Design Considerations of Low Bypass Ratio Mixed Flow Turbofan Engines with Large Power Extraction

The possibility of extracting large amounts of electrical power from turbofan engines is becoming increasingly desirable from an aircraft perspective. The power consumption of a future fighter aircraft is expected to be much higher than today’s fighter aircraft. Previous work in this area has concentrated on the study of power extraction for high bypass ratio engines. This motivates a thorough investigation of the potential and limitations with regards to performance of a low bypass ratio mixed flow turbofan engine. A low bypass ratio mixed flow turbofan engine was modeled, and key parts of a fighter mission were simulated. The investigation shows how power extraction from the high-pressure turbine affects performance of a military engine in different parts of a mission within the flight envelope. An important conclusion from the analysis is that large amounts of power can be extracted from the turbofan engine at high power settings without causing too much penalty on thrust and specific fuel consumption, if specific operating conditions are fulfilled. If the engine is operating (i) at, or near its maximum overall pressure ratio but (ii) further away from its maximum turbine inlet temperature limit, the detrimental effect of power extraction on engine thrust and thrust specific fuel consumption will be limited. On the other hand, if the engine is already operating at its maximum turbine inlet temperature, power extraction from the high-pressure shaft will result in a considerable thrust reduction. The results presented will support the analysis and interpretation of fighter mission optimization and cycle design for future fighter engines aimed for large power extraction. The results are also important with regards to aircraft design, or more specifically, in deciding on the best energy source for power consumers of the aircraft.

Thermodynamic Analysis and Optimization of a Novel Power-Water Cogeneration System for Waste Heat Recovery of Gas Turbine

A power-water cogeneration system based on a supercritical carbon dioxide Brayton cycle (SCBC) and reverse osmosis (RO) unit is proposed and analyzed in this paper to recover the waste heat of a gas turbine. In order to improve the system performance, the power generated by SCBC is used to drive the RO unit and the waste heat of SCBC is used to preheat the feed seawater of the RO unit. In particular, a dual-stage cooler is employed to elevate the preheating temperature as much as possible. The proposed system is simulated and discussed based on the detailed thermodynamic models. According to the results of parametric analysis, the exergy efficiency of SCBC first increases and then decreases as the turbine inlet temperature and split ratio increase. The performance of the RO unit is improved as the preheating temperature rises. Finally, an optimal exergy efficiency of 52.88% can be achieved according to the single-objective optimization results.

Application of Machine Learning Based Surrogate Model for Prediction of Sectional Temperature of Radially Cooled Gas Turbine Blades

Accurate thermal prediction of gas turbine blades is essential to ensure successful operation throughout the design life. Large Gas turbines operate in different conditions based on customer requirements, due to which turbine blades are subjected to variations in thermal loading conditions. Simulating this behavior using conventional finite element modeling involves detailed and time-consuming analyses for calculation of blade temperature, which can be further utilized to assess cyclic and creep life. This paper deals with developing and utilizing machine learning based surrogate models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal parameters for ML model have been obtained from CFD based 3D thermal calculations. A comparative study is presented between linear regression, decision tree, random forest, and gradient boost ML models, to select the model with the least mean absolute error. Additionally, hyperparameter optimization is performed using grid search to minimize the error. The results show that the linear regression-based model outputs the least mean absolute error of 6.5°C and the highest dependence of the output is on the turbine inlet temperature, followed by the cooling air temperature. The findings show a good agreement between the predicted output of the surrogate model and multi-dimensional physics based thermal calculations, while offering a considerate reduction in analysis time.

Machine Learning Based Classification Model for Predicting Cyclic Failure in Radially Cooled Gas Turbine Blades

Gas turbine blades and vanes face very severe operating conditions - high temperature and pressure which necessitates the creation of complex cooling and component designs, resulting in high computational cost. The ability to predict cyclic failure in these components is therefore a critical activity that has been historically performed using 3D commercial finite element (FE) codes for baseload conditions. However, these codes take substantial time and resources which restricts their application in failure prediction at variable operating conditions. Newer data-driven techniques such as machine learning (ML) provide a valuable tool that can be utilized to predict the occurrence of cyclic failure for these conditions with minimal time and resource requirement. In this paper, a machine learning based surrogate model is developed to predict the cyclic failure of a radially cooled turbine blade. The features used as input to machine learning model are turbine inlet temperature, coolant inlet temperature, hot gas mass flow rate, cooling air mass flow rate and blade materials. The output for the model is a binary variable depicting the incident of component failure. 70% of the FE data points are used to train the ML model while the remaining are used for testing. A comparative study between Logistic Regression, Random Forest, K-nearest neighbor, and Support Vector Machine (SVM) was performed to select the most accurate algorithm for the classification model. Finally, the results show that the Random Forest and SVM algorithms predicts failure with the highest f-1 score of 0.92. The model also demonstrates that Turbine Inlet temperature has the highest importance amongst the input features followed by blade material. Additionally, this methodology offers a tremendous advantage for failure prediction by reducing analysis time from multiple hours to a few seconds, rendering this technique especially beneficial for time sensitive business decisions in the gas turbine industry.

Performances of Transcritical Power Cycles with CO2-Based Mixtures for the Waste Heat Recovery of ICE

In the waste heat recovery of the internal combustion engine (ICE), the transcritical CO2 power cycle still faces the high operation pressure and difficulty in condensation. To overcome these challenges, CO2 is mixed with organic fluids to form zeotropic mixtures. Thus, in this work, five organic fluids, namely R290, R600a, R600, R601a, and R601, are mixed with CO2. Mixture performance in the waste heat recovery of ICE is evaluated, based on two transcritical power cycles, namely the recuperative cycle and split cycle. The results show that the split cycle always has better performance than the recuperative cycle. Under design conditions, CO2/R290(0.3/0.7) has the best performance in the split cycle. The corresponding net work and cycle efficiency are respectively 21.05 kW and 20.44%. Furthermore, effects of key parameters such as turbine inlet temperature, turbine inlet pressure, and split ratio on the cycle performance are studied. With the increase of turbine inlet temperature, the net works of the recuperative cycle and split cycle firstly increase and then decrease. There exist peak values of net work in both cycles. Meanwhile, the net work of the split cycle firstly increases and then decreases with the increase of the split ratio. Thereafter, with the target of maximizing net work, these key parameters are optimized at different mass fractions of CO2. The optimization results show that CO2/R600 obtains the highest net work of 27.43 kW at the CO2 mass fraction 0.9 in the split cycle.

Research on Response Characteristics and Control Strategy of the Supercritical Carbon Dioxide Power Cycle

With the development of GEN-IV nuclear reactor technology, the supercritical carbon dioxide (SCO2) Brayton cycle has attracted wide attention for its simple structure and high efficiency. Correspondingly, a series of research has been carried out to study the characteristics of the cycle. The control flexibility of the power generation system has rarely been studied. This paper carried out a dynamic performance of the 20 MW-SCO2 recompression cycle based on the Simulink software. In the simulation, the response characteristics of the system main parameters under the disturbances of cooling water temperature, split ratio, main compressor inlet temperature and pressure were analyzed. The results show that the turbine inlet temperature is most affected by the disturbances, with a re-stabilization time of 2500–3000 s. According to the response characteristics of the system after being disturbed, this study proposed a stable operation control scheme. The scheme is coordinated with the main compressor inlet temperature and pressure control, recompressor outlet pressure control, turbine inlet temperature control and turbine load control. Finally, the control strategy is verified with the disturbance of reduced split ratio, and the results show that the control effect is good.

Can a Wastewater Treatment Plant Power Itself? Results from a Novel Biokinetic-Thermodynamic Analysis

The water–energy nexus (WEN) has become increasingly important due to differences in supply and demand of both commodities. At the center of the WEN is wastewater treatment plants (WWTP), which can consume a significant portion of total electricity usage in many developed countries. In this study, a novel multigeneration energy system has been developed to provide an energetically self-sufficient WWTP. This system consists of four major subsystems: an activated sludge process, an anerobic digester, a gas power (Brayton) cycle, and a steam power (Rankine) cycle. Furthermore, a novel secondary compressor has been attached to the Brayton cycle to power aeration in the activated sludge system in order to increase the efficiency of the overall system. The energy and exergy efficiencies have been investigated by varying several parameters in both WWTP and power cycles. The effect of these parameters (biological oxygen demand, dissolved oxygen level, turbine inlet temperature, compression ratio and preheater temperature) on the self-efficiency has also been investigated. It was found here that up to 109% of the wastewater treatment energy demand can be produced using the proposed system. The turbine inlet temperature of the Brayton cycle has the largest effect on self-sufficiency of the system. Energy and exergy efficiencies of the overall system varied from 35.7% to 46.0% and from 30.6% to 33.55%, respectively.

Cycle-Tempo Simulation of Ultra-Micro Gas Turbine Fueled by Producer Gas Resulting from Leaf Waste Gasification

Leaf waste has the potential to be converted into energy because of its high availability both in the world and Indonesia. Gasification is a conversion technology that can be used to convert leaves into producer gas. This gas can be used for various applications, one of which is using it as fuel for gas turbines, including ultra-micro gas ones, which are among the most popular micro generators of electric power at the time. To minimize the risk of failure in the experiment and cost, simulation is used. To simulate the performance of gas turbines, the thermodynamic analysis tool called Cycle-Tempo is used. In this study, Cycle-Tempo was used for the zero-dimensional thermodynamic simulation of an ultra-micro gas turbine operated using producer gas as fuel. Our research contributions are the simulation of an ultra-micro gas turbine at a lower power output of about 1 kWe and the use of producer gas from leaf waste gasification as fuel in a gas turbine. The aim of the simulation is to determine the influence of air-fuel ratio on compressor power, turbine power, generator power, thermal efficiency, turbine inlet temperature and turbine outlet temperature. The simulation was carried out on condition that the fuel flow rate of 0.005 kg/s is constant, the maximum air flow rate is 0.02705 kg/s, and the air-fuel ratio is in the range of 1.55 to 5.41. The leaf waste gasification was simulated before, by using an equilibrium constant to get the composition of producer gas. The producer gas that was used as fuel had the following molar fractions: about 22.62% of CO, 18.98% of H2, 3.28% of CH4, 10.67% of CO2 and 44.4% of N2. The simulation results show that an increase in air-fuel ratio resulted in turbine power increase from 1.23 kW to 1.94 kW. The generator power, thermal efficiency, turbine inlet temperature and turbine outlet temperature decreased respectively from 0.89 kWe to 0.77 kWe; 3.17% to 2.76%; 782 °C to 379 °C and 705°C to 304 °C. The maximums of the generator power and thermal efficiency of 0.89 kWe and 3.17%, respectively, were obtained at the 1.55 air-fuel ratio. The generator power and thermal efficiency are 0.8 kWe and 2.88%, respectively, with the 4.64 air-fuel ratio or 200% excess air. The result of the simulation matches that of the experiment described in the literature.

Turbine Inlet Temperature Measurements in A T 8200 KW Gas Turbine Using Water Vapor Emission

The measurement of turbine inlet temperature is challenging because of high temperatures and complicated physical access, but continuous measurement of the turbine inlet temperature is very important for maximizing turbine efficiency and increasing durability. This paper provides in-situ turbine rotor inlet temperature (TRIT) measurements in an 8200 kW operating gas turbine engine. The measurements were obtained using integrated spectral infrared (ISIR) emission from the water vapor of the combustion gases entering the turbine rotor. The method utilizes a sapphire optical fiber to convey the signal from the turbine wall to outside the turbine casing. All components are capable of long-term exposure to the turbine operating conditions. The temperature measurements were obtained at 6 operating conditions between 50% and full load. The TRIT temperature was also determined using more than 20 test cell inputs and Solar Turbine's commercial test cell engine model. The two temperatures (measured and modeled) were within 11 K (less than 1%) across the load sweep. Uncertainty calculations suggest that the uncertainty of the measurement can be expected to be ±2.9% within a confidence interval of 95%. The method also yields the nozzle guide vane surface temperature which was found to increase monotonically with increasing load.

Parametric study on exergy and NOx metrics of turbofan engine under different design variables

Examining effects of design variables on performance and emission parameters for gas turbine engines is of high importance. In this study, effects of by-pass ratio (BPR) and turbine inlet temperature (TIT) of turbofan engine on energy, exergy and exhaust emissions are parametrically analyzed at 0.85 Ma and 11 km. Moreover, cruise NOx emission is quantified by Boeing Fuel Flow Method 2 (BFFM2) and DLR methods. As a novelty, Specific NOx Production (SNP) is firstly quantified for PW4000 engine. In this context, parametric cycle equations regarding turbofan engine are encoded so as to compute performance and emission metrics. According to energy analysis, specific fuel consumption (SFC) of the turbofan averagely changes from 19.82 to 18.64 g/ kNs due to rising BPR whereas it increases from 18.62 to 19.93 g/kNs owing to rising TIT.Furthermore, exergy efficiency of turbofan rises from 27.67 % to 29.42 % due to rising BPR whereas it decreases from 29.46 % to 27.65 % owing to rising TIT. As for NOx emission results, the higher BPR leads to the lowering of the SNP index of the turbofan from 0.46 to 0.375 g/kNs while the higher TIT yields to the increase of the SNP index from 0.377 to 0.455 g/kNs. According to the findings of this study, decision mechanism could be improved to find out optimum design variables in terms of eco-friendly aircraft activities.