The suite of software now represented by Road Ranger, had its beginnings in 2005, when James Scandrett began his adventure in tunnel ventilation and simulation modelling whilst in his final year of university and earning his BE Hons(Mechanical) degree. The simulation developed as an exploration tool, and through various iterations of static thrust and airflow prediction calculations, James evolved the complexity of the hardware-in-the loop concept, and its utility for integrating control systems. By 2017, James had proved a methodology for performing dynamic airflow calculations. This methodology found its way into the first application which eventually helped fine tune the integration of a major road tunnel development is Sydney.
The Road Ranger methodology for ventilation systems is a transient (dynamic) set of calculations which considers forces of traffic dynamics, temperature-related buoyancy, plant, and tunnel profiles and resistance, to determine the magnitude of change in tunnel airflows in realtime, throughout the tunnel. With the dynamic air movement comes pollution and temperature generation algorithms, which allow the simulated build up of pollution emissions to estimate the effects of exhaust gases on tunnel patrons.
Road Ranger is thus able to predict the tunnel control compliance to government regulations.
More than just a simulator, the Road Ranger HIL platform allows for a realtime interface to either a real or emulated PLC allowing for realtime exchange of commands to simulated plant, and feedback signals from the simulated plant. These include jet fans, axial fans, dampers of many types, velocity and pollution sensors. A fire can be simulated in intensity at any location.
Where the user wishes to take control of the simulation for trouble-shooting or experimentation, the simulator an be place in “user-in-the-loop” mode allowing manual operation of devices. All sensors have a signal override mode where manually entered values can be injected into the PLC on a device or global basis for individual test cases during the FAT/FIT cycle.
As the testing process requires logging of data as the test progresses, Road Ranger has a comprehensive set of data logging and trending functions. Logging of all frequently used data occurs automatically at a suitable time basis and length profile plots, and/or time plots can be created and saved as the simulation progresses.
The algorithms of Road Ranger have now been used for the integration of several major motorways, and have proven their value as an efficient and cost-effective tool for eradicating defects in the systems without having to resort to road-based regression testing. This represents a major source of savings for Infrastructure developers.
In addition to the above functionality, Road Ranger is also an integral component of a Realtime Procedural Trainer System for Operators of the target infrastructure.
Realistic training in diverse scenarios can be obtained by the Operators running the control system which in turn is connected at the PLC level to the Road Ranger simulation suite, instead of actual plant. Episodes can be created to establish a starting point for the training scenario, from which the Trainer combination runs in real time. Incidents can be created, including fires, to test Operator Reactions. The Instructor is able to insert faults and/or extraordinary monitored values in the simulated plant at the device faceplates, to which the control system and the Operator react as would happen on the real infrastructure.
The Trainer system is indeed another regression testing system for major system changes, and can be used as such. Idetika is constantly enhancing the scope of the simulated subsystems to enrich what the Trainer can apply to in its realtime aspects. An animation suite is used to replace video and enhance realism.
Road Ranger is under constant development by James and Idetika, who has sponsored joint forces with the University of New South Wales (UNSW) and other partners to provide a number of innovations including:
Adaptive methods of utilizing multi-core processors to extend realtime simulation scope.
Incorporating the algorithms into both cloud versions and hosted versions for flexibility in deployment.
Incorporating the environment into realtime training applications with extended UI capabilities.
An AI layer that is able to optimise maintenance activity according to actual plant condition and also the timing to correctly balance repair costs and operating times. This AI layer can also feed developing critical information to Operators presiding over system health.
Use of Ai to enhance system reliability under failure by applying substituted values from an operational Digital Twin environment in the production system.
Incorporating the concepts learnt from AI and simulation to explore the potential of automated integration environments to reduce system deployment times whilst maximizing system integrity.
Extending the AI layer to automatically analyse system operations and advise system owners of potential alterations that could be made to improve system efficiency and increase Return On Investment (ROI).
Idetika has a number of Mottos focused on customer ROI enrichment, including “Know and Grow”, “Only Build what you have Already Simulated and Optimised” and “ Continually Ask the Questions to Drive the Best!”
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