Range Of Input Parameters Research Articles

Optimizing the performance parameters of vacuum evaporation technology for management of anaerobic digestate in a waste water treatment plant using fuzzy MCDM method

Evaporation technology in waste water treatment plants is used to treat excess liquid digestate, eventually reaping the essential nutrients from the process. In the present study, the input conditions have been optimized for implementing and achieving optimal performance of the digestate evaporation technology used in waste water treatment plants by employing fuzzy multi-criteria decision making method. Three input parameters, namely the Temperature, the Pressure, and the Mass Flow rate, each at three levels are considered. Experiments are conducted as per the Taguchi’s L9 standard orthogonal array. The performance of the evaporator is examined based on a few response characteristics, such as payback period, thermal energy consumption, power consumption and cost per unit of electricity. Temperature's impact on multi performance stands out significantly, contributing the most (70.52 %), followed by mass flow rate (18.95 %), and pressure (5.37 %). Result of the study reveals that within the investigated range of input parameters, temperature at 80 ℃, pressure at 0.3 bar and mass flow rate at 20 kg/h is the optimal combination. The results of the present study will help in enhancing the efficiency and effectiveness of the evaporation process, leading to improved digestate treatment that supports circular economy initiatives.

Flow-based Generative Emulation of Grids of Stellar Evolutionary Models

We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multidimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic data sets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age uncertainties can arise when fitting only to global asteroseismic and spectroscopic parameters without prior information on initial helium abundances and mixing length parameter values. We also conduct inference using the flow at a large scale by determining revised estimates of masses and radii for 15,388 field red giants. These estimates show improved agreement with results from existing grid-based modeling, reveal distinct population-level features in the red clump, and suggest that the masses of Kepler red giants previously determined using the corrected asteroseismic scaling relations have been overestimated by 5%–10%.

Utilizing machine learning to forecast mechanical characteristics of NaOH-Treated jute fiber reinforced composite materials

The prediction of mechanical properties in composite materials is crucial for optimizing their performance in various engineering applications. This study focuses on NaOH treated jute fiber reinforced glass epoxy composites, aiming to predict key mechanical characteristics like flexural strength, tensile strength, and the hardness. A comprehensive dataset of 974 samples was analyzed, encompassing a range of input parameters including fiber content, fiber orientation, matrix type, and processing conditions. Advanced statistical and machine learning techniques were employed to develop predictive models, with XGBoost showing itself to be the best model in terms of predicting each of the three mechanical properties. Feature importance analysis revealed that fiber content is the most significant factor influencing the predictions.

Semi-analytical modeling of prompt redeposition in a steady-state plasma

Abstract A steady-state, 1D semi-analytical model for prompt redeposition based on the separation between redeposition caused by the electric field in the sheath and redeposition related to gyromotion is here described. The model allows for the estimation of not only the fraction of promptly redeposited flux but also the energy and angular distribution of the non-promptly redeposited population, along with their average charge state. Thus, the temperature and mean parallel-to-B velocity of the non-promptly redeposited flux are also available. The semi-analytical model was validated against equivalent Monte Carlo simulations across a broad range of input parameters. In this paper the eroded material under exam was tungsten (W) for which the code demonstrated consistent agreement with respect to numerical results, within its defined validity limits. The model can theoretically provide a solution for any material, temperature and electron density profile in the sheath, monotonic potential drop profile, and sputtered particles energy and angular distribution at the wall. As such, this code emerges as a potential tool for addressing the boundary redeposition phenomenon in fluid impurity transport simulations.

Stochastic configuring networks based adaptive sliding mode control strategy to improving stability of SG-VSG paralleled microgrid

This article proposes an adaptive sliding mode control (SMC) strategy based on stochastic configuring networks (SCNs) to improve the stability of synchronous generator (SG) and virtual synchronous generator (VSG) paralleled microgrid and enhance the dynamic performance. First, derives an equivalent swing equation what coupling of inherent inertia/damping of SG and virtual inertia/damping of VSG, the parameters design problem is established to a stabilization problem of second-order uncertain nonlinear system with bounded uncertainties. Second, an adaptive sliding mode control based on variable-speed reaching law is designed to maintain the second-order nonlinear system stable, and the switch function of adaptive reaching law is improved to reduce system chattering. Then, the stochastic configuring networks is introduced to approximate the nonlinear term, and the approximation ability of SCNs is improved by designing the configuration range of input parameters of hidden layer nodes. The stability of proposed control strategy is proved by Lyapunov theory. Finally, the feasibility and effectiveness of the proposed control strategy are verified by the designed simulations and experiments.

Harnessing Artificial Neural Networks for Financial Analysis of Investments in a Shower Heat Exchanger

This study focused on assessing the financial efficiency of investing in a horizontal shower heat exchanger. The analysis was based on net present value (NPV). The research also examined the possibility of using artificial neural networks and SHapley Additive exPlanation (SHAP) analysis to assess the profitability of the investment and the significance of individual parameters affecting the NPV of the project related to installing the heat exchanger in buildings. Comprehensive research was conducted, considering a wide range of input parameters. As a result, 1,215,000 NPV values were obtained, ranging from EUR −1996.40 to EUR 36,933.83. Based on these values, artificial neural network models were generated, and the one exhibiting the highest accuracy in prediction was selected (R2 ≈ 0.999, RMSE ≈ 57). SHAP analysis identified total daily shower length and initial energy price as key factors influencing the profitability of the shower heat exchanger. The least influential parameter was found to be the efficiency of the hot water heater. The research results can contribute to improving systems for assessing the profitability of investments in shower heat exchangers. The application of the developed model can also help in selecting appropriate technical parameters of the system to achieve maximum financial benefits.

Assessment and development of empirical correlations between flat plate dilatometer and cone penetration tests for Auckland soils

ABSTRACT Based on a database of over 2900 cone penetration test (CPT) and flat plate dilatometer test (DMT) data pairs obtained from Auckland, New Zealand, this paper assesses the applicability of several empirical CPT-DMT correlations and presents newly developed regional correlations for use in geotechnical engineering applications. Empirical correlations using the DMT-based material index are assessed. As both the DMT-based material index (ID ) and the soil behaviour type index (Ic ) from CPT data are often used to estimate soil behaviour characteristics when no samples are retrieved, these were used to develop subset databases. In general, the correlation used to estimate the material index (ID ) tended to slightly overestimate the measured values for Auckland soils, while better performance could be observed after removing data points where Ic and ID soil classifications were incompatible. In contrast, the correlations used to estimate horizontal stress index (KD ), and dilatometer modulus (ED ) underestimated the measured values for the database used herein, with the largest underestimates for low cone tip resistance values. This paper presents new correlations for estimating KD and ED based on CPT parameters for Auckland soils, providing improved estimates, with stable bias values across the range of input parameters considered.

Optimization of Pin Fins Using Computational Fluid Dynamics and Machine Learning

This paper presents a two-part study focusing on the optimization of pin-fin arrays for gas turbine blade cooling. The first part of the study examines the thermal performance of various pin-fin shapes and sizes using computational fluid dynamics. The study investigates circular, elliptical, hexagonal, and rectangular cross sections, with emphasis on the hydraulic diameter and Reynolds number. Two-dimensional simulations mimicked a confined, staggered array of uniform-sized pins under different inlet conditions. For a hydraulic diameter of 0.053 m and a Reynolds number of 5500, the rectangular pins showed the highest Nusselt number 8.98, 16.01, and 20.62% larger than circular, elliptical, and hexagonal pins, respectively, but also a pressure increase 68.30% higher on average. For a hydraulic diameter of 0.046 m and a Reynolds number of 1000–15,000, it was shown that all shapes have an exponential increase in pressure and a logarithmic decay in the Nusselt number. Standing out was the rectangular shape, which, at a maximum Reynolds number of 15,000, had a pressure increase of 108% larger than the next in line, the hexagonal pin. In the same range of inlet conditions, the Gee–Webb coefficient was the highest for the circular pin, which was selected as the main shape for the following segment. In the second part of the study, a machine learning framework based on a neural network architecture is presented. The neural network predicts the thermal performance of different pin-fin array configurations. At the end, this framework is used in an optimization process that explores the advantages of nonuniform arrays consisting of differently sized circular pins as opposed to the standard uniform structures of the first part. The model proves to be capable of accurately predicting the characteristic thermal performance coefficients across a wide range of input parameters. The nonuniform array managed a 23% reduction in pressure rise at a 0.2% exit temperature increase compared to a reference standard array of the same hydraulic diameter.

Artificial intelligence model for predicting the load-bearing capacity of eccentrically compressed short concrete filled steel tubular columns

The purpose of this work is to develop the artificial neural network (ANN) model to determine the load-bearing capacity of concrete filled steel tubular (CFST) columns of circular cross-section in a wide range of input parameters. Short columns are considered for which deflections do not lead to a significant increase in the eccentricity of the axial force. The input parameters of the artificial neural network are the outer diameter of the pipe, the wall thickness, the yield strength of steel, the compressive strength of concrete, and the relative eccentricity of the axial force. The artificial neural network is trained on the synthetic data. For training, the dataset of 179,025 numerical experiments with different values of input parameters was generated. Numerical experiments were carried out using the finite element method in a simplified formulation, which makes it possible to reduce the three-dimensional problem of determining the stress-strain state of a CFST column to a two-dimensional problem. The results of testing the developed model on the data from full-scale experiments are presented.

Development of a Model to Support the Management of the Resistance Welding Process

The work was carried out as part of a project to improve the corrosion resistance of welded joints. The optimal selection of input parameters reduces the consumption of welded material as well as the negative impact on the environment. The samples were welded by electrical resistance welding, more precisely by cross-wire welding. Electric resistance welding is a process for welding with electricity in which of high electrical resistance is used to generate heat at the contact point of the welded parts. Steel wire S235 with a diameter of 4 and 3 mm was used. The article shows the creation of the model to predict the percentage of setdown and weld strength as a function of the input parameters, welding current and welding time. The first task was to create a Design of experiments in which the process parameters or the range of input parameters are determined. The Design of the experiments was created for both wire diameters. This covers the range of parameters with which a welded joint of wires can be realised. A statistical analysis of the process follows, where it was found that for 4 and 3 mm diameter wires the percentage setdown increases with increasing welding current and time. An increase in strength as a function of welding current and welding time is also observed, but the results overlap in most cases and the range of their values is greater than that of setdown. The appearance of the weld was analysed for each sample. The visual inspection revealed seven categories of welds, which were divided into two groups, i.e. good and poor welds. All results were listed in a table with the percentage of expectation for a particular category of weld appearance. Based on the analysis, a model for determining the welding result parameters depending on the specified input parameters was developed, which can be used in practise to support the management of the electric resistance welding process.

A comparative study on milling-induced surface roughness during milling of jute–epoxy and carbon–epoxy composites and optimization for better surface finish through Taguchi and RSM techniques

During the present experimentation, milling machining was performed on two different composites, namely carbon fiber-reinforced polymer composite and jute fiber-reinforced polymer composite, using a computer numerical control vertical machining center. The selected machining parameters were spindle speed (S), feed rate (FR), depth of cut (DOC), and flute number or cutting edge number (FN). The output parameter is the machined surface roughness (Ra). Analysis of variance was used to predict the percentage influence of each parameter on machining quality. The parameter feed rate exhibited a higher influence on the machined surface roughness, followed by spindle speed, flute number, and depth of cut in sequence. Similarly, while milling the carbon fiber composite, the feed rate had the highest influence, followed by the parameter flute number. As for the surface roughness, the feed rate had a greater effect, followed by the spindle speed. Under the same machining conditions, it was observed that the sequence of parameters influencing the jute composite and carbon composite changed in the case of cutting force generation, but the sequence of parameters was the same for both cases in terms of roughness. The outcome of the work confirmed that to achieve a smaller value of roughness in the milling of jute–epoxy composite, the optimum combination should be S = 3000 rpm, FR = 800 mm/min, DOC = 0.25 mm, and FN = 6. Similarly, to achieve the minimum surface roughness value in the milling of carbon–epoxy composite, the optimum combination of parameters should be S = 600 rpm, FR = 100 mm/min, DOC = 0.25, and FN = 6. The average roughness values obtained during the milling of jute–epoxy composite and carbon–epoxy composites are 6.685 and 3.08 μm, respectively. In this present work, it is proved that the optimum combination of parameters to get the minimum surface roughness and the amount of surface roughness produced during milling are highly influenced by the type of reinforced material. The graphs are prepared for the entire range of input parameters to identify the intermediate Ra value at any input value without the use of software.

Process Operability Analysis of Membrane-Based Direct Air Capture for Low-Purity CO2 Production.

Addressing climate change constitutes one of the major scientific challenges of this century, and it is widely acknowledged that anthropogenic CO2 emissions largely contribute to this issue. To achieve the "net-zero" target and keep the rise in global average temperature below 1.5 °C, negative emission technologies must be developed and deployed at a large scale. This study investigates the feasibility of using membranes as direct air capture (DAC) technology to extract CO2 from atmospheric air to produce low-purity CO2. In this work, a two-stage hollow fiber membrane module process is designed and modeled using the AVEVA Process Simulation platform to produce a low-purity (≈5%) CO2 permeate stream. Such low-purity CO2 streams could have several possible applications such as algae growth, catalytic oxidation, and enhanced oil recovery. An operability analysis is performed by mapping a feasible range of input parameters, which include membrane surface area and membrane performance metrics, to an output set, which consists of CO2 purity, recovery, and net energy consumption. The base case for this simulation study is generated considering a facilitated transport membrane with high CO2/N2 separation performance (CO2 permeance = 2100 GPU and CO2/N2 selectivity = 1100), when tested under DAC conditions. With a constant membrane area, both membranes' intrinsic performances are found to have a considerable impact on the purity, recovery, and energy consumption. The area of the first module plays a dominant role in determining the recovery, purity, and energy demands, and in fact, increasing the area of the second membrane has a negative impact on the overall energy consumption, without improving the overall purities. The CO2 capture capacity of DAC units is important for implementation and scale-up. In this context, the performed analysis showed that the m-DAC process could be appropriate as a small-capacity system (0.1-1 Mt/year of air), with reasonable recoveries and overall purity. Finally, a preliminary CO2 emissions analysis is carried out for the membrane-based DAC process, which leads to the conclusion that the overall energy grid must be powered by renewable sources for the technology to qualify within the negative emissions category.

A Comparative Study of Embedded Wall Displacements Using Small-Strain Hardening Soil Model

Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM.

A theoretical framework for BL Her stars. II. New period-luminosity relations in Gaia passbands

In the era of the Hubble tension, it is crucial to obtain a precise calibration of the period-luminosity ($PL$) relations of classical pulsators. Type II Cepheids (T2Cs; often exhibiting negligible or weak metallicity dependence on $PL$ relations) used in combination with RR Lyraes and the tip of the red giant branch may prove useful as an alternative to classical Cepheids for the determination of extragalactic distances. We present new theoretical $PL$ and period-Wesenheit ($PW$) relations for a fine grid of convective BL Her (the shortest period T2Cs) models computed using mesa-rsp in the $Gaia$ passbands and we compare our results with the empirical relations from $Gaia$ DR3. Our goal is to study the effect of metallicity and convection parameters on the theoretical $PL$ and $PW$ relations. We used the state-of-the-art 1D non-linear radial stellar pulsation tool mesa-rsp to compute models of BL Her stars over a wide range of input parameters:\ metallicity ($-2.0\; dex Fe/H dex $), stellar mass ($0.5M_ odot -0.8M_ odot $), stellar luminosity ($50L_ odot -300L_ odot $), and effective temperature (across the full extent of the instability strip; in steps of 50K). We used the Fourier decomposition technique to analyse the light curves obtained from mesa-rsp and $Gaia$ DR3 and then compared the theoretical and empirical $PL$ and $PW$ relations in the $Gaia$ passbands. The BL Her stars in the All Sky region exhibit statistically different $PL$ slopes compared to the theoretical $PL$ slopes computed using the four sets of convection parameters. We find the empirical $PL$ and $PW$ slopes from BL Her stars in the Magellanic Clouds to be statistically consistent with theoretical relations computed using the different convection parameter sets in the $Gaia$ passbands. There is a negligible effect coming from the metallicity on the $PL$ relations in the individual $Gaia$ passbands. However, there is a small but significant negative coefficient of metallicity in the $PWZ$ relations for the BL Her models using the four sets of convection parameters. This could be attributed to the increased sensitivity of bolometric corrections to metallicities at wavelengths shorter than the $V$ band. Our BL Her models also suggest a dependence of the mass-luminosity relation on metallicity. We found the observed Fourier parameter space to be covered well by our models. Higher mass models ($> 0.6\ odot $) may be needed to reliably model the observed light curves of BL Her stars in the All-Sky region. We also found the theoretical light curve structures (especially the Fourier amplitude parameters) to be affected by the choice of convection parameters.

Subduction plate interface shear stress associated with rapid subduction at deep slow earthquake depths: example from the Sanbagawa belt, southwestern Japan

Abstract. Maximum shear stress along an active deformation zone marking the subduction plate interface is important for understanding earthquake phenomena and is an important input parameter in subduction zone thermomechanical modeling. However, such maximum shear stress is difficult to measure directly at depths more than a few kilometers and is generally estimated by simulation using a range of input parameters with large associated uncertainties. In addition, estimated values generally represent maximum shear stress conditions over short observation timescales, which may not be directly applicable to long-timescale subduction zone modeling. Rocks originally located deep in subduction zones can record information about deformation processes, including maximum shear stress conditions, occurring in regions that cannot be directly accessed. The estimated maximum shear stress is likely to be representative of maximum shear stress experienced over geological timescales and be suitable to use in subduction zone modeling over timescales of millions to tens of millions of years. In this study, we estimated maximum shear stress along a subduction plate interface by using samples from the Sanbagawa metamorphic belt of southwestern (SW) Japan, in which slivers of mantle-wedge-derived serpentinite are widely distributed and in direct contact with metasedimentary rocks derived from the subducted oceanic plate. These areas can be related to the zone of active deformation along the subduction plate interface. To obtain estimates of maximum shear stress at the subduction interface, we focused on the microstructure of quartz-rich metamorphic rocks – quartz is the main component of the rocks we collected and its deformation stress is assumed to be roughly representative of the stress experienced by the surrounding rock and plate interface deformation zone. Maximum shear stress was calculated by applying deformation temperatures estimated by the crystallographic orientation of quartz (the quartz c-axis fabric opening-angle thermometer) and the apparent grain size of dynamically recrystallized quartz in a thin section to an appropriate piezometer. Combined with information on sample deformation depth, estimated from the P–T (pressure–temperature) path and deformation temperatures, it is suggested that there was nearly constant maximum shear stress of 15–41 MPa in the depth range of about 15–30 km, assuming plane stress conditions even when uncertainties related to the measurement direction of thin section and piezometer differences are included. The Sanbagawa belt formed in a warm subduction zone. Deep slow earthquakes are commonly observed in modern-day warm subduction zones such as SW Japan, which has a similar thermal structure to the Sanbagawa belt. In addition, deep slow earthquakes are commonly observed to be concentrated in a domain under the shallow part of the mantle wedge. Samples showed the depth conditions near the mantle wedge, suggesting that these samples were formed in a region with features similar to the deep slow earthquake domain. Estimated maximum shear stress may not only be useful for long-timescale subduction zone modeling but also represent the initial conditions from which slow earthquakes in the same domain nucleated.

Artificial Neural Network Models for Determining the Load-Bearing Capacity of Eccentrically Compressed Short Concrete-Filled Steel Tubular Columns

Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections, which operated on the widest possible range of input parameters. Short columns were considered for which the amount of deflection does not affect the bending moment. A feedforward network was selected as the neural network type. The input parameters of the neural networks were the outer diameter of the columns, the thickness of the pipe wall, the yield strength of steel, the compressive strength of concrete and the relative eccentricity. Artificial neural networks were trained on synthetic data generated based on a theoretical model of the limit equilibrium of CFST columns. Two ANN models were created. When training the first model, the ultimate loads were determined at a given eccentricity of the axial force without taking into account additional random eccentricity. When training the second model, additional random eccentricity was taken into account. The total volume of the training dataset was 179,025 samples. Such a large training dataset size has never been used before. The training dataset covers a wide range of changes in the characteristics of the pipe metal and concrete of the core, pipe diameters and wall thicknesses, as well as eccentricities of the axial force. The trained models are characterized by high mean square error (MSE) scores. The correlation coefficients between the predicted and target values are very close to 1. The ANN models were tested on experimental data for 81 eccentrically compressed samples presented in five different works and 265 centrally compressed samples presented in twenty-six papers.

Data-driven approach for Cu recovery from hazardous e-waste

The Cu recovery from e-waste is beneficial since it contributes the most economic value to electronic scraps (∼322,000 tons Cu available with PCB). Data Science and Artificial Intelligence (AI) tools, experimental data-driven Adaptive Neuro-Fuzzy Inference System (ANFIS), and Response Surface Methodology (RSM) were adopted for the prediction of Cu recovery from e-waste. These models were developed using four variables (H2SO4, H2O2, Solid/Liquid ratio, and reaction time) and validated by validation experiments. The developed RSM-based model predicts the Cu recovery with an average error of ∼10.51%, whereas the ANFIS-based model predicts the Cu recovery with an error of ∼6.03%. Higher values of R2 (0.99), F (641.59), and a lower value of P (< 0.05) were observed in the ANOVA study indicating better suitability of the model. Based on findings from the comparison, the relevant data-driven ANFIS model is used for further analysis. The 3D interactive plots have been developed to find a suitable range of input parameters to improve the recovery of Cu from e-waste. The results and findings reported in this article will be valuable to Cu recovery, e-waste management, and hazardous waste management. The proposed ANFIS model is highly efficient for effective and accurate automation at the industrial scale. Environmental implicationScientific communities must pay attention to the challenges raised due to surplus e-waste. E-waste is reported to be the most dangerous anthropogenic material due to hazardous chemicals and toxic metals. This article explains how e-waste can be converted into economically beneficial products. Since Cu contributes the most economic value to e-waste, its recovery can minimize its hazardous effect and promote a circular economy. Modern data-driven tools are highly efficient for accurately predicting Cu recovery. In this article, RSM and ANFIS-based prediction models have been developed with high accuracy. It was observed that ANFIS is more efficient than RSM and better for the automation of copper recovery. The results and findings reported in this article will be valuable to Cu recovery, e-waste management, and hazardous management of e-waste. The proposed data-driven approach helps to minimize the load of electronic waste and reduces a significant proportion of hazardous effects of e-waste on the environment.

Optimising FDM printing parameters for improved tensile properties in 3D printed ASTM D638 standard samples

ABSTRACT The present study aims to optimise Fused Deposition Modelling (FDM) printing parameters to enhance the tensile properties of 3D printed specimens adhering to the ASTM D638 standard. The mechanical integrity of printed components is crucial for their successful application in load-bearing scenarios. Through a systematic exploration of key FDM printing parameters, such as layer height, infill density, print speed, and extrusion temperature, their impact on the tensile strength, yield strength, and elongation at break of the ASTM D638 specimens have been explored. The Taguchi design of experiments methodology has been utilised to efficiently traverse the parameter space and identify the optimal combination of printing settings. Tensile tests were conducted on the 3D printed specimens, produced with different parameter configurations, to evaluate their mechanical performance. The results have been analysed to reveal the significant effects of each parameter on the tensile properties and the interactions between multiple parameters. The optimised parameters identified in this research can be directly implemented to enhance the mechanical performance of 3D printed components in practical applications. Additionally, this work contributes to a broader understanding of the interplay between process parameters and material properties in additive manufacturing, facilitating further advancements in the field. Through rigorous experimentation and optimisation, a suitable range of input parameters was identified to achieve maximum tensile strength. Specifically, the extruder temperature is recommended to be within the range of 207.5°C to 210°C, layer thickness between 0.23 and 0.30 mm, print speed between 40 and 44 mm/sec, and infill density between 94% and 100%.

Frequency response of a mass grounded by linear and nonlinear springs in series: An exact analysis

An exact analytical solution for the frequency response of a system consisting of a mass grounded by linear and nonlinear springs in series was derived. The system is applicable in the study of beams carrying intermediate rigid mass. The exact solution was derived by naturally transforming the integral of the governing nonlinear ODE into a form that can be expressed in terms of elliptic integrals. Hence, the exact frequency–amplitude solution was derived in terms of the complete elliptic integral of the first and third kinds. Periodic solutions were found to exist for all real values of [Formula: see text] and for [Formula: see text]. On the other hand, linear or weakly nonlinear frequency–amplitude responses were found to occur during small-amplitude vibrations ([Formula: see text]), very large-amplitude vibrations with strong hardening nonlinearity ([Formula: see text] and [Formula: see text]), and for all amplitudes when [Formula: see text]. Simulations showed that the system’s periodic response is significantly influenced by the nonlinearity parameter ([Formula: see text]), linearity parameter ([Formula: see text]), and amplitude of vibration ([Formula: see text]). Besides, the existence of bifurcation points at [Formula: see text] and different values of [Formula: see text] was confirmed. Lastly, an approximate frequency solution obtained using the He’s frequency–amplitude formulation was found to produce errors less than 1.0% for a wide range of input parameters. In conclusion, the present study provides a benchmark solution for verification of other approximate solutions, and the He’s frequency–amplitude formulation can be used to obtain fast and accurate solutions for complex nonlinear systems.

Large Earthquakes Recurrence Time in the Kefalonia Transform Fault Zone (KTFZ), Greece: Results from a physics-based simulator approach

Large earthquakes mean recurrence time (Tr) on specific fault segments is one of the primary input parameters for developing long-term Earthquake Rupture Forecast (ERF) models in a specific time span considering either a time-independent or an elastic rebound motivated renewal assumption. An attempt is made to define Tr on the major fault segments comprised in Kefalonia Transform Fault Zone (KTFZ), which is an active boundary demarcating from the west the area of central Ionian Islands, namely Lefkada and Kefalonia, and is associated with remarkably high seismic activity. Frequent large (Mw ≥ 6.0) earthquakes are reported to have caused severe damage during the last six centuries. Although the number of large earthquakes (including both historical and instrumental) is satisfactory enough for regional hazard studies, their number become very limited when they are subdivided into subsets assigned to specific fault segments. Physics-based earthquake simulators are approaches to overcome recurrence intervals shortage, due to their ability to generate long lasting earthquake catalogs. The application of a physics-based simulatorn the KTFZ, is attemped upon a detailed fault network model and implemented multiple times and with a wide range of input parameters, aiming at the definition of the most representative simulated catalog in respect to the observed regional seismicity. The most representative simulated catalog is finally used for investigating the recurrence behavior of large (Mw ≥ 6.0) earthquakes and assessing whether the renewal model performs better that the Poisson model, after considering both individual and multiple ruptured segments scenarios.