Fast Method Research Articles

Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework

AbstractWe propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.

Measurement of bending deformation of sheet metal using machine vision

ABSTRACT The aim of this study is to propose a fast and effective method to measure the curvature distribution and bending angle of sheet metal in the bending deformation using machine vision. An open-source computer vision library (OpenCV) was used for image processing. First, the image is calibrated for perspective and lens distortion. After that, the image is subjected to color extraction and binary conversion. The outer geometry of the material was obtained using edge detection. With the proposed method, the bending angle and curvature distribution in the four-point bending and V-bending tests are measured. The proposed machine vision method exhibits errors of 0.55% and 0.12% compared with the 3D scanner measurements for the bending angle in the four-point bend and V-bend tests after springback, respectively. The curvature distribution in bending deformation was also obtained. As an application of the proposed method, the moment – curvature diagram of the material was determined based on the obtained curvature at the center. Additionally, the optimal stroke was calculated for the target bend angle during the V-bending process. The proposed method proved to be an accurate and efficient way to measure bending deformation of sheet metal.

Deep‐Learning‐Assisted Affinity Classification for Humoral Immunoprotein Complexes

Immunoglobulins are important building blocks in biology and biotechnology. With the emergence of comprehensive deep‐learning approaches, there are enormous opportunities for fast and accurate methods of classification of protein–protein interactions to arise. Herein, widely accessible image classification algorithms for species‐specific typification of a range of different immunoglobulin G (IgG) complexes are repurposed. Droplets of various immunoglobulins mixed with a B‐cell superantigen (SAg) (recombinant staphylococcal Protein A) are deposited onto hydrophobic polymer substrates and the resulting protein stains are imaged using polarized light microscopy. A comprehensive study based on 23 745 images finds that the pretrained convolutional neural network (CNN) InceptionV3 not only successfully categorizes IgGs from four different species but also predicts their binding affinity to Protein A: averaged over 36 binding pairs, the following are observed: 1) an overall accuracy of 81.4%, 2) the highest prediction accuracy for human IgG, the antibody with the highest binding affinity for Protein A, and 3) that the classification accuracy regarding the various IgG/Protein A ratios generally correlates with the binding strength of the protein–protein–complex as determined via circular dichroism spectroscopy. In addition, the CNN pretrained with IgG/Protein A stain images has been challenged with a new set of images using a different superantigen (SAg, Protein G). Despite the use of the unknown SAg, the CNN correctly classifies the images of human IgG and Protein G as indicated by a 94% accuracy over the various molar binding ratios. These findings are noteworthy because they demonstrate that appropriately pretrained CNNs can be used for the prediction of protein–protein interactions beyond the scope of the original training set. Aided by deep‐learning methods, simple stains of mixed protein solutions may serve as accurate predictors of the strength of protein–protein interactions with relevance to protein engineering, self‐aggregation, or protein stability in complex media.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

Markov-chain sampling for long-range systems without evaluating the energy.

In past decades, enormous effort has been expended to develop algorithms and even to construct special-purpose computers in order to efficiently evaluate total energies and forces for long-range-interacting particle systems, with the particle-mesh Ewald and the fast multipole methods as well as the "Anton" series of supercomputers serving as examples for biomolecular simulations. Cutoffs in the range of the interaction have also been used for large systems. All these methods require extrapolations. Within Markov-chain Monte Carlo, in thermal equilibrium, the Boltzmann distribution can, however, be sampled natively without evaluating the total energy. Using as an example the Lennard-Jones interaction, we review past attempts in this direction and then discuss in detail the class of cell-veto algorithms that allow for the fast, native sampling of the Boltzmann distribution without any approximation, extrapolation, or cutoff even for the slowly decaying Coulomb interaction. The computing effort per move remains constant with increasing system size, as we show explicitly. We provide worked-out illustrations and pseudocode representations of the discussed algorithms. Python scripts are made available in an associated open-source software repository.

Exploring the molarity of Lithium Fluoride in minimally intensive layer delamination (MILD) method for efficient room temperature synthesis of high quality Ti3C2Tx free-standing film

In recent years, MXene has become the most demanding material in the 2D family, leading to their increased use in research for a diverse range of applications, such as energy storage, water purification, optoelectronics, electromagnetic interference (EMI) shielding and medicine. However, recent research has made it increasingly important to synthesize high-quality MXene in a safe, reliable, fast, reproducible, and good-yielding single-step method. To achieve high-quality MXene by etching the MAX phase precursors, it is very important to critically optimize synthesis parameters, such as etching method, etchant concentrations, temperature, time, etc. Minimally intensive layer delamination (MILD) method for MXene synthesis using Lithium Fluoride (LiF) salt and hydrochloric (HCl) acid seems to be a better choice in the context of improved performance in different applications as well as its scalability and reproducibility. Here, the molar ratio of HCL/LIF is critical for Aluminium etching at room temperature to produce a high-quality MXene with = O, -OH, -F termination groups in the MILD method. However, in case of MILD method the effect of LiF/HCL concentrations on quality of MXene need to be addressed. In this study, we focus on room temperature etching of Ti3AlC2 using MILD method to synthesize quasi two-dimensional carbide (Ti3C2) MXene. The influence of molarity concentration of etchants (LiF and HCl) on quality of MXene has been thoroughly investigated. Different molar concentrations of Lithium Fluoride (LiF) with fixed HCL concentration were tested at room temperature for 24 h in the etching process. The crystal structure, microstructure and composition of the MXene were characterized using X-ray diffraction, Raman spectroscopy, and electron microscopy. Flexible free-standing MXene film with largest shift in the characteristic (002) peak, indicating large interplanar spacing, has been successfully achieved repeatedly for an optimum molar concentration of etchants at room temperature.

Radiation Impedance of Rectangular CMUTs.

Recently, capacitive micromachined ultrasound transducers (CMUTs) with long rectangular membranes have demonstrated performance advantages over conventional piezoelectric transducers; however, modeling these CMUT geometries has been limited to computationally burdensome numerical methods. Improved fast modeling methods, such as equivalent circuit models, could help achieve designs with even better performance. The primary obstacle in developing such methods is the lack of tractable methods for computing the radiation impedance of clamped rectangular radiators. This paper presents a method that approximates the velocity profile using a polynomial shape model to rapidly and accurately estimate radiation impedance. The validity of the approximate velocity profile and corresponding radiation impedance calculation was assessed using finite element simulations for a variety of membrane aspect ratios and bias voltages. Our method was evaluated for rectangular radiators with width:length ratios from 1:1 up to 1:25. At all aspect ratios, the radiation resistance was closely modeled. However, when calculating the radiation reactance, our initial approach was only accurate for low aspect ratios. This motivated us to consider an alternative shape model for high aspect ratios, which was more accurate when compared with FEM. To facilitate the development of future rectangular CMUTs, we provide a MATLAB script that quickly calculates radiation impedance using both methods.

Experimental study of wear and rolling contact fatigue in railway wheel steels coupled with various brake block materials: Insights from innovative small-scale testing

This study presents a comprehensive analysis using an innovative testing method of two wheel steels paired with cast iron and organic composite brake block materials. By conducting tests under consistent conditions and varying in duration, the study examines temperature profiles, friction coefficients, surface characteristics, weight loss, and microstructural changes in wheel samples, emphasizing the distinct behaviour of these materials in braking applications and the damage evolution over time. The results demonstrate that organic composite brake samples outperform those in cast iron, showcasing smoother wheel sample surfaces and stable friction coefficients. Weight loss analysis reveals the environmental benefits of organic composite brakes, emitting fewer particulates than cast iron counterparts. Microstructural examinations uncover the formation of a Thermal White Etching Layer (T-WEL) on wheel samples tested with cast iron samples, leading to cracks and material detachment. Conversely, extended use of organic composite samples led to a "thermal fuse effect", impacting their efficiency and suggesting the need of careful temperature management in sustained braking scenarios. Despite significant differences in wheel steels, the study underscores the critical role of brake material in braking improvements. The findings not only enhance the scientific understanding of brake material behaviour but also introduce an innovative, cost-effective, and fast 4-contact machine testing method.

Accurate and fast quaternion fractional-order Franklin moments for color image analysis

Quaternion type moments (QTM) based on hypergeometric functions and harmonic functions have attracted interest in the imaging field. The Franklin system is a complete orthonormal system consisting of piecewise linear continuous functions with Haar wavelet collocation points. However, due to the lack of efficient computational methods and explicit expressions, research on piecewise polynomials with Haar wavelet collocation points remains underexplored. Furthermore, the impact of magnitude and phase of quaternion on the color image features extracted by QTM has not been comprehensively explored. To address these problems, a fast and accurate computation method for Franklin functions is proposed. Quaternion fractional-order Franklin moments (QFFM) are proposed and using the Minkowski distance as a prior parameter for selecting unit pure quaternions. Experiments are conducted to demonstrate the superior performance of QFFM in image reconstruction and color image classification tasks compared with QTM-based and deep learning-based methods. Moreover, with the help of experiments, it is confirmed the effectiveness of using the Minkowski distance to select magnitude and phase of quaternion.

Identification of a Clade-Specific HLA-C*03:02 CTL Epitope GY9 Derived from the HIV-1 p17 Matrix Protein.

Efforts towards an effective HIV-1 vaccine have remained mainly unsuccessful. There is increasing evidence for a potential role of HLA-C-restricted CD8+ T cell responses in HIV-1 control, including our recent report of HLA-C*03:02 among African children. However, there are no documented optimal HIV-1 CD8+ T cell epitopes restricted by HLA-C*03:02; additionally, the structural influence of HLA-C*03:02 on epitope binding is undetermined. Immunoinformatics approaches provide a fast and inexpensive method to discover HLA-restricted epitopes. Here, we employed immunopeptidomics to identify HLA-C*03:02 CD8+ T cell epitopes. We identified a clade-specific Gag-derived GY9 (GTEELRSLY) HIV-1 p17 matrix epitope potentially restricted to HLA-C*03:02. Residues E62, T142, and E151 in the HLA-C*03:02 binding groove and positions p3, p6, and p9 on the GY9 epitope are crucial in shaping and stabilizing the epitope binding. Our findings support the growing evidence of the contribution of HLA-C molecules to HIV-1 control and provide a prospect for vaccine strategies.

Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California

Rare earth elements (REEs) exhibit diagnostic absorption features in the visible-near infrared region, enabling their detection and identification via spectroscopic methods. Satellite-based remote sensing mapping of REEs, however, has not been attainable so far due to the necessity for high-quality hyperspectral data to resolve their narrow absorption features. This research leverages EnMAP hyperspectral satellite data to map REEs in Mountain Pass, California—a mining area known to host bastnaesite-Ce ore in sövite and beforsite carbonatites. By employing a polynomial fitting technique to characterize the diagnostic absorption features of Neodymium (Nd) at ∼740 and ∼800 nm, the surface occurrence of Nd was successfully mapped at a 30m pixel resolution. The relative abundance of Nd was represented using the continuum-removed area of the 800 nm feature. The resulting map, highlighting hundreds of anomalous pixels, was validated through laboratory spectroscopy, surface geology, and high-resolution satellite imagery. This study marks a major advancement in REE exploration, demonstrating for the first time, the possibility of directly detecting Nd in geologic environments using the EnMAP hyperspectral satellite data. This capability can offer a fast and cost-effective method for screening Earth’s surfaces for REE signature, complementing the existing exploration portfolio and facilitating the discovery of new resources.

Evaluation of Earthquake Performance of Reinforced Concrete Buildings with Fuzzy Logic Method

Reinforced concrete building is a type of building whose structural system consists of reinforced concrete columns, beams, wall wall, slabs and foundations. Due to the strong earthquakes in our country, it has been observed that reinforced concrete buildings have been severely damaged or collapsed from past to present. The evaluation of buildings after earthquakes and their performances are quite important for the safety of life and property. In the literature, different methods have been developed for post-earthquake evaluation of buildings, either low-cost and fast or slow and high-cost and tool-demanding. In this study, in order to overcome the gap in the literature, the evaluation of earthquake performance of buildings with fuzzy logic method is discussed. In this context, the performance of the building was evaluated by taking into account the concrete compressive strength, number of floors, ground floor area, area of column and shear walls, ground floor area and architectural parameters. The data of 18, 28 and 146 buildings affected by the earthquakes in Afyon, Bingöl and Van provinces in 2002, 2003 and 2011, respectively, were used in the study. Out of the total 192 building data, 94 buildings were processed as data and fuzzy logic rules were applied. The remaining 98 buildings were tested with this method. The buildings considered are light, moderate and severely damaged or collapsed. Matlab program was used in the study. The method and result used in the study are in the class of fast and second stage methods in the literature, which are reliable but do not reflect the final result. With the method revealed in the study, the damage status of reinforced concrete buildings can be determined quickly and reliably. In this context, the buildings were evaluated as no damage/slight, moderate and severely damaged or collapsed. According to the results, the correct estimation rate of the damage status of 98 buildings was found to be 88%. It is seen that the fuzzy logic method can predict building performances at a quite high rate. Compared to the existing second stage assessment methods in the literature, this result is more conservative.

The fast Euler-Maruyama method for solving multiterm Caputo fractional stochastic delay integro-differential equations

The fast Euler-Maruyama method for solving multiterm Caputo fractional stochastic delay integro-differential equations

2D/0D Heterojunction Fluorescent Probe with Schottky Barrier Based on Ti3C2TX MXene Loaded Graphene Quantum Dots for Detection of H2S During Food Spoilage

AbstractHydrogen sulfide (H2S) contamination of food has raised widespread public health concerns, leading to substantial medical and economic burdens. Herein, a 2D/0D heterojunction fluorescent probe (TCTG) with Schottky barriers (SB) is designed and synthesized, utilizing Ti3C2Tx MXene‐loaded graphene quantum dots (GQDs), for the detection of H2S during food spoilage. The microstructures observed through SEM and TEM reveal that uniformly sized GQDs are evenly attached to the surface of a monolayer Ti3C2Tx. The chemisorption between GQDs and Ti3C2Tx facilitates charge transfer and the formation of SB, resulting in intramolecular charge transfer (ICT) effects. With the introduction of H2S, TCTG(50%) exhibits the highest sensitivity, selectivity, and anti‐interference properties, with ultra‐fast fluorescence transient reaction (3s) and remarkably low detection limit of 41.82 ppb as well as noticeable color change. When TCTG(50%) reacted with H2S, the ICT effects are inhibited, leading to the recovery of photoinduced electron transfer (PET) and fluorescence quenching. Notably, probe TCTG is effectively utilized to detect changes in H2S levels in raw foods to assess their quality. Overall, the significance of this study is its potential to revolutionize food spoilage detection, offering a fast, reliable, and sensitive method to ensure food safety and reduce associated health and economic burdens.

EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency

Despite recent advancement in 3D molecule conformation generation driven by diffusion models, its high computational cost in iterative diffusion/denoising process limits its application. Here, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation. In EC-Conf, a modified SE (3)-equivariant transformer model was directly used to encode the Cartesian molecular conformations and a highly efficient consistency diffusion process was carried out to generate molecular conformations. It was demonstrated that, with only one sampling step, it can already achieve comparable quality to other diffusion-based models running with thousands denoising steps. Its performance can be further improved with a few more sampling iterations. The performance of EC-Conf is evaluated on both GEOM-QM9 and GEOM-Drugs sets. Our results demonstrate that the efficiency of EC-Conf for learning the distribution of low energy molecular conformation is at least two magnitudes higher than current SOTA diffusion models and could potentially become a useful tool for conformation generation and sampling.Scientific ContributionsIn this work, we proposed an equivariant consistency model that significantly improves the efficiency of conformation generation in diffusion-based models while maintaining high structural quality. This method serves as a general framework and can be further extended to more complex structure generation and prediction tasks, including those involving proteins, in future steps.

Unveiling facet engineering of ultrathin Pt overlayers for enhanced ammonia oxidation reaction

Utilizing ammonia for hydrogen production via ammonia oxidation reaction (AOR) remains a challenge owing to insufficient electrode performance and high cost of noble metal. Herein, we performed repeated self-terminated electrodeposition (SED) of Pt on Ni substrate to obtain Pt/Ni electrodes. The electron-deficient Ni sites prefer *OH adsorption and serve as a reservoir for NH3 dehydrogenation, thereby promoting NH3 adsorption on Pt sites. Additionally, this iterative process enabled precise regulation of the Pt loading mass. Pt5/Ni electrode exhibited the highest catalytic activity for AOR. Subsequently, immersing this electrode in various acid solutions resulted in surface morphologies with numerous well-defined pits, driven by the adsorption of different electrolyte ions. Specifically, etching in HNO3 led to a higher Pt(100) density, further enhancing the electrode’s catalytic performance for AOR. Thus, this study offers a simple, fast, and spontaneous method for facet engineering ultrathin Pt overlayers on Ni substrate under ambient conditions.

Efficient and Low-Drift Point-Cloud Mapping Method for GNSS Weak and Obstructed Areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.

Efficient interfacial solar evaporation using a novel carbonized foam as photo-thermal converter

Recent research shows that one of the effective and green methods to reduce freshwater scarcity is interfacial solar evaporation. This method does not require energy consumption. One of the important challenges that should be taken into consideration in interfacial solar evaporator systems is the fabrication of solar absorbers with easy, environmentally friendly, cost-effective, efficient and scalable methods. Herein, a carbonized melamine foam (CMF) fabricated via a one-step, fast, low-cost and easy method for solar desalination. The CMF is fabricated by an electrical heat gun within a few minutes and is characterized by various techniques. The prepared CMF as a black foam shows excellent properties for solar desalination such as superhydrophilicity, high light absorption, high water transportation rate, good photo-thermal conversion, high rate of water evaporation and salt resistance. The prepared CMF shows repeatable performance in solar evaporation at consecutive evaporation cycles with solar evaporation rate of 3 kg m−2h−1 and high solar thermal conversion efficiency of 90 % under 1000 W m−2 illumination.

Fast and scalable preparation of metal–organic coordination polymeric precursor for bifunctional electrocatalysts for high performance lithium–oxygen batteries

The Metal-organic coordination polymers (MOCPs) with structural and functional diversity are considered effective cathode materials for Lithium-oxygen batteries (LOBs). Conventional synthesis methods make it difficult to obtain large quantities of materials. Here, A highly active low-cost Co-decorated N-doped carbon oxygen cathode catalyst (Co–N–C) was synthesized using a fast and scalable method based on positive and negative charge coordination. Rapid synthesis of MOCPs depends on the addition of base sites. A theory of “Lewis’s base-assisted rapid synthesis” was proposed. Co-N-C as LOBs air cathode showed 150 cycles with 1000 mAh g-1 at 500 mA g-1. The fast and scalable synthesis method can provide a feasible strategy for large-scale production of oxygen electrocatalysts for LOBs.