Numerical study on laminar burning velocity of ammonia flame with methanol addition

The combustion characteristics of ammonia/methanol mixtures were investigated numerically in this study. Methanol has a dramatic promotive effect on the laminar burning velocity (LBV) of ammonia. Three mechanisms from literature and another four self-developed mechanisms constructed in this study were evaluated using the measured laminar burning velocities of ammonia/methanol mixtures from Wang et al. (Combust.Flame. 2021). Generally, none of the selected mechanisms can precisely predict the measured laminar burning velocities at all conditions. Aiming to develop a simplified and reliable mechanism for ammonia/methanol mixtures, the constructed mechanism utilized NUI Galway mechanism (Combust.Flame. 2016) as methanol sub-mechanism and the Otomo mechanism (Int. J. Hydrogen. Energy. 2018) as ammonia sub-mechanism was optimized and reduced. The reduced mechanism entitled ‘DNO-NH3’, can accurately reproduce the measured laminar burning velocities of ammonia/methanol mixtures under all conditions. A reaction path analysis of the ammonia/methanol mixtures based on the DNO-NH3 mechanism shows that methanol is not directly involved in ammonia oxidation, instead, the produced methyl radicals from methanol oxidization contribute to the dehydrogenation of ammonia. Besides, NO_x emission analysis demonstrates that 60% methanol addition results in the highest NO_x emissions. The most important reactions dominating the NO_x consumption and production are identified in this study.     

Sulfur doped biocarbon obtained from Sargassum spp. for the oxygen reduction reaction

Sulfured doped carbon electrocatalysts is synthesized from the waste biomass Sargassum spp. Two doping procedures are examined to determine which is better for Oxygen Reduction Reaction (ORR); one by doping biocarbon obtained from the pyrolysis of the biomass and the second through a process of in situ doping in autoclave. The electrocatalyst are obtained from pyrolysis of the sample at 700 °C, which is finally characterized as a metal free electrocatalyst for the ORR. The electrocatalyst are characterized by BET surface area analysis, Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS) and the electrochemical characterization is determined in 0.1 M KOH. The sample SSKPT-1 exhibits a promising electrocatalytic activity with an onset potential of 0.896 V vs RHE and a current density of 5 mA cm^?2 (at 0.2 V vs. RHE) which could be partly attributed to its high BET surface area of 2755 m² g^?1.     

工业园区混合能源优化配置方法应用与实践

为了充分挖掘多能源之间的互补潜力,使工业园区混合能源系统的配置方式由传统粗放式转变为精确优化配置并降低系统配置方案的保守性,基于粒子群联合CPLEX求解算法对工业园区的系统配置和运行调度方案进行分析优化,并以某生物园区为例进行了实例分析,得到了目标函数下的最优设计方案和运行策略。仿真分析结果表明,优化配置方法的应用能够协调冷热电多种能源的混合利用,提升系统的综合经济性。同时,为工业园区能源供应项目的规划、设计提供参考性支撑,丰富设计手段。     

Physicochemical and technological properties of beef burger as influenced by the addition of pea fibre

This study aimed to evaluate the physicochemical characteristics and sensory attributes of beef burgers with the addition of pea fibre as a partial substitute of meat or fat. Three formulations were prepared: control (CON) – similar to the commercial formulation; fibre/less meat (FLM)—5% meat reduction and addition of 1% pea fibre; fibre/less fat (FLF)—7% fat reduction and addition of 1% pea fibre. Non-significant differences were obtained for pH, colour parameters (L* and b*), texture profile, cooking loss and size reduction among formulations. Moreover, sensory analysis with consumers of beef burgers did not indicate differences among the formulations for all the analysed attributes. Therefore, pea fibre is a promising partial replacer for meat and fat in beef burgers due to the preservation of technological parameters and sensory acceptance.     

基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.     

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition-based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.