Numerical Study on Hydrogen Flow Behavior in Two Compartments with Different Connecting Pipes

Hydrogen accumulation in the containment compartments under severe accidents would result in high concentration, which could lead to hydrogen deflagration or detonation. Therefore, getting detailed hydrogen flow and distribution is a key issue to arrange hydrogen removal equipment in the containment compartments. In this study, hydrogen flow behavior in local compartments has been investigated in two horizontal compartments. The analysis model is built by 3-dimensional CFD code in Cartesian coordinates based on the connection structure of the Advanced Pressurized Water Reactor (PWR) compartments. It consists of two cylindrical vessels, representing the Steam Generator compartment (SG) and Core Makeup Tank compartment (CMT). With standard k-ε turbulence model, the effects of the connecting pipe size and location on hydrogen concentration distribution are investigated. Results show that increasing the diameter of connection pipe (IP) which is located at 800 mm from 150 mm to 300 mm facilitates hydrogen flow between compartments. Decreasing the length of IP which is located at 800 mm from 1000 mm to 500 mm can also facilitate hydrogen flow between compartments. Lower IP is in favor of hydrogen mixing with air in non-source compartment. Higher IP is helpful for hydrogen flow to the non-source term compartment from source term compartment

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme

Nanochanneled Device and Related Methods

A capsule configured for in vivo refilling of a therapeutic agent. In certain embodiments, the capsule may contain methotrexate

Ni-doped TiO2 nanotubes for wide-range hydrogen sensing

Doping of titania nanotubes is one of the efficient way to obtain improved physical and chemical properties. Through electrochemical anodization and annealing treatment, Ni-doped TiO(2) nanotube arrays were fabricated and their hydrogen sensing performance was investigated. The nanotube sensor demonstrated a good sensitivity for wide-range detection of both dilute and high-concentration hydrogen atmospheres ranging from 50 ppm to 2% H(2). A temperature-dependent sensing from 25°C to 200°C was also found. Based on the experimental measurements and first-principles calculations, the electronic structure and hydrogen sensing properties of the Ni-doped TiO(2) with an anatase structure were also investigated. It reveals that Ni substitution of the Ti sites could induce significant inversion of the conductivity type and effective reduction of the bandgap of anatase oxide. The calculations also reveal that the resistance change for Ni-doped anatase TiO(2) with/without hydrogen absorption was closely related to the bandgap especially the Ni-induced impurity level

Combinatorial Multidomain Mesoporous Chips and a Method for Fractionation, Stabilization, and Storage of Biomolecules

A new fractionation device shows desirable features for exploratory screening and biomarker discovery. The constituent MSCs may be tailored for desired pore sizes and surface properties and for the sequestration and enrichment of extremely low abundant protein and peptides in desired ranges of the mass/charge spectrum. The MSCs are effective in yielding reproducible extracts from complex biological samples as small as 10 microliter in a time as short as 30 minutes. They are inexpensive to manufacture, and allow for scaled up production to attain the simultaneous processing of a large number of samples. The MSCs are multiplexed, label-free diagnostic tools with the potential of biological recognition moiety modification for enhanced specificity. The MSCs may store, protect and stabilize biological fluids, enabling the simplified and cost-effective collection and transportation of clinical samples. The MSC-based device may serve as a diagnostic tool to complement histopathology, imaging, and other conventional clinical techniques. The MSCs mediated identification of disease-specific protein signatures may help in the selection of personalized therapeutic combinations, in the real-time assessment of therapeutic efficacy and toxicity, and in the rational modulation of therapy based on the changes in the protein networks associated with the prognosis and the drug resistance of the disease

Efficient strategies for constrained black-box optimization by intrinsically linear approximation (CBOILA)

In this paper, a novel trust-region-based surrogate-assisted optimization method, called CBOILA (Constrained Black-box Optimization by Intrinsically Linear Approximation), has been proposed to reduce the number of black-box function evaluations and enhance the efficient performance for solving complex optimization problems. This developed optimization approach utilizes an assembly of intrinsically linear approximations to seek the optimum with incorporation of three strategies: (1) extended-box selection strategy (EBS), (2) global intelligence selection strategy (GIS) and (3) balanced trust-region strategy. EBS aims at reducing the number of function evaluations in current iteration by selecting points close to the given trust region boundary. Whilst, GIS is designed to improve the exploration performance by adaptively choosing points outside the trust region. The balanced trust-region strategy works with four indicators, which will be triggered by the quality of the approximation, the movement direction of the search, the location of the sub-optimum, and the condition of the termination, respectively. By modifying the move limit of each dimension accordingly, CBOILA is capable of attaining a balanced search between exploitation and exploration for the optimal solutions. To demonstrate the potentials of the proposed optimization method, four widely used benchmark problems have been examined and the results have also been compared with solutions by other metamodel-based algorithms in published works. Results show that the proposed method can efficiently and robustly solve constrained black-box optimization problems within an acceptable computational time

Predictive role of blood-based indicators in neuromyelitis optica spectrum disorders

IntroductionThis study aimed to assess the predictive role of blood markers in neuromyelitis optica spectrum disorders (NMOSD).MethodsData from patients with NMOSD, multiple sclerosis (MS), and healthy individuals were retrospectively collected in a 1:1:1 ratio. The expanded disability status scale (EDSS) score was used to assess the severity of the NMOSD upon admission. Receiver operating characteristic (ROC) curve analysis was used to distinguish NMOSD patients from healthy individuals, and active NMOSD from remitting NMOSD patients. Binary logistic regression analysis was used to evaluate risk factors that could be used to predict disease recurrence. Finally, Wilcoxon signed-rank test or matched-sample t-test was used to analyze the differences between the indicators in the remission and active phases in the same NMOSD patient.ResultsAmong the 54 NMOSD patients, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) (platelet × NLR) were significantly higher than those of MS patients and healthy individuals and positively correlated with the EDSS score of NMOSD patients at admission. PLR can be used to simultaneously distinguish between NMOSD patients in the active and remission phase. Eleven (20.4%) of the 54 patients had recurrence within 12 months. We found that monocyte-to-lymphocyte ratio (MLR) (AUC = 0.76, cut-off value = 0.34) could effectively predict NMOSD recurrence. Binary logistic regression analysis showed that a higher MLR at first admission was the only risk factor for recurrence (p = 0.027; OR = 1.173; 95% CI = 1.018–1.351). In patients in the relapsing phase, no significant changes in monocyte and lymphocyte count was observed from the first admission, whereas patients in remission had significantly higher levels than when they were first admitted.ConclusionHigh PLR is a characteristic marker of active NMOSD, while high MLR is a risk factor for disease recurrence. These inexpensive indicators should be widely used in the diagnosis, prognosis, and judgment of treatment efficacy in NMOSD

Non-invasive load monitoring of induction motor drives using magnetic flux sensors

Existing load monitoring methods for induction machines are generally effective, but suffer from sensitivity problems at low speeds and non-linearity problems at high supply frequencies. This study proposes a new noninvasive load monitoring method based on giant magnetoresistance flux sensors to trace stray flux leaking from induction motors. Finite element analysis is applied to analyse stray flux features of test machines. Contrary to the conventional methods of measuring stator and/or rotator rotor voltage and current, the proposed method measures the dynamic magnetic field at specific locations and provides time-spectrum features (e.g. spectrograms), response time load and stator/rotor characteristics. Three induction motors with different starting loading profiles are tested at two separate test benches and their results are analysed in the time-frequency domain. Their steady features and dynamic load response time through spectrograms under variable loads are extracted to correlate with load variations based on spectrogram information. In addition, the transient stray flux spectrogram and time information are more effective for load monitoring than steady state information from numerical and experimental studies. The proposed method is proven to be a low-cost and non-invasive method for induction machine load monitoring