裂解态正癸烷点火延迟时间的理论研究（英）

在先进飞行器发动机中,吸热碳氢燃料在进入燃烧室之前会发生热裂解反应,生成未反应燃料和小分子裂解产物的混合物(称为裂解态燃料)。本工作研究了在1300~1800 K、0.1~3.0 MPa和当量比为1.0的条件下,不同的裂解转化率、裂解压力、点火压力和自由基对正癸烷裂解着火特性的影响。通过采用一种精确的组合机理,从理论上计算了流动反应器中3.0和5.0 MPa下正癸烷裂解组分,与文献中的实验结果吻合较好。结果表明,正癸烷在3 MPa和5 MPa下裂解的出口转化率分别为46.2%和58.8%,裂解产物分布一致,但乙烯的含量随着压力的升高明显的降低,而烷烃含量随着压力的增大而增加。尽管自由基总体含量很低,但在3 MPa条件下裂解产物中的自由基浓度依然高于5 MPa条件下。对于点火延迟时间的计算结果则表明,裂解态正癸烷的点火延迟时间随着转化率的增大而延长,且在5 MPa下随着转化率的变化更明显。相同转化率下,5 MPa下的裂解态正癸烷的点火延迟时间比3 MPa下更短。此外,与无自由基的裂解正癸烷相比,裂解正癸烷中自由基的存在可以加速着火过程,转化率小于40%时,着火延迟时间缩短15%以上。     

Quantile regression on the nonlinear relationship between land use and trip time

This study conceptually confirms and empirically tests the potential that the significance and magnitude of the compact land use–trip time relationship differ by the degree of compactness and trip time. Based on a quantile regression of about 25,000 commuters in Seoul, Korea, the empirical analysis suggests a significant level for each neighbourhood land use variable and a magnitude change within the level. Weekend trip and school densities are significant for shorter trip time commuters, population density is significant for longer trip time commuters, and pedestrian density is significant for all commuters. Wherever significant, the land use variables exert stronger effects as their values increase.     

Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay

In this paper, the parameter estimation issue of Wiener system with random time delay and missing output data is studied. The linear part of Wiener system is described by Finite Impulse Response (FIR) model. The mathematical formula of the Expectation Maximum algorithm to identify Wiener-FIR system that contains the random time delay and the nonlinear output data in missing completely at random mechanism is derived, which is never considered before. To obtain the unmeasurable intermediate variable in Wiener-FIR system, the idea of auxiliary model is adopted. The time delay and system parameters can be estimated simultaneously by this method. Numerical example and the identification of water tank system example are carried out, the effectiveness of the algorithm is proved.     

Adaptive backstepping repetitive learning control design for nonlinear discrete‐time systems with periodic uncertainties

This paper addresses a tracking problem for uncertain nonlinear discrete‐time systems in which the uncertainties, including parametric uncertainty and external disturbance, are periodic with known periodicity. Repetitive learning control (RLC) is an effective tool to deal with periodic unknown components. By using the backstepping procedures, an adaptive RLC law with periodic parameter estimation is designed. The overparameterization problem is overcome by postponing the parameter estimation to the last backstepping step, which could not be easily solved in robust adaptive control. It is shown that the proposed adaptive RLC law without overparameterization can guarantee the perfect tracking and boundedness of the states of the whole closed‐loop systems in presence of periodic uncertainties. In addition, the effectiveness of the developed controller is demonstrated by an implementation example on a single‐link flexible‐joint robot. Copyright © 2014 John Wiley & Sons, Ltd.     

一种胶体电解液胶凝时间的测定方法

本文建立了浊度法测定胶体电解液胶凝时间的方法。该方法能简便、直观、准确地测定胶体电解液的胶凝时间,为胶体电池的电解液研制提供实验方法支持。     

The effect of ignition delay time on the explosion behavior in non-uniform hydrogen-air mixtures

The purpose of this study is to examine the explosion characteristics of non-uniform hydrogen-air mixtures with turbulent mixing. In the experiment, hydrogen is first filled into a 20 L spherical chamber to a desired initial pressure, then air is introduced into the same chamber through a fast response solenoid valve, by adjusting the ignition delay time (t_d), i.e., the time period between the end of air injection and the action of ignition, the turbulent mixing strengthen (or called uniformity of hydrogen-air mixture) is then changed. The experimental results show that the explosions are overall enhanced as t_d decreases, which indicates that turbulence plays a leading role in enhancing the explosion behaviors. In addition, it is found that the effect of turbulence on p_max is more prominent in end-wall ignition than that in center ignition. This is because the heat loss per unit time is higher in end-wall ignition due to the flame front continuously contacts with inner wall of the chamber throughout the explosion process, although the explosion duration time t_e for both ignition cases is reduced when turbulence is introduced, heat loss reduction for end-wall ignition is generally larger than that in center ignition. Lately, a systematical analysis of the turbulent effect associated with various equivalence ratios on the explosion characteristics is conducted in end-wall ignition. Those experimental results illustrate that the turbulence-enhancing influence is more noticeable when hydrogen-air mixtures move toward the lower explosion limit. However, no significant influence of turbulence on explosion process can be found as combustible mixtures tend to the fuel-rich side. This is mainly because that when hydrogen-air mixtures tend to fuel-rich side, τ_e reduction caused by the presence of turbulence is relatively weak as compared with that under quiescent condition, resulting in heat loss during explosion process changes slightly, hence there is no significant impact on explosion parameters.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Validation of Models Predicting Nucleation Rates from Induction Times and Metastable Zone Widths

Three approaches for estimation of nucleation rates from induction time and metastable zone width (MSZW) were validated based on directly measured nucleation rates for paracetamol in ethanol. To quantitatively predict nucleation kinetics using Kubota's methods it is necessary to know the minimum detectable number concentration of nuclei. This was found by determination of light transmission of a series of diluted suspensions of newly nucleated crystals where the size had already been assessed by optical reflection measurement (ORM). The measured nucleation rates strongly depended on both temperature and supersaturation. The Nyvlt method predicted nucleation rates in this system reasonably well; however, it gave slightly low estimates for all temperatures. The methods of Kubota provided nucleation rates that were low by an order of magnitude.     

Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution

The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T₁) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T₁ value was better than the transverse relaxation time constant (T₂) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry     

Effect of pre-treatment and hydraulic retention time on biohydrogen production from organic wastes

This study investigated the effect of pre-treatment and hydraulic retention time (HRT) on biohydrogen production from organic wastes. Various pre-treatments including thermal, base, acid, ultrasonication, and hydrogen peroxide were applied alone or in combination to enhance biohydrogen production from potato and bean wastewater in batch tests. All the pre-treated samples showed higher hydrogen production than the control tests. Hydrogen peroxide pre-treatment achieved the best results of 939.7 and 470 mL for potato and bean wastewater, respectively. Continuous biohydrogen production from sucrose, potato and bean wastewater was significantly influenced by reducing the HRT as 24, 18 and 12 h. Sucrose and potato showed similar behavior, where the hydrogen production rate (HPR) increased with decreasing the HRT. Optimum hydrogen yield results of 320 mL-H₂/g-VS (sucrose) and 150 mL-H₂/g-VS (potato) were achieved at HRT of 18 h. Bean wastewater showed optimum HPR of 0.65 L/L.d with hydrogen yield of 80 mL-H₂/g-VS at 24 h HRT.