Qom University Of Technology

This study aims to introduce a risk-targeted probabilistic model that facilitates seismic design hazard mapping. Here, we investigate how to incorporate hazard regional variations and different structural fragility typologies so as to achieve a uniform target risk across the map. This paper takes Tehran, the capital city, and its surrounding area as a case study and explores the highly digitized hazard curves conditioned on the time-dependent occurrence of an earthquake at a 50-year exposure time. We introduce a new set of parametrized quantile functions for Risk-targeted design Intensity Measure (IM¬R) as the seismic design hazard. A group of IMR maps is subsequently prepared based on a 1% collapse probability during the next 50-year lifetime. Furthermore, the quantile function corresponding to each fragility type is able to show the aleatory uncertainty at each site on the region map. The uncertainty maps illustrate relatively less inherent variability than what exists in the hazard curves themselves. Then, we tackle the source of uncertainty arising from percentile hazard curves into IMR, known as epistemic uncertainty, by deriving a new closed-form expression, which allows for a sampling-free estimation of the epistemic uncertainty. The risk-targeted seismic design hazard with its accompanying uncertainties can lead to a promising seismic design hazard with potential applications for the insurance industry, urban planners, and risk-aware building owners. Keywords: Time-dependent hazard; risk-targeted design; epistemic uncertainty; aleatory randomness; target risk; Iran

This paper proposes a multiobjective hybrid Taguchi genetic algorithm (HTGA) to optimize the speed range of a yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. HTGA combines Taguchi’s local optimization with the global optimization of traditional genetic algorithms (GAs), facilitating faster and more accurate solutions. The Taguchi method is employed to generate offspring individuals within GA; it inherits parameter characteristics from stronger offspring, saving considerable computation time. The objective is to achieve a motor with low cogging torque, high average torque, and an expanded speed range in the field weakening area. Various parameters of the motor, such as the split ratio, stator axial length, pole angles, PM arc, and number of conductors per slot, are selected as optimization variables. The optimization constraints include the field-weakening rate, saliency rate, cogging torque, and average torque. The optimized motor parameters are determined, and the speed range before and after optimization is evaluated. Cosimulation analysis using a 3-D finite element method (FEM) is performed under no-load and full-load conditions to compare the motor’s speed regulation range. The optimized motor exhibits a maximum speed that is almost 1.5 times higher than the initial design, with improvements of 11.3% in average torque and 9% in cogging torque. Experimental results compared to 3-D FEM simulations demonstrate the superior performance of the optimized motor in terms of speed, torque, power, and efficiency.

Barite concrete, due to its significantly higher specific gravity and robust structural strength, proves to be an ideal radiation shield for nuclear centers. Any structural anomalies in such critical establishments could potentially trigger catastrophic consequences. Therefore, ensuring durability in nuclear centers assumes paramount importance. This research employs an experimental program to study the durability performance of barite concrete thoroughly. The program investigates and analyzes the influence of several variables, including concrete strength class, pH levels of the surrounding environment, aggregate type, and the number of wet and dry cycles, on the compressive and tensile strength of barite concrete. To achieve these goals, 141 cubic specimens and 70 cylindrical specimens were made, and four sulfuric acid pools with pH values of 2, 3, 4, and 7 were utilized. Subsequently, the compressive and tensile strength results of the samples were measured after 7 (28 days), 14 (56 days), 23 (92 days), and 30 (120 days) cycles exposure to acid pools. The findings of this research illuminate that barite concrete exposed to acid precipitation experiences an 18% reduction in compressive strength and a 35% decrease in tensile strength. A comparison between barite concrete and conventional aggregate concrete shows that barite concrete has almost double the strength of ordinary concrete against acid cycles.

The first polychrome glazed tiles in the Middle East, widely utilized for architectural ornamentation, can be traced back to the first millennium BCE. The seven-color (haft-rang) Safavid tiles represent an evolution of the mina’i and lajvardina ceramics in Iran. These exquisite seven-color tiles gained popularity in the central Iranian platue during the Safavid dynasty (approximately 1501-1736 CE) and graced various architectural marvels, including palaces, madrasas, and mosques. The glazed tiles examined in this study were retrieved from the Safavid segment of the Meymeh great mosque, which is situated in the central region of the Iranian Plateau. Given that glazed tiles are integral to architectural design, conducting destructive analyses on such glazed ceramics is unfeasible. Thus, noninvasive methods are essential for their analysis. In this study, we determined the coloring agents responsible for the different hues of the seven-color tiles. Notably, the results of hyperspectral imaging, which identified the coloring agents and glaze types, corroborated the findings derived from the chemical composition analysis through portable XRF. Furthermore, our research revealed a fascinating aspect of the black lines used to separate colored areas in seven-color glazed tiles. These lines feature two distinct coloring agents, chromium and manganese (without chromium), which are employed together (the manganese black lines above the chromium black lines). Specifically, the chromium-infused black lines adorned the base motifs, while the manganese-infused black lines were introduced after the addition of other colors, marking the chromium black lines and revealing finer details.

This paper proposes an adaptive transmission scheme for two-way amplify-and-forward (AF) relaying over optical wireless communications. The two terminals utilize subcarrier phase shift keying (S-PSK) intensity modulation for data transmission, and the atmospheric turbulence impacts the transmitted signals. The proposed transmission scheme exploits the efficient utilization of the optical wireless channel capacity by adjusting the modulation order of S-PSK. While meeting predetermined bit error rate (BER) requirements for each terminal, the selected modulation order of each terminal corresponds to the instantaneous condition of turbulence-induced fading. A new expression is derived for the spectral efficiency of the proposed two-way relaying scheme, and then performance investigations are carried out under various turbulence conditions. Moreover, we derive a closed form expression for the BER performance and analyze the proposed system model from the outage probability point of view. Numerical examples illustrate that the proposed two-way relaying scheme enjoys better spectral efficiency compared with the traditional direct transmission scheme in the high signal-to-noise-ratio regime. The simulation results show that for a fixed turbulence, the TWR saves the electrical signal-to-noise-ratio (SNR) about 2.5 dB compared to the direct transmission scheme when the target spectral efficiency is set to 1.2 bits/s/Hz.

This study examines the impact of different circular shell and tube heat exchanger configurations on the system charging time. Various configurations will be evaluated, namely A, B, C, and D, with 1, 2, 3, and 4 tubes, respectively. The shell contains a phase change material (N-eicosane), and the location of tubes changes in each configuration, forming a new case. A total of 38 numerical experiments were conducted using a commercial software to investigate various tube locations and arrangements. While previous studies have primarily focused on total charging time as the fitness measure for different configurations, this study considers the need to achieve high charging levels within limited time windows. Therefore, a configuration that takes longer to charge completely may have a shorter time to reach 90% of charging, making it more reliable. The top configurations were assessed with gold, silver, and bronze rankings based on their agility to reach milestones (at every level of 50%, 80%, and 90% charging). For instance, case C6 achieved a golden ranking for reaching 90% of full charge in about 328 seconds, 12.5% faster (41 seconds) than the efficient configuration (C5) with the minimum total time. However, C6 has a longer total charge time of 516 seconds (about 20% extra time). On the other hand, C5 also has the fastest time for reaching milestones at 50% and 80% of charging, which shows that it responds to the thermal source very rapidly. The results also demonstrate that increasing the tubes significantly decreases total melting time. For instance, the efficient case of type D, D7, takes only about one-fifth (80% improvement) of the total charging time compared to the base case, A1. Finally, the performance of various cases during the discharge process is also evaluated.

In this paper, we have proposed a new type of multi-layer solar cell structure based on multi-walled carbon nanotube (MWCNT) photonic crystals grown on a silicon substrate. The structure is constructed by stacking layers of MWCNTs array with different lattice constants from 100 to 800 nm as an active layer. It exhibits a remarkable absorption efficiency, reaching a peak value of 92.61% within a broad absorption spectrum spanning from 100 to 2000 nm. The absorption capacity and range are significantly improved compared to conventional solar cell structures. The effect of the filling factor on the absorption coefficient was examined. Various filling factor values ranging from 4 to 60% were evaluated to optimize the structure, with the most favorable outcome observed at a filling factor of f = 30%. The host medium is silicon, which is fully compatible with Si technology, enabling seamless easy integration with other Si-based technology devices.

This research focuses on utilizing non-uniform magnetic fields, induced by dipoles, to control and enhance thermal energy transfer in a two-dimensional cooling conduit including a double backward-facing step. The presence of electronic equipment along the straight channel path creates such arrangements, and cooling is often ineffective in the corners of the formed steps. The use of a non-constant magnetic field is a passive technique to improve the cooling rate in these sections without changing the internal geometry, thereby increasing the heat transfer rate. A commercial software based on the finite volume technique is employed to solve the governing equations of fluid flow and heat transfer. Multiple parameters are examined in this study, including the flow Reynolds number (12.5–50), dipole location and strength (0.1–5 A-m), and the number of dipoles (single or double). The results indicate that all of these parameters have a significant impact on the thermal energy transfer. The results of the study show that a single dipole increase the average heat transfer by about 22%, two magnetic fields by 40%, the strength of the magnetic source by 24% with respect to the non-magnetic field in the present study.

Physical layer key generation (PLKG) can significantly enhance the security of classic encryption schemes by efficiently providing secret keys in resource-limited network like the Internet of Things (IoT). However, reaching a high key generation rate (KGR) is challenging in applications like smart home or remote area sensing with quasi-static channels. Recently, exploiting reconfigurable intelligent surface (RIS) to induce randomness in quasi-static wireless channels has received significant research interest. However, the inherent spatial correlation among the RIS elements is rarely studied, which can alter the optimum PLKG approach in terms of KGR and randomness in the key sequence. Specifically, for the first time, in this contribution, we take into account a spatially correlated RIS, which intends to enhance the KGR in a quasi-static medium. Novel closed-form analytical expressions for KGR are derived for the two cases of random phase shift (RPS) and our proposed equal phase shift (EPS) in the RIS elements. We also analyze the correlation between the channel samples to ensure the randomness of the generated secret key sequence. It is shown that the EPS scheme can effectively exploit the inherent spatial correlation between the RIS elements and it leads to a higher KGR compared to the widely used RPS strategy. We further formulate an optimization problem in which we determine the optimal portion of time dedicated to direct and indirect channel estimation, which has never been addressed in the previous studies. We show the accuracy and the fast convergence of our sequential convex programming (SCP) based algorithm and discuss the various parameters affecting spatially correlated RIS-assisted PLKG.

In this letter, we initially present a formulation of Received Signal Strengths (RSS) in a scenario involving one source (or emitter), one Intelligent Reflecting Surface (IRS), and a Wireless Sensor Network (WSN). To determine the source location based on RSS measurements, we carefully select IRS phases to simplify the relationship between them. Subsequently, we solve the resulting rather simple nonlinear equations using a Least Squares (LS) approach. The solution involves an initial estimation through a course search, followed by a Steepest-Descent (SD) recursion. Simulation results highlight the superior performance of SD compared to course search and some state-of-the-art RSS localization techniques in the literature.

This paper studies a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted wireless system where a multi-antenna base station (BS) transmits both wireless information and energy-carrying signals to single-antenna users. To explore the trade-off between spectral efficiency (SE) and energy efficiency (EE) in this system, a multi-objective optimization problem (MOOP) is formulated to maximize SE and EE. The beamforming vector at the BS, the power splitting ratio at each user, phase shifts and amplitude coefficients of the STAR-RIS are jointly optimized, subject to the constraints of the maximum transmit power of the BS and the minimum harvested energy of users. To tackle this MOOP, we propose a Meta-DDPG algorithm that combines deep deterministic policy gradient (DDPG) and meta-learning approaches. Simulation results demonstrate that the Meta-DDPG algorithm outperforms the classic DDPG and genetic algorithms in terms of EE. Besides, via simulation results, it is illustrated that Meta-DDPG reaches a close performance to the exhaustive search and optimization-based solutions.

Several models have been used in literature studies to describe the mechanical behavior of hydroxyapatite (HA) polymer nanocomposites. However, previous models have often overlooked the size of HA and the properties of the interphase. In this study, we further develop an equation originally proposed by Kolarik. This advanced equation predicts the tensile strength of polymer/HA nanocomposites by considering the interphase parameter (A), rod‐like HA size, HA concentration, and interphase properties, including depth and strength. We validate our method using experimental data from various examples and through parametric inspections. Both the strength and the depth of the interphase directly affect the ‘A’ value and, consequently, the strength of the nanocomposites. For instance, an HA radius (R) of 6 nm yields the highest ‘A’ value of 4.97, enhancing the nanocomposite strength by up to 250%. In contrast, ‘R’ value of 20 nm fails to reinforce the samples effectively. Additionally, the thickness of the interphase (t) and the concentration of HA directly handle the nanocomposite strength. The strength of the samples significantly improves by 137% and 160% with an interphase thickness of 50 nm and an HA volume fraction of 0.2, respectively. Generally, the length of HA and interphase characteristics (thickness and strength) directly control the strength of samples, but the HA radius has an inverse relationship with nanocomposite strength. Highlights Kolarik equation is developed to predict the tensile strength of polymer HA nanocomposites. Interphase parameter, HA size, HA concentration and interphase properties are considered. HA radius of 6 nm produces the maximum enhancement of nanocomposite strength by 250%. Interphase properties and concentration of HA directly control the nanocomposite strength. All parameters reasonably influence the strength of samples confirming the developed model.

The high weight-to-strength ratio of AA6061 aluminum alloys presents increased potential applications in industries such as automotive and aircraft. However, its limited formability at room temperature (RT) restricts its usage. Therefore, in the conducted study, the formability of AA6061-T6 sheets with a thickness of 2 mm was investigated at different temperatures in the range of RT up to 300°C. Both experimental and numerical methods were employed to investigate the forming limit diagram (FLD) of an AA6061-T6 sheet. The tests were conducted using a non-isothermal Nakajima standard die under dry contact conditions. Two damage criteria, the Johnson–Cook and the ductile fracture criterion (DFC), were used in a thermomechanically coupled finite element analysis in Abaqus/Explicit to predict fracture in the AA6061 sheet. To examine the impact of temperature on the friction coefficient in the punch and sheet contact, an atomic force microscope was used to measure the roughness of the sheet, after the FLD tests, were conducted at different temperatures. Results indicate an increase in FLD levels from RT up to 100°C, followed by a decrease, for temperatures surpassing 100°C. Experimental findings underscored the significance of the adhesive wear at elevated temperatures, acting as a decisive factor that hampers the material flow and the sheet deformation, in the contact between the sheet and punch.

An experimental investigation has been performed to explore the Laser Forming Process (LFP) of Fiber Metal Laminates (FMLs). To achieve this, samples of FMLs were manufactured, comprising two outer layers of cold-rolled aluminum alloy 3105-H14, each with a thickness of 0.3 mm, and a middle layer of epoxy/unidirectional glass fiber composite, with a thickness of 0.2 mm. These samples were fabricated through manual impregnation and subsequently cured under hot pressing. Following this, 27 FML specimens were formed by controlling three key parameters of the LFP: the number of scan passes, scan velocity, and laser power, each varying across three levels. A linear scan path was employed for the laser beam to induce a simple 2D bend. Subsequent measurements were taken to assess the bending angles of each sample after forming, allowing for a comprehensive investigation into the impact of these forming parameters. Evaluation of potential issues, such as aluminum/composite delamination and thermal degradation of the epoxy matrix within the bent area, was carried out using optical as well as scanning electron microscope (SEM) images. Additionally, the effects of the LFP on the bonding strength between the aluminum and composite layers were investigated through peel tests, conducted in accordance with ASTM D1876-61T standards. Following this investigation, it was observed that elevated laser power combined with an increased number of scan passes, alongside a reduced scan speed, resulted in notably significant bent angles of up to 17˚. However, this was accompanied by a corresponding 15% reduction in the delamination peak force, highlighting the intricate trade-offs associated with these forming parameters.

Electric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK‐2 market, as shown by multiple analyses.

In this paper, a private distributed estimation algorithm is proposed. In this algorithm, a differential-privacy noise is added to the intermediate estimation to be exchanged among nodes. Two types of differential noise is regarded in the paper which are Gaussian and Laplacian. Also, in each case, two approaches are used to recover the true intermediate estimations. In the first approach, we estimate the true intermediate estimation and in the second approach, we estimate the noise vector and then subtract it from the noise intermediate estimation. We show that both approaches lead to the same formula for denoised intermediate estimation. Simulation experiments corroborate the effectiveness of the proposed algorithm when variance of added privacy noise is high and the privacy is guaranteed with high confidence.