Thermal management in PEMFCs: The respective effects of porous media in the gas flow channel

This study aims to evaluate the convective heat transfer enhancement of the proton exchange membrane fuel cells (PEMFC) numerically. As the higher heat transfer surfaces lead to higher heat transfer rates, a flat plate porous layer is utilized in the gas flow channel (GFC). This enhancement in heat transfer stems from the corresponding modification in the temperature and velocity profiles. The influencing parameters on these profiles are the thickness, permeability, and porosity of the GFC porous layer. After performing the simulations, the results indicate that convective heat transfer has a direct relationship with GFC porous layer's thickness and permeability. However, lower values of porosity lead to the higher Nusselt numbers. Previous investigations have also mentioned the positive impact of the microporous layer (MPL) on the water management of these fuel cells. Therefore, six different sizes of MPL and the gas diffusion layer (GDL) are utilized to evaluate their impacts on the thermal management. Results indicate that although these sizes have negligible effects on the heat transfer, Nu increases by enhancing the total size of MPL and GDL. The results also show that thicker MPLs lead to higher heat transfer rates. The evaluation of the friction factor also indicates the adverse effect of the GFC porous layer, although this undesirable effect is negligible. Finally, all the simulated values are utilized to train an artificial neural network (ANN) model with high precision. This ANN model can produce more data for sensitivity analysis and presenting respective 3D diagrams of the influencing parameters on heat transfer.     

An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control

In the present work, an augmented subcutaneous (SC) model of type 1 diabetic patients (T1DP) is proposed first by estimating the model parameters with the aid of nonlinear least square method using the physiological data. Next, a nonlinear adaptive controller is proposed to tackle two important issues of intra-patient variability (IPV) and uncertain meal disturbance (MD). The proposed patient model agrees quite well with the responses of one of the most popular existing nonlinear model used in the research of artificial pancreas. Further, the developed adaptive control is shown to be capable of providing desired glycemic control without feed-forward action for meal compensation or safety algorithms to avoid hypoglycemia. Due to the simple structure and capability of handling intra-patient variability of the adaptive controller, it can find immediate applicability in the development of the in-silico artificial pancreas.     

Robustness analysis and distributed control of a networked system with time-varying delays

This paper is concerned with the robustness analysis and distributed output feedback control of a networked system with uncertain time-varying communication delays. This system consists of a collection of linear time-invariant subsystems that are spatially interconnected via an arbitrary directed network. Using a dissipation inequality that incorporates dynamic hard IQCs (integral quadratic constraints) for the delay uncertainties, we derive some sufficient robustness conditions in the form of coupled linear matrix inequalities, in which the coupled parts reflect the interconnection structure of the system. We then provide a procedure to construct a distributed controller to ensure the robust stability of the closed-loop system and to achieve a prescribed $\ell_2$-gain performance. The effectiveness of the proposed approach is demonstrated by some numerical examples.     

基于多元Taylor级数和AFS的混合定位算法

针对传统Taylor级数定位算法存在精度严重依赖初始值,导致定位精确度不高的缺陷,结合人工鱼群算法和多元Taylor级数展开算法的优点,提出了一种基于人工鱼群算法初值选取与多元Taylor级数展开算法精确求解的混合定位方法。算法充分发挥了人工鱼群算法初值估计性能良好和多元Taylor级数展开算法求解精度高的优点。仿真结果表明:上述算法减少了鱼群数目和迭代次数的选取对定位精度的影响,混合定位算法的精度更高。     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders

A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

ANN–supported control strategy for a solid oxide fuel cell working on demand for a public utility building

The idea of control strategy of SOFC operating to meet demand of a public utility building was presented. The strategy was formulated with the support of Artificial Neural Network. The network was used to predict the demand for electricity. The calculations were carried out on the example of a building of the Institute of Heat Engineering Warsaw University of Technology. The control strategy is influenced by various factors depending on changes in market conditions and operating characteristics of the cell. We can define different objective functions eg: working for own needs, for maximum profit and maximum service life. The article presents a simulation of SOFC operation for demand profile of the IHE building from the selected time period.     

Assessing Industrial Robot agility through international competitions

Manufacturing and Industrial Robotics have reached a point where to be more useful to small and medium sized manufacturers, the systems must become more agile and must be able to adapt to changes in the environment. This paper describes the process for creating and the lessons learned over multiple years of the Agile Robotics for Industrial Automation Competition (ARIAC) being run by the National Institute of Standards and Technology.