Using genetic algorithm for the optimization of seismic behavior of steel planar frames with semi-rigid connections

Beam-column connections have a significant role in the results of the analysis and the design of steel frames. In this paper, a genetic algorithm has been used for the non-linear analysis and design of steel frames. For minimizing the weight of frames, while satisfying the applied constraints and restraints such as the limits of normal and combined stresses, criteria such as target displacement(s) and the number and locations of plastic hinges were used. To analyze and design the frame elements, I and box-shaped standard sections were used for beams and columns, respectively. Finally, some clues for finding optimizing semi-rigid connection stiffness values for beam-to-column connections have been obtained. The degrees of these rigidities are obtained by a genetic algorithm during the procedure of optimization in order to reach a frame with the minimum weight. SAP2000 structural analysis program was used to perform modal analysis and linear and non-linear static solutions as well as the design of the elements. A MATLAB program was written for the process of optimization. The procedure of optimization was based on a weight minimization carried out for 9 steel frames. Thus, the optimum connection stiffness could be obtained for minimizing the weight of the structure. The results show that the non-linear analysis gives less weight for short period frames with semi-rigid connections compared to those of linear ones. However, by increasing the periods of frames, much less weights are obtained in the case of non-linear analysis with semi-rigid connections.     

Dam break flood wave under different reservoir’s capacities and lengths

Dam failure has been the subject of many hydraulic engineering studies due to its complicated physics with many uncertainties involved and the potential to cause many losses of lives and economical losses. A primary source of uncertainties in many dam failure analyses refers to prediction of the reservoir’s outflow hydrograph, which is studied in the present investigation. This paper presents an experimental study on instantaneous dam failure flood under different reservoir’s capacities and lengths in which the side slopes change within a range of 30°–90°. Thus, several outflow hydrographs are calculated and compared. The results reveal the role of the side slopes on dam break flood wave, such that lower side slope creates more catastrophic outflow. The reservoir capacity and length are also recognized to be important factors, such that they do affect peak discharge and time to peak of the outflow hydrograph. Finally, the paper presents two simple relations for peak discharge and maximum water level estimation at any downstream location.     

Engineering Next Generation Cyclized Peptide Ligands for Light-Controlled Capture and Release of Therapeutic Proteins

Photo-affinity adsorbents (i.e., translucent matrices functionalized with ligands featuring light-controlled biorecognition) represent a futuristic technology for purifying labile biologics. In this study, a framework for prototyping photo-affinity adsorbents comprising azobenzene-cyclized peptides (ACPs) conjugated to translucent porous beads (ChemMatrix) is presented. This approach combines computational and experimental tools for designing ACPs and investigating their light-controlled isomerization kinetics and protein biorecognition. First, a modular design for tailoring ACP's conformation, facilitating sequencing, and streamlining the in silico modeling of cis/trans isomers and their differential protein binding is introduced. Then, a spectroscopic system for measuring the photo-isomerization kinetics of ACPs on ChemMatrix beads is reported; using this device, it is demonstrated that the isomerization at different light intensities is correlated to the cyclization geometry, specifically the energy difference of trans versus cis isomers as calculated in silico. Also, a microfluidic device for sorting ACP-ChemMatrix beads to select and validate photo-affinity ligands using Vascular Cell Adhesion Molecule 1 (VCAM-1) as target protein and cyclo_AZOB[GVHAKQHRN-K*]-G-ChemMatrix as model photo-affinity adsorbent is presented. The proposed ACPs exhibit rapid and defined light-controlled isomerization and biorecognition. Controlling the adsorption and release of VCAM-1 using light demonstrates the potential of photo-affinity adsorbents for targets whose biochemical liability poses challenges to its purification.     

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.     

Architected Origami Materials: How Folding Creates Sophisticated Mechanical Properties

Origami, the ancient Japanese art of paper folding, is not only an inspiring technique to create sophisticated shapes, but also a surprisingly powerful method to induce nonlinear mechanical properties. Over the last decade, advances in crease design, mechanics modeling, and scalable fabrication have fostered the rapid emergence of architected origami materials. These materials typically consist of folded origami sheets or modules with intricate 3D geometries, and feature many unique and desirable material properties like auxetics, tunable nonlinear stiffness, multistability, and impact absorption. Rich designs in origami offer great freedom to design the performance of such origami materials, and folding offers a unique opportunity to efficiently fabricate these materials at vastly different sizes. Here, recent studies on the different aspects of origami materials—geometric design, mechanics analysis, achieved properties, and fabrication techniques—are highlighted and the challenges ahead discussed. The synergies between these different aspects will continue to mature and flourish this promising field.     

EPR Relaxation‐Enhancement‐Based Distance Measurements on Orthogonally Spin‐Labeled T4‐Lysozyme

Lanthanide‐induced enhancement of the longitudinal relaxation of nitroxide radicals in combination with orthogonal site‐directed spin labeling is presented as a systematic distance measurement method intended for studies of bio‐macromolecules and bio‐macromolecular complexes. The approach is tested on a water‐soluble protein (T4‐lysozyme) for two different commercially available lanthanide labels, and complemented by previously reported data on a membrane‐inserted polypeptide. Single temperature measurements are shown to be sufficient for reliable distance determination, with an upper measurable distance limit of about 5–6 nm. The extracted averaged distances represent the closest approach in Ln^III–nitroxide distance distributions. Studies of conformational changes and of bio‐macromolecule association‐dissociation are proposed as possible application area of the relaxation‐enhancement‐based distance measurements.     

An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units

Abstract

This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24?h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.     

Modified variable return to scale back-propagation neural network robust parameter optimization procedure for multi-quality processes

Selecting the optimum process parameter level setting for multi-quality processes is cumbersome. Previous methods were plagued by complex computational search, unrealistic assumptions, ignoring the interrelationship between responses and failure to select optimum process parameter level settings. The methods of variable return to scale (VRS) back-propagation neural network (BPNN) previously adopted were limited by the use of weak models, poor discriminatory tendency and an inability to select the optimum parameter level setting. This study applied a modified VRS–adequate BPNN topology model in the robust parameter procedure to solve this problem. Here, standard VRS models are allowed to self-assess, leading to partitioning. The upper bound of the free variable of the VRS model is restricted and the VRS penalization coefficient is adopted to determine the optimum process parameter level setting. The effectiveness of the proposed model measured by the total anticipated improvement yielded the highest total improvement over the existing methods.     

A ghost fluid method for sharp interface simulations of compressible multiphase flows

A ghost fluid based computational tool is developed to study a wide range of compressible multiphase flows involving strong shocks and contact discontinuities while accounting for surface tension, viscous stresses and gravitational forces. The solver utilizes constrained reinitialization method to predict the interface configuration at each time step. Surface tension effect is handled via an exact interface Riemann problem solver. Interfacial viscous stresses are approximated by considering continuous velocity and viscous stress across the interface. To assess the performance of the solver several benchmark problems are considered: One-dimensional gas-water shock tube problem, shock-bubble interaction, air cavity collapse in water, underwater explosion, Rayleigh-Taylor Instability, and ellipsoidal drop oscillations. Results obtained from the numerical simulations indicate that the numerical methodology performs reasonably well in predicting flow features and exhibit a very good agreement with prior experimental and numerical observations. To further examine the accuracy of the developed ghost fluid solver, the obtained results are compared to those by a conventional diffuse interface solver. The comparison shows the capability of our ghost fluid method in reproducing the experimentally observed flow characteristics while revealing more details regarding topological changes of the interface.     

Numerical and experimental study of hysteretic behavior of cylindrical friction dampers

Frictional dampers utilize the mechanism of friction for absorbing and dissipating the energy imparted to the dynamic systems. Frictional dampers are widely used in mechanical systems in various industries in order to mitigate the impact and vibration effects. Frictional dampers are also utilized in structures as means of passive control to improve the seismic behavior of structures.In this investigation, an innovative type of frictional damper called cylindrical friction damper (CFD) is proposed. This damper consists of two main parts, the inner shaft and the outer cylinder. Dimensions and properties of the main parts are defined based on seismic demand of structures. These two parts are assembled such that one is shrink fitted inside the other. Upon application of proper axial loading to both ends of the CFD, the shaft will move inside the cylinder by overcoming the friction. This in turn leads to considerable dissipation of mechanical energy. In contrast to other frictional dampers, the CFDs do not use high-strength bolts to induce friction between contact surfaces. This reduces construction costs, simplifies design computations and increases reliability in comparison with other types of frictional dampers.The hysteretic behavior of CFD is studied by experimental and numerical methods. The results show that the proposed damper has great energy absorption by stable hysteretic loops, which significantly improves the performance of structures subjected to earthquake loads. Also, a close agreement between the experimental and numerical results is observed.