Scientific and technological progress has been possible thanks to our ability to identify patterns in nature. This ability allows us to solve problems and advance in various disciplines. In fact, Carl Sagan described her as “the best quality of the human being.”
Since Archimedes (287-212 AEC) humanity has sought to understand the universe through these patterns. However, knowledge has grown in complexity. Therefore, current scientific challenges combine theoretical and experimental analyzes.
In recent decades, computational tools have acquired a key role. These allow to find patterns, reduce errors and improve efficiency in many applications.
In fact, simulation and computer modeling are today research pillars. They allow to validate experiments and explore new theories in difficult conditions to reproduce. In addition, they optimize processes in multiple areas of knowledge. A clear example is the study of magnetism.
The high cost of experiments
Since Hans Christian Ørsted discovered in 1820 the relationship between electricity and magnetism, Maxwell’s equations – which describe electromagnetic phenomena – have evolved. These advances have improved transmission, storage and reduction of energy losses.
Since energy is an essential resource, understanding magnetism is key to optimizing its use. Therefore, the computer modeling of the magnetic field is crucial in many sectors. It is used in fusion reactors, particle accelerators, in renewable energies and in the production of isotopes to treat cancer.
The importance of these models is that, despite the advances, Maxwell’s equations – which explain electromagnetic phenomena – only have exact solutions in simple cases. Experimental essays, due to their high cost and duration, are used only for validations.
Therefore, the computational simulation has become essential in the analysis of complex phenomena. The so -called “Finite Elements Modeling”, as the name implies, divides a problem into small and manageable parts.
However, current computational limitations are more expensive.
How to improve the efficiency of models?
To improve efficiency innovative methods have been developed. Its objective is to simplify the models without affecting the precision of the results. A recent approach promises to overcome the barriers of conventional tridimensional simulation.
This method modifies electrical wiring geometry. In other words, it reduces the number of finite elements to which the problem was reduced and improves calculation efficiency.
The key is to adjust the properties of the materials in the premodle phase. Thus the electrical and magnetic characteristics are preserved without compromising accuracy.
In magnetism, an essential parameter is the frequency, which measures the speed with which a periodic phenomenon is repeated. The low frequencies correspond to slow processes, such as the Tic-Tac of a clock; The half frequencies include the transmission of AM radio; and high frequencies cover FM radio signals and wireless communications.
At high frequencies, phenomena appear, such as the film effect and the proximity effect, which affect the efficiency of electrical and electronic devices. Understand them improves the efficiency of these systems.
Scientists around the world publish work on those effects every year, and each advance has meant a milestone. But this novel study in cables with polygonal sections has provided new contributions in this field of research.
Thus, a new approach proposes to use corrective coefficients once the simulation is over (what experts call “postmodelled”). This allows to obtain electrical and magnetic results very similar to those that would be achieved if the real forms of the cables had been modeled in total.
In other words, it is like using a “magical” formula that, at the end of the process, adjusts the result so that it seems a lot to which we would have obtained if we had made a more complex and slow simulation.
This technique accelerates calculations in components with irregular or not very symmetrical shapes, which are normally more difficult to recreate. In addition, it allows to calculate two fundamental properties (resistance and inductance) that are key parameters to design efficient electrical devices.
Each resolved challenge makes us advance
Innovation in scientific computing remains key to understanding nature with greater precision. Each advance brings us to solve great unknowns and improve the quality of life on Earth.
Despite the computational challenges, each problem solved and every successful simulation brings us closer to a future with more opportunities.
Human curiosity and technology drive new discoveries; They help us find patterns where we didn’t see them before. Thanks to the development of advanced computing, knowledge continues to expand.
This article was originally published in The Conversation.