assignments, one for each couple of hours or so of study. These computer-marked assignments give automatic feedback, depending on your answers, and questions can be repeated if necessary. The computer-marked assignments provide a pass/fail assessment only. There is also a nal tutor-marked assignment. On completing the study material, you will have the opportunity to complete a 3-day residential course where you will be able to put your new knowledge into practice, using both low-level and state-of-the-art equipment, on location at a race track, and in the new Motorsport Engineering Centre at Oxford Brookes University. This element of the course is optional and additional tuition fees are payable.
Course Requirements You will need a computer running Windows XP or 2000 with a DVD-ROM drive to access the course material. You will also need access to the Internet to use online resources and undertake the computermarked assignments. The course makes extensive use of the MATLAB computer program, and a copy of this will be provided as part of the course material. It is NOT necessary to know how to use MATLAB before you start the course. Full instructions in use of the relevant features of the program will be given.
Tuition Fees The tuition fees for this course are £1200*. The practical element of the course is an additional £600.
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The overall coursework material is excellent and well prepared.
Peter Jackson, General Manager, MoTeC (Europe)
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I think that it is a well put-together course. Easy to follow and instructive at the same time. Peter Van Manen, CEO, McLaren Electronics
http://motorsport.brookes.ac.uk
*Oxford Brookes Alumni are eligible for a 10% discount.
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Course Content The courses covers a number of topics in the eld of racing car data acquisition, such as: Basic processes of data acquisition, and actions of the different sub-systems Sensors: basic action, errors, and related issues Using MATLAB to produce calibration equations for linear- and non-linear transducers Operation of the main types of sensor e.g. pressure, temperature, acceleration, ow-rate An understanding of the problems of accuracy and resolution Analogue to digital conversion: the process, and how practical issues affect data quality Matching sampling rate to signal: Nyquist frequency, sampling theorem, and aliasing Practicalities of connecting sensors to loggers, especially as applied to noise reduction CAN bus connection of sensors – how this works, and the main advantages and problems Memory requirement of data loggers, and the main technologies for data storage; data transfer Nature of signals (Fourier analysis) Filter behaviour and purpose: signal extraction; noise reduction; the importance of phase Basics of digital signal processing (DSP): how digital lters work; advantages of digital over analogue; effect of lter order and type of frequency response and phase; implementing lters in data acquisition systems Use of DSP for Fourier transforms; time and frequency domains; moving between domains Processing of acceleration data (with MATLAB and in the logger) to produce 2-dimensional speed and position data; use of GPS data to enhance acceleration, speed and position data Other signal processing both with external programs (MATLAB) and using the data logger’s math channels How data from key channels can be used in vehicle performance improvement and driver coaching.
For more information please contact: Rebecca Price, School of Technology, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford, OX33 1HX, UK t: +44 (0) 1865 484227 e:
[email protected] Oxford Brookes University reserve the right to alter the course and fees without notice.
MKE/CPD/06_2010
