The Critical Role of Data Science in Automotive and Aerospace Engineering


Aerospace Engineering

Over the past few years, the global marketplace has witnessed exponential growth in data volumes, created and consumed by a diverse cross-section of stakeholders.

The term “Big Data” which has gained popularity in recent times, signifies large datasets in multiple formats. This is growing at an enormous rate and posing problems for traditional storage and analytical platforms. Data science has seen an increased focus with the advent of analytics mostly around Big Data. The main goal of Data Science is to turn data into useful, actionable intelligence.

The three components involved in Data Science are organising, packaging and delivering data. Organising is where the physical location and structure of the data is planned and executed. Packaging is where the prototypes are built; the statistics are performed, and the visualisation is created. Delivering is where the story gets told, and the value is obtained.

Design has been revolutionised by the development of Data Science. Especially in the areas of aerospace and automotive engineering, Data Science has helped in improving product quality and safety with innovative 3D design software applications.

Innovative manufacturers are moving away from just “selling a product”. Instead, they are moving towards providing mobility solutions, especially in automotive and aerospace engineering. These are solutions that are conceptualised after profound research through data analytics.

Data Science in Aerospace EngineeringAerospace Engineering

When it comes to Aerospace Engineering, Data Science has been helping to collect data on aircrafts for years ranging from binary data such as speed, altitude, and stability of the aircraft during flight. Damage and crack growth progression are also analysed at service intervals. To enhance the design accuracy in the field of aerospace engineering, routine data has been gathered periodically from failures to raise safety standards.  Aerospace engineering has grown multifold due to the advancement of Data Science and Analytics. When converting data to actionable information, one of the many challenges in aerospace engineering is how to account for as much of the data as possible before reaching a conclusion.

A high velocity, high volume, and diverse data stream may not be the most important characteristic for data analytics. Rather, it is more important that the data be relevant, complete and measurable. Useful insights can be gathered from smaller data if the data analytics is powerful which leads to innovative in aerospace engineering.

In the area of Aerospace engineering, aircraft manufacturers are leveraging Data Science to make flying safer than before.

  • Weather patterns and maintenance schedules are thoroughly analysed.
  • Data is collected, starting from flight takeoff down to landing.
  • Data patterns are driven and supported by analysis and algorithms to help create a safer and more expedited travel experience.

Data Science Applications for Automotive Engineering

Data Science, just like in aerospace engineering has multiple functions in automotive engineering. Companies gather data from all sources and use it to enhance existing advancements in automotive engineering and streamlining product lifecycle management.

Automotive Engineering organisations use GPS data to identify important patterns around speeding, understand vehicle impact, rationalise driver behaviour. It also helps to provide assistance to the driver; enabling him to make smarter decisions like Judging how long an accident and related traffic would take to clear, vehicle routes can be optimised in real-time etc.

Data Science Analysis for Automotive engineering is used to :

  • Understand driving behaviours –  frequently used routes patterns and counts of turns taken; as well as motion and standstill.
  • Provide a more accurate and detailed view of rush hour by time and road segment.
  • Predict time taken for incidents to clear,
  • Optimise directions and reduce travel delays in real-time
  • Optimise traffic light patterns to reduce congestion.

Ultimately, these types of data science insights can be woven together to help in Automotive Engineering on car design, predict wear, and tear and make fuel consumption improvements.

There is an immense potential for growing power, importance, and responsibility of applying Data Science methodologies to address existing challenges in aerospace and automotive engineering. Data Scientists are discovering newer and radical ways of thinking about data and the world around us.'

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