Coupled heat and mass transfer analysis of NH<Subscript>3</Subscript>-H<Subscript>2</Subscript>O falling film absorption on inner tube surface with low solution flow rates

NH₃-H₂O falling film absorption usually takes place with low solution flow rate in real absorption refrigeration system. An experimental study of inner vertical absorption is carried out for the consideration of air-cooling absorber. Variable working conditions are tested to evaluate the heat and mass transfer performances.The traditional evaluation method based on log-mean-temperature（concentration） difference is criticized for its lack of theoretical basis while simultaneous heat and mass transfer process occurs. A new method proposed by Kim and Infante Ferreira is modified to evaluate the experimental results with reasonable assumptions. The method is based on the derivation of coupled heat and mass transfer differential equations of NH₃-H₂O absorption process.The analysis of the same experimental data shows that the new method realizes better consistency with smaller error, especially in heat transfer aspect. Heat and mass transfer performance is enhanced with the increase of solution Reynolds number. Sub-cooling of inlet weak solution also has positive influence on the absorption process,which should be evaluated by the new method correctly. Two correlations are developed to evaluate both Nusselt and Sherwood numbers for the design of air-cooling absorber.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Brittleness analysis and important nodes discovery in large time-evolving complex networks

The brittleness analysis and important nodes detection have been a hot spot in the complex networks. How to get the overall feature of the whole network and how to find out some important nodes are requisites to solve these problems. In this paper, we adopt the trace of the adjacency matrix and the centrality of the complex networks to give a quantitative and qualitative analysis of networks being studied. Results show that the k-shell plays a more important role than the degree centrality and the betweenness in finding important nodes, and it can also be used to give direction on the immunization and maintenance of complex networks.     

Long-term tracking based on spatio-temporal context

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.     

Poisson noise removal based on nonlocal total variation with Euler’s elastica pre-processing

An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.     

Mathematical model validated by a crash test for studying the occupant’s kinematics and dynamics in a cars’ frontal collision

The aim of the paper was to determine the kinematic parameters that influence the occupant injury risk through a mathematical model. The developed model is a 2D model composed of 4 bodies (2 vehicles, thorax and head). The head and thorax are interconnected with a rotation joint and a torsion spring meant to stiffen the relative movement between the bodies. The thorax is connected with the vehicle body by a linear spring meant to simulate the seatbelt stiffness. The model was solved using Lagrange principle and the validation of the model was made through a crash test performed using the same initial conditions and comparing the obtained values of the displacement, velocity and acceleration parameters with the ones obtained with the mathematical model. The head and torso were chosen due to the fact that they are the common parts of the body that get injured, especially the head with the change of 80 % to cause fatal injury in car’s frontal collision. Once the model was validated, the stiffness of the seatbelt was modified in order to determine the behavior of the occupant in case of car frontal collisions. When the seatbelt stiffness was reduced, the occupant displacement and velocity increased, while by increasing the stiffness, these parameters decreased. The values of the developed model presented a high degree of similarity with the results obtained from the crash test with an error of 10 %. This model can be used by engineers to easily asses the occupant injury risk in case of vehicle frontal collisions.     

Application of the reverse 3D scanning method to evaluate the wear of forging tools divided on two selected areas

This study is focused on tools used in the industrial hot forging process of a front wheel forging (eventually–gear wheel) manufactured for the automotive industry. Four different variants were applied for the tools: 2 die inserts were coated with two different hybrid layers (GN + PVD type), i.e. AlCrTiN and AlCrTiSiN, one insert was only nitrided, and one was pad welded, to improve tool durability. The tool wear was analysed and represented by the material degradation on the working surface, based on the 3D scanning and the material growth of the periodically collected forgings. Additionally, the scanned tools were divided into two areas, in which it was found, based on the reliminary analysis, that various degradation mechanisms are predominant. Microstructural and hardness measurements of the analyzed tools were also performed. Based on the results, it was found that, in the central part of the die insert (area A), thermo-mechanical fatigue and wear occurred, while in the area of the bridge insert (area B), only abrasive wear could be observed. For these areas (A and B), the loss of material was determined separately. In area A for the inserts with hybrid layer GN+AlCrTiSiN and gas nitrided, an intensive increase of wear took place, which was not observed for the pad welded and GN+AlCrTiN layer insert, for which, together with the increase of the forging number, a proportional growth of the loss of material occurred. In area B the weakest results were obtained for the insert with GN+AlCrTiSiN layer, while wear of other die inserts grew similar and proportional.     

Lightweight mixture faults detection method for gasoline engine using on-line trend analysis

Mixture faults detection is meaningful for gasoline engines because proper mixture is the basic prerequisite for healthy running of a combustion engine. Among existing methods for faults detection, the data-driven trend analysis technique is widely used due to the simplicity and efficiency in time-domain. The CUSUM (Cumulative Sum Of Errors) algorithm is good at real-time trend extraction, but it’s easy to be costly on the fuel trim signal during the engine in normal working conditions, which will increase battery energy consumption because engine failure is rarely occurs. Hence, the conventional treatment methods of artifacts in the CUSUM algorithm are modified by means of decay function and detection time analysis. The thresholds are tuned according to the characteristics of artifacts instead of residual variability, which leads to better results of trend extraction and less computation. Then, the revised CUSUM algorithm is used for monitoring the mixture abnormal behaviors, and the mixture faults can be detected in real time through analyzing the variation features of fuel trim signal. The lightweight faults detector using the advanced CUSUM algorithm (FD-A-CUSUM) is evaluated on the experimental data collected from a Ford engine. The validation results show that while engine works under normal conditions, the computation of FD-A-CUSUM has decreased by 72.79 % in comparison with the detection method using the original CUSUM algorithm (FD-O-CUSUM), and the false alarm ratio of FD-A-CUSUM is 3.37 %. Futhermore, the detection results of FD-A-CUSUM for two leakage faults have achieved 91.18 % test accuracy.     

Effective method for knock signal denoising in internal combustion engine

Uncontrolled expansion of combustion wave in spark ignited internal combustion engine causes knock effect which seriously degrades efficiency and lifetime of the engine. Thus, accurate knock detection and control are essential for obtaining a desired performance from the engine. Usually, knock sensor is used to detect this phenomenon but it has limited accuracy especially at engine high-speed rotations because of natural vibration and external noises. In this study an effective method based on Non-Local Mean (NLM) algorithm has been proposed to improve the knock detection accuracy. This method is evaluated based on four different indicators and four engine cylinders. The results show 52.9 % improvement in knock detection. Also feasibility of real time execution of this method based on embedded hardware has been studied.     

Improvement of the second order approximation of the smoothed particle hydrodynamics

The smoothed particle hydrodynamics (SPH), as a fully Lagrangian particle method, has been successfully applied to astrophysical problems and extended to elastic dynamics and computational fluid dynamics.High order derivatives have to be approximated when elastic dynamics problems are modeled. However, the approximation errors in SPH could lead to computational failure in the case that the order of derivative is high.A novel method was proposed in order to improve the accuracy of SPH method, which shows the relationship between the selected functions and their SPH approximations. The entire involved system was represented by a finite number of particles that carry individual mass and occupy individual space, and the integral interpolation was approximated by a summation interpolation. In addition, error comparison was made between SPH method with and without the present improvement.