Case Studies

A client in the power generation sector recognized that digital twin technologies were being developed inconsistently across their organization, leading to duplicated efforts and challenges in deploying these tools effectively. Without a standardized approach, teams struggled to leverage real-world operational data, create adaptable models, and integrate emerging technologies like AI and machine learning into their workflows.

A client in the power generation industry sought to enhance their operational capabilities through advanced digital modeling of gas turbine assets. The goal was to create a sophisticated monitoring solution that could provide deeper insights into turbine performance and support data-driven decision making.

A power generation operator sought to expand their monitoring and diagnostic capabilities for aeroderivative gas turbines. While digital twin technology had proven valuable for frame-type units, the client needed similar capabilities extended to a different class of equipment, along with support for implementing advanced combustion monitoring techniques.

A client needed a way to evaluate the economic impact of different air filtration strategies for their gas turbine operations. The challenge involved balancing multiple competing factors: filter efficiency, pressure drop effects on turbine performance, maintenance costs, water wash scheduling, and long-term operational expenses.

A client sought to improve their ability to detect and diagnose equipment faults in their gas turbine fleet before they led to costly unplanned outages. Traditional monitoring approaches were reactive, often identifying problems only after performance had already degraded significantly.

A client in the power generation sector sought to explore how emerging digital twin technologies could be applied to improve asset reliability and operational intelligence. While the concept of digital twins had gained significant traction across industries, there was uncertainty about which specific applications would deliver meaningful value for their particular equipment and operational challenges.