Date of Award


Document Type


Degree Name

Master of Engineering (ME)


Mechanical Engineering

Committee Chair/Advisor

Dr. John Wagner

Committee Member

Dr. Laura Redmond

Committee Member

Dr. Cameron Turner


Digital twin technology builds upon virtual engineering models, computer simulation, and real-time field data streaming to enable next-generation designs and predictive maintenance. A digital twin is a computer-based high-fidelity collection of models that predicts the performance of dynamic systems per operating cycles, input feature parameters, and data communication from a physical plant. Product Lifecycle Management (PLM) is growing in importance and is central to virtual design processes where the digital twin toolset fits into this emerging architecture. The product design process can be advanced using digital twin resources by eliminating the need for, and cost from, continual physical prototyping, reliability testing, and outdated maintenance practices. Digital twin virtual tools enable improved product performance evaluations early in the design cycle which leads to manpower savings for the enterprise. The mobility needs of individuals, corporations, and government entities require careful attention to product design. With increasing ground vehicle complexity in electrical components and propulsion hybridization, the digital twin plays a role in predicting, understanding, and designing vehicle systems. The ability to couple streaming field data with digital twin estimates generates a powerful tool for diagnostic and prognostic methods.

In this research project, digital twin technology is explored, developed, and applied to off- road ground vehicles for design engineering studies and predictive health maintenance. The research goals included integrating mathematical models for ground vehicle components into a digital twin, application of the digital twin for vehicle design, examination of predictive maintenance methods, and creation of two surveys to measure usefulness and time savings metrics available with digital twin technology. The modeling of wheeled and tracked vehicles in MATLAB/Simulink/Simscape enabled the assembly of a 14-degree-of-freedom virtual vehicle system consisting of body dynamics, engine curves, wheel kinematics, driveline systems, and suspension characteristics piloted by a virtual driver and environment inputs. The digital twin tool assisted in tradespace analysis studies within the Clemson University VIPR-GS Center. Predictive maintenance, with machine learning, was applied to the off-road digital twin by seeding 6 anomalies into a virtual model and leveraging statistical algorithms based on neural networks. For the numerical study, 176 logged signals across 275 total simulations were utilized in the predictive maintenance framework to successfully predict 92% of trained validation results and 40% accuracy of untested compound anomalies. Metric surveys evaluating usefulness and time savings were deployed to DO13/14 vehicle engineering teams on the Clemson University International Center for Automotive Research (CUICAR) campus and the results showed a positive Likert scale response to digital twin usefulness with the greatest perceived benefits in model organization and design validation. The advantages and opportunities available with digital twin technology have been explored in these individual studies and deployed in the VIPR-GS Center. Given the growing awareness of digital engineering design methodologies, digital twins represent a cornerstone of many future endeavors.



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