Thermodynamic Entropy-Based Fatigue Life Assessment Method for Nickel-Based Superalloy GH4169 at Elevated Temperature Considering Cyclic Viscoplasticity.

This paper develops a thermodynamic entropy-based life prediction model to estimate the low-cycle fatigue (LCF) life of the nickel-based superalloy GH4169 at elevated temperature (650 °C). The gauge section of the specimen was chosen as the thermodynamic system for modeling entropy generation within the framework of the Chaboche viscoplasticity constitutive theory. Furthermore, an explicitly numerical integration algorithm was compiled to calculate the cyclic stress-strain responses and thermodynamic entropy generation for establishing the framework for fatigue life assessment. A thermodynamic entropy-based life prediction model is proposed with a damage parameter based on entropy generation considering the influence of loading ratio. Fatigue lives for GH4169 at 650 °C under various loading conditions were estimated utilizing the proposed model, and the results showed good consistency with the experimental results. Finally, compared to the existing classical models, such as Manson-Coffin, Ostergren, Walker strain, and SWT, the thermodynamic entropy-based life prediction model provided significantly better life prediction results.

Analysis and prediction of residual stresses based on cutting temperature and cutting force in rough turning of Ti-6Al-4V.

Turning is a typical machining process, which is widely used in the manufacturing process of parts. The residual stress introduced by turning has a significant influence on the mechanical properties, fatigue performance, and service safety, and is one of the key factors affecting the fatigue life of parts. Conventional residual stress prediction models based on cutting parameters cannot consider all the influencing factors of turning and are strongly dependent on the specific cutting environment and tool, so they have limitations. Therefore, a residual stress analysis and prediction method based on cutting temperature and cutting force is proposed in this paper for the rough turning process of Ti-6Al-4V. Firstly, the sensitivity analysis of turning residual stress is carried out on eight cutting variables to determine the key cutting variables affecting the residual stress. Subsequently, the influence of the above key variables on residual stress is analyzed from the perspective of cutting temperature and cutting force. Finally, the residual stress prediction model based on cutting temperature and cutting force is established. The results show that the three variables that have the greatest influence on residual stresses are friction coefficient, tool edge radius, and cutting speed. The friction coefficient and tool edge radius affect the thickness of the residual stress layer. The cutting speed has little effect on the thickness of the residual stress layer, but increasing the cutting speed will lead to the transformation of residual stress to tensile stress. The relative error between the predicted value and the simulated value of residual stress is less than 6%, indicating that the prediction model has high accuracy and can effectively predict the residual stress. The prediction method proposed in this paper is not limited by the specific turning condition and provides a new perspective for the analysis and prediction of turning residual stress.

Mitigation effects of alternative aviation fuels on non-volatile particulate matter emissions from aircraft gas turbine engines: A review.

Global air transportation has grown rapidly in the past decade until the recent coronavirus pandemic. Previous research has demonstrated that particulate matter (PM) emissions from aircraft gas turbine engines can impair human health and environment, and may play a significant role in global climate change via direct absorption of solar radiation and indirect effect by their interaction with clouds. Using alternative aviation fuels (AAFs) from different sources have become a promising means to reduce aviation PM emissions and ensure energy sustainability. This work presents a review of non-volatile PM (nvPM) emission characteristics of aircraft gas turbine engines burning conventional aviation fuel (CAF) and CAF/AAF blends from recent ground and cruise tests. Current engine emission regulations, as well as available aviation PM emission prediction models and inventories are also discussed. Available nvPM emission characteristics, including particle number, particle mass, and particle size distribution (PSD), are analyzed and compared among different studies. The synthesized results indicate that burning AAFs tends to generate smaller size nvPM and reduce up to 90% nvPM number as well as 60-85% nvPM mass. The reduction is the most significant at low engine power settings, but becomes marginal at high engine power settings. The utilization of AAF blends reduces nvPM emission yet increases water vapor emission, which may promote contrail and even widespread cirrus cloud formation. Therefore, more investigation is required to quantify the potential impact of burning AAF at cruise altitudes on cloud formation and climate change. An appropriate estimation method for the particle number emissions from aircraft gas turbine engines fueled by both CAF and CAF/AAF blends is also in need aiming to establish a global aviation nvPM emission inventory and improve relevant global climate models.

Eddy Current Sensor System for Blade Tip Clearance Measurement Based on a Speed Adjustment Model.

Blade tip clearance (BTC) measurement and active clearance control (ACC) are becoming crucial technologies in aero-engine health monitoring so as to improve the efficiency and reliability as well as to ensure timely maintenance. Eddy current sensor (ECS) offers an attractive option for BTC measurement due to its robustness, whereas current approaches have not considered two issues sufficiently. One is that BTC affects the response time of a measurement loop, the other is that ECS signal decays with increasing speed. This paper proposes a speed adjustment model (SAM) to deal with these issues in detail. SAM is trained using a nonlinear regression method from a dynamic training data set obtained by an experiment. The Levenberg⁻Marquardt (LM) algorithm is used to estimate SAM characteristic parameters. The quantitative relationship between the response time of ECS measurement loop and BTC, as well as the output signal and speed are obtained. A BTC measurement method (BTCMM) based on the SAM is proposed and a geometric constraint equation is constructed to assess the accuracy of BTC measurement. Experiment on a real-time BTC measurement during the running process for a micro turbojet engine is conducted to validate the BTCMM. It is desirable and significative to effectively improve BTC measurement accuracy and expand the range of applicable engine speed.