ONE DIMENSIONAL MODELLING FOR PULSED FLOW TWIN-ENTRY TURBINE

One-dimensional (1D) modelling is critical for turbomachinery unsteady performance prediction and system response assessment of internal combustion engines. This paper uses a novel 1D modelling (TURBODYNA) and proposes two additional features for the application to a twin-entry turbocharger turbine. Compared to single-entry turbines, twin-entry turbines enhance turbocharger transient response and reduce engine exhaust valve overlap periods. However, out-of-phase high frequency pulsating pressure waves lead to an unsteady mixing process from the two flows and pose great challenges to traditional 1D modelling. The present work resolves the mixing problem by directly solving mass, momentum and energy conservation equations during the mixing process instead of applying constant pressure assumption at the limb-rotor joint. Comparisons of TURBODYNA and an experimentally validated CFD suggest that TURBODYNA can not only provide a very good agreement on turbine performance, but also accurately capture unsteady features due to flow field inertial and pressure wave propagation. Levels of accuracy achieved by TURBODYNA have proved superior to traditional 1D modelling on turbine performance and the generality of the current 1D modelling has been explored by extending the application to another turbine featuring distinct characteristics.

A Mathematical Model of the Production Inventory Problem for Mixing Liquid Considering Preservation Facility

The mixing process of liquid products is a crucial activity in the industry of essential commodities like, medicine, pesticide, detergent, and so on. So, the mathematical study of the mixing problem is very much important to formulate a production inventory model of such type of items. In this work, the concept of the mixing problem is studied in the branch of production inventory. Here, a production model of mixed liquids with price-dependent demand and a stock-dependent production rate is formulated under preservation technology. In the formulation, first of all, the mixing process is presented mathematically with the help of simultaneous differential equations. Then, the mixed liquid produced in the mixing process is taken as a raw material of a manufacturing system. Then, all the cost components and average profit of the system are calculated. Now, the objective is to maximize the corresponding profit maximization problem along with the highly nonlinear objective function. Because of this, the mentioned maximization problem is solved numerically using MATHEMATICA software. In order to justify the validity of the model, two numerical examples are worked out. Finally, to show the impact of inventory parameters on the optimal policy, sensitivity analyses are performed and the obtained results are presented graphically.

Diagnosing underdetermination in stable isotope mixing models

Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named β-dependent SIMM) using two motivated examples. Using a simulation example, we showed that the proposed method can rigorously quantify the expected underdetermination (i.e., intervals of β-dependent posterior) of relative contributions. Moreover, the application to the published field data highlighted two problematic aspects of the underdetermination: 1) ordinary SIMMs was difficult to quantify underdetermination of each source, and 2) the marginal posterior median was not necessarily consistent with the joint posterior peak in the case of underdetermination. Our study theoretically and numerically confirmed that β-dependent SIMMs provide a useful diagnostic tool for the underdetermined mixing problem. In addition to ordinary SIMMs, we recommend reporting the results of β-dependent SIMMs to obtain a biologically feasible and sound interpretation from stable isotope data.

Audio Information Camouflage Detection for Social Networks

Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.

EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

One Dimensional Modelling for Pulsed Flow Twin-Entry Turbine

One-dimensional (1D) modelling is critical for turbomachinery unsteady performance prediction and system response assessment of internal combustion engines. This paper uses a novel 1D modelling (TURBODYNA) and proposes two additional features for the application to a twin-entry turbocharger turbine. Compared to single-entry turbines, twin-entry turbines enhance turbocharger transient response and reduce engine exhaust valve overlap periods. However, out-of-phase high frequency pulsating pressure waves lead to an unsteady mixing process from the two flows and pose great challenges to traditional 1D modelling. The present work resolves the mixing problem by directly solving mass, momentum and energy conservation equations during the mixing process instead of applying constant pressure assumption at the limb-rotor joint. Comparisons of TURBODYNA and an experimentally validated CFD suggest that TURBODYNA can not only provide a very good agreement on turbine performance, but also accurately capture unsteady features due to flow field inertial and pressure wave propagation. Levels of accuracy achieved by TURBODYNA have proved superior to traditional 1D modelling on turbine performance and the generality of the current 1D modelling has been explored by extending the application to another turbine featuring distinct characteristics.

Virtual Experiments of Particle Mixing Process with the SPH-DEM Model

Particle mixing process is critical for the design and quality control of concrete and composite production. This paper develops an algorithm to simulate the high-shear mixing process of a granular flow containing a high proportion of solid particles mixed in a liquid. DEM is employed to simulate solid particle interactions; whereas SPH is implemented to simulate the liquid particles. The two-way coupling force between SPH and DEM particles is used to evaluate the solid-liquid interaction of a multi-phase flow. Using Darcy’s Law, this paper evaluates the coupling force as a function of local mixture porosity. After the model is verified by two benchmark case studies, i.e., a solid particle moving in a liquid and fluid flowing through a porous medium, this method is applied to a high shear mixing problem of two types of solid particles mixed in a viscous liquid by a four-bladed mixer. A homogeneity metric is introduced to characterize the mixing quality of the particulate mixture. The virtual experiments with the present algorithm show that adding more liquid or increasing liquid viscosity slows down the mixing process for a high solid load mix. Although the solid particles can be mixed well eventually, the liquid distribution is not homogeneous, especially when the viscosity of liquid is low. The present SPH-DEM model is versatile and suitable for virtual experiments of particle mixing process with different blades, solid particle densities and sizes, and liquid binders, and thus can expedite the design and development of concrete materials and particulate composites.

EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.