https://bura.brunel.ac.uk/bitstream/2438/22049/1/FullText.pdf

Review of supercritical CO2 technologies and systems for power generation

Supercritical CO2 Brayton cycle: A state-of-the-art review

Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes

A systematic review of supercritical carbon dioxide(S-CO2) power cycle for energy industries: Technologies, key issues, and potential prospects

In the European Industry, 275 TWh of thermal energy is rejected into the environment at temperatures beyond 300 °C. To recover some of this wasted energy, bottoming thermodynamic cycles using supercritical carbon dioxide (sCO2) as working fluid are a promising technology for the conversion of the waste heat into power. CO2 is a non-flammable and thermally stable compound, and due to its favourable thermo-physical properties in the supercritical state, can lead to high cycle efficiencies and a substantial reduction in size compared to alternative heat to power conversion technologies. In this work, a brief overview of the sCO2 power cycle technology is presented. The main concepts behind this technology are highlighted, including key technological challenges with the major components such as turbomachinery and heat exchangers. The discussion focuses on heat to power conversion applications and benefits of the experience gained from the design and construction of a 50 kWe sCO2 test facility at Brunel University London. A comparison between sCO2 power cycles and conventional heat to power conversion systems is also provided. In particular, the operating ranges of sCO2 and other heat to power systems are reported as a function of the waste heat source temperature and available thermal power. The resulting map provides insights for the preliminary selection of the most suitable heat to power conversion technology for a given industrial waste heat stream.

/pdf/review-of-supercritical-carbon-dioxide-sco2-technologies-for-5f75woxwba.pdf

Review of supercritical carbon dioxide (sCO2) technologies for high-grade waste heat to power conversion

Reliability-based design optimization (RBDO) of the NASA stage 37 axial compressor is performed using an uncertainty model for stall margin in order to guarantee stable operation of the compressor. The main characteristics of RBDO for the axial compressor are summarized as follows: First, the values of mass flow rate and pressure ratio in stall margin calculation are defined as statistical models with normal distribution for consideration of the uncertainty in stall margin. Second, Monte Carlo Simulation is used in the RBDO process to calculate failure probability of stall margin accurately. Third, an approximation model that is constructed by an artificial neural network is adopted to reduce the time cost of RBDO. The present method is applied to the NASA stage 37 compressor to improve the reliability of stall margin with both maximized efficiency and minimized weight. The RBDO result is compared with the deterministic optimization (DO) result which does not include an uncertainty model. In the DO case, stall margin is slightly higher than the reference value of the required constraint, but the probability of stall is 43%. This is unacceptable risk for an aircraft engine, which requires absolutely stable operation in flight. However, stall margin obtained in RBDO is 2.7% higher than the reference value, and the probability of success increases to 95% with the improved efficiency and weight. Therefore, RBDO of the axial compressor for aircraft engine can be a reliable design optimization method through consideration of unexpected disturbance of the flow conditions.

/pdf/reliability-based-design-optimization-of-axial-compressor-wuzw5g01go.pdf

Reliability-based design optimization of axial compressor using uncertainty model for stall margin

Partial Admission, Axial Impulse Type Turbine Design and Partial Admission Radial Turbine Test for SCO2 Cycle

The multidisciplinary design optimization method, which integrates aerodynamic performance and structural stability, was utilized in the development of a single-stage transonic axial compressor. An approximation model was created using artificial neural network for global optimization within given ranges of variables and several design constraints. The genetic algorithm was used for the exploration of the Pareto front to find the maximum objective function value. The final design was chosen after a second stage gradient-based optimization process to improve the accuracy of the optimization. To validate the design procedure, numerical simulations and compressor tests were carried out to evaluate the aerodynamic performance and safety factor of the optimized compressor. Comparison between numerical optimal results and experimental data are well matched. The optimum shape of the compressor blade is obtained and compared to the baseline design. The proposed optimization framework improves the aerodynamic efficiency and the safety factor.

Multi-disciplinary design optimization and performance evaluation of a single stage transonic axial compressor

It is well-known that nonuniform tip clearance in an axial compressor induces pressure and velocity perturbations along the circumferential direction. This study develops a numerical modeling to predict perturbed flows in an axial compressor with a nonuniform tip clearance and presents a mechanism of the flow redistribution in the axial compressor at design and off-design conditions. The modeling results are compared with CFD results (2006, “Prediction of the Fluid Induced Instability Force of an Axial Compressor,” ASME FEDSM 2006, Miami, FL) not only to validate the present modeling, but also to investigate more detailed flow fields. In an axial compressor, nonuniform tip clearance varies local flow passage area and resultant axial velocity along the circumferential direction. There are small axial velocity differences between maximum and minimum clearances near the design condition, while large pressure differences are investigated according to local locations. However, contribution of the main flow region overrides the tip clearance effect as the flow coefficient deviates from the design condition. Moreover, the flow field redistribution becomes noticeably strong when the off-design effects are incorporated. In case of high flow coefficients, the low relative flow angle near the minimum clearance regions results in a large negative incidence angle and forms a large flow recirculation region and a corresponding large amount of loss occurs near the blade pressure surface. It further promotes strong flow field perturbations at the off-design conditions. The integration of these pressure and blade loading perturbations with a control volume analysis leads to the well-known Alford’s force. Alford’s force is always negative near the design condition; however, it reverses its sign to positive at the high flow coefficients. At the high flow coefficients, tip leakage flow effects lessen, while increased off-design effects amplify blade loading perturbations and a steep increase in Alford’s force. This study enables that nonuniform flow field, and the resultant Alford’s force, which may result in an unstable rotor-dynamic behavior, can be easily evaluated and assessed during the compressor, fan, or blower design process.

Prediction of the Nonuniform Tip Clearance Effect on the Axial Compressor Flow Field

The development of a 60-kWe turbo generator that uses supercritical carbon dioxide (sCO2) cycle technology at the lab scale is described herein. The design concept for the turbo generator involved using commercially available components to reduce the developmental time and to increase the reliability of the machine. The developed supercritical partial-admission CO2 turbine has a single-stage axial-type design with a 73-mm rotor mean diameter. The design of the sCO2 turbine uses impulse and partial admission to reduce the axial force and rotational speed. We simulated the flow of the designed sCO2 turbine. To increase the simulation accuracy, a real gas property table is coupled with the flow solver. The turbine performance test apparatus and test results are described; then, the turbine is continuously operated for 44 min. The maximum turbine power is 25.4 kW, and the maximum electric power is 10.3 kWe.

Young-Seok Kang

Papers

Reliability-based design optimization of axial compressor using uncertainty model for stall margin

Partial Admission, Axial Impulse Type Turbine Design and Partial Admission Radial Turbine Test for SCO2 Cycle

Multi-disciplinary design optimization and performance evaluation of a single stage transonic axial compressor

Prediction of the Nonuniform Tip Clearance Effect on the Axial Compressor Flow Field

Design, Flow Simulation, and Performance Test for a Partial-Admission Axial Turbine Under Supercritical CO2 Condition