Input Design Parameters Research Articles

Establishment of a semi-continuous nano-production line using the Microfluidizer® technology for the fabrication of lipid-based nanoparticles part 1: Screening of critical parameters and Design of Experiment optimization studies

A variety of strategies for producing high-quality nanoparticles have been reported in recent years. Batch-based bottom-up and top-down technologies are generally the most efficient methods, but present a number of challenges, particularly in terms of variability, safety, sustainability and large-scale production. In this study, a scalable, semi-continuous production line was built by connecting individual processing units, including a high shear mixing device, the Microfluidizer® technology and a cooling system. Each unit was equipped with an adequate temperature control to allow solvent-free production of solid lipid nanoparticles (consisting of Precirol® ATO 5 or Gelucire® 43/01) and nanostructured lipid carriers (additionally comprising Labrafac™ lipophile WL 1349). Subsequently, critical formulation parameters and critical process parameters (CPPs) of the individual processing units and their effects on particle size (i.e., critical quality attribute (CQA)) were investigated to identify appropriate input parameters for the subsequent Design of Experiment (DoE) studies conducted after linking the process units to a semi-continuous production line. For particle size monitoring, spatially resolved dynamic light scattering (SR-DLS) measurements were conducted and compared to standard DLS measurements to evaluate the applicability of SR-DLS as an inline monitoring tool. It was found that matrix composition, emulsifier concentration, pressure and number of cycles when processing through Microfluidizer® processor were the most influencing parameters. By optimizing these parameters, five-times higher throughputs could be achieved by the semi-continuous manufacturing line. In addition, the particle size measurements with SR-DLS confirmed the feasibility of implementing this technology for real-time particle size monitoring as an important safety factor in quality control.

Data-driven approach for design and optimization of rotor–stator mixers for miscible fluids with different viscosities

Rotor–stator mixers (RSMs) are essential equipment used in the food, pharmaceutical, and cosmetic industries. The performance of RSMs is conventionally analyzed using three approaches based on empirical correlations and experimental and numerical analyses. This paper introduces a new approach based on a multifidelity data-driven framework for designing and optimizing RSMs that systematically combines elements of experimental and high-resolution computational fluid dynamics (CFD) models to train a machine learning model to predict RSM performance. It was subsequently coupled to an optimization solver based on a genetic algorithm. The developed framework was applied to a small-scale RSM with variable rotor blade radius, rotor speed, stator mesh width, and mass flow rate that is operated to mix two miscible fluids with different viscosities. The RSM was optimized at two regimes of low and high viscosities to achieve the best performance in terms of shaft power and mixing indices. The results demonstrate the successful application of the developed frameworks. The optimization of the mixing of high-viscosity fluids yielded a higher improvement than that for low-viscosity fluids. A decrease in shaft power of approximately 50% was obtained for the high-viscosity fluids, whereas the mixing indices increased up to 20% by the optimization of the geometrical input design parameters. The proposed framework can be advantageous for designers and operators at the industrial scale.

A Framework for Multi-Physics Modeling, Design Optimization and Uncertainty Quantification of Fast-Spectrum Liquid-Fueled Molten-Salt Reactors

The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Hydrogen storage systems performance and design parameters using response surface methods and sensitivity analysis

DesignDesign of metal hydride-based hydrogen storage reactors is often performed using numerical/experimental modelling which is computationally/economically difficult. This paper investigates the applicability of Response Surface Methodology (RSM) coupled Local/Global Sensitivity Analysis (L/GSA) to investigate – i) the applicability of advanced RSMs in predicting the responses for storage systems efficiently, ii) the applicability of advanced RSMs to perform L/GSA to identify the sensitive input design parameters based on their effect on the Outputs of Interests (OIs), i.e., reaction fraction (i.e., C) and bed temperature (i.e., T), and iii) the dependence of importance ranking of design parameters on the employed L/GSA methodology. The study is conducted in two stages. In the first stage, the most accurate RSM was identified among fourteen traditional and advanced RSMs, i.e., radial basis, kriging, quadratic, moving least square, support vector machine etc., employing a measure of precision, i.e., Nash–Sutcliffe Efficiency (NSE). RSMs were constructed based on the values of OIs estimated using finite element simulation using COMSOL software for random realizations of inputs generated via Latin Hypercube Sampling (LHS). In the second stage, the importance ranking of design parameters was estimated for both OIs using six different L/GSAs based on the input-output relationships estimated in stage one. All the codes of RSMs and L/GSAs were written and validated in MATLAB. Finite element simulations of the random realizations were performed using COMSOL software. For the present study, NSEs of the considered RSMs were ranging between 0.6262-0.8544 and 0.4652–0.8081 for C and T respectively, indicating the importance of selection of appropriate RSM. RBF-augmented Compact-I and kriging were the most accurate RSMs with NSEs approximately 10%–20 % higher to those of frequently used polynomial RSM. Time (t) and mass of hydrogen to be stored (MH) were the most; and external temperature (Text) and porosity (E) were the least sensitive inputs corresponding to C and T, with differences of 80–90 % in the sensitivity indices respectively. The ranking prediction was highly dependent upon the employed L/GSA methodology, with Morris's screening observed to be the least accurate. The RSM methods described in this study help to design and investigate the metal hydride reactors for various applications (space heating, hydrogen storage, storage for vehicular application, metal hydride compressor) without undergoing detailed mathematical modelling of the system. The proposed methodology may significantly assist the designers to focus (or vary) on the sensitive inputs only during the physical modelling of systems to improve their performance. This sensitivity analysis is helpful to find out the most advance sensitivity analysis method which can be used to find out the most sensitivity parameter which can be varied according to their rankings to achieve the required performance of metal hydride reactor for the particular application. The advanced RSMs assist to identify these sensitive inputs quickly by reducing the mathematical efforts in the L/GSA by providing the accurate input-OIs relationships based on the limited numerical simulations. This will significantly save the resources and time of industries required in physical modelling/numerical simulations significantly, which otherwise would have been invested on investigating the non-sensitive inputs.

Probabilistic design and optimization of thermal protection system with variable thickness based on non-uniform aerodynamic heating

Thermal Protection System (TPS) is a critical component of space vehicles to protect them from damage by extreme aerothermal heating conditions. The uncertainty of the aerothermal heating conditions, including approaching velocities, atmospheric density, pressure, etc., significantly affects the aerothermal heating imposed on the TPS, vastly changing the initial design conditions. In this study, we developed a new uncertainty quantitative (UQ) analysis method to comprehensively consider all the uncertainty of TPS materials, structures, and aerothermal heating conditions. To address the challenges of a massive amount of input and determined optimization parameters, we first induced the response surface method and Monte Carlo algorithm to produce a large number of analysis samples for Probabilistic Design. Then, the key affecting factors, not all the input design parameters, were chosen for optimization with sequential optimization and reliability assessment (SORA) strategy by a sensitivity analysis. With such improvements, the optimization for UQ analysis becomes practical. We used this new UQ method to analyze the effects of uncertainty of the aerothermal heating conditions on the thermal protection layer. A variable thickness design is proposed to adapt the non-uniform aerothermal heating conditions for the TPS. The results show that significant light-weighting can be achieved without the reliability penalty. The optimized structure also reduces internal temperature gradients and facilitates a more uniform temperature field distribution for the TPS. This study provides a new UQ engineering design approach for the system with many designed parameters.

A reduced order/simplified method for uncertainty quantification on the floating wind turbine design basis

The cost of floating wind turbines (FWTs) is currently higher than onshore and bottom fixed offshore wind turbines. An important step towards more cost-efficient FWT structures is understanding the uncertainties and how they affect the design performance. On the path towards developing a fully probabilistic design approach, an important initial step is defining the variables and uncertainties that significantly impact the final design performance. This study aims to conduct a design sensitivity analysis on a FWT system consisting of the VolturnUS Semisubmersible floater coupled with an IEA 15 MW turbine. First, a Python-based design evaluation framework is implemented that computes operational limit states as a function of design input parameters defining the floater design. The system responses and the loads are obtained using Horizontal Axis Wind turbine simulation Code 2nd generation (HAWC2). Time domain outputs of the design evaluation framework are compared to a frequency domain response using Quick Load Analysis of Floating Wind Turbines (QuLAF). Initial results provide that buoyancy column diameter and the floater radius have the highest effects on the general floater response.

Explainable artificial intelligence framework for FRP composites design

Fiber-reinforced polymer (FRP) materials are integral to various industries, from automotive and aerospace to infrastructure and construction. While FRP composite design guidelines have been established, the process of obtaining the desired strength of an FRP composite demands considerable time and resources. Despite recent advancements in Machine Learning (ML) models which are commonly used as predictive models, the inherent 'black box' nature of those models poses challenges in understanding the relationship between input design parameters and output strength of the composite. Moreover, these models do not provide tools to facilitate the designing process of the composite. The current study introduces an explainable Artificial Intelligence (XAI) framework that will provide understanding for the input–output relationships of the model through SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs). In addition, the framework provides for the first time a designing approach for adjusting the important design parameters to obtain the desired composite strength by the designer through utilizing an explainability technique called Counterfactual (CF). The framework is evaluated through the design of a 14-ply composite, successfully identifying critical design parameters, and specifying necessary adjustments to meet strength requirements.

Performance prediction and Bayesian optimization of screw compressors using Gaussian Process Regression

Optimizing the performance of screw compressors is critical for achieving high efficiency and reducing costs in various industrial and engineering applications. Often, the design and optimization processes are time-consuming owing to the underlying iterative complex analyses. In this context, the present research investigates the potential of Gaussian Process Regression (GPR) and Bayesian optimization for the prediction and optimization of the performance of an oil-flooded screw compressor. Specifically, the GPR-based surrogate model is developed to predict the compressor performance characteristics based on its four main geometrical design parameters such as wrap angle, relative length, tip speed of the male rotor and built-in volume ratio. The model is trained using a dataset comprising 19,200 data points relating the input design parameters with the compressor performance, obtained using physics-based multi-chamber thermodynamic models. While four different learning algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Polynomial regression and GPR are explored, the GPR performed the best resulting in an R2 value of 0.99 for the test dataset after hyperparameter tuning. Further, the model is also experimentally validated on a completely unseen dataset, showing very good predictions with a maximum error of 5%. The resulting surrogate model is then used to optimize the compressor design parameters using Bayesian optimization. The results are compared with optimization using Genetic Algorithm (GA) and physics-based multi-chamber thermodynamic model. It was shown the proposed approach results in similar optimal design parameters but with a significantly less optimization time by a factor of 7. The study highlight the potential of machine learning-based prediction and optimization of screw compressors in engineering applications.

A methodical approach for the design of thermal energy storage systems in buildings: An eight‐step methodology

AbstractRecent research focuses on optimal design of thermal energy storage (TES) systems for various plants and processes, using advanced optimization techniques. There is a wide range of TES technologies for diverse thermal applications, each with unique technical and economic characteristics. Matching an application with the most suitable TES system remains challenging. This study proposes an eight‐step design methodology guiding the process from describing the thermal process to defining the most appropriate TES based on constraints and requirements. The steps include specifying the thermal process, system design parameters, storage characteristics, integration parameters, key performance indicators, optimization method, tools, and design robustness. Seven already‐designed TES systems are evaluated to assess the methodology's effectiveness, where the design procedures have been adapted to the proposed steps. Case studies involve various applications with both sensible and latent TES systems, demonstrating the applicability of the proposed design procedure. A significant diversity exists among the design cases regarding the design objective, input, design, and output parameters. Nevertheless, the design procedure in each case can be deconstructed into the outlined design steps. The last design step has been excluded from all case studies due to insufficient information regarding the robustness of the design process. The paper demonstrates how a methodical approach can be applied to examine the TES design and the integration. The design steps proposed in this study can serve as a foundation for developing a more systematic approach for designing TES systems in future works, resulting in simplifying the design process.

Analysis of Bridge Tests on Sandy Overburden Site with Fault Dislocating

Performance-based seismic design methods for bridges are advancing, yet limited research has explored the damage mechanisms of bridges subjected to extreme seismic effects, such as those near or across faults. To investigate the damage mechanisms under bedrock dislocation and bridge rupture resistance, providing essential insights for the standardized design and construction of bridges in close proximity to seismic rupture sites, we developed a large-scale device to model bridges in the immediate vicinity of tilted-slip strong seismic rupture sites. This included a synchronous bedrock dislocation loading system. Four sets of typical sandy soil modeling tests were concurrently conducted. The results indicate: (1) The overall shear deformation zone of the foundation and surface uneven deformation primarily concentrate the overburdened soil body along the fault dip. The damaged area under the low-dip reverse fault is lighter on the surface and inside the soil body compared to the high-dip-positive fault. (2) The presence of bridges reduces the width of the main rupture zone and avoidance distance to some extent. However, this reduction is not as significant as anticipated. The damage to the bridge pile foundation along the fault dislocation tendency notably leads to the bending damage of the bridge deck. (3) Input parameters for fracture-resistant bridge design (surface rupture zone location, extent, maximum deformation, etc.) can be deduced from the free site. Within the rupture zone, a “fuse” design can be implemented using simply supported girders. Additionally, combining the “fuse” design with simple supported girders on both sides and utilizing simple support beams for “fuse” design within the rupture zone, along with structural “disconnection”, allows for reinforcing measures on the bridge structure’s foundation platform and pile in the soil body.

Multi-objective optimization of a pipe energy pile with heat exchanger pipes subjected to chaotic advection

Shallow geothermal energy extraction through an energy pile system relies on the constant temperature available below the earth's surface at shallow depths. The energy pile system transfers the heat stored in the earth to a place of utility for space heating and cooling through fluid circulation, thus extracting geothermal energy. In this study, a 3D energy pile model comprising a helical heat exchanger pipe (HHEP) was developed and validated with the existing experimental data using a FEM-based software package. Further, it was hypothesized that replacing HHEP with a chaotic configured helical heat exchanger pipe (CHEP) would enhance thermal performance. The numerical analyses supported the hypothesis, where there was about a 15 % increase in heat release rate for energy piles containing CHEP. The above results form a basis for exploring the simulation results through laboratory scale model tests. Thus, an experimental setup was fabricated to conduct geothermal energy tests on a scaled pipe energy pile (PEP) model. The investigation was carried out by considering three design-input parameters: flow rate, pitch, and inlet temperature at three different levels with water and ethylene glycol as heat exchanger fluid (HEF) in a 4:1 ratio. The heat release rate of the PEP (Qp), heat flux per unit length of HEP (qpl), soil excess temperature (θs), total thermal resistance (TTR), and energy consumed (EC) were analyzed. Furthermore, multi-objective optimization using the MO-Jaya algorithm and multiple attribute decision-making (MADM) R-method were used to predict an optimal set of design-input parameters. Finally, a confirmation test was conducted to test the competence of the predicted parameters. The percentage variation between the predicted design input parameters and the experimental values was below 10 %.

Assessment of annual performance of quonset GiPVT system combined with an earth-air heat exchanger (EAHE) for hot and dry climatic conditions

Annual performance of a Quonset-shaped GiPVT system combined with an earth air heat exchanger (EAHE) has been evaluated for hot and dry climatic conditions. A periodic thermal model for the GiPVT system has been developed in terms of input climatic and design parameters by employing the energy balance equations of the greenhouse system. The present model is used to calculate the effective cooling and heating capacity of the EAHE, which is further utilized to determine the optimum operating hours of EAHE for different months of the year. Furthermore, the electrical energy of the GiPVT system and the thermal energy of EAHE are calculated, enabling a comprehensive performance evaluation of the greenhouse system. Results show that the EAHE increases the greenhouse air temperature by 8 °C on the coldest day and decreases the greenhouse air temperature by 21 °C on the hottest day of the year. EAHE has a lower potential for heating than cooling because of the hot and dry climatic conditions. Moreover, the system generates 17.6 kWh and 29.22 kWh of electrical energy on an extremely cold and hot day, respectively making the system self-sustainable. The EAHE significantly reduced TLL and DF values, which gives thermal comfort to the greenhouse.

A Study of Seating Suspension System Vibration Isolation Using a Hybrid Method of an Artificial Neural Network and Response Surface Modelling

A reliable prediction model can greatly contribute to the research of car seating system vibration control. The novelty of this paper lies in the development of a hybrid method of an artificial neural network (ANN) and response surface methodology (RSM) to predict the peak seat-to-head transmissibility ratio of a seating suspension system and to evaluate its ride comfort for different seat design parameters. Additionally, this method can remove the experimental design of the RSM model. In this paper, four seat design parameters are selected as input parameters and arranged using the central composite design method. The peak transmissibility ratio from seat to head at 4 Hz is chosen as the response target output value. To illustrate this hybrid method, the response target output value of the peak transmissibility ratio is calculated from the frequency response of a five-degrees-of-freedom (5-DOF) lumped-parameter biodynamic seating suspension model. The input design parameters and the response target output values are used to train an ANN to establish the relationship between the seat design parameters and the peak transmissibility ratio. At the same time, the input design parameters and the response target output values predicted by the ANN are used to develop the relationship between the seat design parameters and the peak transmissibility ratio using the response surface method and linear regression models. The hybrid of the ANN and response surface methods makes the planning or design of experiments not essential. The hybrid model of the ANN and response surface method is more accurate and convenient than a linear regression model for the study of seating system vibration isolation.

Synergizing Neutrosophy and Randomized Blocks Design: Development and Analytical Insights

The design of the experiment is a strategy for effectively examining the relationship between input design parameters and process output and developing a greater understanding. A randomized block design is an experimental design that has two primary factors and is widely used in agriculture, environment, biological, animal, and food sciences, where experimental material is heterogeneous and precise. In a randomised block design, one or more observations may lose their true significance due to an accident, poor handling, pest infestations in agricultural trials, or other factors. It is prudent to treat this value as missing and estimate it. In today’s practical situations, uncertainty and inaccuracies are inevitable in most research areas. It is important to handle such data, which can lead to inaccurate and unreliable results. Neutrosophy is the branch of philosophy that provides an efficient method to study impreciseness among the data. Some of the common sources of Neutrosophy in randomised block design are incorrect blocking factor selection, measurement error, subjective factors, and natural variability. It is paramount to handle the Neutrosophy in a randomised block design; otherwise, it may lead to various problems, like a high risk of false positives. In this paper, the Neutrosophic Randomised Block Design (NRBD) is introduced to tackle data impreciseness. The study also, outlines a methodology for estimating missing observations in NRBD and presents its analysis. Additionally, the study compares the efficiency of NRBD to that of the Neutrosophic Completely Randomised Design (NCRD).

Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine

Parametric cycle analysis, an on-design engine study, specifies the required design characteristics that optimize engine performance. This study aimed to conduct a parametric analysis of a low-bypass turbofan engine with an afterburner, F100-PW229, and develop a technique for estimating its performance based on data using machine learning and deep learning. Commercially available gas turbine simulation software, GasTurb 14, was used to create a dataset of engine performance response variables and input design parameters. The effects of the Mach number, fan pressure ratio, altitude, turbine entry temperature, and bypass ratio on the specific thrust, propulsive efficiency, specific fuel consumption, and total fuel flow were investigated. Regression learning models and deep neural networks were then programmed on this dataset to predict responses for new input data. In MATLAB, a total of 24 regression models were trained with cross-validation, and the model with the least root mean square error was selected as the final model. The machine learning regression models produced reliable output parameter predictions, with the least root mean square error of 9.076 × 10−5. Among the numerous regression models tested, Gaussian process regression, the quadratic support vector machine, and the wide neural network emerged to be the most successful in predicting turbofan engine performance metrics. A multilayer perceptron model was coded in Python with two hidden layers that accurately predicted the performance parameters. The mean square error value on test data was found to be as low as 0.0046. In comparison to intensive computational simulations, machine learning and deep learning models offer an efficient method for conducting parametric analysis of turbofan engines.

Optimisation of design parameters of the finned tube heat exchanger by numerical simulations and artificial neural networks for the condensing wall hang boilers

This research investigates the use of computational fluid dynamics (CFD) and artificial neural networks (ANNs) to optimise the design of finned tube heat exchangers for use in condensing wall-mounted boilers (WHBcs). Fin height, thickness, and distance are selected as the input design parameters, and the internal volume of the heat engine is modelled using the CFDHT (CFD and heat transfer) method. Different ANN structures are trained and tested on the resulting data to identify the optimal training process. The trained ANN is then used to predict various output parameters, including total heat transfer on the inner surface of the tube, maximum temperature on the fins, total heat transfer per unit volume of the heat exchanger, and pressure drop between the inlet and outlet of the internal volume. The optimal design scenarios are evaluated based on design criteria, and the ANN is found to have good statistical performance, with an average accuracy of 1.00018 and a maximum relative error of 9.16%. The ANN is able to accurately estimate the optimal design case.

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance.

In recent years, there has been a growing interest in the field of 3D printing technology. Among the various technologies available, fused deposition modeling (FDM) has emerged as the most popular and widely used method. However, achieving optimal results with FDM presents a significant challenge due to the selection of appropriate process parameters. Therefore, the objective of this research was to investigate the impact of process parameters on the tribological and frictional behavior of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D-printed parts. The design of experiments (DOE) technique was used considering the input design parameters (infill percentage and layer thickness) as variables. The friction coefficient values and the wear were determined by experimental testing of the polymers on a universal tribometer employing plane friction coupling. Multi-response optimization methodology and analysis of variance (ANOVA) were used to highlight the dependency between the coefficient of friction, surface roughness parameters, and wear on the process parameters. The optimization analysis revealed that the optimal 3D printing input parameters for achieving the minimum coefficient of friction and linear wear were found to be an infill percentage of 50% and layer thickness of 0.1 mm (for ABS material), and an infill percentage of 50%, layer thickness of 0.15 mm (for PLA material). The suggested optimization methodology (which involves minimizing the coefficient of friction and cumulative linear wear) through the optimized parameter obtained provides the opportunity to select the most favorable design conditions contributing to a more sustainable approach to manufacturing by reducing overall material consumption.

Effect of the foundation soil properties on the optimum design of reinforced concrete retaining walls and defining critical boundary lines

The optimum design of reinforced concrete retaining wall (RCRW) is among the common optimization problems in civil engineering as it is affected by many different failure modes and input parameters. The properties of the foundation soil are one of the most critical input parameters in the optimum design of the retaining wall, just as in other civil engineering structures. While the existing foundation soil conditions on the site may sometimes be sufficient for an optimum design or it is necessary to consider alternative solutions such as ground improvement or foundation type change. This paper aims to determine the effects of the shear strength parameters of the foundation soil on the optimum wall cost using the modified artificial bee colony algorithm. For this purpose, optimum designs were performed separately for a total of 9246 different shear strength parameter combinations for a wide range, where the cohesion parameter was also considered in addition to the internal friction angle only used in the literature. The analysis results showed that the shear strength parameters of the foundation soil are highly influential in wall costs under weak soil conditions but not under strong soil conditions. In order to make this distinction easier, three important cost regions and two boundary lines separating these regions were defined depending on the shear strength parameters of the foundation soil, and an approach was proposed to determine the positions of these boundary lines. Moreover, the 125 wall models were investigated under 9246 different foundation soil conditions to reveal the effects of important parameters such as wall height, surcharge load, and backfill internal friction angle on the critical boundary lines. Knowing the boundary lines separating weak and strong soil conditions depending on the wall input parameters will significantly contribute to the truly optimum design of retaining walls.

Perovskites informatics: Studying the impact of thicknesses, doping, and defects on the perovskite solar cell efficiency using a machine learning algorithm

AbstractThe integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively.