Abstract
Abstract Dynamic measurement challenges are ubiquitous in metrology and can be found in many measurement applications that are not conventionally regarded as dynamic. We present four recent examples of work at the UK's National Physical Laboratory that demonstrate how modelling and simulation can contribute to improved understanding of dynamic measurement tasks. The examples are (i) a software simulation of a lock-in amplifier, (ii) a simulation of a sensor network in which one of the sensors has insufficient bandwidth for the measurement task and Kalman filter based data fusion is used to aggregate the sensor outputs, (iii) a study of performance imperfections in a clock embedded in a wireless sensor node using a Monte Carlo based method for simulating counting errors, and (iv) a simulation of wave propagation in a shock tube in which the lattice Boltzmann method was used to study non-ideal behaviour of the shock tube. It is shown that simulation (both physically based and phenomenological) is useful in designing measuring systems and identifying and quantifying measurement uncertainties and that the development of simulation software requires the developer to have a clear understanding of the measuring system of interest.
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