The Taguchi Method: Justification for Robustification

The Taguchi Method: Justification for Robustification

Within the scientific method and its techniques for pursuing knowledge, experiments are the vehicle through which empirical facts are established. The early approach of “trial and error” is what design of experiments (DOE) aims to improve upon. By applying statistical analysis to natural phenomena, experimenters can improve the setup, execution, and conclusions drawn from trials – and errors. Experiments in modern times are critical for researchers and manufacturers alike, and occur much earlier in product life cycles than in pre-industrial eras. As good as a product concept may be, manufacturers must provide quality not only by making an efficient product, but by making it consistently throughout the product’s lifecycle.

Through experimentation, something as potentially simple as establishing causation between two factors can create a cascade of effects that ripple through a product’s design, saving costs while improving functionality. But how can these factors be considered and quantified along with the design of the product? And what if these same factors could provide knowledge about what aspects of the product are most central in determining quality and satisfaction?

These were among the questions that Genichi Taguchi considered as he worked to improve Japan’s telephone network in the 1950s. Himself an engineer, Taguchi proposed a design of experiments that coupled critical thought of a product and its crucial factors to a statistical, numerical process. This approach not only aimed to cut costs by establishing a single, optimal iteration of a product, but also sought to cut deviation from that optimal state by considering the relationship between noise (uncontrollable) and signal (controllable) factors in design and improvement.

The Taguchi method utilizes the concept of a loss function to determine quality of a product, which can offer experiment facilitators and data analysts alternative perspectives over data being collected and processed. For Taguchi, loss is measured as a product’s loss to society, which is calculated as variations in performance as well as their effects. A product that functions despite environment and user is considered robust, and for Taguchi, this is the key feature of a high-quality product. At a glance, the Taguchi method presents the case for robustification, and an associated methodology for achieving this result.



If a company were developing a laser used to create tiny patterns on materials (a rudimentary description of the etching process used in semiconductor manufacturing), then the quality of the laser would be, in part, determined by the amount of variance from the standard found in the patterns it creates. In a case where one such laser could cost millions to develop and produce, the Taguchi method would devote greater time to the research and development stage to establish that every laser will etch a pattern that meets specified requirements.

Following the Taguchi method, loss could be measured as any negative effect resulting from design of the product. The potential for an operator to be injured while operating the laser, materials made defunct via incorrect or imprecise patterns are two clear ways that loss could occur, and would receive special attention at design and early iteration stages for a product.

Additional considerations for loss would include loftier aspects of negative results from the product. Waste produced, loss of future sales due to a drop in brand confidence, and any post-production costs to fix problems with the product can and would be included in Taguchi’s loss function.



Certain aspects of the Taguchi method are philosophical in nature, and describe the way that a company should analyze or conceptualize their products. They include three main points that are sometimes referred to as fundamental concepts. They are:

1) Quality Must be Designed into the Product: 

Understanding the aspects of product design that influence quality implies an understanding of the product itself. Knowing the product, the user, and its intended use-cases may seem a simple task, but accounting for them in a pre-manufacturing stage – that is, before the product, user, or use-case exist – is part of the overall reimagination of how products should be created.

Implicit in the Taguchi method is the belief that manufacturing processes are flawed and can only introduce problems into design. Thus, adjustments and iterations take place at a preceding stage before they reach any potential manufacturing or assembly line. Consequently, this approach is also called “off-line design” or “off-line quality control.”

2) Quality is Realized by Minimizing Deviation from the Target: 

Investments that reduce variation from a target optimal state in a product have favorable return on investment (ROI), especially when customer satisfaction, replacements, and post-production improvements are factored into cost. Along with bolstering brand loyalty, addressing these factors early – and continuously – makes design robust and helps eliminate loss resulting from the aforementioned pathways.

(Fig. 1) Taguchi’s loss function, L(Y), features a quadratic formula that illustrates a product’s performance (Y) as it deviates from its target (t). The vertical bars (D) show customer tolerance. Their intersection with monetary loss (M) represents when this tolerance is exceeded.

3) Quality is a Function of Deviation:

Placing a primary emphasis on the relationship between quality and cost of failure establishes a guide for optimal improvement of a product. The Taguchi method measures losses at the systemic level and can factor in any costs associated with the return of a product; warranty, re-inspection, replacement, and even costs extended to the customer are all factors that contribute to loss under the Taguchi method.



Taguchi’s approach allows for experiments where facilitators can choose factors that are more consistent, and approaches design with consideration for uncontrollable factors. There are three central aspects of the overall structure:

Systems Design: 

The “brainstorming” and synthesis of a product or process to be used. Systems design occurs early on during conceptualization of a product. This stage focuses on achieving functionality through innovation. After these creative avenues have been exhausted, the basis for parameter design should be established, as it is the next stage in Taguchi’s process.

Parameter Design: 

Parameter design in the Taguchi method achieves the goal of creating a product that is robust enough for both the environment and the user. Designing a set of rules that determine design elements, then defining each rule using parameters and components helps quantify and diagnose variation in a given product. The term “parametric design” is often used interchangeably with “robust design” given its focus.

However, Taguchi also makes use of orthogonal arrays, which fall under a greater scheme of orthogonal array testing strategies (OATS) and are meant to provide an alternative to other quality control methods which can be prohibitive as a result of setup cost, time constraints, or other factors that make them otherwise impractical. In this sense, Taguchi’s orthogonal arrays are an alternative to full factorial experimental design, which simply – and exhaustively – tests every possible combination of states and variables.

The robustness of a product is determined using a signal-to-noise ratio (signal / noise or S/N) which is determined by (mean / variation) as well as mean response, or mean output variables. Whereas other methodologies may look to minimize noise in the experiment, Taguchi’s approach makes use the signal – or desired value – and noise – or undesired value. The resulting distribution around desired values show which control factors are most robust to noise factor variation.

Tolerance Design:

Tolerance design generally comes after parameter design studies. This stage specifies tightening tolerances for a product in order to improve quality, and also identifies crucial tolerances in a product or process design. A complex process or product could already have tight tolerance requirements, but selecting materials (preferably during system or parameter design stages) that have high tolerance to variability can quantify how crucial improvements are to achieving a robust design. In Taguchi’s experience, simply meeting tolerances is not as favorable as an approach seeking to meet the target while minimizing variance around it.

Although it may intuitively make sense that a product with tolerated deviation of ± 1 micrometers could be made better by tolerating only ± .5 micrometers, such a change could be cost prohibitive for a manufacturer. Without a preliminary parameter design and subsequent testing, the effect may also be minimal in increasing overall quality of the product.

Taguchi’s consideration for product deviation from target values is also contrary to a mindset in manufacturing that treats quality as a binary process, where items are either within or beyond specification. Taguchi includes tolerance ranges, with different levels of tolerance for components of varying importance to the overall design. As a result, quality under the Taguchi method is a curve, and takes on a parabolic shape when factored into the loss function (Fig. 1, above).



Many modern manufacturing life cycles reflect values inherent in the Taguchi method. Greater emphasis on R&D and baselining means that many companies go through more iterations of a product or prototype before continuing to the production and logistics stages. In such cases, this is generally a result of the cost-effectiveness that greater off-line quality control offers. For some specific industries like software and digital products, physical manufacturing may not even factor into a product’s lifecycle. However, system, parameter, and tolerance controls under Taguchi’s approach are still applicable, and quality assurance continues to play a major role in identifying and fixing problems in digital environments.

Established automobile manufacturers represent the opposite side of the spectrum, where complex manufacturing processes take considerable effort and resources. Mistakes in design (for Taguchi, the only mistakes there are) can result in global recalls of their vehicles and unforeseen repair costs. Any manufacturer facing bankruptcy as a result of a recall would likely determine that their testing and design experiments were not robust enough. The incurred costs would also factor into the Taguchi loss function for the company.



A thorough understanding of a product’s physics is relevant to its adoption. In the case of an electric circuit breaker, it is important to understand how fast it disconnects from the circuit, how resilient it is to mechanical impacts, and its effectiveness despite adverse weather conditions. Given the variety of conditions that equipment could be exposed to, experimental testing – and implementation of the Taguchi method – for the circuit breaker design becomes challenging. A thorough sweep of parameters via experimental investigation becomes non-plausible given the manual effort and experimental costs involved.

A high-fidelity multiphysics solver presents a solution for design insights in these cases. Understanding the physics behind the product for various operating and abuse conditions not only makes a product robust, but also catalyzes a revolution in product design. VizSpark™, as a high-fidelity thermal plasma flow solver, is already being used in the industry to provide further insights on conventional design, achieve faster design iterations, and reduce product iteration cycle times.

The figures below show published work by Ranjan et al., where VizSpark™ was used to simulate the electric disconnection in electric vehicle relays. The varying factor across the simulations are their gas compositions. The assessment was made for different levels of hydrogen in the hydrogen-nitrogen mixtures. Taguchi’s approach could be implemented in a similar way for different levels of purity of a certain gas-mixture. The use of multiphysics solvers and simulations offers system, parameter, and tolerance insights without the attached costs of physical experiments.

Thanks for reading! If you’re still curious about the topics discussed in this article, check out the following journal papers (and ask us for a free copy!):

Ranjan, R., Thiruppathiraj, S., Raj, N., Karpatne, A. et al., “Modelling of Switching Characteristics of Hydrogen-Nitrogen Filled DC Contactor Under External Magnetic Field,” SAE Technical Paper 2022-01-0728, 2022


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EsgeeTech Presenting at IPMHVC 2022

EsgeeTech Presenting at IPMHVC 2022

This week, IEEE begins its International Power Modulator and High Voltage Conference, along with the jointly held Electrical Insulation Conference (IPMHVC/EIC) in Knoxville, Tennessee. Engineers and scientists involved in applications for power modulators and high voltage technologies will converge to share and discuss their knowledge and work over the next few days.

Esgee Technologies will be among the presenters this year, represented by Douglas Breden. Our papers, “Computational Study of Plasma Flow in Arcing Horns During a Voltage Surge” and “Numerical Simulation of Arcing During Contact Separation in SF6-Filled High Voltage Circuit Breaker” will be presented back-to-back on June 21st at 10am. These papers are part of the “Plasmas, Discharges, and Electromagnetic Phenomena” session within the conference.

Both of our papers include simulations made with VizSpark, our plasma-flow solver for thermal (arc) plasmas. These talks are our first open demonstration to the high-voltage community and insulation researchers of how thermal arcs can be modeled with high-fidelity multiphysics software. We present the three-dimensional simulation of arcing in high-voltage interrupters and the plasma-flow simulation of arcing between arcing horns.

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Using VizSpark to Model Electrical Discharge in Combustion Engines

Using VizSpark to Model Electrical Discharge in Combustion Engines

Argonne National Laboratory represents the United States’ Department of Energy’s commitment to cooperative research and scientific discovery. Since its inception in 1946, Argonne has pioneered laboratory research and experimentation as the first national laboratory in the United States. While a significant amount of research in the decades following its founding centered around nuclear energy and applications, Argonne has transitioned from nuclear research to include additional energy sources and storage since the beginning of the 21st century. Now, Argonne constitutes a scientific community of leading researchers, with projects across a spectrum of computational, quantum, and interdisciplinary fields.

Among the contributors in this area are Dr. Joohan Kim and Dr. Riccardo Scarcelli. Their work on modeling spark discharge processes in spark-ignition (SI) engines was recently recognized by Argonne. Dr. Kim received a Postdoctoral Performance Award in the area of Engineering Research, along with ten other postdoctoral appointees whose contributions set a standard not only for the quality of their discoveries, but also for the ingenuity of their techniques and demonstrated leadership capabilities. According to Argonne, awardees’ works have upheld core values of scientific impact, integrity, respect, safety, and teamwork.

Within the highly competitive automotive industry, the need for innovation through design presents opportunities for new tools and technologies to be utilized. Regulations from governing entities seek to strike a balance between meeting climate goals through greater restrictions on CO2 emissions from automobiles, while relying on the transportation industry and automotives to fuel trade and commerce. With restrictions focused solely on reducing emissions, applications that meet these criteria without sacrificing capabilities stands out for both manufacturers and legislators alike.

Dr. Kim’s work highlights the need for predictive models which can optimize operational parameters for SI systems in order to maximize thermal efficiency gain and lower engine development costs. Creating these predictive models requires advanced simulation software capable of solving and coupling electromagnetic physics and fluid dynamics into a computational framework. When we asked about his use of simulations, Dr. Kim said, “high-fidelity simulations enable us to perform in-depth analysis of the spark-ignition process, including energy transfer, birth of flame kernel, and thermo-chemical properties; these would be difficult to obtain using experimental techniques only.” He went on to add that, “with a fundamental understanding of complex physics, we can develop predictive models that make simulation-based optimization robust and reliable.”

“VizSpark provided a fully-coupled framework between electromagnetic physics and fluid dynamics, and thereby we were able to diagnose the plasma properties occurring within tens of nanoseconds without many assumptions.”

Dr. Kim’s study utilized VizSpark simulations to accurately estimate electrical discharge shape, as well as temperature and pressure of plasma kernels, thus providing a set of robust initial and boundary conditions for studying flame kernel growth under engine-like conditions. He noted “VizSpark provided a fully-coupled framework between electromagnetic physics and fluid dynamics, and thereby we were able to diagnose the plasma properties occurring within tens of nanoseconds without many assumptions.”

VizSpark is a robust, industrial simulation tool for high-fidelity modeling of thermal (arc) plasmas. Additionally, VizSpark is fully parallelized and can be used to perform large, 3D simulations with complex geometries. Its comprehensive solvers and scalability make it ideal for solving real world engineering problems.

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Mirroring the World with Digital Twins

Mirroring the World with Digital Twins

Twins in literature and mythology are a shared theme across cultures and ontologies, exploring early concepts like duality, polarity, and unity. However, equal to these themes were explorations of concepts like loss, fratricide, and self-realization through remorse. Indeed, for every Castor and Pollux, there is a Cain and Abel, or a Romulus and Remus. Twins in myth evoke an impressionistic reaction to the triumphs and tragedy that they represent. Efforts of the current decade may tell us which of the two will ultimately characterize the concept of digital twins and their implementation.

Since being coined in 2003 by NASA executive Michael Grieves, the term “digital twin” has become an ambiguous term for the future of simulation and modeling applications. While Grieves’ earliest intention was in improving product life-cycles, the idea of high-fidelity, virtual representations of physical objects seemed like a certain future for computational modeling given technological capabilities and their increasing role in product design and iteration processes.

What was once Grieves’ insight into the future of technological applications has become a catch-all for any number of virtual models for physical entities, as well as the flow of data between them that provides parity. The resulting ambiguity in the phrase is due to its widespread usage across industries and the dynamic nature of evolving methodologies to reach the virtual “mirrored” / “twinned” ideal.

As with any other technology, there are limitations to simulations and computational models that tend to be overshadowed by their perceived benefits and desired insights. In departure from the abstract, requirements and standardizations for what constitutes a digital twin are yet to be seen. What’s more is that the concept of a digital twin is arguably not new at all, but simply an aggregation of techniques and research already in existence.





An issue with the popularity of terms like “digital twin” is that they risk becoming a misnomer due to a lack of common development methodology, much like the internet of things (IoT) platforms they rely on which require no internet connection at all. Digital twins face difficulties in procuring enough data from sensors to mirror physical entities, but also procuring and applying the correct data to become accurate representations. For example, a digital twin for a car’s braking system could use data to predict when maintenance will be needed by using predictive models for determining wear on the brake pad. However, even a specific system would rely on numerous external factors like environment, temperature, and lubrication, as well as an IoT platform for sensors that communicate and collect data from connected assets, or parts. The absence of any one of these parameters could result in incomplete or erroneous data that leads to faults in the virtual entity. Identifying missing parameters and diagnosing inconsistencies between physical and virtual entities can make their usage prohibitive in terms of both cost and labor.

The figure below shows hypothetical examples of digital twin implementations for an atomic layer deposition reactor, a complex machine used to deposit thin films onto materials.


At its core, digital twins are real-time, virtual representations of physical entities enabled by sensors and data. Twins can take on specific roles depending on the type of problem they solve or the advantages they offer. Adopting the model introduced by Oracle, there are three primary implementations for twins:


Virtual Twins

A virtual representation of a physical entity or asset. These contain manually provided data to the virtual twin from the physical counterpart best described as parameters, and requires the ability for the virtual twin to establish a connection in order to retrieve information from the physical environment. The type and number of parameters sent across this connection – as well as their accuracy – are primary attributes in grading and defining the “fidelity” of the virtual entity.


Predictive Twins

As the name suggests, this implementation focuses on creating predictive models and is not a static representation of a physical entity, but one based on data gathered from historic states. These twins serve to detect problems that could occur at a future state and proactively protect against them or allow designers the opportunity to diagnose and prevent the problem. Predictive twins are potentially much more simple than other implementations, and can focus on specific parameters like machine data rather than constantly receiving information from sensors and recreating a full virtual environment.


Twin Projections

This implementation is also used to create predictive models, but relies heavily on IoT data exchange between individually addressable devices over a common network, rather than sensors or physical environments. Applications or software that generate insights from the IoT platforms generally have access to aggregate data that is used to predict machine states and alleviate workflow issues.

There are a number of issues that each implementation faces. Maintaining connectivity to sensors for data transfer from physical entities, volume of network traffic between devices, and identification of key parameters are make-or-break in implementing successful twins. The yet ununified methods of collecting data further exacerbate the situation, with most vehicles for standardization lying in sharing models and information. 

The issue that results from relying on such collaborations has to do with data ownership; an issue already marred by controversies both moral and legal. Nonetheless, the promises of improvements for behavior, conformity, design, manufacturability, and structure have already attracted major attention from researchers.





Given the broad applications and ambitious tech behind the concept, the question of what cannot be digitally twinned is interesting to consider, especially given that a digital twin of Earth is already in production. The answer depends ultimately on what a digital twin’s use-case is, and to what degree it is able to achieve and produce desired results.

Using this as a criteria doesn’t help the already broad definition of what constitutes a digital twin; one could argue that established technologies like Google Maps and Microsoft Flight Simulator are digital twins. While this may detract from its novelty, digital twin as a term also carries an undertone of possibility through connectivity. Excitement surrounding digital twins is heavily tied to the anticipation of a new level of interconnectedness between devices that enables automation and machine learning. This is seen as a new phase for technology – even a new, fourth industrial revolution, commonly referred to as Industry 4.0.

Still, the complexity of digital twins creates a high barrier for production and implementation for many prospective innovators. A general misconception is that digital twin production requires that a company simply hire data scientists and provide them an analytics platform. Domain expertise and product lifecycle management tend to be overlooked as a result.

Configuration of assets on a product also impact design and are subject to changes in scale and capabilities. Divergence from original, pilot assets can create a cascading effect of incorrect or outdated information between iterations or generations of a product. Asset changes are not always anticipated, certain assets outlast others, and asset replacement in cases of failure can mean drastic changes in design. In the case of products that go through several generations or are sold for decades on the market, synchronization of digital twins is the only solution. This could occur as often as changes are made to the product itself.

It can be challenging to coordinate with manufacturing processes and across iterations or versions as a product makes its way to the consumer. One of the primary use-cases for digital twins in manufacturing has to do with shop floor optimization. Similar focuses on improving operations are found for supply chain use-cases seeking to optimize warehouse design. Generally, study and expertise surrounding these kinds of improvements and optimizations falls under maintenance, repair, and operations (MRO).





Computational simulations are a core feature that facilitates the development of digital twins. By combining high-fidelity simulations and fully coupled multiphysics solvers, companies can create models for assets and tweak them using their own data. Simulation insights create robust iteration phases that can cut process and testing costs, ultimately leading to shorter cycle times and greater management of product life cycles. Regardless of the size of a company or the scale of its products, simulations can connect the earliest designs made by research and development teams to final iterations made by manufacturing teams by providing clear, relevant physical and chemical insights.

“Ultimately, an industrial simulation that does not incorporate high-fidelity physics is essentially digital art.”

Given the increasing market focus on visual and virtual utility, impressive graphics could be misleading when it comes to digital twins. Ultimately, an industrial simulation that does not incorporate high-fidelity physics is essentially digital art. Within technical domains, the centermost aspect of a digital twin should be the fidelity with which it can predict not only steady-state processes, but also edge cases where physics is set to be challenging.

Of all the engineering design problems with applications for digital twins, problems experienced within the semiconductor industry are perhaps the most complex. In this industry’s “race to the bottom,” providing high-fidelity models requires the capability to determine the effects of disruptors like chemical impurities – which can threaten the functionality of critical components like wafers – at a margin of one part per trillion (or one nanogram per kilogram). Additional processes like atomic layer deposition are extremely sensitive to local species concentration as well as pressure profiles in the vicinity of the wafer being produced. While these are examples of restrictions based on the difficulty of working at an atomic scale, insight and perspective in the design and manufacturing process for semiconductors represents one of the most rigorous testing grounds for digital twins.




Thanks for reading! If you’re still curious about the topics discussed in this article, check out the following journal papers (and ask us for a free copy!):

Rasheed, Adil, Omer San, and Trond Kvamsdal. “Digital twin: Values, challenges and enablers from a modeling perspective.” Ieee Access 8 (2020): 21980-22012.


Rajesh, P. K., et al. “Digital twin of an automotive brake pad for predictive maintenance.” Procedia Computer Science 165 (2019): 18-24.

Interested in learning more about plasma flow simulations? Click here to take a look at our previous article. Feel free to follow us on Twitter and LinkedIn for more related news, or reach out to us directly at

Esgee to Present at SAE WCX 2022

Esgee to Present at SAE WCX 2022

Esgee Technologies will be presenting at this year’s Society of Automotive Engineers (SAE) WCX World Congress Experience held in Detroit, Michigan from April 5th to 7th. Our paper, “Modeling of Switching Characteristics of Hydrogen-Nitrogen Filled DC Contactor Under External Magnetic Field,” was chosen from hundreds of submissions to be featured at the event.

WCX is among the top annual gatherings which provides an intersectional forum between automotive engineers, researchers, scientists, and technical innovators. This year’s topics include EV technology and electrical infrastructure, energy storage and battery disposition, as well as design and safety for automated vehicles.

We sat down with Dr. Rakesh Ranjan, who will be presenting on behalf of EsgeeTech this year, in order to learn more about the applications for this research and how they align with the conference’s goals:


What applications are there for EV relay arcs? And why choose SAE to discuss them?

SAE is the biggest confluence of engineers dedicated to enhancing our mobility in an environmentally friendly manner. If you are excited about the prospect of buying a cleaner vehicle which won’t contribute to environmental pollution, it’s likely that the EV technologies behind it started as concepts presented at an SAE conference. Technologies for the future of mobility have their beginnings right here at SAE conferences.

As for EV relays, it is a critical component for the safety of electric vehicles. With increasing power needs for electric vehicles, there comes an increase in things like battery size and voltage levels required to drive vehicles. An increase in voltage means that electric isolation of safety-critical components would be delayed due to prolonged arcing. So, how safe your vehicle is could ultimately depend on how quickly the arc channel inside the EV relay quenches.

Perhaps it may not be the first feature that consumers think of when it comes to vehicle safety, but for manufacturers and anyone involved in future maintenance on the vehicle, arc-resistant equipment is key to creating a safe environment. For the owner of an electric vehicle, arc-quenching is also a means of decreasing or completely removing the risk of damage from arc flash events. That, of course, is desirable because it means lowered maintenance costs and higher longevity for critical automotive components.

What is the quick takeaway from your talk?

A one-minute synopsis of my talk would be about the use of hydrogen-nitrogen mixtures for quenching of arcs. One typically associates hydrogen with flammability, but it also has fantastically high diffusive properties which could lead to quicker arc quenching. We report how hydrogen concentration leads to smaller arc lifetimes, which in turn improves a circuit’s interruption performance. We simulated contact separation in hydrogen-enriched and pure air environments using VizSpark which showed us that a strong external magnetic field can stretch the arc and reduce its extinction time.

You mention that you used VizSpark™ in your research. Why choose VizSpark™ specifically? What scenarios / applications is it useful for?

VizSpark is a multiphysics CFD solver which is capable of capturing the interaction between the plasma and flow with high fidelity. One thing which I really like about it is its robustness for a wide range of thermal plasma problems. You can throw in tough multiphysics problems: permanent magnets, high voltages and currents, supersonic flows, conjugate heat transfer. In terms of industrial applications, I could think of EV Relays, fuses, and high-voltage circuit breakers. It could also be used for safety assessment in high-voltage applications. For example, if there is local arcing inside a battery pack and you want to assess the root-cause through V-I traces, you could potentially do it in VizSpark.

WCX ’22 Attendees can view Dr. Ranjan’s presentation in the “Electric Motor & Power Electronics” session from 10:00 AM to 10:30 AM CDT on Wednesday, April 6th.



Thanks for reading! If you’re still curious about the topics discussed in this article, check out the following journal papers (and ask us for a free copy!):

Ranjan, Rakesh, et al. Modelling of switching characteristics of hydrogen-nitrogen filled DC contactor under external magnetic field. No. 2022-01-0728. SAE Technical Paper, 2022.

Interested in learning more about plasma flow simulations? Click here to take a look at our previous article. Feel free to follow us on Twitter and LinkedIn for more related news, or reach out to us directly at



Esgee to Present at the International Conference on Electric Power Equipment – Switching Technology (ICEPE-ST) 2022

Esgee to Present at the International Conference on Electric Power Equipment – Switching Technology (ICEPE-ST) 2022

Esgee Technologies will be participating in the 6th International Conference on Electric Power Equipment – Switching Technology (ICEPE-ST 2022), held in Seoul, Republic of Korea. This year’s conference will be held on both live and virtual platforms in a “hybrid” format. This means that participants from across the globe will be participating in sessions all week, thus taking advantage of the very technologies they seek to improve upon. 

Our publication, “Numerical Study of Ablation-dominated Arcs in Polyamide Enclosure” by Ranjan et al. is also being featured during the conference, on March 17th at 9:20am local time (GMT+9).

Esgee Technologies is proud to be a partner for this year’s conference, and would like to thank Cigre Korea and KOEMA for putting together what’s sure to be an amazing conference!