Turbomachinery aerodynamic and aeromechanic design optimization using the adjoint method

The thesis documents the investigation of the application of the adjoint method to turbomachinery blading design optimization, with emphasis on blading aerodynamic design optimization in a multi-bladerow environment and concurrent blading aerodynamic and aeromechanic design optimization for a single bladerow. Based on the nonlinear flow equations, a steady adjoint system has been developed using the continuous adjoint approach. The capability of the conventional adjoint system has been augmented by the introduction of an adjoint mixing-plane treatment. This treatment is a counterpart of the flow mixing-plane treatment, enabling the steady adjoint equations to be solved in multi-bladerow computational domains. This allows turbomachinery blades to be optimised to enhance their aerodynamic performance in a multi-bladerow environment with matching between adjacent bladerows dealt with in a timely manner. The Nonlinear Harmonic Phase Solution method, a neat frequency domain method catered specifically for turbomachinery aeromechanics prediction, has been chosen to integrate with the adjoint method to calculate objective function sensitivities efficiently for concurrent aeromechanic and aerodynamic design optimization for single row turbomachinery blades. The Nonlinear Harmonic Phase Solution method, unlike the time-linearized methods, solves the unsteady flow equations at two or three carefully selected phases of a period of unsteadiness. This approach not only can conveniently turn a steady flow solver to one solving the unsteady flow equations efficiently, but also provides a good basis on which the corresponding adjoint system can be formulated and solved in a similar manner by extending a steady adjoint system. In order to resolve the issue of having a good blading performance over a whole operating range at a given operation speed, a multi-operating-point design optimization is implemented by formulating an objective function of a weighted sum of performance at more than one operating poin

Computing Networks Enabled Semantic Communications

Semantic communication has shown great potential in boosting the effectiveness and reliability of communications. However, its systems to date are mostly enabled by deep learning, which requires demanding computing resources. This article proposes a framework for the computing networks enabled semantic communication system, aiming to offer sufficient computing resources for semantic processing and transmission. Key techniques including semantic sampling and reconstruction, semantic-channel coding, semantic-aware resource allocation and optimization are introduced based on the cloud-edge-end computing coordination. Two use cases are demonstrated to show advantages of the proposed framework. The article concludes with several future research directions

Revisiting the Space-Time Gradient Method: A Time-clocking Perspective, High Order Difference Time Discretization and Comparison with the Harmonic Balance Method

This paper revisits the Space-Time Gradient (STG) method which was developed for efficient analysis of unsteady flows due to rotor–stator interaction and presents the method from an alternative time-clocking perspective. The STG method requires reordering of blade passages according to their relative clocking positions with respect to blades of an adjacent blade row. As the space-clocking is linked to an equivalent time-clocking, the passage reordering can be performed according to the alternative time-clocking. With the time-clocking perspective, unsteady flow solutions from different passages of the same blade row are mapped to flow solutions of the same passage at different time instants or phase angles. Accordingly, the time derivative of the unsteady flow equation is discretized in time directly, which is more natural than transforming the time derivative to a spatial one as with the original STG method. To improve the solution accuracy, a ninth order difference scheme has been investigated for discretizing the time derivative. To achieve a stable solution for the high order scheme, the implicit solution method of Lower-Upper Symmetric Gauss-Seidel/Gauss-Seidel (LU-SGS/GS) has been employed. The NASA Stage 35 and its blade-count-reduced variant are used to demonstrate the validity of the time-clocking based passage reordering and the advantages of the high order difference scheme for the STG method. Results from an existing harmonic balance flow solver are also provided to contrast the two methods in terms of solution stability and computational cost

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

BackgroundAcute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.MethodsIn this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.ResultsEleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.ConclusionThe ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources

Uncovering the Root Causes of Stall Flutter in a Wide Chord Fan Blisk

Flutter was encountered at part speeds in a scaled wide chord fan blisk designed for a civil aeroengine during a rig test when the fan bypass flow was throttled toward its stall boundary. Analysis of the blade tip timing measurement data revealed that the fan blades vibrated at the first flap (1F) mode with nodal diameters of two and three. To facilitate a further rig test and ultimately eliminate the flutter problem, a numerical campaign was launched to help understand the root causes of the flutter. Both the influence coefficient method (ICM) and the traveling wave method (TWM) were employed in the numerical investigation to analyze unsteady flows due to blade vibration, with the intention to corroborate different numerical results and take advantage of each method. To eliminate nonphysical reflections, a sponge layer with an inflated mesh size was used for the extended inlet and outlet regions. Steady flow field and unsteady flow field were examined to relate them to the blade flutter. The influences of vibration frequency, mass flow rate, shock, boundary layer separation and acoustic mode propagation behaviors on the fan flutter stability were also investigated. Particular attention was paid to the acoustic mode propagation behaviors

Multi-Row Turbomachinery Aerodynamic Design Optimization by an Efficient and Accurate Discrete Adjoint Solver

This paper proposes an approach that combines manual differentiation (MD) and automatic differentiation (AD) to develop an efficient and accurate multi-row discrete adjoint solver. In this approach, the structures of adjoint codes generated using an AD tool are first analyzed. Then, the AD-generated codes are manually adjusted to reduce memory and CPU time consumption. This manual adjustment is performed by replacing the automatically generated low-efficient differentiated codes with manually developed ones. To demonstrate the effectiveness of the proposed approach, the single-stage transonic compressor–NASA Stage 35 and the 1.5-stage Aachen turbine–are used. The solution information exchange at a rotor-stator/stator-rotor interface is achieved by a conservative, non-reflective, and robust discrete adjoint mixing plane method. The results show that the discrete adjoint solver developed by hybrid automatic and manual differentiation is more economical in computational cost than that developed purely by an AD tool and has higher sensitivity accuracy than the adjoint solver with the constant eddy viscosity (CEV) assumption. Moreover, the multi-row turbomachinery design optimizations can be efficiently performed by the discrete adjoint solver developed by the hybrid automatic and manual differentiation

Polyphyllin I alleviates neuroinflammation after cerebral ischemia–reperfusion injury via facilitating autophagy-mediated M2 microglial polarization

Abstract Microglial activation and polarization play a central role in poststroke inflammation and neuronal damage. Modulating microglial polarization from pro-inflammatory to anti-inflammatory phenotype is a promising therapeutic strategy for the treatment of cerebral ischemia. Polyphyllin I (PPI), a steroidal saponin, shows multiple bioactivities in various diseases, but the potential function of PPI in cerebral ischemia is not elucidated yet. In our study, the influence of PPI on cerebral ischemia–reperfusion injury was evaluated. Mouse middle cerebral artery occlusion (MCAO) model and oxygen–glucose deprivation and reoxygenation (OGD/R) model were constructed to mimic cerebral ischemia–reperfusion injury in vivo and in vitro. TTC staining, TUNEL staining, RT-qPCR, ELISA, flow cytometry, western blot, immunofluorescence, hanging wire test, rotarod test and foot-fault test, open-field test and Morris water maze test were performed in our study. We found that PPI alleviated cerebral ischemia–reperfusion injury and neuroinflammation, and improved functional recovery of mice after MCAO. PPI modulated microglial polarization towards anti-inflammatory M2 phenotype in MCAO mice in vivo and post OGD/R in vitro. Besides, PPI promoted autophagy via suppressing Akt/mTOR signaling in microglia, while inhibition of autophagy abrogated the effect of PPI on M2 microglial polarization after OGD/R. Furthermore, PPI facilitated autophagy-mediated ROS clearance to inhibit NLRP3 inflammasome activation in microglia, and NLRP3 inflammasome reactivation by nigericin abolished the effect of PPI on M2 microglia polarization. In conclusion, PPI alleviated post-stroke neuroinflammation and tissue damage via increasing autophagy-mediated M2 microglial polarization. Our data suggested that PPI had potential for ischemic stroke treatment