Enabling high-value manufacturing industries to optimise performance of dynamic products and processes
Improving the performance of many high-value manufacturing products and processes, such as in automotive engine development, requires precise understanding of dynamic forces. For example, designing engines for fuel-efficiency requires manufacturers to measure mechanical torque to understand the causes of power transmission losses. Torque, a measure of rotational forces, is dynamic, meaning it changes continuously over time rather than in discrete steps. However, the sensors used in industry to measure torque can only be calibrated using constant torque values that do not account for dynamic sensor responses. Such mechanical sensors exhibit dynamic behaviour, including resonance, most notably affecting measurement reliability at high frequencies. This dynamic response problem applies to almost all mechanical sensors. Indeed, many mechanical parameters, such as force and pressure, are dynamic. Mechanical sensors were calibrated using static procedures mainly due to a lack of accepted procedures or documentary standards for dynamic calibration.
The EMRP project IND09 Traceable dynamic measurement of mechanical quantities established primary and secondary traceability at NMIs for dynamic force, dynamic torque and dynamic pressure. This was achieved by developing dynamic mathematical models for the complete calibration measurement chain, procedures for uncertainty evaluation of dynamic measurements, and procedures correcting for measurements for dynamic effects. However, none were incorporated into documentary standards, international guidance or industrial software during the project.
This EMPIR project built on the advice for using calibration results corrected for dynamic effects and demonstrated compliance with the Guide to the expression of uncertainty in measurement (GUM). The project developed, validated, and tested software to support dynamic sensor calibrations and dynamic measurement problems, that enables users to demonstrate compliance with the GUM. Written in Python, PyDynamic was made available via the PyDynamic GitHub repository and PyDynamic PyPi Website. It was developed in partnership with the testing, measuring and weighing technology company Hottinger Baldwin Messtechnik GmbH (HBM), and communicated to users via conference presentations, journal papers and contributions to Supplements to the GUM.
As reported by the consortium in the October 2018 issue of Precision magazine, PyDynamic has been applied in industry to calibrate piezoelectric fibre-optic sensors, analyse medical ultrasound signal measurements, and even to the study of invasive blood pressure measurements. HBM used the software to test its conditioning amplifiers, finding not all signals produced by connected pressure sensors registered as intended, causing likely response errors. It subsequently applied an improved signal analysis technique in the firmware of its Quantum MX410B universal 4-channel measuring amplifier. This resulted in improved reliability and accuracy over a wide frequency range, and suitability for highly-dynamic measurement tasks, such as supporting designers and developers looking to deliver improved engine efficiency and performance.
In the longer term, outputs of the project will assist high-value manufacturing industries to further optimise products and processes where dynamic measurements are necessary, supporting improved competitiveness of high-value manufacturing in Europe.
Coordinator: Trevor Esward (NPL)
For more information, please contact the EURAMET Management Support Unit:
Phone: +44 20 8943 6666