Digital engineering is the practice of creating repeatable frameworks to bring the power of automation and information technologies to complex systems. Systems engineering is an essential part of the digital engineering practice. Autonomous and remote operation of physical assets can provide nu-merous benefits to organizations and industries that deal with complex and distributed systems. The automation of the operation of a physical asset can be achieved through a digital twin, connected to the inputs and outputs of the asset and using machine learning (ML) and artificial intelligence (AI). Development of the digital twin requires understanding of systems interfaces and incorporating this understanding in digital systems. The effort described herein aims to prove the feasibility and benefit of such a process through the development and evaluation of a digital twin connected to a heat-pipe test-bed environment.