Taking the right decision along the product lifecycle: what we can learn from Weather Forecasting

In the last decades, physics-based simulation has represented a revolution, allowing companies to design, optimize and produce complex products faster and faster, while cutting costs but not quality and reliability. Of course, the aerospace industry has been one of the first to adopt the technology, given the complexity of its products and its need to test everything to certify products and processes.

Towards technology convergence

Over the years, simulation software has become more and more accurate, able to work on multiple physics and domains, easy to use, and accessible, allowing every company, no matter how big and in which industry, to take advantage of it. This extensive adoption was made easier by the rise of cheap computer power and, more recently, HPC and Cloud. It was (and is) possible to accelerate virtual testing, analyzing hundreds or thousands of different design hypotheses at a fraction of the cost and time of a physical test.

We are now witnessing a new revolution fostered by the convergence of simulation, data analytics and AI/Machine Learning. It brings the promise of being able to simulate very complex systems, make very reliable predictions and, above all, suggest actions to reach very ambitious performance goals.

Self improving models

To better understand how it works, let's use as an example the weather forecast, one of the most challenging if not "impossible" simulations to be run. By harnessing vast amounts of data and leveraging sophisticated algorithms, data analysis has emerged as a transformative force, significantly enhancing physic-based predictions. Thanks to sensors, satellites, weather stations, buoys, and other sources, meteorologists can now collect massive datasets on various atmospheric phenomena. Scientists can build more accurate models and understand the complex interactions within the Earth's atmosphere by extracting patterns and trends from this data. Machine learning algorithms can detect intricate patterns and non-linear relationships within historical weather data, leading to more refined predictions. AI-driven approaches can also adapt and fine-tune forecast models in real time, enhancing their adaptability to rapidly changing weather conditions.

One of the most critical aspects of weather forecasting is predicting extreme events like hurricanes, heat waves, and flash floods. Data analysis is pivotal in forecasting these high-impact weather events by identifying early indicators, even weak ones, and risk factors.

The Chaos theory tells us that it is impossible to have 100% accurate weather forecasts if not in a short timeframe and a precise location. Data analysis enables scientists to quantify the uncertainty associated with predictions. This valuable information helps to understand the range of potential outcomes and make more informed choices in response to forecasted weather conditions.

Taming complexity

Going back to industrial applications, what we see in weather forecasting applies to other complex systems or systems of systems. AI-powered physics+data based digital twins are on the agenda of several executives; prescriptive maintenance is taking the place of predictive maintenance; data-driven decisions on fast-changing scenarios are a must in times of uncertainties.

From product design and certification to production, up to its lifetime cycle management, the convergence of numerical simulation, HPC and data analytics is paving the way to long-lasting and successful digitalization programs.

Interested in knowing more? Have a look at this short e-book https://altair.com/resource/transform-development-with-altair-digital-twins?lang=en