ANSYS > Case Studies > Enhancing Microturbine Efficiency with IoT: A Case Study of Connecticut Reserve Technologies

Enhancing Microturbine Efficiency with IoT: A Case Study of Connecticut Reserve Technologies

Technology Category

Infrastructure as a Service (IaaS) - Cloud Middleware & Microservices
Sensors - Electrical Conductivity Sensors

Applicable Industries

Electrical Grids
Life Sciences

Applicable Functions

Product Research & Development

Use Cases

Structural Health Monitoring

Services

System Integration

About The Customer

Connecticut Reserve Technologies, LLC (CRT) is an engineering consulting firm based in the United States. They specialize in the development of distributed electrical power systems, with a particular focus on microturbines for compact cogeneration units. These units are designed to provide on-site power for manufacturing plants and other facilities, offering an economical and reliable alternative to public utility lines. CRT's work often involves the use of advanced structural ceramics, such as silicon nitride, which allow microturbines to operate at higher temperatures than conventional metal alloys. This leads to significant fuel savings and emissions reductions. CRT's work is often conducted under the U.S. Department of Energy's Distributed Energy Program.

The Challenge

Connecticut Reserve Technologies (CRT) faced a significant challenge in the development of microturbines for compact cogeneration units. These units, designed to provide economical and reliable power for manufacturing plants and other facilities, relied on advanced structural ceramics like silicon nitride. While these ceramics allowed the microturbines to operate at higher temperatures than conventional metal alloys, leading to significant fuel savings and emissions reductions, they also exhibited large variations in fracture strength. This was particularly true when considering the inherent flaws resulting from various surface treatments. The challenge was to account for these complex statistical strength distributions to make more accurate predictions of expected component life. Another challenge was defining and implementing a method that establishes Weibull distribution metrics for silicon nitride suppliers based on the particular component. This required combining service stress states from the various treated surfaces of a rotor blade with a stipulated component reliability to develop material performance curves.

The Solution

CRT utilized ANSYS Structural analysis software to address these challenges. Through the use of ANSYS Parametric Design Language (APDL), surfaces of a rotor component with specified finishes were identified, the ANSYS results file was queried, and stresses were mapped to the relevant element surfaces. Failure data was then analyzed using the CRT WeibPar algorithm. Using information generated by ANSYS (geometry and stress state), the CARES algorithm computed component reliability. The openness of ANSYS technology and the ease of integration with other software enabled the ANSYS, CARES, and WeibPar programs to operate together in a smooth and efficient manner. This solution allowed for the development of material performance curves that could be scaled to standard ceramic bend bar test specimens, making component requirements more readily understood by material suppliers.

Operational Impact

The use of ANSYS Structural analysis software, in conjunction with the CARES and WeibPar algorithms, resulted in a more efficient and accurate design process for CRT. The resulting design approach allowed changes and improvements in system requirements to take place readily in parallel with enhancements in material properties. In the past, this typically was a series process in which system engineering would follow improvements in ceramic materials. Now, material characterization maps can be generated quickly for a given component under specified operating conditions. This information can influence the goals of a ceramic materials development program and better guide engineers toward an optimal design.