Demonstration of a small‐scale power generator using supercritical CO_(2)

The supercritical CO_(2)(sCO_(2))power cycle could improve efficiencies for a wide range of thermal power plants.The sCO_(2)turbine generator plays an important role in the sCO_(2)power cycle by directly converting thermal energy into mechanical work and electric power.The operation of the generator encounters challenges,including high temperature,high pressure,high rotational speed,and other engineering problems,such as leakage.Experimental studies of sCO_(2)turbines are insufficient because of the significant difficulties in turbine manufacturing and system construction.Unlike most experimental investigations that primarily focus on 100 kW‐or MW‐scale power generation systems,we consider,for the first time,a small‐scale power generator using sCO_(2).A partial admission axial turbine was designed and manufactured with a rated rotational speed of 40,000 rpm,and a CO_(2)transcritical power cycle test loop was constructed to validate the performance of our manufactured generator.A resistant gas was proposed in the constructed turbine expander to solve the leakage issue.Both dynamic and steady performances were investigated.The results indicated that a peak electric power of 11.55 kW was achieved at 29,369 rpm.The maximum total efficiency of the turbo‐generator was 58.98%,which was affected by both the turbine rotational speed and pressure ratio,according to the proposed performance map.

A goal-oriented Design Method of CO_(2) Power Cycle(CPC)System

The CO_(2)power cycle(CPC)system is an efficient and environmentally friendly method for waste heat recovery(WHR).However,the traditional design and optimization process of a CPC system is very complex and timeconsuming.This paper proposes a novel goal-oriented design method based on machine-learning methods for quickly designing an optimized CPC system with given performance indicators.And taking the design of the CO_(2)transcritical power cycle(CTPC)system for internal combustion engines(ICEs)as an example.Firstly,the net output power and the total cost of the system prediction models are trained by simulated data.Then the multiobjective optimization of the system is carried out by using the genetic algorithm coupled with the prediction models,and the optimization results are used to train a classification model.Finally,the given target indicators are input into the classification model for goal-oriented designing and getting the optimal configuration.The results of the goal-oriented design validation show that the goal-oriented design method can design the CTPC system well.And,once the classification model is trained,the CTPC system’s future goal-oriented design process only needs to be calculated once,significantly reducing design time.In conclusion,the goal-oriented design method based on machine-learning proposed is a novel and promising method.This is a technology that combines computer science and energy science and can provide users with a quick and reliable CPC system design method.