Gas Turbine Efficiency Control Using Adaptive Operating States
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Solution Overview
Problem
Existing gas turbine systems face inefficiencies due to heat loss during energy conversion and transmission, which are not effectively managed by current control methods, leading to suboptimal mechanical efficiency.
Innovation Solution
A system comprising a gas turbine with a compressor and turbine coupled through a shaft, along with a processing system that automatically adjusts operating conditions based on real-time efficiency calculations and ambient conditions, using multi-dimensional maps to optimize performance and minimize heat loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional control methods are used for gas turbine systems, then the system operation is simple, but mechanical efficiency is suboptimal due to unmanaged heat loss
Solution Approach 1:
The control system continuously monitors operating conditions and efficiency metrics, then adjusts control parameters in real-time to maximize mechanical efficiency. This closed-loop feedback mechanism enables the system to adapt to changing conditions and minimize heat loss dynamically.
Solution Approach 2:
The control system transitions from static, pre-programmed control to dynamic, real-time adjustment of operating parameters. The system continuously iterates through operating states and selects optimal parameters based on current efficiency calculations, allowing adaptive optimization rather than fixed control schedules.
2Productivity
If the control system iterates through multiple operating states to maximize efficiency, then mechanical efficiency improves, but the control process complexity increases
Solution Approach 1:
The control system autonomously iterates through operating states, calculates efficiency metrics, and selects optimal parameters without external intervention. The system self-regulates by continuously evaluating its own performance and making independent adjustments to maximize mechanical efficiency.
Solution Approach 2:
The patent replaces complex mechanical control mechanisms with computational algorithms that iterate through operating states and calculate optimal parameters. This substitution of mechanical control with intelligent software-based control achieves high efficiency through information processing rather than mechanical adjustment.
3Measurement precision
If real-time efficiency calculations are performed, then operating efficiency optimizes, but computational requirements and system complexity increase
Solution Approach 1:
The control system uses intermediate calculations and models to estimate efficiency metrics based on readily available sensor data. Rather than requiring complex direct measurements, the system employs computational intermediaries that translate simple sensor readings into meaningful efficiency indicators for control optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances mechanical efficiency by iteratively adjusting operating states to maximize efficiency, reducing heat loss and improving overall performance, thereby optimizing energy conversion and transmission.
Implementation Method 1
a gas turbine can convert chemical energy stored in jet fuel into mechanical energy stored in a spinning turbine
Implementation Method 2
some of the mechanical energy transmitted from a spinning turbine to a mainshaft will be lost as heat as mainshaft frictionally engages radial support bearings
Implementation Method 3
electrical resistance will convert electrical energy transmitted through wires into heat
Data Source
AI summary
A system can include a gas turbine and a processing system. The gas turbine can include a compressor coupled to a turbine through a shaft. The processing system can be configured to: automatically transition an operating condition of the system through a plurality of operating states; determine an efficiency of the system at each of a plurality of the operating states; for each of the plurality of operating states: select a future operating state of the system based on the determined efficiency of the current operating state.