Rajshahi University of Engineering and Technology

Optimizing the hydraulic efficiency of submersible drainage pumps (SDPs) is crucial for energy conservation and performance enhancement, especially in emergency applications. Therefore, this study aims to develop an energy-efficient SDP by designing an optimized impeller model using a numerical scheme and introducing a flow balance block (FBB) as an alternative to modifying the pump casing, thereby reducing internal space and improving performance. Experimental validation of the optimized impeller and FBB was conducted by comparing their performance with the original pump model. The results demonstrated that the optimized impeller increased efficiency by 3.35% at a flow rate of 0.195 m³/min, with an overall average efficiency improvement of 2.33%. Additionally, when the optimized impeller and FBB were combined, the pump's efficiency was further enhanced by 5%, and the flow rate increased by approximately 10%. The study also proposed that the tap bolt method is more effective for installing the FBB than the silicone attaching method. This research provides a comprehensive experimental approach to improving SDP efficiency and offers valuable insights for future pump optimization.

The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively impact classification performance. Traditional CNN-based methods predominantly utilize 2D CNNs for feature extraction, which inadequately exploit the inter-band correlations in HSIs. While 3D CNNs can capture joint spectral-spatial information, they often encounter issues related to network depth and complexity. To address these issues, we propose an innovative land cover object classification approach in HSIs that integrates segmented principal component analysis (Seg-PCA) with hybrid 3D-2D CNNs. Our approach leverages Seg-PCA for effective feature extraction and employs the minimum-redundancy maximum relevance (mRMR) criterion for feature selection. By combining the strengths of both 3D and 2D CNNs, our method efficiently extracts spectral-spatial features. These features are then processed through fully connected dense layers and a softmax layer for classification. Extensive experiments on three widely used HSI datasets demonstrate that our method consistently outperforms existing state-of-the-art techniques in classification performance. These results highlight the efficacy of our approach and its potential to significantly enhance the classification of land cover objects in hyperspectral imagery.

The exponential rise of electric vehicles (EVs) is transforming the global automobile industry, driving a shift towards greater cleanliness and environmental sustainability. EV charging stations (EVCSs) play a pivotal role in this massive transition towards EVs, where accurate forecasting of EVCS demand is crucial for seamlessly integrating EVs into existing power grids. Most of the existing research mainly concentrates on univariate forecasting, neglecting the multiple factors influencing EVCS demand. Hence, this study offers a comparative analysis of different algorithms for univariate forecasting and multivariate forecasting, where the multivariate scheme incorporates metadata such as charging time, greenhouse gas savings, and gasoline savings. The experimental results indicate the superiority of the multivariate scheme over the univariate forecasting. For multivariate forecasting, the gated recurrent unit (GRU) has outperformed other models such as categorical boosting (Catboost), recurrent neural network (RNN), long short‐term memory (LSTM), extreme gradient boosting (XGBoost), random forest, convolutional neural network (CNN), CNN + LSTM, and LSTM + LSTM. The results of this study emphasize the significance of using the GRU model for multivariate forecasting with metadata during normal and noisy scenarios to yield more reliable and accurate predictions. This approach enhances decision‐making, policy development, and efficient grid integration in the growing EV sector.

Garment products are international trade items and essential consumption items. The readymade garment industry of Bangladesh is the second largest garment exporter in the world. The garment industry of Bangladesh consumes one-third of the country's total industrial energy. Such industry consumes significant energy and contributes greenhouse gas (GHG) emissions into the atmosphere. Assessment of energy consumption and GHG emission of garment products helps to understand the baseline status of the production and benchmarking the energy and emission leveling of the product. This paper aims to investigate the energy consumption and GHG emissions of readymade garment (T-shirt) production in Bangladesh. Gate-to-gate production stages have been considered for the investigation. The strategies for the energy and GHG emission-saving potential have been also analyzed. The result showed that the total energy consumption in terms of natural gas, oil, and electricity found are 15,105 m³, 49.93 kg, and 36,265 kWh respectively per day. The total CO2 emission found is 56.65 tons per day. The specific energy consumption found is 12 MJ/piece and the specific emissions of the CO2, CO, CH4, NOx, and SOx are 0.94 kg/piece, 1.69 gm/piece, 3.53 gm/piece, 4.1 gm/piece, and 0.89gm/piece, respectively. The proposed strategies could save energy in the machinery section in the range of 22–70%. The energy saving potential of 25% is possible in the steam generation sector by employing the proposed electric steam generator. The steam generation section gives the highest CO2 emission and the compressor section gives the lowest emission. The specific energy consumption and carbon footprint of Bangladesh-manufactured garments are in very close agreement with the literature studied.

In this current research multiple elements i.e., Co, Mn, Mg, Cu, and Ni were doped to produce compositionally complex ZnO (CCZO) thin films which are denoted by (CuxCoxMnxMgxNix)Zn1-xO (where x = 0.02, 0.04, and 0.06). Spin coating technique and sol-gel method was used to prepare the pure ZnO and CCZO thin films. The structural properties of the prepared thin film samples were analyzed using X-ray Diffraction (XRD), where the morphological properties were revealed using Scanning Electron Microscopy (SEM), the elemental composition was determined using Energy-dispersive X-ray Spectroscopy (EDS), and finally the optical properties were studied using UV-Visible Spectroscopy analysis. From XRD analysis it was observed that ZnO could incorporate the mentioned elements to a high level of 30% without altering its crystal structure and emerging a new phase. The crystallite size increases from 36.90 nm to 41.36 nm for 10% doping followed by a decline to 28.64 nm and 26.05 nm for 20% and 30% doping respectively. The spherical particle-shaped morphology of pure ZnO thin film, revealed from SEM investigation changed to the wrinkle type after doping. The successful incorporation of the targeted elements in CCZO thin films was confirmed by the EDS analysis. The transparency of the fabricated CCZO thin films increases gradually in the visible wavelength region. The optical band gap of the pure ZnO thin film was found to be 3.27 eV which increases in CCZO thin films to around 3.38 eV for 30% doping. Graphical Abstract

Earlier, an approximation technique was presented for solving strong nonlinear oscillator equations. Due to arising algebraic complexities, the method fails to determine suitable solutions of some nonlinear oscillators such as quadratic oscillators, the cubical Duffing oscillator of softening springs, and pendulum equations. Then, rearranging an algebraic equation related to amplitude and frequency, the method covers the noted problems. In this article, the latter technique is applied to handling anti-symmetric constant force oscillators and anti-symmetric quadratic nonlinear oscillators.

This paper presents a comprehensive comparison between the modified harmonic balance method (MHBM) and He’s frequency formulation (HFF) for solving the nonlinear dynamics of an excited pendulum constrained by a crank-shaft-slider mechanism (CSSM). The pendulum’s motion, influenced by the CSSM, introduces strong nonlinearity, making the analysis challenging using conventional methods. The harmonic balance method (HBM), a widely used technique for approximating solutions to nonlinear differential equations, often becomes cumbersome due to its complex nature, particularly when higher-order approximations are necessary. This complexity leads to the emergence of a set of intricate nonlinear complex equations that are not easy to solve. To overcome the limitations of HBM, a modified form of the HBM is adopted in this study. The performance of the MHBM is thoroughly evaluated by comparing the results with those obtained using HFF. Additionally, the numerical solutions are obtained using the Runge–Kutta fourth-order method, serving as a benchmark to assess the accuracy of both analytical approaches. Remarkably, the first approximation provided by the MHBM exhibits superior accuracy compared to the solutions obtained from the HFF method and other existing analytical methods. The results highlight the potential of MHBM as a reliable and accurate method for solving nonlinear differential equations in various engineering applications, particularly in systems exhibiting strong nonlinearity.

This study presents a numerical approach to achieve high efficiency using a novel dual‐absorber perovskite solar cell (PSC) utilizing environmentally friendly inorganic perovskite materials focusing on the optimization of different parameters. Ca3SbI3 and Sr3SbI3 are employed as the upper and lower absorber layer, respectively, in the proposed PSC structure. The device architecture also incorporates SnS2 as the electron transport layer (ETL) and Spiro‐OMeTAD as the hole transport layer (HTL). The further investigation explores the effect of ETL and HTL thicknesses and doping concentrations on device performance, revealing significant impact on photovoltaic parameters. Using double‐graded materials of Ca3SbI3/Sr3SbI3 with ETL and HTL, the PSC in this study achieves an optimized efficiency of 32.74% with JSC of 34.17 mA cm⁻², fill factor of 83.77%, and VOC of 1.14 V having an optimized level of doping 1 × 10¹⁷ cm⁻³ for Sr3SbI3 and 1 × 10¹⁶ cm⁻³ for Ca3SbI3 perovskite materials, with a thickness of 800 and 200 nm for Sr3SbI3 and Ca3SbI3, respectively, and defect density of 1 × 10¹² cm⁻³ for both the materials at room temperature. These findings provide a blueprint for developing highly efficient and cost‐effective PSCs, emphasizing the importance of dual‐absorber configurations in surpassing limit of efficiency of single‐junction solar cells.

The paper presents a thorough investigation into the design of a Modified Core Hexa–Deca Photonic Crystal Fiber (MHD-PCF) with adjustable features to regulate dispersion and birefringence. At the target wavelength of 1550 nm, the suggested MHD-PCF exhibits extraordinary optical properties, including an ultra-high negative dispersion coefficient of − 7755 ps/(nm km) and significant birefringence of 1.905 × 10⁻². The analysis entails regular changes in lattice constants and center air hole parameters, which provide insights into optical property trends. The MHD-PCF regularly exceeds existing benchmarks across a wide range of parameter adjustments. Notably, this fiber has strong nonlinearity (59.12 W⁻¹ km⁻¹) and low confinement loss (2.896 × 10⁻³ dB/cm), as well as an enhanced numerical aperture (0.5144), demonstrating its potential for efficient light coupling and supercontinuum production. These results put the Modified Core Hexa–Deca Photonic Crystal Fiber at the forefront of contemporary communication systems, and its optical enhancements and flexible features present exciting opportunities for novel Terahertz technology breakthroughs, especially in the areas of communication systems and sensing applications.

Efficient resource allocation for production and cost-effective dispatch under uncertainty is crucial for a streamlined supply chain. Traditional segregated and deterministic production–distribution models often fall short in providing robust solutions due to dynamic market demands, supplier reliability, and resource availability. In this context, this paper focuses on integrating production and distribution problems while managing conflicting goals and addressing uncertainties. A fuzzy multi-objective linear programming (FMOLP) model is developed to simultaneously minimize the total integrated production cost and delivery time amid uncertainties. This article aims to shed light on the practical implications and benefits of adopting integrated production and distribution models. Here, triangular fuzzy numbers and the concept of the minimum accepted level method are employed to formulate the problem. The minimum operator and weighted additive operator methods are employed to aggregate all fuzzy sets with non-linear exponential membership functions for a better representation of the FMOLP problem. A scenario-based analytical hierarchy process (AHP) is devised, integrating expert knowledge and experience to rank multiple objectives, thereby enhancing realism for the respective case. The experimental results reveal that the proposed integration increases the satisfaction level of decision makers from 0.684 to 0.747. Furthermore, the higher degree of closeness of the proposed model compared to the employed approach validates the final results. Impacts and interaction analysis also demonstrate the necessity of an integrated approach for production and distribution planning under uncertainty.

This research investigates different pretreatments and drying temperatures influence on the dehydration kinetics, thermodynamic parameters, and functional properties of green mango pulp slices dried in an oven as sustainable preservation technique. Green mangoes were pretreated with hot water blanching and a 1% citric acid solution, then dried at 60°C, 65°C, and 70°C. Among these, HWB samples demonstrated the shortest drying times, with drying rates increasing with temperature. The Midilli model provided the best fit for describing thin-layer drying behavior. Effective moisture diffusivity ranged from 1.50 × 10⁻⁶ to 2.92 × 10⁻⁶ m²/s, following an Arrhenius-type temperature dependence, with activation energies of 28.67, 26.22, and 39.74 kJ/mol for control, citric acid, and HWB samples, respectively. Functional property analysis revealed significant impacts of temperature and pretreatment on bulk density (BD), water absorption capacity (WAC), and oil absorption capacity (OAC). HWB pretreatment, in particular, resulted in higher BD and WAC, while OAC was lower compared to control samples. These findings highlight the importance of optimizing pretreatment and drying conditions to enhance the quality and functional properties of dried green mango powder, contributing to its potential as a sustainable food ingredient.

As climate change intensifies coastal hazards globally, understanding and quantifying community vulnerability is crucial for effective adaptation strategies. This study examines the application of indicators in planning adaptive capacity strategies to reduce community vulnerability to tsunami hazards (THs). Using a case study approach in Mehuin, a coastal community in southern Chile susceptible to tsunamis, we identified and validated specific indicators through both quantitative and qualitative methods. Secondary data sources were used to initially identify indicators, which were then validated through 12 in-depth interviews with community representatives and local emergency management actors. The Analytic Hierarchy Process was employed to determine the relative weights of indicators in adaptive capacity planning. A total of 25 specific indicators were identified for Mehuin, grouped into 7 key issues: human exposure, physical exposure, geographical factors, socioeconomic conditions, psychological factors, governance, and planning capacities. The most influential indicators for adaptive capacity planning were found to be preparedness and planning-related, including the existence of emergency plans (14.33% weight), quality of urban/regional plans (14.20%), and building codes (10.79%). Socioeconomic factors like collective action (9.06%) and social networks (5.61%) were also significant. The least influential factors were building structure (0.16%) and housing density (0.36%). The indicators provide insights into community vulnerabilities and can inform targeted strategies to enhance adaptive capacity. This study concludes that local-scale indicators are crucial for identifying community-specific vulnerabilities and priorities in tsunami preparedness. However, their application requires careful contextual consideration. The findings demonstrate the importance of integrating community perspectives in vulnerability assessments and adaptive planning for coastal hazards. This research advances evidence-based approaches for disaster risk reduction and sustainable development in coastal regions, aligning with UN Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action).

A novel PCF-SPR sensor with hybrid sensing capability is presented in this study for detecting both coronavirus and alcohol concentration simultaneously. The sensor utilizes both external and internal sensing approaches to enable simultaneous multi-analyte detection. A thin gold film applied uniformly to the entire external surface as well as the external section of the internal circle that contains the analyte facilitates the surface plasmon resonance (SPR) phenomenon. This hybrid sensor demonstrates excellent performance across the RI range of 1.29 to 1.42 (external, 1.29–1.37; internal, 1.34–1.42). The sensor’s simulation results and performance are analyzed using the finite element method (FEM)–based COMSOL Multiphysics 5.3a. The performance parameters, including wavelength sensitivity (WS), amplitude sensitivity (AS), figure of merit (FOM), and sensor resolution (SR), are assessed for both external and internal sensing modes. The maximum WS and AS are observed to be 13,000 nm/RIU (external) and 5000 nm/RIU (internal), and $-$1216.45 RIU$^{-1}$ (external) and $-$85.8465 RIU$^{-1}$ (internal), respectively. The minimum SR is found to be 7.69×$10^{-6}$ RIU (external) and 2.00×$10^{-5}$ RIU (internal), while the highest FOM is noted as 650 RIU$^{-1}$ (external) and 66.667 RIU$^{-1}$ (internal). The hybrid sensor is capable of detecting coronavirus samples at concentrations ranging from 0 to 62.5 nM and determining COVID-positive or negative status using a threshold of 1.953125 nM. It also detects alcohol concentrations in water mixtures ranging from 15 to 70%. With its compact design and high sensitivity, this sensor shows promise for biological analyte sensing.

Lean manufacturing has become a buzzword for the past few years due to its capability to reduce waste and make the system more efficient. To meet the numerous demands of consumers, companies are moving toward Industry 4.0, which can be accomplished through automation. Prior research demonstrated a conceptual integration of lean tools' effects into automation, but it lacked a quantitative analysis. The purpose of this study is to ascertain the effects of implementing lean tools prior to automation. To get real‐time data for the analysis of applying lean and automation, an empirical study was conducted on a manufacturing line. Using ARENA simulation software, four distinct simulation models were created using the data: the current assembly line model, the model with a lean tool (Inline Quality Check), the model with only automation, and the model with lean applied first and automation applied later. The productivity of the assembly line is used to compare the results. The comparative analysis demonstrated that applying lean tools prior to automation increases productivity in automated lines. This study could assist businesses understand that, before implementing automation, they should use lean technologies to improve system efficiency. An effective system can become considerably more efficient by being automated.

This thesis investigates the relationship between optimal pile foundation depth and building height. Twenty-one building models of various heights were made using CSI ETABS. The models' base reactions were determined, and soil parameters were used to determine pile foundation depth using CSI SAFE. Microsoft Excel data analysis was used to develop an empirical equation relating pile depth to building height. The study included relevant analyses, like base shear, story drift, and building modes. Soil parameters were determined based on SPT number. The effects of wind and seismic loads were investigated using international standards and Bangladesh design codes (BNBC 2020). The technique includes the steps for developing building models, applying loads, conducting analyses, and verifying member design. The static and dynamic parameters obtained will be presented in the results section, together with natural frequencies, base shear values, and axial forces. The findings will be discussed, with a focus on the relationship between building height and pile depth requirements. If an equation is established, the validity and limitations will be discussed. This thesis aims to advance the subject of geotechnical engineering by providing insights into pile foundation design for high-rise structures. The findings may be useful for engineers looking to improve foundation systems for safe and efficient building construction.

In this paper, we present and numerically investigate an auxetic metamaterial absorber based on vanadium dioxide (VO2), achieving more than 90% absorption of the incident terahertz (THz) waves between 4.15 THz to 8.43 THz with an average absorption of 98.4%. To our knowledge, the absorption bandwidth is higher than previously reported VO2-based absorbers. The proposed absorber contains two VO2 based resonator rings placed diagonally at the top of the dielectric substrate in such a way as to create an auxetic shape and provide more design space than the existing absorbers. Considering the vector nature of electromagnetic fields in three-dimensional space, numerical analysis is performed while keeping the phase-changing material VO2 in the metallic state. According to the full wave simulation, it is shown that under normal incidence, the proposed absorber provides almost flat and near-unity absorption, which covers from 4.82 THz to 7.53 THz. We describe the physical mechanism of the absorber through impedance matching theory, and the absorber performance is evaluated by observing the electric field distributions at various frequencies. The proposed structure also exhibits a satisfactory tunable range from 2% to 100%, which may satisfy the requirements of re-configurable metamaterial absorbers. We also show that the absorber provides better wide-angle absorption performance than the existing VO2-based models for both transverse polarization i.e., transverse electric (TE) and transverse magnetic (TM) modes. Owing to the higher absorption bandwidth and better tunable range, the proposed auxetic metamaterial has great potential applications in THz imaging, detectors, and sensing.

This paper introduces a new structure of switched-capacitor (SC) based 3-phase single-source multilevel inverter (MLI), which is designed to generate nine different output voltage (line-to-line) levels. A low-voltage grid-feeding photovoltaic (PV) system is the perfect use case for the inverter. The switching pattern is modeled in such a way that during voltage generation, the main dc source gets isolated and voltage is supplied based on the SC units. As a result, the voltage of SC units is balanced automatically, and the SC units also provide boosted output voltage up to four times higher than the single input dc source. Comparing this to other series-connected SC-based inverter topologies, such as flying capacitor (FC) and neutral point clamped (NPC) inverters, no additional control complexity is mandatory. The system can be made modular to produce a higher-level voltage output as well. The MLI topology, along with the whole system, is simulated in MATLAB/Simulink and PLECS simulation environment. A laboratory-scaled prototype was also built to justify the theoretical claim more boldly.

Transformerless grid-connected inverters are gaining popularity due to their high efficiency, compact design, and cost-effectiveness than transformer-based inverters. However, these inverters face significant challenges, particularly leakage current and limited voltage boost capability. This paper presents a novel solution to these issues through a five-level transformerless inverter (TLI) topology utilizing switched capacitors (SCs). The inverter utilizes seven insulated gate bipolar transistors (IGBTs), two diodes, and two SCs to achieve a five-level boosted output while ensuring low leakage current. Near-zero leakage current is enabled by maintaining a constant common mode voltage (CMV). Finite control set model predictive control (FCS-MPC), instead of traditional modulators, facilitates grid current tracking and reduced current harmonics. The use of only seven switches and two SCs results in increased efficiency and reduced losses. Comprehensive MATLAB/Simulink simulations and an experimental prototype validate the effectiveness of the proposed TLI for solar PV integration.

Pulsewidth modulation (PWM) techniques play a crucial role in determining the power quality of multilevel-inverter-based grid-tied solar photovoltaic (PV) fed systems. However, the existing PWM techniques suffer from the heat dissipation of the switches and power loss issues. In view of this concern, a new PWM technique is proposed to mitigate the junction temperature as well as the power loss of a cascaded H-bridge (CHB) inverter employed in a grid-tied solar PV system. Apart from the junction temperature of the power switch and power loss, different steady-state and dynamic responses of the CHB inverter are investigated using MATLAB/Simulink and PLECS software environments. Experimental results are also provided to support the simulation analysis.