Using Engineering Intuition and Strategic Experimentation to Overcome Technical Challenges

Research and development processes are becoming ever more important with the advent of AI and the speed at which new technologies are being developed. Exploring new technological applications through experimentation demands an expedited time frame and rapid results. I spoke with Dr. David Parsi, an experienced practitioner and doctor of mechanical engineering, concerning experimental design principles and efficient research methodologies.

Figure 1: David Parsi in his research lab. Image provided by David Parsi.

David has recently completed his Ph.D. at Brigham Young University. His dissertation, entitled “An Electrowetting on Dielectric Mircopump for a Microfluidic Reconfigurable Radio Frequency Device,” was based on research he performed with a grant from the National Science Foundation. David’s work has also been published in top-tier journals, and he has presented at prominent conferences in Canada and the United States. He’s a leading researcher in his field and offers insightful experiences for anyone aspiring to surpass technical challenges in research.

Identifying Problems and Possibilities

David’s research delved into microfluidic-based techniques to overcome obstacles in reconfigurable radio frequency (RF) instruments. This began when he saw potential in novel systems that make use of a selectively metalized plate (SMP). He said that “practical application was often hindered by integration challenges” between the various subsystems of these RF devices, and because he was aware of the problem, he capitalized quickly on technology that could simplify and remediate those issues. David showcases that every problem can be turned into possibility with the right combination of technical know-how and an awareness of the situation, both of which are important aspects of a design thinking mindset.

Figure 2: An example of using an SMP in his experiments. Illustration courtesy of David Parsi.

Before receiving his Ph.D., David had taken charge of the experiments and data analysis from another student. He wasted no time in becoming acquainted with his predecessor’s methodologies and equipment, noticing that the prototype that had been built was “useful for exploratory work,” but “the design suffered from several critical flaws.” Rather than letting this setback hamper his research and progress, he saw the challenge as an opportunity: “I applied CAD software and rapid prototyping methods to redesign the system from scratch. Within a week, I produced a fully functional, high-performance prototype” that “significantly improved the accuracy of experimental data collection.” Moreover, his invaluable work was quickly noticed by his sponsoring professor.

Figure 3: On the left is David’s CAD model of the prototype; on the right, the prototype is placed on the far left of the table along with a camera and light source for gathering data. Courtesy of David Parsi.

It’s All About the Data

In the end, the usefulness of an experiment is predominantly defined by what insights or patterns can be observed and what conclusions can be drawn. In order for industry professionals or other scientists to be confident in applying the results of previous experiments, there must be substantial data. If the data being recorded is plagued with inconsistency, it could be entirely unusable. Variation is natural and inherent in experimentation, but if data collecting techniques or conditions vary substantially from sample to sample without being recorded, serious errors can be introduced into the data. David noted that for one colleague’s experiments, “Despite nearly nine months of work on this prototype, the collected data was not sufficiently reliable for robust analytical modeling.” Hence, when David was collecting data, he put in as much initial effort as necessary to ensure his data was valid enough for application.

Back to the Basics

Simulations are often powerful tools in the hands of researchers, but they can become overly complex. On his project, David realized that “Initial attempts using 3D fluid-structure interaction simulations proved computationally infeasible, taking over a week without convergence on a high-performance workstation.” Knowing that he could not afford to waste more time, he conferred with his research advisor, made necessary approximations, and “applied fundamental principles of fluid mechanics to develop a simplified model that captured the system’s steady-state behavior with over 90% accuracy.” Because he was willing to go back to basic principles and once again think through his research process, David was able to design a far more accurate model, one that could also record high-quality data.

Figure 4: A diagram of the experimental model and the data recording instruments. This illustration is a schematic of the setup shown in Figure 2. Courtesy of David Parsi.

Branching Out

David’s route to his Ph.D. was a long one. After receiving his first master’s degree in mechanical engineering from Amirkabir University of Technology (Tehran Polytechnic) in Tehran, Iran in 2014, he later studied at Memorial University of Newfoundland in Canada, receiving his second master’s, this time in mechatronics engineering, in 2018. During his time in Canada, he broadened his technical knowledge and picked up valuable skills including calibration, 3D printing, CAD design, and finite element analysis (FEA). 

After a stint in industry, David decided to return to academia once more to pursue a doctorate. Rather than narrowing his field of interests, he expanded, and researched across a variety of topics such as optimization, vibration analysis, MEMS designs, and eventually microfluidics, which allowed him to apply expertise from a swath of different areas as he continued his research. “My research exemplifies how engineering intuition, interdisciplinary training, and strategic modeling can rapidly overcome long standing technical challenges,” David reflected. 

Building on his foundational work, David proposed and began implementing a new actuation mechanism using electrowetting-on-dielectric (EWOD) technology. By seeing problems as possibilities, rigorously collecting data, knowing how to model his work theoretically, and harnessing skills from the vast array of technical areas, modeling, and experimentation, David exemplifies world-class engineering intuition and technical thinking.

To cite this article:
Conover, Dylan. “Using Engineering Intuition and Strategic Experimentation to Overcome Technical Challenges.” The BYU Design Review, 19 May 2025, https://www.designreview.byu.edu/collections/using-engineering-intuition.