It has been years since virtual engineering was first introduced and the practice started to get adopted by engineers in a wide range of industries. Nowadays, various tools, methodologies and approaches, which belong to this practice, have been subsumed under standard engineering.
Notwithstanding the hyped use of “digital” and “virtual” in the press and in market research pieces, the underlying engineering toolset has helped transform the way in which the digital world informs engineering applications and decision making.
From the very first use of CADs (computer aided designs) and other analysis tools up to recent machine learning techniques and simulations, virtual engineering has contributed substantially in evolving the way in which engineers frame and solve problems. Most importantly, by replicating machinery, assets and systems in a virtual environment, engineers became able to test the behavior of these in more dimensions, experiment at lower cost and deliver faster understanding.
Automotive R&D Applications of Virtual Engineering
Take as an example the testing process involved in the production of a new car model. An automotive OEM used to manufacture prototypes and have them run for a great many kilometers, mostly in test-tracks, so as to validate design, performance and assembly. By stress-testing components, underlying issues would hopefully emerge before commercial release. Importantly, every iteration or change in the assembly during this process often necessitated a new round of such “manual”/corporeal testing.
Even if we bracket the high costs incurred in these processes, time-to-market and the resulting ceding of market space to competition could be too painful for teams that were struggling to release a new product. In this sense, what virtual engineering tools masterfully achieve is to compress time. Well calibrated models could suddenly simulate vehicle laps in virtual space and stress-test/ fine-tune different car part assemblies.
One of the domains of virtual engineering that stands out during the last years in the space of data-driven manufacturing is “digital twins”.
“A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making” — IBM Business Operations Blog
Digital twins allow engineers to inspect, troubleshoot and optimize the workings of physical assets and machinery. Some of the automotive use cases that “digital twins” are applied to include:
Engine failure rate minimization
Predictive maintenance of vehicle parts
Linkage of complex systems e.g. a modern powertrain
Smart assembly and assembly verification
Duty or life cycle analysis of vehicle parts
Connected cars simulation for real environments
The Data Fuel of Virtual Environments
For these applications to be successful, digital environments need to be correctly engineered and digital twins dynamically curated and “updated”. The necessary ingredient to achieve this and provide dynamic information bridges between the physical and virtual world is no other than data. This includes contextual data that update, inform and shape the environment of a digital space (e.g. traffic, weather, road conditions, driver behavior) and asset-specific data, usually captured by some IOT or other sensor that connects digital to corporeal (asset) twin. In both cases, data is the digital oxygen that permeates a virtual space and renders a model a dynamic counterpart of the real world . Without appropriate evolving data, a “digital twin”remains a model.
Tech infrastructure has evolved to accommodate these needs. Streaming processing platforms, like Apache Kafka or Amazon Kinesis, facilitate the dynamic streaming and utilization of big data from a variety of data capturing activities.
Data Ops Challenges
So how does an engineering team or data science team ensure a reliable and purified supply of data? Where does a team that needs to apply virtual engineering concepts or techniques find appropriate data for their projects? This is indeed a rapidly evolving space where engineering teams are re-skilling, hiring for new roles and adopting novel tools. Teams are learning to grapple with data operations challenges including data procurement, data management, data governance and data vendor partnership management. In large organisations, “Data Partnership” and “Data Procurement” job roles are emerging, highlighting the time and resources that are being invested in this direction.
From our experience at Mobito, we have seen data teams spending months to source, assess, select and integrate external data. At the same time, more data is being generated, captured and made available for third party-usage increasing the capabilities of virtual engineering applications. Cognizant of these opportunities, 92% of data analytics professionals report that their companies need to increase use of external data sources — 2019 Deloitte.
Vehicle Data at Mobito Data Marketplace
At Mobito, we offer such teams the opportunity to outsource their data procurement and expand their data ecosystem by streamlining their access to external data. We tailor to the needs of OEMs with driving behavior, vehicle performance and contextual data including weather and pollution data, accessed through our Data Catalogue. Additional data is generated and captured, at-request, through our list of data partners and data procurement missions. Reach out to us if you are interested to learn more about the automotive-relevant data projects we serve or if you want to share with us your own challenges and opportunities in the space.