#42🚀 Design Magnetics as a Scientific

SuperPowers for Electrical Engineers 🦸🏻‍♂️

👋 Hello Supergineers! Dr. Molina here! 👨‍🔧

Welcome to Dr.Molina Newsletter, where I break down Magnetic designs or theories for you every week, in 4 minutes.

First of all. Thank you all of you for the amazing support I have received this year. We have built a community of 983 engineers, every week I have received more than 10 emails from the community.

🚀 In June 2022, we have already multiplied the revenues of 2021.

🚀 We have users with more than 60 designs per month (3 per working day) in Frenetic Online.

🚀 More than 50 engineers have already completed the Training.

🚀 Our database of magnetics is growing, helping us to find the limits of the state of the art.

Some of our detractors tried to criticize our work doing references to specific designs or bad results. To be honest, failures are the feed of my brave engineers. They analyze the failures, save the data and work on the models.

When you work using a scientific approach, failure is necessary. Scientific work is our second value, the first one is honesty.

In this edition, I’m going to talk about our Scientific Method for magnetic design and you will understand why failures are good for us 🦸🏻‍♂️.

Design Magnetics as a Scientific

The classic process of designing magnetics is based on iterations.

There are a few calculations about the known part:

• Number of turns for an expected inductance

• Gap estimation

• DC resistance

Expert designers use their experience (and spreadsheets) to predict the most unknown part of the design, where models aren´t accurate.

• Core Losses

• Leakage inductance

• Winding losses

• Temperature prediction (Heat dissipation)

After the design decisions, they start doing some tests in the lab. After each test, they make decisions about what was wrong, iterate, and try it again until is working.

There is no database of results.

With the current needs of the industry, reaching power densities of 50 kW/l, this legendary process only works if:

1. You own 20 years of experience

2. You don´t have deadlines

3. You don´t care about the future

4. You are working with J.W. Kolar at ETH

Figure 1. Magnetics Power density was published in [1] by Kolar.

For the rest, we need a different process. In my opinion, the best solution is to apply the scientific method and create a scientific process.

Scientific Method

Let’s start defining the basic concepts.

The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century [2].

These are the steps of the method.

1. Observe and question

2. Research

3. Formulate hypothesis

4. Test your hypothesis

5. Analyze and conclude

To apply this method to design magnetic elements, you need to go step by step for each variable and analyze the results.

Scientific Method for Magnetic Design at Frenetic

We are constantly working on improving our method for designing magnetic components. However, there are some basic concepts I want to reveal to you.

The first step is to observe which variables you can measure and with which error.

The second step is to create a process of measurements, data collection, storing samples, and digital documents organization.

The digital part is hard but is key.

We have created a process for measuring inputs, physical variables, and results. Probably, we have the biggest database of magnetic designs together with results. The key points for the database are:

1. Create a measurement procedure (to keep the consistency of the data)

2. Introduce data of designs working

3. Introduce data on designs that DOESN´T work (this is the most important part because these designs will create limits)

4. Create a model with the data obtained to predict solutions (With AI, regression, or more simple methods)

Once you have that, every step of the design is created following a scientific structure.

Let’s apply the Frenetic method for a specific design for an 11 kW OBC transformer.

1. Assuming I have chosen a core in a previous scientific approach. Which is the best winding configuration in a custom PQ60 for an 11 kW LLC transformer?

2. I need to find out the electrical parameters like switching frequency, voltage/current, and resonant inductance ranges.
   I can research in our database of designs, which includes results of Llk and temperature, to find out what was working previously and what WASN´T working. With this research and the goals, I can see if we are in the state of the art of power density or if we are challenging a new space 👨🏻‍🚀.

3. Create a hypothetical design of the winding with the model of the Winding generation.

4. Measure the electrical specifications using the defined process.
   If the error of the Leakage or any other parameter is higher than 30%, the design would be considered not valid, but we need to save the data in the database. Even if the design is not valid, I will test it in a power setup to save its behavior in terms of temperature.
   In case the physical predictions are correct and the power tests are correct, we repeat the test out of the initial ranges to characterize the magnetic completely.

5. We conclude with a report of results, which are always the same, analyzing the error of the predictions and reporting to the engineer who tunes the model.

The creation of the whole scientific process could be the main barrier for companies because it takes time. Besides, we already have done it, why would you repeat yourselves, when you can just use our technology?

In any case, I want to highlight some companies that are doing something more than just using the models. For example, Wurth, measures the losses of the inductors, instead of predicting them. They use equipment (I know which one, but I’m not going to reveal it ;), that allows them to measure the losses of an inductor when you apply high frequency (think in a buck converter).

That’s all folks.

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References

[1] J. W. Kolar et al., "PWM Converter Power Density Barriers," 2007 Power Conversion Conference - Nagoya, 2007, pp. P-9-P-29, doi: 10.1109/PCCON.2007.372914.

[2] https://en.wikipedia.org/wiki/Scientific_method