An experimental analysis of subcooled leakage flow through slits from high pressure high temperature pipelines

The work presented here is an experimental investigation of the critical flashing flow of initially subcooled water through circumferential slits in pipes. The study provides first hand information about the prediction of leak flow rates in piping and pressure vessels retaining high temperature and high pressure. The dedicated experimental facility loop simulates the thermal hydraulic condition of Pressurized Heavy Water Reactors (PHWR). The critical flow characteristics found for varying leakage cross sections at different stagnation pressure and different degree of subcooling has been demonstrated in this paper. A marked decrease in mass flux has been found as subcooling decreases for a fixed stagnation pressure. More observation has revealed that the tighter slits or openings with very short duct as small as 0.8 cm flow length have different flow behavior than greater opening dimensions or with longer flow channels or that for nozzles. The critical flow has been seen to occur at higher pressure differentials along the flaws and prominent changes in the flow rate is reported to occur with varying dimensional parameters of the slit or cracks.     

Precision design aspects for friction actuation with error compensation

Some key aspects in the design of actuators for ultra precision systems will require particular attention as actuators have to comply with ultra precision positioning and related tight specifications. The actuation is an important sub-group within the overall system to be designed. Any design failure in this sub-group will degrade drastically the machine performance. An interface analysis with immediate surrounding sub-groups is important to secure better generation of motion. In this paper, a design procedure for an actuator dedicated to generate high precision motion is presented with a discussion of some important aspects encountered when designing a friction drive. The procedure encompasses design concepts, axis stiffness, control, system dynamics, and, error compensation using a dedicated control strategy compensating for errors. A case study is discussed.     

Biosynthesis of reusable and recyclable CuO@Magnetite@Hen Bone NCs and its antioxidant and antibacterial activities: a highly stable magnetically nanocatalyst for excellent reduction of organic dyes and adsorption of polycyclic aromatic hydrocarbons

For the first time, through a fast, eco‐friendly and economic method, the aqueous extract of the leaf of Euphorbia corollate was used to the green synthesis of the highly stable CuO@Magnetite@Hen Bone nanocomposites (NCs) as a potent antioxidant and antibacterial agent against Pseudomonas aureus, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae pathogenic bacteria. The biosynthesised NCs were identified using the scanning electron microscopy (SEM), energy dispersive X‐ray spectroscopy, elemental mapping, X‐ray diffraction (XRD), Fourier transforms infrared spectroscopy and UV–vis analytical techniques. Also, the radical scavenging activity using (2,2‐diphenyl‐1‐picrylhydrazyl) method was used to evaluate the antioxidant activity of the NCs. The stability of nanocatalyst was monitored using the XRD and SEM analyses after 30 days from its synthesis. Furthermore, its excellent catalytic activity, recycling stability, and high substrate applicability were demonstrated to the adsorption of the polycyclic aromatic hydrocarbons of the light crude oil from Shiwashok oil fields and destruction of methylene blue and methyl orange as harmful organic dyes at ambient temperature using UV–vis spectroscopy. Moreover, the green CuO@Magnetite@Hen Bone NCs were recovered and reused several times without considerable loss of its catalytic activity.Inspec keywords: nanobiotechnology, X‐ray diffraction, infrared spectra, catalysis, crude oil, Fourier transform spectra, ultraviolet spectra, scanning electron microscopy, dyes, catalysts, photochemistry, iron compounds, X‐ray chemical analysis, antibacterial activity, adsorption, visible spectra, microorganisms, organic compounds, reduction (chemical), nanomedicine, toxicology, recycling, chemical industryOther keywords: antioxidant activity, XRD, SEM analyses, recycling stability, polycyclic aromatic hydrocarbons, harmful organic dyes, UV–vis spectroscopy, green CuO@Magnetite@Hen Bone NCs, reusable CuO@Magnetite@Hen Bone NCs, recyclable CuO@Magnetite@Hen Bone NCs, antioxidant activities, antibacterial activities, highly stable magnetically nanocatalyst, eco‐friendly method, economic method, euphorbia corollate, green synthesis, CuO@Magnetite@Hen Bone nanocomposites, antibacterial agent, pseudomonas aureus, staphylococcus aureus, escherichia coli, klebsiella pneumoniae pathogenic bacteria, biosynthesised NCs, X‐ray spectroscopy, X‐ray diffraction, radical scavenging activity, antioxidant agent, 2,2‐diphenyl‐1‐picrylhydrazyl, catalytic activity, organic dye reduction, light crude oil, CuO     

Self-tuning dynamic models of HVAC system components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO₂ concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.     

Mechanical Properties of Dry-Pressed Ceramic Green Products: The Effect of the Binder

The strength of alumina green samples prepared by uniaxial pressing of powders spray-dried with two binders such as poly(ethylene glycol) or poly(vinyl alcohol) is discussed in terms of (i) the adhesive properties of the polymer-rich external layer of spray-dried granules and (ii) the mechanical properties of this layer. A transition from intra- to intergranular fracture is observed when the glass transition temperature of the binder increases. The influence of the binder on the defect size distribution in the green products is investigated using the classical Weibull statistics.     

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.     

Data-driven approach to solve vertical drain under time-dependent loading

Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.     

Interleukin-1 Receptor Antagonist Reduces Neonatal Lipopolysaccharide-Induced Long-Lasting Neurobehavioral Deficits and Dopaminergic Neuronal Injury in Adult Rats

Our previous study showed that a single lipopolysaccharide (LPS) treatment to neonatal rats could induce a long-lasting neuroinflammatory response and dopaminergic system injury late in life. This is evidenced by a sustained activation of microglia and elevated interleukin-1β (IL-1β) levels, as well as reduced tyrosine hydroxylase (TH) expression in the substantia nigra (SN) of P70 rat brain. The object of the current study was to test whether co-administration of IL-1 receptor antagonist (IL-1ra) protects against LPS-induced neurological dysfunction later in life. LPS (1 mg/kg) with or without IL-1ra (0.1 mg/kg), or sterile saline was injected intracerebrally into postnatal day 5 (P5) Sprague-Dawley male rat pups. Motor behavioral tests were carried out from P7 to P70 with subsequent examination of brain injury. Our results showed that neonatal administration of IL-1ra significantly attenuated LPS-induced motor behavioral deficits, loss of TH immunoreactive neurons, as well as microglia activation in the SN of P70 rats. These data suggest that IL-1β may play a pivotal role in mediating a chronic neuroinflammation status by a single LPS exposure in early postnatal life, and blockading IL-1β might be a novel approach to protect the dopaminergic system against perinatal infection/inflammation exposure.     

Generalized Method for Gun Propellant Formulation Design

Determination of propellant formulation by ballistic requirement is an important area of research in recent times. In this study, a theoretical method for the design of gun propellant formulation using primary data of ingredients and necessary thermochemical properties of the resultant propellant was established. The employed method is based on a mathematical model of thermochemical properties of the propellant by optimizing the heat of explosion of the propellant using the fmincon tool in MATLAB. A graphical user interface (GUI) based code was generated and developed for the formulation design of solid gun propellants. The designed code was verified by available data in the literature. Such code will be useful to the researchers working in the area of high energy materials for the design of unknown propellant compositions. Further, it can be extended to redesign the existing propellant formulation in order to enhance the ballistic performance.     

Design of survivable WDM networks using pre-configured protection structures with unrestricted shapes

We propose a novel protection approach for the design of link-protection schemes in survivable Wavelength Division Multiplexing mesh networks by merging the well-known p-cycle- and p-tree-protection structures. So doing, we aim at gathering the advantages of p-cycles in terms of protection capabilities, and of p-trees in terms of protection flexibilities (local re-routing, scalability) in a single protection scheme. As opposed to existing protection schemes based on protection structures with a pre-defined shape, the building blocks of the new scheme are protection structures with unrestricted shapes. Thus, they allow more flexibility in provisioning spare capacity, and provide higher capacity efficiency when compared to the shaped-protection schemes that have been proposed so far. In order to cope with the size of the solution space which includes all the possible protection structures, we propose an efficient and scalable optimization technique in large-scale systems named column generation (CG). In our CG-based optimization approach, the shape of a candidate protection structure is dynamically decided during the optimization process according to a link spare capacity budget. Experimental results on different network instances show that the protection plan resulting from the merging of p-cycle and p-tree structures is, on average, ~15% less capacity redundant and ~15% more reliable than the pure p-cycle one. It also requires, on average, ~30% less protection structures. In addition, those structures provide backup paths ~30% smaller than those of the p-cycle-based scheme.