Impact of the anode operating conditions on the liquid water distribution in the cathode gas diffusion layer

Previous experimental results indicate that the humidification conditions at the anode have an impact on the liquid water distribution in the cathode gas diffusion layer. Numerical simulations are developed to reproduce and analyze this effect. Results consistent with the experimental results are first obtained by playing with the partition coefficients of an advanced pore network model computing the liquid water formation and transfer in the cathode gas diffusion layer (GDL) for a large range of operating conditions. Then, a model for the full anode – cathode assembly is developed by combining the pore network model of the cathode GDL and a 1D model describing the heat and water transfer in the various components of the anode-cathode assembly. This enables one to generalize the dry – wet regime diagram introduced in a previous work by incorporating the effect of the humidity condition at the anode.     

Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data

This research is concerned with the problem of 4 degrees of freedom (DOF) ship manoeuvring identification modelling with the full-scale trial data. To avoid the multi-innovation matrix inversion in the conventional multi-innovation least squares (MILS) algorithm, a new transformed multi-innovation least squares (TMILS) algorithm is first developed by virtue of the coupling identification concept. And much effort is made to guarantee the uniformly ultimate convergence. Furthermore, the auto-constructed TMILS scheme is derived for the ship manoeuvring motion identification by combination with a statistic index. Comparing with the existing results, the proposed scheme has the significant computational advantage and is able to estimate the model structure. The illustrative examples demonstrate the effectiveness of the proposed algorithm, especially including the identification application with full-scale trial data.     

Impact sensitivities of energetic materials: Exploring the limitations of a model based only on structural formulas

Using a comprehensive set of drop weight impact test data (h₅₀) newly compiled from literature for 308 materials, a recent approach to predict impact sensitivities of nitro compounds is generalized to most explosive substances of interest. Compared to previous ones, this procedure is more thoroughly validated and exhibits a good predictive value. Furthermore, it yields new insight into the physical mechanisms involved, explaining for instance the unexpected desensitization of some oxygen-deficient triazoles upon nitration.     

Modelling of black carbon statistical distribution and return periods of extreme concentrations

Eight datasets of 1-h black carbon (BC) concentrations measured in Warsaw agglomeration (Poland), at urban background and sub-urban sites, and in Racibórz, a small town in Upper Silesia district (regional background site) were analyzed to evaluate BC levels, daily profiles and statistical distributions of concentrations in Central-Eastern European region. The observed mean levels ranged from 1483 ng m⁻³ in suburban site during summer to 3358 ng m⁻³ in regional background site in winter. Observed diurnal patterns were bimodal in the locations dominated by traffic emissions, but unimodal, with elevated evening peak in individually heated residential area. Three theoretical frequency distributions were applied to fit analyzed datasets separately. The lognormal distribution was the most appropriate to represent the middle-range values, while the high concentrations were satisfactorily predicted by the type I two-parameter exponential distribution which was used to estimate the return periods of extreme concentrations for winter months.     

Assessment of spray quality from an external mix nozzle and its impact on SQL grinding performance

Spray quality is the critical factor which decides the efficacy of Small Quantity Lubrication (SQL) technology in a high specific energy involved machining process like grinding. Yet, the understanding about spray quality, the actual process mechanics and its effect on machining performance is inadequate. The present work is an attempt to establish a correlation between the spray input variables, quality of the spray and machining performance of SQL grinding through modelling and experiments. Using computational fluid dynamic techniques, the variation of droplet size, droplet velocity, number of droplets and heat transfer coefficient have been analysed at different input parameters and the computed trends have been verified and validated. CFD modelling of spray indicates that it is possible to produce aerosol medium with high heat dissipation ability at moderately high air pressure and low flow rate. It also shows that any increase in atomising air pressure favourably leads to notable increase in wetting area and also results in substantial enhancement in heat dissipation ability. Reduction of residual stress is thus remarkably good. On the other hand, grinding fluid flow rate, if increased, offers significantly better lubricity and reduces the grinding force which also reduces tensile residual stress. Short spell grinding test results are found to be in good agreement with CFD results.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Assessment of the effectiveness of steel fibre reinforcement for the punching resistance of flat slabs by experimental research and design approach

The present paper deals with the experimental assessment of the effectiveness of steel fibre reinforcement in terms of punching resistance of centrically loaded flat slabs, and to the development of an analytical model capable of predicting the punching behaviour of this type of structures. For this purpose, eight slabs of 2550 × 2550 × 150 mm³ dimensions were tested up to failure, by investigating the influence of the content of steel fibres (0, 60, 75 and 90 kg/m³) and concrete strength class (50 and 70 MPa). Two reference slabs without fibre reinforcement, one for each concrete strength class, and one slab for each fibre content and each strength class compose the experimental program. All slabs were flexurally reinforced with a grid of ribbed steel bars in a percentage to assure punching failure mode for the reference slabs. Hooked ends steel fibres provided the unique shear reinforcement. The results have revealed that steel fibres are very effective in converting brittle punching failure into ductile flexural failure, by increasing both the ultimate load and deflection, as long as adequate fibre reinforcement is assured. An analytical model was developed based on the most recent concepts proposed by the fib Mode Code 2010 for predicting the punching resistance of flat slabs and for the characterization of the behaviour of fibre reinforced concrete. The most refined version of this model was capable of predicting the punching resistance of the tested slabs with excellent accuracy and coefficient of variation of about 5%.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.