ALBERT

Beschreibung: Recently, the frequent seasonal drought in Southwest China has brought considerable concerns and continuous heated arguments on the “water pump” viewpoint (i.e., the water demand from Hevea spp. and Eucalyptus spp. can be treated as a water pump) once again. However, such viewpoint just focused on water consumption from vegetation transpiration and its ecoenvironment impacts, which had not considered other attributes of vegetation, namely, water saving and drought resistance, and hydrological regulation (water conservation) into consideration. Thus, in this paper, the synthesized attributes of regional vegetation water use had been mainly discussed. The results showed that the study on such aspects as the characters of water consumption from vegetation transpiration, the potential of water saving and drought resistance, and the effects of hydrological regulation in Southwest China lagged far behind, let alone the report on synthesized attributes of water utilization with the organic combination of the three aspects above or the paralleled analysis. Accordingly, in this paper, the study on the synthesized attributes of water use by regional vegetation in Southwest China was suggested, and the objectives of such a special study were clarified, targeting the following aspects: (i) characters of water consumption from transpiration of regional typical artificial vegetation; (ii) potential of water saving and drought resistance of regional typical artificial vegetation; (iii) effects of hydrological regulation of regional typical artificial vegetation; (iv) synthesized attributes of water use by regional typical artificial vegetation. It is expected to provide a new idea for the scientific assessment on the regional vegetation ecoenvironment effects and theoretical guidance for the regional vegetation reconstruction and ecological restoration.

Beschreibung: The relative contributions of different factors to the variation in surface runoff have been broadly quantified. However, little attention has been paid to how these relative contributions have changed over time. We analyzed the changes in surface runoff during 1980–2010 in six subbasins in the mountainous region of the Haihe River Basin, one of the most serious water shortage regions in China, and identified the changes in the relative contributions of climate (precipitation and temperature) and land-use to surface runoff decrease. There was a decreasing tendency in surface runoff in all subbasins, four of which had an abrupt change point around 1998. Comparing the relative contributions before and after 1998 in the four subbasins, the average influence of climate was found to decline dramatically from 67.1% to 30.5%, while that of land-use increased from 23.9% to 69.5% mainly due to the increase of forest area. Our results revealed that the primary environmental factor responsible for runoff variations was not constant, and an alternation may accentuate the impact and stimulate an abrupt change of runoff in semiarid and semihumid mountainous regions. This will help in taking tracking measures to deal with the complex water resource challenges according to different driving factors.

Beschreibung: Hypersonic aircraft have been rapidly developed in recent years both theoretically and experimentally. Aerothermoelastic simulation is very challenging due to its inherent complexity, but physical tests are a workable approach. Flutter tests with variable speed are a popular alternative to hypersonic tests which provide nonstationary structural response data. This paper proposes a nonstationarity assessment method based on energy distribution in the time-frequency domain. The proposed method reveals the nonstationarity level corresponding to the appropriate modal identification algorithm or flutter boundary prediction (FBP) method. Several classic flutter criteria are utilized to build a hypersonic aircraft FBP framework. Numerical simulation and experimental applications demonstrate the effectiveness and feasibility of the proposed method, which facilitates accurate flutter predictions for the subcritical turbulence response during hypersonic flutter flight.

Beschreibung: Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

Beschreibung: Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. In this study, a signal processing method was established to use the structural response signals from an aeroelastic model to classify flutter signals via deep learning algorithm. This novel flutter signal processing and classification method works by combining a convolutional neural network (CNN) with time-frequency analysis. Flutter characteristics are revealed in both time and frequency domains, which are harmonic or divergent in the time series; the flutter model energy is singular and significantly increases in the frequency view, so the features of the time-frequency diagram can be extracted from the dataset-trained CNN model. As the foundation of the subsequent deep learning algorithm, the datasets are placed into a collection of time-frequency diagrams calculated by short-time Fourier transform (STFT) and labeled with two artificial states, flutter or no flutter, depending on the source of the signal measured from a wind tunnel test on the aeroelastic model. After preprocessing, a cross-validation schedule is implemented to update (and optimize) CNN parameters though the trained dataset. The trained models were compared against test datasets to validate their reliability and robustness. Our results indicate that the accuracy rate of test datasets reaches 90%. The trained models can effectively and automatically distinguish whether or not there is flutter in the measured signals.

Beschreibung: The aeroengine control system is a piece of complex thermal machinery which works under high-speed, high-load, and high-temperature environmental conditions over lengthy periods of time; it must be designed for the utmost reliability and safety to function effectively. The consequences of sensor faults are often extremely serious. The inherent complexity of the engine structure creates difficulty in establishing accurate mathematical models for the model-based sensor fault diagnosis. This paper proposes an intelligent fault diagnosis method for aeroengine sensors combining a deep learning algorithm with time-frequency analysis wherein the signal recognition problem is transformed into an image recognition problem. The continuous wavelet transform (CWT) is first applied to seven common health condition signals in an engine control system sensor in order to generate scalograms that capture the characteristics of the signal. A convolutional neural network (CNN) model trained with preprocessed and labeled datasets is then used to extract the features of a time-frequency graph based on which faults can be identified and isolated. This method does not require modeling and design thresholds, so it has strong robustness and accuracy rate of over 97%. The trained model effectively reveals faults in sensor signals and allows for accurate identification of fault types.

Beschreibung: Using the experimental data from the ALICE program on the centrality dependence of the transverse momentum (pT) spectra in Pb+Pb collisions at sNN=2.76 TeV, we show that the double-Tsallis distribution and the generalized Fokker-Planck (FP) solution cannot describe the spectra of pions, kaons, and protons from central to peripheral collisions in the entire pT region, simultaneously. Hence, a new two-component distribution, which is a hydrodynamic extension of the generalized FP solution accounting for the collective motion effect in heavy-ion collisions, is proposed in order to reproduce all the identified particle spectra. Our results suggest that the particle production dynamics may be different for different particles, especially at very low pT region.