Meiyin Zhu

Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.

Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.

Flame liftoff poses a significant risk in industrial systems, necessitating reliable liftoff monitoring methods. Electrical capacitance tomography (ECT) is widely used in non-contact flame detection. However, direct detection of flame liftoff remains challenging for conventional ECT arrangements, because flame liftoff is mainly characterized by axial flame displacement rather than a significant change in combustion intensity. This study utilizes the fringe effect of ECT sensors to achieve real-time detection and characterization of flame liftoff. During liftoff, flame displacement away from the sensor weakens the fringe field, causing a detectable change in the capacitance value. Experiments were conducted using a bluff-body combustor with acoustic excitation to generate controllable liftoff. The Calderon method adapted for annular regions was employed to reconstruct flame combustion images. The comparison between the ECT reconstructed images and the high-speed camera images was completed to verify the effectiveness of the method. The results indicate that the reconstructed grayscale values decrease with the increase of flame liftoff intensity, confirming the method’s ability for real-time and accurate detection. To quantitatively characterize the liftoff intensity, a liftoff index ( LI ) was proposed, integrating the frequency, height, and duration of liftoff events. At a fixed acoustic frequency, LI increased monotonically with sound pressure level (SPL), whereas at a fixed SPL, LI exhibited nonlinear variation with excitation frequency. Effective liftoff monitoring and characterization are crucial for the safe operation of combustors.

Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.

Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.

Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.

As a key parameters of the real high-altitude environment, humidity has a pronounced impact on the performance of aviation equipment and test results. However, current dynamic models for Altitude Ground Test Facilities (AGTF) control research only consider the effects of temperature and pressure, ignoring the influence of humidity. To address this issue, this paper incorporates humidity as a key variable into the dynamic modeling of AGTF, performs dynamic modeling research on the general components of a typical AGTF, and develops a general underlying component model library that accounts for temperature, humidity, and pressure. Based on the established model library, a numerical simulation system for a small-scale AGTF is built and validated against actual test data. The results show that the simulated temperature, pressure, and relative humidity in the environmental chamber follow the dynamic trends of the actual test data, with maximum absolute errors of 0.59 K, 288.68 Pa, and 0.44%, respectively. To evaluate the system's suitability for control studies, a Proportional-Integral (PI) controller is employed to conduct closed-loop control analyses of temperature, pressure, and humidity on the validated model. The dynamic response curves of the environmental chamber temperature, pressure, and relative humidity output by the simulation system exhibit strong agreement with the prescribed setpoint values; during a typical dynamic regulation process, the relative errors for temperature, pressure, and relative humidity are all within 1%.