CFD simulation of the IAEA 10 MW generic MTR reactor under loss of flow transient

Three-dimensional simulation of the IAEA 10 MW generic reactor under loss of flow transient is introduced using the CFD code, Fluent. The IAEA reactor calculation is a safety-related benchmark problem for an idealized material testing reactor (MTR) pool type specified in order to compare calculational methods used in various research centers. The flow transients considered include fast loss of flow accidents (FLOFA) and slow loss of flow accidents (SLOFA) modeled with exponential flow decay and time constants of 1 and 25 s, respectively. The transients were initiated from a power of 12 MW with a flow trip point at 85% nominal flow and a 200 ms time delay. The simulation shows comparable results as those published by other research groups. However, interesting 3D patterns are shown that are usually lost based on the one-dimensional simulations that other research groups have introduced. In addition, information about the maximum clad surface temperature, the maximum fuel element temperature as well as the location of hot spots in fuel channel is also reported.     

Activated carbon incorporated with first-row transition metals as catalysts in hydrogen production from propane

Transition metals (V, Mn, Fe, Co and Ni) were mixed with bituminous coal and carbonized at 600 °C to obtain activated carbons (AC) with different catalytic and physical properties. A variety of analytical techniques - XRD, SEM, FTIR and BET surface area - were used to characterize the samples. The presence of different proportions of crystalline structures and surface functional groups were observed depending on the metal added in the carbon matrix. The samples surface areas changed from 140.9 to 296.0 m²/g for V- and Ni-AC, with corresponding increase in pore volumes and diameters. There are graphitic and metal-carbon phases as evidenced by XRD. The aromatic stacking appears to decrease from V- to Co-AC. V-AC exhibited fewer occurrences of turbostratic carbon interlayers. In the FTIR spectra, region assigned to aromatic CC stretching and CH-bending have been observed. Their relative proportions diminish based on the nature of the transition metal used. The samples were tested as catalysts at 450-550 °C using a feed mixture of propane and argon - in the presence or absence of CO₂ in the feed stream. Propylene selectivities per unit area increase along the groups of the metals. Ni-AC exhibited the best performance per gram in terms of propane conversions (1.5-17.7%) and selectivities to propylene (39.9-49.0%) in the temperature range. Partial substitution of argon with CO₂ in the feed exhibited general increase in propane conversion with no significant change in propylene selectivities.     

Novel dispersed magnetite core–shell nanogel polymers as corrosion inhibitors for carbon steel in acidic medium

Novel core–shell preparing poly(2-acrylamido-2-methylpropane sulfonic acid) (PAMPS) and copolymers with acrylic acid (AA) or acrylamide (AM) magnetic nanogels with controllable particle size produced via free aqueous polymerization at room temperature have been developed for the first time. The crosslinking polymerization was carried out in the presence of N,N′-methylenebisacrylamide (MBA) as a crosslinker, N,N,N′,N′-tetramethylethylenediamine (TEMED) and potassium peroxydisulfate (KPS) as redox initiator system. The structure and morphology of the magnetic nanogels were characterized by Fourier transform infrared spectroscopy (FTIR), transmission and scanning electron microscopy (TEM and SEM). The effectiveness of the synthesized compounds as corrosion inhibitors for carbon steel in 1 M HCl was investigated by various electrochemical techniques such as potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The results showed enhancement in inhibition efficiencies with increasing the inhibitor concentrations and temperatures. The results showed the nanogel particles act as mixed inhibitors. Adsorption of nanogel particles was found to fit the Langmuir isotherm and was chemisorption.     

Hydrogen refueling station compression and storage optimization with tube-trailer deliveries

Hydrogen refueling stations require high capital investment, with compression and storage comprising more than half of the installed cost of refueling equipment. Refueling station configurations and operation strategies can reduce capital investment while improving equipment utilization. Argonne National Laboratory developed a refueling model to evaluate the impact of various refueling compression and storage configurations and tube trailer operating strategies on the cost of hydrogen refueling. The modeling results revealed that a number of strategies can be employed to reduce fueling costs. Proper sizing of the high-pressure buffer storage reduces the compression requirement considerably, thus reducing refueling costs. Employing a tube trailer to initially fill the vehicle's tank also reduces the compression and storage requirements, further reducing refueling costs. Reducing the cut-off pressure of the tube trailer for initial vehicle fills can also significantly reduce the refueling costs. Finally, increasing the trailer's return pressure can cut refueling costs, especially for delivery distances less than 100 km, and in early markets, when refueling stations will be grossly underutilized.     

Preparation, Characterization, and Microwave Dielectric Properties of Sr2La3Nb1−x Ta x Ti4O17 (0 ≤ x ≤ 1) Ceramics

Sr₂La₃Nb_1?x Ta _x Ti₄O₁₇ (0 ≤ x ≤ 1) ceramics were processed via a solid-state mixed oxide route. Sr₂La₃Nb_1?x Ta _x Ti₄O₁₇ (0 ≤ x ≤ 1) solid solutions were single phase in the whole range of x values within the x-ray diffraction (XRD) detection limit. The microstructure comprised elongated and needle-shaped grains. The ceramics exhibit relative permittivity (ε _r) of 73 to 68.6, product of unloaded quality factor and resonant frequency (Q _u f ₀) of 7100 GHz to 9500 GHz, and temperature coefficient of resonant frequency (τ _f) of 78.6 ppm/°C to 56.6 ppm/°C.     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Energy-efficient operation of indirect adiabatic data center cooling systems via Newton-like phasor extremum seeking control

This paper considers the problem of optimizing the operation of Indirect Adiabatic Cooling (IAC) systems with application to data centers. Optimal operation is achieved when the required cooling demand is satisfied at the minimum energy cost. For this purpose, we design a supervisory control system, where the higher layer determines the optimal set-points for the local controllers by employing an Extremum Seeking Control (ESC) scheme. In particular, we consider a Newton-like phasor ESC, which augments the derivative estimator underlying the phasor approach to capture also the Hessian of the plant index and then it uses these estimates to steer the system along a Newton-like direction. The effectiveness of the considered approach is tested in simulation by exploiting a Matlab-based simulation environment including an IAC system and a computer room.     

Synthesis of bisphenol a novolac epoxy resins for coating applications

Bisphenol A novolacs were synthesized in both melting and solution processes using p‐formaldehyde and formalin solution in presence of oxalic acid catalyst, respectively. Hydrogen nuclear magnetic resonance, ¹H NMR, investigations show a high methylene bridge contents in the novolacs synthesized in a melting process. These novolacs were analyzed by gel permation chromatography (GPC) and fourier transform infrared spectroscopy (FTIR). The bisphenol A novolac was cured with 1‐(2‐amino ethyl) piprazine (AEP) as a curing agent for epoxy resins. The cured resins were evaluated as organic coating for steel. The mechanical properties of the cured epoxy resins were evaluated. The chemical resistances of the cured resins were evaluated through salt spray resistance, hot water, solvents, acid and alkali resistance measurements. The data indicate that the cured epoxy resins have excellent chemical resistances as organic coatings among other cured resins. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008