Bridging the Technology Readiness Gap with Simulation and Virtual Labs
With an increasing emphasis on using smart technologies and innovative techniques to support the objectives of industry, the connection between academia and industry must facilitate the effective transfer of fundamental science to proven technology. Learn how simulation and virtual labs can support effective collaboration between academia and industry by helping bridge the technology readiness gap through providing a rich environment for engineering development and knowledge sharing.
Published: 8 Jul 2022
Hello, everyone. My name is Graham Dudgeon, and I am Principal Product Manager for Electrical Technology at MathWorks. In this presentation, I will be providing a perspective on how the Technology Readiness Gap may be bridged with simulation and virtual and remote test benches.
These are the main themes that I will be looking to address over the next 10 minutes or so. When I looked at these themes, it struck me that they all relate to the topic of technology readiness, specifically the role that both academia and industry have in contributing to technology readiness and how a phenomena known as the Technology Readiness Gap can be mitigated or shortened through simulation, real-time testing, and industry academic collaboration.
As I discuss this phenomena, I will be giving both my own perspective and the perspective of my colleagues who work closely with academia. As such, I am hesitant to give specific recommendations, as this is such a rich area of opportunity where many viewpoints may be formed. But I hope the story I tell will provide a useful perspective.
MathWorks has the privilege of partnering with customers across diverse industries. We work with over 6,500 universities worldwide and have over 90,000 businesses and organizations that we partner with ranging from aerospace and automotive to semiconductors and utilities and energy. As a result, we have a front row seat to learn about emerging challenges being tackled by both industry and academia.
One of these challenges is, of course, the Technology Readiness Gap. So what is the Technology Readiness Gap? With an increasing emphasis on using smart technologies and innovative techniques to support the objectives of industry, the connection between academia and industry must facilitate the effective transfer of fundamental science to proven technology. Many observers view this transfer area as the Valley of Death, but I prefer calling it the Technology Readiness Gap. This gap is where many fundamental ideals can drop off the cliff as they attempt to move through the proven operating technology.
Another view of this process is so-called technology readiness levels, which is a scale of one through nine, where one is the eureka moment, and nine is proven field operation. Academia tends to move up to TRL three or four, where initial research and de-risking is performed, whereas industry tends to accept technology around TRL six or seven . The Technology Readiness Gap can therefore be considered as a gap that spans by TRL three to four to six to seven.
Closing this gap requires collaboration and also tools and processes that facilitate the transfer of both technology and knowledge. I'd like to acknowledge that universities can be at different stages in this process and that many universities already emphasize their capacity to get students and faculty through the Technology Readiness Gap through both direct industry collaboration and startup incubation. MathWorks and industry in general are collaborative partners that can help bolster these efforts.
While NASA has published specific definitions for each TRL number, the TRL scale is open to some interpretation. In other words, one person's TRL five may be another person's TRL six. But the scale does consistently follow key development attributes that you can see on the left.
Assigning technology attributes to specific TRL numbers is typically done by the engineering teams working on the technology. If we first think in terms of model fidelity for desktop simulation, then we typically see different levels of model fidelity coming in at different technology readiness levels.
As you begin developing fundamental science and conducting basic research, you will have models that are low-fidelity. For example, you may have models that have basic functionality in that they convey some high-level system information such as power flows and component sizing but which are technology agnostic, meaning the models do not capture specific technology characteristics.
As your technology matures, then simulation models that capture more specific technology attributes will be created. But you may still not have vendor-specific information on physical components and control components. A high fidelity simulation model would have vendor-specific architectures, including parameterization of physical components, control system architectures, and communication protocols.
Notice that all fidelity levels can track up to TRL nine. The reason for this is, as your technology matures, you can develop models with different levels of detail that not only supports implementation of your technology but can also be used for other reasons, such as coaching technicians, as an operator training simulator, or as a digital twin to support in-service operations. Each of these applications require different levels of model detail.
A key point for our discussion is that modeling and simulation is firmly in the realm of virtual engineering, and the key reason that modeling and simulation continues to grow in importance is that simulation technologies have matured to support the development of highly accurate models which can be verified against real equipment, can be run over fault and degraded conditions, and will run in a reasonable amount of time.
We are seeing more universities moving to online learning and establishing virtual laboratories, meaning experiments are created around simulation models. One challenge is to ensure that simulation models mirror the technology as accurately as possible. Industry can help provide very detailed models of a given technology to support the creation of virtual test benches from which online experiments can be run.
For certain experiments, the details of implementation of simulation models may need to be hidden, and so tools that support hiding implementation details are important. From an industry perspective, hiding implementation details is necessary where the protection of intellectual property is paramount.
I would like to quickly reference John Hopkins University, establishing a virtual lab for wireless communications. John Hopkins has seen a high level of student enthusiasm coupled with a learning environment that nurtures deeper understanding. Both of these points, of course, are tremendously exciting and convey the potential that can be achieved with virtual lab environments.
Another view we can take is to explore desktop simulation, real-time testing, and production implementation as a function of technology readiness. When we look at this view, initial de-risking and feasibility assessment is explored on the desktop before moving to a real-time testing environment where hardware-in-the-loop and rapid control prototyping is conducted to further de-risk control algorithms and physical architectures.
The production stage is where algorithms are deployed on actual production hardware, and this is where automatic code generation, which deploy straight to target, can offer significant benefits in verification and validation as you near full system deployment.
With this view, it is real-time testing labs which are the key to bridging the Technology Readiness Gap. Many universities already have strong collaboration with industry, and you will find the real-time testing lab at the heart of the infrastructure that supports this collaboration. There are many benefits to collaboration over TRL four to six. Industry can invest in intellect and gain access to many ideas and feasibility assessments.
Academia can expose students to the needs of industry and provide an environment which prepares students to hit the ground running when they enter industry. Over the years, I have seen universities and industry organizations creating collaboration through real-time laboratories across numerous application and industry areas, including power and utilities, power electronics, microgrid, and electric ship.
These collaborations are always vibrant, with tremendous rapport and bidirectional knowledge sharing. When we consider adding to this, the emerging trend of remote access, this vibrancy can only increase. With remote access, industry and academia can collaborate as a team on running hardware in the loop and rapid control prototyping experiments.
Engineering data can be viewed immediately and assessed together. There is also potential for industry to directly access the academic labs and for academia to directly access industry labs, which can provide academia with access to more specialized and mature technologies.
In conclusion, the emergence of online learning, virtual test benches, and remote access to real-time testing laboratories can only benefit collaboration between industry and academia in the TRL four to six area and build upon already successful collaborative efforts to better prepare students for industry and to give industry further access to the considerable intellect that academia offers. Thank you.