Material Parameters Research Articles

Comparison of a quasi Newton method using Broyden’s update formula and an adjoint method for determining local magnetic material properties of electrical steel sheets

Purpose Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data. Design/methodology/approach The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered. Findings The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed. Originality/value The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

Non-similar analysis of bioconvection MHD micropolar nanofluid on a stretching sheet with the influences of Soret and Dufour effects

In this study, we investigate the capabilities of magnetohydrodynamic bioconvective micropolar nanofluids, considering the impact of Soret and Dufour effects using a non-similarity analysis. Our objective is to forecast the complex heat and mass transfer phenomena observed in both biological and industrial systems. In recent years, notable progress in energy applications has spurred our inquiry and exploration. To augment thermal conductivity and explore potential biocompatibility, we utilize blood as the base fluid, incorporating silver Ag\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({\ ext{Ag}}\\right)$$\\end{document} and copper oxide (CuO)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{CuO}})$$\\end{document}. This distinctive configuration offers improved control over thermal properties and supports the exploration of advanced applications across various domains. In our analysis, we also consider factors such as viscous dissipation, the influences of Soret and Dufour effects, the existence of a magnetic field, and the occurrence of heat generation. The governing PDEs and their corresponding boundary conditions are transformed into dimensionless form through the use of suitable non-similar transformations. The outcomes generated by the modified model are obtained through the application of a local non-similarity approach, extended up to the second degree of truncation, and integrated with a finite difference code (bvp4c). Furthermore, the effects of different factors on fluid flow, micro-rotation, heat transfer, volume fraction, and microorganism properties in the analyzed flow scenarios are demonstrated and examined through visual representations, following the attainment of satisfactory agreement between the obtained results and those reported in prior studies. The tables are designed to present numerical variations for the drag coefficient and Nusselt number. A comparative analysis is conducted on previously published work, despite certain limitations, in order to evaluate the accuracy of the numerical scheme. It can be shown that the material parameter K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document} has two effects on micropolar fluid dynamics: it increases the micro-rotation profile, which leads to higher fluid stiffness, and it reduces the velocity profile in response to an angled magnetic field. Furthermore, in bio-convective micropolar fluid, greater K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document} values are correlated with an elevated temperature profile, showing enhanced heat transfer efficiency via increased fluid speed and kinetic energy production. The velocity profiles in bioconvective micropolar fluids rise with higher magnetic field values (M)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(M)$$\\end{document}, highlighting the significance of magnetic field orientations for a thorough comprehension of the behavior of fluids in these systems. Increasing the Dufour effect (Du)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{Du}})$$\\end{document} raises the temperature profile, whereas increasing the Soret effect (Sr)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{Sr}})$$\\end{document} lowers the concentration profile. Furthermore, increasing the bio-convective Lewis numbers (Le)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{Le}})$$\\end{document} results in larger concentrations of moving microorganisms, but increasing the Peclet number (Pe)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({\ ext{Pe}})$$\\end{document} results in a drop in microbe concentrations. The main focus of our study is to devise unique transformations customized to address the intricacies of the specific problem under investigation. These transformations aim to produce precise and efficient outcomes, offering valuable insights for future research in the realm of nanofluid flows, particularly concerning pressure ulcer problems.

Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques

Effect of Process and Material Parameters on the Permeabilities of Reinforcement Mats: Experimentations and Machine Learning Techniques

Hybrid modeling of an ultracentrifugation process for separation of full and empty adeno-associated virus particles.

Ultracentrifugation is an attractive method for separating full and empty capsids, exploiting their density difference. Changes of the serotype/capsid, density of loading material, or the genetic information contained in the adeno-associated viruses (AAVs) require the adaptation of the harvesting parameters and the density gradient loaded onto the centrifuge. To streamline these adaptations, a mathematical model could support the design and testing of operating conditions.Here, hybrid models, which combine empirical functions with artificial neural networks, are proposed to describe the separation of full and empty capsids as a function of material and operational parameters, i.e., the harvest model. In addition, critical quality attributes are estimated by a quality model which is operating on top of the harvest model. The performance of these models was evaluated using test data and two additional blind runs. Also, a "what-if" analysis was conducted to investigate whether the models' predictions align with expectations.It is concluded that the models are sufficiently accurate to support the design of operating conditions, though the accuracy and applicability of the models can further be increased by training them on more specific data with higher variability.

Advancing Rock-Socketed Pile Design with a Unified Interface Shear Strength Framework for Soft Rocks

AbstractThe shaft resistance of rock-socketed piles (RSPs) is primarily influenced by the interactions between the pile, rock and any soft interface materials. This study presents a fundamental experimental and numerical investigation to predict the shaft resistance of model RSPs in soft rocks through a comprehensive shear strength framework incorporating the major variables such as roughness and smear fabric. By calibrating the Discrete Element Method (DEM) results with the experimental outcomes, this study evaluates the load-resistance attributes of RSPs in three different soft rocks for a wide range of roughness and smear configurations. The interface roughness effect of the RSPs in terms of the friction coefficient was correlated with the ultimate shaft resistances, uniaxial compressive strength and Young’s modulus of the rock. The study then incorporated the effect of smear fabric (placement, thickness and area proportion) in the shear strength framework of clean shafts. Comprehensive review of the DEM results revealed that the socket roughness effect diminishes at a critical roughness factor $$({\text{RF}})$$ ( RF ) of 0.4, beyond which the smear predominantly influences the shaft capacity. Following this, the effects of roughness and smear fabric were incorporated into a single equation representing the interface shear strength of RSPs, where the existence of smear results in a maximum reduction of up to 75% of the ultimate shaft resistance. The distinctive feature of this unified interface shear strength framework lies in its integration of the new smear fabric parameter and the linkage to the mechanical properties of soft rocks, which is the limitation of the earlier studies. It thereby sets a strong base for future studies aimed at advancing the pile-rock interface models.

Effective Carrier Lifetime in Ultrashort Pulse Laser Hyperdoped Silicon: Sulfur Concentration Dependence and Practical Limitations

Charge carrier lifetime is a crucial material parameter in optoelectronic devices and knowing the dominant recombination channels points the way for improvements. The effective carrier lifetime τeff of surface‐passivated hyperdoped (hSi) and nonhyperdoped “black” (bSi) silicon by quasi‐steady‐state photoconductance decay (QSSPC) measurements and its evolution upon controlled wet‐chemical etching are studied. Sample preparation involves the irradiation of Si by numerous ultrashort laser pulses either in SF6 for hSi or ambient atmosphere for bSi. Findings suggest that the hSi is composed of a double layer: 1) an amorphous resolidified top layer with about 92% of the total incorporated sulfur that accounts for the sub‐bandgap absorptance and 2) a crystalline layer underneath in which sulfur concentration tails off toward the Si substrate. The effective lifetime is deconstructed by a 1D simulation to quantify the impact of the local lifetime of the defect‐rich top layer, τtop. It is found that by the QSSPC method, a maximum τtop for 1) can be estimated. For 2), τtop between 2 and 8 ns is estimated. The bSi sample shows a faster lifetime recovery upon etching which suggests that in hSi samples purely laser‐induced defects are not limiting the carrier lifetime compared to sulfur‐related defects.

Mechanical and morphological characterization of the emphysematous lung tissue.

Irreversible alveolar airspace enlargement is the main characteristic of pulmonary emphysema, which has been extensively studied using animal models. While the alterations in lung mechanics associated with these morphological changes have been documented in the literature, the study of the mechanical behavior of parenchymal tissue from emphysematous lungs has been poorly investigated. In this work, we characterize the mechanical and morphological properties of lung tissue in elastase-induced emphysema rat models under varying severity conditions. We analyze the non-linear tissue behavior using suitable hyperelastic constitutive models that enable to compare different non-linear responses in terms of hyperelastic material parameters. We further analyze the effect of the elastase dose on alveolar morphology and tissue material parameters and study their connection with respiratory-system mechanical parameters. Our results show that while the lung mechanical function is not significantly influenced by the elastase treatment, the tissue mechanical behavior and alveolar morphology are markedly affected by it. We further show a strong association between alveolar enlargement and tissue softening, not evidenced by respiratory-system compliance. Our findings highlight the importance of understanding tissue mechanics in emphysematous lungs, as changes in tissue properties could detect the early stages of emphysema remodeling.

Numerical Analysis of the Blade Coating Process Using Non‐Newtonian Nanofluid with Magnetohydrodynamic (MHD) and Slip Effects

AbstractThe coating process is widely used in various industries to enhance the production quality and efficiency. This study gives a comprehensive analysis of non‐isothermal blade coating of non‐Newtonian nanofluid incorporating magnetic, thermophoresis, and Brownian effects. The mathematical equations derived from mass, momentum, and energy conservation laws are initially streamlined by means of lubrication approximation theory (LAT). Subsequently, these dimensionless equations are solved in dimensionless form numerically using fourth order Runge–Kutta and Newton–Raphson methods. This study includes the effects of the slip parameter, magnetohydrodynamic (MHD) and other material parameters on the coating thickness (), blade load, velocity, temperature, concentration, and pressure profiles through graphs and tables. The velocity of molten polymer increases near the substrate while it decreases near the blade surface as the slip parameter increases. The temperature distribution increases as the Brinkman number rises, with the maximum temperature occurring in the nip region of the flow. The coating thickness and load‐carrying force for both plane and exponential coater increase with higher values of the magnetohydrodynamic (MHD) parameter.

Phenomenological sintering model and experimental validation of gravity-induced distortions in binder-jetted stainless steel components

Sintering is an important consolidation step employed in sinter-based metal additive manufacturing processes. Binder Jetting (BJT) starts with green components with low green density (40-60%) that results in large sintering shrinkages and geometrical shape distortions caused by external forces (e.g. gravity). Consequently, the prediction of the final sintered geometry is crucial during the design process. In this work, a novel sintering simulation framework for gravity-affected sintering of stainless-steel components is presented, including the Rios-Olevsky-Hryha sintering model and the methodology for the identification of the required material parameters. The constitutive law includes material constants to account for the powder packing effects and the delta-ferrite transformation occurring at high temperatures. The material shear viscosity was explicitly related to the equilibrium phase fraction of austenite and delta-ferrite during sintering temperatures. Dilatometry experiments were conducted and followed by the data postprocessing for the model calibration. The calibrated model was incorporated in a FEM code and validated against experimental data from BJT sintered components, showing the remarkably accuracy of the numerical simulations, with small geometric deviations (0.56 mm) related to the assumption of isotropic shrinkage in the model proposed. In parallel, other alternative models were implemented based on different normalized bulk viscosity formulations, which underestimate/overestimate the sintered distortions.

Numerical modeling of LEAP-2022 dynamic centrifuge tests adopting a multi-surface plasticity model

This study presents the numerical results of a series of laboratory and dynamic centrifuge tests conducted by the team at Universidad del Norte, as part of the LEAP-2022 project. The soil's mechanical behavior was simulated using a pressure-dependent multi-surface plasticity constitutive model, which was carefully calibrated based on cyclic soil tests performed on Ottawa F-65 sand. These tests covered a wide range of initial densities, initial effective stresses, and cyclic stress ratios. The comparison between laboratory and numerical element tests revealed that the adopted constitutive model reasonably replicated most features of the material's undrained cyclic response, including liquefaction occurrence and the progressive development of double-amplitude permanent shear strains. The calibrated constitutive model was then used to blindly predict the dynamic behavior of centrifuge experiments composed of a sheet pile-soil system using the OpenSees finite elements software framework; these simulations are referred to as Type-B predictions. The numerical simulation showed that the model provided reasonable representation of soil responses in terms of accelerations and pore water pressure build-up; however, the simulations consistently overpredicted the displacements of the sheet piles. Therefore, based on the centrifuge experimental results, minor adjustments of the material parameters were performed, and the centrifuge tests were re-simulated; these simulations are referred to as Type-C predictions. The comprehensive evaluation exposed both the strengths and weaknesses of the modeling approach for the simulation of liquefiable deposits. Despite the discrepancies in sheet pile displacement, the study instills confidence in the model's applicability to liquefaction-related projects with similar conditions.

Research on the power generation performance and optimization of thermoelectric generators for recycling remaining cold energy

Compared with extensive research on the power generation of thermoelectric generators (TEGs) under medium-high temperatures, research on power generation for cold energy utilization is still limited. To study the performance and feasibility of low-temperature power generation, this paper combines simulation and experiments for research. Firstly, a simulation model based on actual material parameters is established. Then, a thermoelectric low-temperature power generation experimental platform is established to conduct experimental research on TEG in different temperature zones and temperature differences and compared with simulation results. Finally, optimization research is conducted by changing thermoelectric materials and structures. The research results indicate that the maximum errors between the dates of simulation and experiment in terms of output voltage, conversion efficiency, and exergy efficiency are 11.3 %, 10.57 %, and 10.57 %, respectively. Compared with Bi2Te3, the output voltage, conversion efficiency, and exergy efficiency of CsBi4Te6 are improved by 21.95 %, 34.64 %, and 34.64 %, respectively. The maximum output power and conversion efficiency are linearly related to the output current. The performance is optimal when the thermoelectric unit height is 1.0 mm and the side length is 1.6 mm. This proves the feasibility of TEG application at low temperatures and lays the foundation for low-temperature power generation.

Biaxial Testing of EPDM Rubbers for Automotive Applications Using a Uniaxial Testing Machine

The FE-based structural design of components made of rubber materials requires an experimental characterization to calibrate a proper hyperelastic material model. A strain energy density (SED) parameter is typically adopted to describe the hyperelastic behavior of isotropic rubbers. This can be written as a function of the strain tensors, the strain invariants or the stretch ratios and proper material parameters. The latter must be calibrated on experimental data generated by testing rubber materials under different loading conditions, typically two among uniaxial tension, pure shear and biaxial tension. In this context, the mechanical behavior of Ethylene–Propylene–Diene Monomer (EPDM) rubbers, both in the dense and expanded configurations, which are typically employed to manufacture automotive seals, has been investigated. First, uniaxial tension tests have been performed. Afterwards, a new fixture has been developed to perform equi-biaxial tension tests using a uniaxial testing machine. The fixture has been adopted to test EPDM cruciform specimens under equi-biaxial tension loading. Finally, both uniaxial and equi-biaxial data have been employed to fit a Mooney–Rivlin material model for each tested EPDM rubber, which has been validated by comparing the results of FE simulations with the experimental ones.

Modeling the performance and bandwidth of single-atom adiabatic quantum memories

Quantum memories are essential for quantum repeaters, which will form the backbone of the future quantum internet. Such memory can capture a signal state for a controllable amount of time, after which this state can be retrieved. In this work, we theoretically investigated how atomic material and engineering parameters affect the performance and bandwidth of a quantum memory. We have applied a theoretical model for quantum memory operation based on the Lindblad master equation and adiabatic quantum state manipulation. The materials’ properties and their uncertainty are evaluated to determine the performance of Raman-type quantum memories by showcasing two defects in two-dimensional hexagonal boron nitride. We have derived a scheme to calculate the signal bandwidth based on the material parameters as well as the maximum efficiency that can be realized. The bandwidth depends on four factors: the signal photon frequency, the dipole transition moments in the electronic structure, the cavity volume, and the strength of the external control electric field. As our scheme is general and independent of materials, it can be applied to many other quantum materials with a suitable three-level structure. We, therefore, provided a promising route for designing and selecting materials for quantum memories. Our work is, therefore, an important step toward the realization of a large-scale quantum network.

Entropy production on mixed convection stagnation point flow of nonlinear radiative fourth-grade hybrid nanofluid through a stretchable Riga surface

The purpose of the current study is to inspect entropy generation, mixed convective stagnation point flow, and thermal transfer features of nonlinear radiative fourth-grade hybrid nanofluid (NF) confined by a convectively heated Riga surface. The heat transport is examined with the existence of two disparate heat source modulations, variable thermal conductivity, and viscous dissipation. The original flow equations are first transmuted using appropriate transformations into non-dimensional ordinary differential equations, which are then solved via an overlapping grid-based spectral collocation scheme. The upshots of various pertinent parameters on velocity, temperature, entropy generation, and valuable engineering quantities are deliberated. Pivotal results obtained reveal that speedily flow of fluid and skin friction can be accelerated by strong magnetic force, rising mixed convection, and material parameters. Also, convective boundary conditions, along with nonlinear radiation and fluctuating thermal conductivity, are recommended for boosting fluid temperature, rates of entropy generation and heat transport. Thermal mechanism in hybrid NF is dominant over simple NF, which implies that performance of hybrid NF is better than that of simple NF. The outcomes of this study can be useful in enriching thermal performance of the working fluid, assisting in diagnosing causes of incompetency in thermal systems, and discovering suitable means of minimizing entropy generation with the intention of mitigating the loss of useful and scarce energy resources.

Impact location of metal structures based on time-frequency image features and deep residual network

Abstract Impact location detection plays an important role in the structural health monitoring of metal materials. However, the methods of metal material impact location detection based on physical analysis are often limited by the extraction accuracy of some parameters such as material and structure parameters and time difference calculation. Therefore, this paper develops a deep residual network method for impact location detection, time-frequency characteristic deep residual network (TF-DRN). This method takes the four-channel short-time Fourier transform (STFT) time-frequency graph as input, uses the unique residual network architecture to automatically extract the advanced features, and then uses the global average pooling layer and the full connection layer to establish the mapping between the advanced features and the impact location, so as to detect the collision location. By introducing regularization and batch normalization, the problems of gradient disappearance and gradient explosion are alleviated, and the generalization and efficiency of impact location detection are further improved. The experimental results show that on an 800mm×800mm×2.5mm aluminum plate, the average error of the validation set and the test set are 0.85cm and 1.33cm respectively, and the performance of the method is significantly better than that of CNN, ResNet18 and ResNet33 networks.

Random dynamic response analysis of an asphalt concrete core wall dam on deep overburden with double random factors

The spatial variability of material parameters and the randomness of ground motion are factors that influence the seismic response of a dam. To obtain more reliable seismic response results, it is crucial to study the influence of various uncertain factors on the response to earthquakes. In this paper, the K-L series expansion method and LH sampling technique are combined to characterize the spatial correlation of material parameters using a Gaussian autocorrelation function. This allows for the simulation of random fields of material parameters. By improved the Clough-Penzien power spectrum model and incorporating the concept of a random function, it becomes possible to generate nonstationary random ground motion that accurately reflects the site conditions. Taking an asphalt concrete core dam on deep overburden as an example, a random dynamic response analysis was conducted, considering various random factors. Statistical analysis and distribution testing were conducted on the horizontal acceleration and vertical permanent deformation responses. The process of the evolution of the probability density of the horizontal acceleration at the top of the dam was determined using the generalized probability density evolution method. This method helps to reveal the influence of various random factors on the response of the dam body. The research results show that among the spatial variability of material parameters and the randomness of ground motions, the randomness of ground motions is the main influencing factor of the random response results of the dam body. The mean response of two random factors is significantly larger than that of a single random factor. When considering dual random factors simultaneously, the complexity of the random factors increases, resulting in statistical properties that differ from those of single random factors. Therefore, to obtain more reliable random seismic response results, it is necessary to consider both the spatial variability of material parameters and the randomness of ground motions.

An Improved Hidden Markov Model with Magnetic Barkhausen Noise and Optimized Gaussian Mixture Feature for Fatigue Prediction

Abstract Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback-Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation (GCO) algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model (GMM) and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.

This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

A comparative research on knitted fabrics washed with eco‐friendly detergent for different washing cycles

AbstractThe laundering process stands as the most commonly employed method for the maintenance of textile products. This investigation focused on boron, a pivotal mineral resource for our nation, and aims to elucidate alterations in fabric properties, specifically about colour changes and pilling, after the utilisation of various washing detergents. Comparative analyses and assessments were conducted across three distinct washing cycles, employing three distinct detergents: a boron‐infused washing detergent, a standard detergent conventionally utilised in textile trials, and a widely adopted commercial detergent. The investigation encompassed three diverse fabric constituents—cotton, polyester, and viscose—subjected to scrutiny in terms of fabric parameters subsequent to 1, 5, and 25 washing cycles. Following the laundering processes conducted at 40°C, evaluations were conducted concerning the pilling degree and colour alteration of the fabrics. Consumer perception regarding colour alterations, and subjective colour assessments was undertaken with a panel of 10 individuals. Furthermore, the research delved into the stain removal efficacy of the boron‐infused washing detergent. To assess stain removal, all fabrics were intentionally stained with six diverse contaminants: ketchup, mayonnaise, coffee, sweetened coffee, tea, and sweetened tea. A post‐washing evaluation of stain conditions was carried out subjectively by an assembled arbitration committee. Over the course of 25 washing cycles, it was observed that the boron‐containing detergent notably diminished colour strength, particularly evident in polyester and cotton fabrics. Pilling outcomes indicated a decrease in pilling as the number of washing cycles increased. Boron‐containing detergent, denoted as detergent B, consistently demonstrated commendable stain removal properties, particularly on cotton fabric.

Mechanically induced long period gratings in different silica multi-layered optical fibers

This work presents a thorough comparative investigation of mechanically induced long period gratings (MILPGs) realized in different unconventional multi-layered silica optical fibers: two double cladding fibers (DCF), with progressively three layered (PTL) and W-type structures, and a solid core photonic crystal fiber (PCF). A periodic interdigitated grooved structure, based on the stereo-lithography 3D printing technique, is developed for the grating formation in these fibers with simplicity and cost-effectiveness. Multiple grating periods are developed in the range 480–660 μm, resulting in attenuation bands with depth within 15–25 dB and wide range of tunability over the wavelength range of 1100–1700 nm. The results demonstrate effective management of transmission spectra evolution as a function of the applied weight, with minimal alteration in wavelength position when testing the fiber with or without its coating. Polarization dependent properties of these gratings are also investigated demonstrating a negligible impact. This is the first time that such a broad investigation is conducted about MILPGs in the fibers mentioned earlier with broad scouting in fabrication parameters, opening to the application of such devices for sensing and communication purposes.