Computational Analysis of Active and Passive Flow Control for Backward Facing Step

The internal steady and unsteady flows with a frequency and amplitude are examined through a backward facing step (expansion ratio 2), for low Reynolds numbers (Re=400, Re=800), using the immersed boundary method. A lower part of the backward facing step is oscillating with the same frequency as the unsteady flow. The effect of the frequency, the amplitude, and the length of this oscillation is investigated. By suitable active control regulation, the recirculation lengths are reduced, and, for a percentage of the time period, no upper wall, negative velocity, region occurs. Moreover, substituting the prescriptively moving surface by a pressure responsive homogeneous membrane, the fluid–structure interaction is examined. We show that, by selecting proper values for the membrane parameters, such as membrane tension and applied external pressure, the upper wall flow separation bubble vanishes, while the lower one diminishes significantly in both the steady and the unsteady cases. Furthermore, for the time varying case, the length fluctuation of the lower wall reversed flow region is fairly contracted. The findings of the study have applications at the control of confined and external flows where separation occurs.

Improved Modular Division Implementation with the Akushsky Core Function

The residue number system (RNS) is widely used in different areas due to the efficiency of modular addition and multiplication operations. However, non-modular operations, such as sign and division operations, are computationally complex. A fractional representation based on the Chinese remainder theorem is widely used. In some cases, this method gives an incorrect result associated with round-off calculation errors. In this paper, we optimize the division operation in RNS using the Akushsky core function without critical cores. We show that the proposed method reduces the size of the operands by half and does not require additional restrictions on the divisor as in the division algorithm in RNS based on the approximate method.

A Physically Consistent Model for Forced Torsional Vibrations of Automotive Driveshafts

The aim of this research was to design a physically consistent model for the forced torsional vibrations of automotive driveshafts that considered aspects of the following phenomena: excitation due to the transmission of the combustion engine through the gearbox, excitation due to the road geometry, the quasi-isometry of the automotive driveshaft, the effect of nonuniformity of the inertial moment with respect to the longitudinal axis of the tulip–tripod joint and of the bowl–balls–inner race joint, the torsional rigidity, and the torsional damping of each joint. To resolve the equations of motion describing the forced torsional nonlinear parametric vibrations of automotive driveshafts, a variational approach that involves Hamilton’s principle was used, which considers the isometric nonuniformity, where it is known that the joints of automotive driveshafts are quasi-isometric in terms of the twist angle, even if, in general, they are considered CVJs (constant velocity joints). This effect realizes the link between the terms for the torsional vibrations between the elements of the driveshaft: tripode–tulip, midshaft, and bowl–balls–inner race joint elements. The induced torsional loads (as gearbox torsional moments that enter the driveshaft through the tulip axis) can be of harmonic type, while the reactive torsional loads (as reactive torsional moments that enter the driveshaft through the bowl axis) are impulsive. These effects induce the resulting nonlinear dynamic behavior. Also considered was the effect of nonuniformity on the axial moment of inertia of the tripod–tulip element as well as on the axial moment of inertia of the bowl–balls–inner race joint element, that vary with the twist angle of each element. This effect induces parametric dynamic behavior. Moreover, the torsional rigidity was taken into consideration, as was the torsional damping for each joint of the driveshaft: tripod–joint and bowl–balls–inner race joint. This approach was used to obtain a system of equations of nonlinear partial derivatives that describes the torsional vibrations of the driveshaft as nonlinear parametric dynamic behavior. This model was used to compute variation in the natural frequencies of torsion in the global tulip (a given imposed geometry) using the angle between the tulip–midshaft for an automotive driveshaft designed for heavy-duty SUVs as well as the characteristic amplitude frequency in the region of principal parametric resonance together the method of harmonic balance for the steady-state forced torsional nonlinear vibration of the driveshaft. This model of dynamic behavior for the driveshaft can be used during the early stages of design as well in predicting the durability of automotive driveshafts. In addition, it is important that this model be added in the design algorithm for predicting the comfort elements of the automotive environment to adequately account for this kind of dynamic behavior that induces excitations in the car structure.

Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy.

Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase

The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.

Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

The present study evaluated a newly developed computational tool (CT) to assess the alveolar bone space and the alveolar crest angle and compares it to dentist assessment (GT). The novel tool consisted of a set of processes initiated with image enhancement, points localization, and angle and area calculations. In total, we analyzed 148 sites in 39 radiographic images, and among these, 42 sites were selected and divided into two groups of non-periodontitis and periodontitis. The alveolar space area (ASA) and alveolar crest angle (ACA) were estimated. The agreement between the computer software and the ground truth was analyzed using the Bland–Altman plot. The sensitivity and specificity of the computer tool were measured using the ROC curve. The Bland–Altman plot showed an agreement between the ground truth and the computational tool in all of the parameters assessed. The ROC curve showed 100% sensitivity and 100% specificity for 12.67 mm of the alveolar space area. The maximum percentage of sensitivity and specificity were 80.95% for 13.63 degrees of the alveolar crest angle. Computer tool assessment provides accurate disease severity and treatment monitoring for evaluating the alveolar space area (ASA) and the alveolar crest angle (ACA).

Cohesive Zone Model Modification Techniques According to the Mesh Size in Finite Element Models of Stiffened Panels with Debonding

When catastrophic failure phenomena in aircraft structures, such as debonding, are numerically analyzed during their design process in the frame of “Damage Tolerance” philosophy, extreme requirements in terms of time and computational resources arise. Here, a decrease in these requirements is achieved by developing a numerical model that efficiently treats the debonding phenomena that occur due to the buckling behavior of composite stiffened panels under compressive loads. The Finite Element (FE) models developed in the ANSYS© software (Canonsburg, PA, USA) are calibrated and validated by using published experimental and numerical results of single-stringer compression specimens (SSCS). Different model features, such as the type of the element used (solid and solid shell) and Cohesive Zone Modeling (CZM) parameters are examined for their impact on the efficiency of the model regarding the accuracy versus computational cost. It is proved that a significant reduction in computational time is achieved, and the accuracy is not compromised when the proposed FE model is adopted. The outcome of the present work leads to guidelines for the development of FE models of stiffened panels, accurately predicting the buckling and post-buckling behavior leading to debonding phenomena, with minimized computational and time cost. The methodology is proved to be a tool for the generation of a universal parametric numerical model for the analysis of debonding phenomena of any stiffened panel configuration by modifying the corresponding geometric, material and damage properties.

On Numerical Modeling of Thermal Performance Enhancementof a Heat Thermal Energy Storage System Using a Phase Change Material and a Porous Foam

In this investigation, a comprehensive numerical analysis of the flow involved in an open-ended straight channel fully filled with a porous metal foam saturated and a phase change material (paraffin) has been performed using a single relaxation time lattice Boltzmann method (SRT-LBM) at the representative elementary volume (REV) scale. The enthalpy-based approach with three density functions has been employed to cope with the governing equations under the local thermal non-equilibrium (LTNE) condition. The in-house code has been validated through a comparison with a previous case in literature. The pore per inch density (10≤PPI≤60) and porosity (0.7≤ε≤0.9) effects of the metal structure were analyzed during melting/solidifying phenomena at two Reynolds numbers (Re = 200 and 400). The relevant findings are discussed for the LTNE intensity and the entropy generation rate (Ns). Through the simulations, the LTNE hypothesis turned out to be secure and valid. In addition, it is maximum for small PPI value (=10) whatever the parameters deemed. On the other hand, high porosity (=0.9) is advised to reduce the system’s irreversibility. However, at a moderate Re (=200), a small PPI (=10) would be appropriate to mitigate the system irreversibility during the charging case, while a large value (PPI = 60) might be advised for the discharging case. In this context, it can be stated that during the melting period, low porosity (=0.7) with low PPI (=10) improves thermal performance, reduces the system irreversibility and speeds up the melting rate, while for high porosity (=0.9), a moderate PPI (=30) should be used during the melting process to achieve an optimal system.

Analysis of Thermal Performances in a Ventilated Room Using LBM-MRT: Effect of a Porous Separation

This article demonstrates the feasibility of porous separation on the performance of displacement ventilation in a rectangular enclosure. A jet of fresh air enters the cavity through an opening at the bottom of the left wall and exits through an opening at the top of the right wall. The porous separation is placed in the center of the cavity and its height varies between 0.2 and 0.8 with three values of thickness, 0.1, 0.2, and 0.3. The heat transfer rate was calculated for different intervals of Darcy (10−6 ≤ Da ≤ 10), Rayleigh (10 ≤ Ra ≤ 106), and Reynolds (50 ≤ Re ≤ 500) numbers. The momentum and the energy equations were solved by the lattice Boltzmann method with multiple relaxation times (LB-MRT). Schemes D2Q9 and D2Q5 were chosen for the velocity and temperature fields, respectively. For porous separation, the generalized Darcy–Brinkman–Forchheimer model was adopted. It is represented by a term added in the standard LB equations. For the dynamic domain, numerical simulations revealed complex flow structures depending on all control parameters. The results showed that the thermal field, mainly in the second compartment, is very dependent on the size and permeability of the porous separation. However, they have no influence on the transfer rate.

A Simulation Study on Spread of Disease and Control Measures in Closed Population Using ABM

An infectious disease can cause a detrimental effect on national security. A group such as the military called a “closed population”, which is a subset of the general population but has many distinct characteristics, must survive even in the event of a pandemic. Hence, it requires its own distinct solution during a pandemic. In this study, we investigate a simulation analysis for implementing an agent-based model that reflects the characteristics of agents and the environment in a closed population and finds effective control measures for making the closed population functional in the course of disease spreading.