Phase-field simulation of dendritic solidification using a l＇ull threaded tree with adaptive meshing

Simulation of the microstructure evolution during solidification is greatly beneficial to the control of solidification microstructures. A phase-field method based on the full threaded tree （FTT） for the simulation of casting solidification microstructure was proposed in this paper, and the structure of the full threaded tree and the mesh refinement method was discussed. During dendritic growth in solidification, the mesh for simulation is adaptively refined at the liquid-solid interface, and coarsened in other areas. The numerical results of a three- dimension dendrite growth indicate that the phase-field method based on FTT is suitable for microstructure simulation. Most importantly, the FTT method can increase the spatial and temporal resolutions beyond the limits imposed by the available hardware compared with the conventional uniform mesh. At the simulation time of 0.03 s in this study, the computer memory used for computation is no more than 10 MB with the FTT method, while it is about 50 MB with the uniform mesh method. In addition, the proposed FTT method is more efficient in computation time when compared with the uniform mesh method. It would take about 20 h for the uniform mesh method, while only 2 h for the FTT method for computation when the solidification time is 0.17 s in this study.     

Co-fermentation of sewage sludge and algae and Fe2+ addition for enhancing hydrogen production

Co-fermentation of sewage sludge and algae was performed for enhancing the hydrogen production, and the effect of Fe²⁺ on co-fermentation process was examined. Results showed that both co-fermentation process and Fe²⁺ addition promoted hydrogen production. Highest hydrogen production of 28 mL/100 mL (14.8 mL H₂/g VS_added) was obtained from the co-fermentation group with 600 mg/L Fe²⁺ addition, which was 2.15 times, 2.00 times and 1.87 times of mono-fermentation of sludge, mono-fermentation of algae, and the co-fermentation group without Fe²⁺ addition. Both volatile solids and protein degradation were stimulated by co-fermentation process. Microbial analysis showed that co-fermentation groups with Fe²⁺ addition enriched Clostridium sensu stricto 13, Clostridium tertium and Terrisporobacter, which were positively correlated with cumulative hydrogen production. This study suggested that the co-fermentation of sludge and algae in the presence of Fe²⁺ could significantly improve the hydrogen production by stimulating the hydrogen-producing metabolism.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

Inverse design of the thermal environment in an airplane cockpit using the adjoint method with the momentum method

Currently, the thermal environment in airplane cockpits is unsatisfactory and pilots often complain about a strong draft sensation in the cockpit. It is caused by the unreasonable air supply diffusers design. One of the best approaches to design a better cockpit environment is the adjoint method. The method can simultaneously and efficiently identify the number, size, location, and shape of air supply inlets, and the air supply parameters. However, the real air diffuser needed to design often have grilles, especially in the airplane cockpit, and the current method can only design the inlet as an opening. This study combined the adjoint method with the momentum method to directly identify the optimal air supply diffusers with grilles to create optimal thermal environment in an airplane cockpit (1) under ideal conditions and (2) with realistic constraints. Under the ideal conditions, the resulting design provides an optimal thermal environment for the cockpit, but it might not be feasible in practice. The design with realistic constraints provides acceptable thermal comfort in the cockpit, but it is not optimal. Thus, there is an engineering trade-off between design feasibility and optimization. All in all, the adjoint method with the momentum method can be effectively used to identify real air supply diffusers.     

Structural analysis of network traffic matrix via relaxed principal component pursuit

The network traffic matrix is widely used in network operation and management. It is therefore of crucial importance to analyze the components and the structure of the network traffic matrix, for which several mathematical approaches such as Principal Component Analysis (PCA) were proposed. In this paper, we first argue that PCA performs poorly for analyzing traffic matrix that is polluted by large volume anomalies, and then propose a new decomposition model for the network traffic matrix. According to this model, we carry out the structural analysis by decomposing the network traffic matrix into three sub-matrices, namely, the deterministic traffic, the anomaly traffic and the noise traffic matrix, which is similar to the Robust Principal Component Analysis (RPCA) problem previously studied in [13]. Based on the Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated Proximal Gradient (APG) algorithm, we present an iterative approach for decomposing a traffic matrix, and demonstrate its efficiency and flexibility by experimental results. Finally, we further discuss several features of the deterministic and noise traffic. Our study develops a novel method for the problem of structural analysis of the traffic matrix, which is robust against pollution of large volume anomalies.     

探地雷达在公路工程冻土调查中的应用

探地雷达是一种高频电磁波勘探技术,具有对探测对象不造成任何损伤,抗干扰能力强,成果直观、准确、高效等特点而被广泛应用于公路勘察中。该文主要介绍了利用地质雷达技术并结合探坑调查,查明了路线多年冻土的分布范围、上限及厚度,并提出了相应的处理措施。     

Cyclo-energy detector for spectrum sensing in cognitive radio

In this paper, a cyclo-energy detector is proposed for the spectrum sensing problem based on the cyclostationary signal analysis. The proposed detector can determine the existence of primary user by estimating the primary user's received power according to the cyclostationarity features of the secondary user. By using this detector, the secondary user does not need to stop transmitting signals when sensing the frequency spectrum. Simulation results are presented to confirm the performance of the proposed detector.