The conventional powertrain has seen a continuous wave of energy optimization, focusing heavily on boosting and engine downsizing. This trend is pushing OEMs to consider turbocharging as a premium solution for exhaust energy recovery. Turbocharger is an established, economically viable solution which recovers waste energy from the exhaust gasses, and in the process providing higher pressure and mass of air to the engine. However, a turbocharger has to be carefully matched to the engine. The process of matching a turbocharger to an engine is implemented in the early stages of design, through air system simulations. In these simulations, a turbocharger component is represented largely by performance maps and it serves as a boundary condition to the engine. The thermodynamic parameters of a turbocharger are calculated through the performance maps which are usually generated experimentally in gas test stands and used as look-up table in the engine models. Thus, the operational of the engine is dictated by the air flow thermodynamic parameters (pressure, temperature and mass flow) from the turbocharger compressor; this in turn will determine the thermodynamic parameters for the exhaust gas entering the turbocharger turbine. The importance and its sensitivity dictate that any heat transfer affecting the experiments to acquire the performance maps will cause errors in the characterization of a turbocharger. This will consequently lead to inaccurate predictions from the engine model if the heat transfer effects are not properly accounted for. The current paper provides a comprehensive review on how the industry and academics are addressing the heat transfer issue through advancing researches. The review begins by defining the main issues related with heat transfer in turbochargers and the stateof-the-art research looking into it. The paper also provides some inputs and recommendations on the research areas which should be further investigated in the years to come.

Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of microelectromechanical system (MEMS)-based barometers and their systematic embedding in smartphones and wearable devices, a vast breadth of new applications for the use of barometers has emerged. For instance, it is now possible to use barometers in conjunction with other sensors to track and identify a wide range of human activity classes. However, the effectiveness of barometers in the growing field of human activity recognition critically hinges on our understanding of the numerous factors affecting the atmospheric pressure, as well as on the properties of the sensor itself—sensitivity, accuracy, variability, etc. This review article thoroughly details all these factors and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition. In addition, we specifically collected some experimental data to illustrate the effects of these factors, which we observed to be in good agreement with the findings in the literature. We conclude this review with some suggestions on some possible future uses of barometric sensors for the specific purpose of tracking human activities.

To improve the efficiency of mixed flow pump, Computational Fluid Dynamics (CFD) analysis is one of the advanced tools used in the pump industry. A detailed CFD analysis was done to predict the flow pattern inside the impeller which is an active pump component. From the results of CFD analysis, the velocity and pressure in the outlet of the impeller is predicted. CFD analyses are done using Star CCM+ software. These outlet flow conditions are used to calculate the efficiency of the impeller. The calculated value of efficiency from the empirical relations is 55%. The optimum inlet and outlet vane angles are calculated for the existing impeller by using the empirical relations. The CAD models of the mixed flow impeller with optimum inlet and outlet angles are modeled using CAD modelling software ProE WF3. To find the relationship between the vane angles and the impeller performance the optimum vane angle is achieved step by step. Three CAD models are modeled with the vane angles between existing and optimum values. These models are analyzed individually to find the performance of the impeller. In the first case, outlet angle is increased by 5°. From the outlet flow conditions, obtained from the CFD analysis, it is evident that the reduced outlet recirculation and flow separation cause the improved efficiency. By changing the outlet angle the efficiency of the impeller is improved to 59%. In the second case inlet angle is decreased by 10%. The efficiency of the impeller in this case is 61%. From this analysis it is understood that the changes in the inlet vane angle did not change the efficiency of the impeller as much as the changes in outlet angle. In the third case, impeller with optimum vane angles is analyzed and the outlet flow conditions are predicted. From the CFD analysis the efficiency of the impeller with optimum vane angles is calculated as 65%. Thus, efficiency of the mixed flow impeller is improved by 18.18% by changing the inlet and outlet vane angles.

Comprehensive and quantitative investigations of social theories and phenomena increasingly benefit from the vast breadth of data describing human social relations, which is now available within the realm of computational social science. Such data are, however, typically proxies for one of the many interaction layers composing social networks, which can be defined in many ways and are typically composed of communication of various types (e.g., phone calls, face-to-face communication, etc.). As a result, many studies focus on one single layer, corresponding to the data at hand. Several studies have, however, shown that these layers are not interchangeable, despite the presence of a certain level of correlations between them. Here, we investigate whether different layers of interactions among individuals lead to similar conclusions with respect to the presence of homophily patterns in a population-homophily represents one of the widest studied phenomenon in social networks. To this aim, we consider a dataset describing interactions and links of various nature in a population of Asian students with diverse nationalities, first language and gender. We study homophily patterns, as well as their temporal evolutions in each layer of the social network. To facilitate our analysis, we put forward a general method to assess whether the homophily patterns observed in one layer inform us about patterns in another layer. For instance, our study reveals that three network layers-cell phone communications, questionnaires about friendship, and trust relations-lead to similar and consistent results despite some minor discrepancies. The homophily patterns of the co-presence network layer, however, does not yield any meaningful information about other network layers.Keywords: social networks; multilayer networks; temporal homophily defined: e.g., friendship relations, patterns of communications, co-presence, face-toface interactions. These different types of relations form a multilayer network [3,4], for which each layer can be explored using possibly different methods. Friendship relations are typically mined through surveys, physical interactions and proximity by diaries or more recently using wearable sensors [5,6], and communication patterns are extracted from mobile phone call records [7][8][9]. In recent times in particular, technological developments have allowed researchers to gather increasing amounts of digital data on face-to-face contacts, phone communication patterns and online relationships, at widely different scales in terms of population size, space and time resolution. These data have been widely used to investigate the structure of social networks, the patterns of social interactions and social theories, such as the strength of weak ties [7], homophily patterns (the tendency of individuals to have social links with similar individuals, with respect to gender, nationality, social class, etc.[10]) [11][12][13][14][15][16], mechanisms of link formation and persistence [11,12,17], social strategies linked to limited ...

With new cities increasingly expanding vertically, there is a pressing need to shed light on human vertical mobility, which can readily be achieved with existing sensor technology. To date, the methodology to track and identify vertical movement from large-scale unstructured data sets is lacking. Here, we design and develop such a framework to accurately and systematically identify the sparse human vertical displacement activity that is typically buried into the predominantly horizontal mobility. Our framework uses sensor data from barometer, accelerometer and Wi-Fi scanner coupled with an extraction step involving a combination of feature engineering and data segmentation. This methodology is subsequently integrated into a machine-learning-based classifier to automatically distinguish vertical displacement activity from its horizontal counterpart. We confirm the high accuracy of this approach by a thorough validation and testing showing a 98% overall accuracy and a 92% F1-score in classifying vertical displacement activity.We illustrate the potential of the developed framework by applying it to an unstructured large-scale data set associated with over 16,000 participants going about their daily activity in the city-state of Singapore. This gives us access to all the vertical movements of this large population, and we investigate the statistical distribution of vertical activity, both in terms of number of events and size of vertical jumps, and their temporal heterogeneity across the day. The approach developed here could be used in massive human experiments to uncover the hidden patterns of human vertical mobility. This new knowledge would have significant ramifications for the architectural design of vertical cities.