Analysis and assessment of a nanoparticle seeded small scale absorption refrigeration system driven by a low‐grade waste heat source

The thermoeconomic behaviour of a nanoparticle seeded single effect LiBr‐H₂O absorption refrigeration system (ARS) is investigated for a small scale application. In the proposed method, alumina nanoparticles with volume concentrations of 3%, 5%, and 7% are dispersed into an aqua lithium bromide solution. The multiobjective heat transfer search algorithm is employed to examine the design trade‐off between the coefficient of performance (COP) and total annualized cost (TAC). To analyze the overall performance of the system, the influence of five design parameters, namely the temperatures of the generator, absorber, evaporator, condenser and heat exchanger pipe diameter, are studied. It is found that with an increase in the COP, the TAC of the system is initially raised marginally, and after that, raised rigorously with further increment. The comparative results indicate that the COP and TAC of the nanofluid based ARS system are increased by about 7% and decreased by about 3.2%, respectively, corresponding to the Pareto points of the base ARS system. A lower break‐even point of about 2.6 years is achieved for the ARS system containing nanoparticles compared to the base ARS system. Overall, the ARS system containing 5% nanoparticles is the best solution from a thermodynamic and economic point of view.     

水电站大型竖井施工安全风险控制措施研究

竖井是大型水电工程中广泛采用的重要建筑物,但竖井的传统施工方法存在施工程序复杂、人工劳动强度大、施工效率不高等特点,同时还存在较大的安全风险。通过对杨房沟水电站大型竖井施工过程的研究,总结出精细化控制爆破、小溜渣井施工方式,同时也设计了一套用于运输的综合提升系统。采用上述创新方法不仅减少了繁琐的施工程序,降低了安全施工风险,而且大量采用机械化手段进一步提高了施工效率,确保了该工程竖井施工的安全风险可控。相关措施可为类似工程的竖井施工提供较好的借鉴作用。     

Steganography using a 3-player game

Image steganography aims to securely embed secret information into cover images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, were among the most secure and most often used methods for image steganography. With the arrival of deep learning and more specifically, Generative Adversarial Networks (GAN), new steganography techniques have appeared. Among them is the 3-player game approach, where three networks compete against each other. In this paper, we propose three different architectures based on the 3-player game. The first architecture is proposed as a rigorous alternative to two recent publications. The second takes into account stego noise power. Finally, our third architecture enriches the second one with a better interaction between embedding and extracting networks. Our method achieves better results compared to existing works Hayes and Danezis (2017), Zhu et al. (2018), and paves the way for future research on this topic.     

Hollow mesoporous silica nanoparticles as nanocarriers employed in cancer therapy: A review

Hollow mesoporous silica nanoparticles (HMSNs) have become an attractive drug carrier because of their unique characteristics including stable physicochemical properties, large specific surface area and facile functionalization, especially made into intelligent drug delivery systems (DDSs) for cancer therapy. HMSNs are employed to transport traditional anti-tumor drugs, which can solve the problems of drugs with instability, poor solubility and lack of recognition, etc., while significantly improving the anti-tumor effect. And an unexpected good result will be obtained by combining functional molecules and metal species with HMSNs for cancer diagnosis and treatment. Actually, HMSNs-based DDSs have developed relatively mature in recent years. This review briefly describes how to successfully prepare an ordinary HMSNs-based DDS, as well as its degradation, different stimuli-responses, targets and combination therapy. These versatile intelligent nanoparticles show great potential in clinical aspects.     

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSP_max) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSP_max values as compared with PV–battery and WT–battery systems.     

Reduced basis method for managed pressure drilling based on a model with local nonlinearities

To circumvent restrictions of conventional drilling methods, such as slow control actions and inability to drill depleted reservoirs, a drilling method called managed pressure drilling (MPD) has been developed. In MPD, single-phase flow processes can be modeled as a feedback interconnection of a high-order linear system and a low-order nonlinear system. These nonlinearities appear locally both inside and at the boundaries of the computational domain. To obtain a fast simulation platform for real-time purposes (eg, online model-based controller implementation), model order reduction is required for MPD. However, the local nonlinearities render applying model order reduction techniques challenging. In this study, a new approach is proposed to deal with such nonlinearities within the reduced basis (RB) context and it is successfully tested on a model for MPD. Contrary to the classical RB technique, the proposed approach not only does not generate nonphysical spikes at the locations of these local nonlinearities but also yields high speedup factors. The obtained reduced-order model can be used for efficient online simulation and controller design for drilling systems with MPD.     

宁夏红寺堡盆地隐伏古隆起成因机制及其对区域沙漠化的影响

目前对于青藏高原东北缘活动构造研究多集中于活动断裂带的活动性,而对夹持于其间的沉积盆地构造格架及活动性研究甚少。以夹持于烟筒山断裂与牛首山—罗山断裂之间的宁夏红寺堡盆地为研究对象,采用重力资料重处理解译、音频大地电磁测深(AMT)和地震勘探相结合,揭示红寺堡盆地隐伏构造特征,并进一步采用盆山一体化思路分析隐伏构造的成因机制及其对区域沙漠化的控制作用。结果表明:青藏高原在中新世末发生强烈的NE向推挤、扩展和隆升,红寺堡盆地由坳陷盆地转变为挠曲盆地; 在青藏高原NE向扩展的影响下,烟筒山断裂发生强烈的逆冲作用,古生代—中生代基底逆冲于古近系—新近系之上,受构造变形影响的最新地层为中新统彰恩堡组; 红寺堡盆地内的隐伏古隆起呈NW—SE向,与烟筒山构造带具有相同的构造动力学背景,受青藏高原NE向扩展影响形成于中新世末,并且至今仍具有活动性; 隐伏古隆起周缘断裂的活动破坏了地表稳定性、蓄水能力和地表植被,导致区域沙漠化呈现有规律的NW—SE向带状展布。该研究成果对于宁夏红寺堡盆地区域稳定性评价及沙漠化的综合治理具有现实指导意义。     

起重机械防碰装置试验系统的设计

防碰装置是起重机械重要的安全防护装置,其性能质量直接关系到人员和设备安全。常规的防碰装置性能试验方法难以保证试验数据的精度和可靠性。针对防碰装置的工作特点和试验要求,从试验原理、系统结构、运行逻辑等方面入手,设计一套能模拟防碰装置实际运行工况,自动进行性能试验和数据采集处理,精度高、重复性好的试验系统,可以有效提升防碰装置检测能力。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

预压力对压电驱动履带移动系统的性能影响

为了提高夹心式压电驱动移动系统的机械输出性能，提出了一种U型预压力调节机构，并开展了预压力对夹心式压电驱动履带移动系统输出性能影响关系的实验研究。首先，针对U型预压力调节机构的安装对夹心式压电振子的振动特性影响关系开展了有限元仿真分析，发现U型预压力调节机构始终处在夹心式压电振子的两相工作模态振动节点位置；其次，开展了夹心式压电驱动履带移动系统的原理样机的预压力调节测试实验，确定了系统的最大输出牵引力及其所对应的最佳预压力；最后，在最佳预压力工作状态下，开展了原理样机的牵引力特性、越障性能以及模拟月壤环境下的运动特性实验。研究结果表明，在最佳预压力工作状态下，夹心式压电驱动履带移动系统的机械输出性能最佳，为其进一步在月面巡视器上的应用提供了技术支持和试验基础。