Low Computational Cost Research Articles

A hybrid modeling approach: dynamic simulation of two-phase fluid flow in compacting gas condensate reservoirs using potential flow theory

Simulating the flow processes of complex hydrocarbon mixtures, such as gas condensate mixtures and volatile oils, in deep reservoirs is one of the most challenging problems in reservoir hydrodynamics. The challenge lies in meeting three requirements simultaneously when creating a simulator: high accuracy, low computational cost, and high reliability. These metrics determine the performance of the simulation. Meeting all these requirements at the same time necessitates a hybrid approach to solving the problem and optimizing the computational algorithm in terms of CPU time. A new technique has been developed for hybrid modeling of the development of deposits of complex hydrocarbon mixtures. This technique integrates the theory of potential flow, the Binary Flow Model (which accounts for rock compaction, PVT properties of reservoir fluids, phase transformation, and mass transfer between phases), and the material balance equations of the hydrocarbon system using time discretization. In this case, to linearize nonlinear differential equations for the flow of a gascondensate mixture, the method of averaging reservoir pressure along the radial coordinate was used. By introducing the Khrestianovich function, an algorithm was obtained to determine the rate of inflow of the gas-condensate mixture to the well. Using material balance equations for gas and condensate, it was possible to obtain differential equations that describe changes in reservoir pressure and condensate saturation over time. Based on this technique, a simulator for a gas condensate reservoir was created, enabling computer studies to be conducted. The results demonstrated the proposed technique's good accuracy compared with the results of the semi-analytical solution. Keywords: dynamic modeling; simulation; monitoring; streamline; hybrid modeling; binary model.

ІНКЛЮЗІЯ В УКРАЇНІ: СУЧАСНІ ВИКЛИКИ

The article is devoted to a theoretical and practical analysis of the problem of inclusion: formation and development, features of educational inclusion, and mechanisms of implementation of inclusive higher education. Based on the analysis of regulatory documents and scientific research, the authors determine the most significant stages of the formation and development of inclusion in Ukraine. The requirements for specialists needed by the modern domestic education system have been analyzed. The article emphasizes the trends in the process of invalidation of the population of Ukraine. The authors determine the expediency and necessity of wide implementation of educational inclusion in the system of higher education, in particular: ensuring free access of all higher education applicants to educational and methodical materials and creating the necessary special units. On the basis of the analysis of the results of the conducted survey, the possible ways of solving the current problems of educational inclusion in the higher education system are substantiated and characterized: support of the teaching staff in the organization of an inclusive educational environment in the institution of higher education; support for education seekers who belong to the category of persons with special needs; financing and implementation of regulated and guaranteed mechanisms for financial support of higher education institutions; technical and methodological support of the educational process. Also, emphasis is placed on electronic inclusion, which is interpreted as a social movement aimed at overcoming the digital divide among different categories of the population. It was found that the appropriate level of organization of an inclusive educational environment is observed in institutions of general secondary education. Instead, HEIs need more active involvement of specialists, technical equipment and financial support for effective educational inclusion. An important factor is the availability of computer equipment: ensuring access to free or low-cost computers, in particular through the implementation of special target programs, charity projects, etc. It is also relevant to search and exchange data to diversify the ways of obtaining education for persons with special educational paths, to ensure effective interpersonal communication despite temporal and spatial boundaries.

Aggregate-aware model with bidirectional edge generation for medical image segmentation

Accurate segmentation of lesion areas plays an important role in medical imaging-assisted diagnosis and treatment. Accurate boundary information can help doctors develop precise surgical plans and improve patient prognosis. However, automatic segmentation methods often struggle to accurately segment edges due to the random shape, size, and location of regions of interest (ROI). This problem is compounded in medical images, where the difference in pixel intensity between foreground and background is significantly smaller than in natural images. In this study, we propose an aggregate-aware model with bidirectional edge generation (Ambeg) for medical image segmentation. To overcome the problem of blurred edges between foreground and background in medical images, we design a deep learning model via a multi-task learning strategy and obtain richer visual features to guide segmentation. Furthermore, an Edge Feature Fusion (EFF) module is developed to combine spatial correlation information of lesion edges between adjacent images for more accurate edge segmentation. Finally, we design a new evaluation metric, the Boundary DSC segmentation consistency measure, to evaluate the edge segmentation accuracy of medical image segmentation methods. We utilize dilation and erosion operations in morphological methods to construct lesion edge labels. In addition, we use expansion and erosion rates to regulate the dimensions of the edge region to assess the requirements of different diseases for edge segmentation accuracy. The proposed approach is particularly noteworthy for achieving state-of-the-art results on medical image segmentation datasets, including BraTS 2022 (MRI), BraTS 2020 (MRI) and COVID-19–20 (CT), which have different modalities of datasets. It has an impressive Hausdorff distance of 4.62 mm and a sensitivity score of 92.45 % on BraTS 2020. Compared with existing assessment methods such as Dice score, Boundary DSC segmentation consistency measure focuses on the hard-to-segment lesion edge region rather than the easy-to-segment lesion center region, which provides a more comprehensive reference for physicians to choose automatic segmentation methods. In addition, since the boundary shapes of medical images are complex and diverse, we utilize morphological methods to obtain the boundary labels to ensure the smoothness of the boundary. Moreover, the approach is easy to implement and has a low computational cost, making it an attractive option for practical medical imaging applications.

A Piecewise Linear Approach for Implementing Fractional-Order Multi-Scroll Chaotic Systems on ARMs and FPGAs

This manuscript introduces a piecewise linear decomposition method devoted to a class of fractional-order dynamical systems composed of piecewise linear (PWL) functions. Inspired by the Adomian decomposition method, the proposed technique computes an approximated solution of fractional-order PWL systems using only linear operators and specific constants vectors for each sub-domain of the PWL functions, with no need for the Adomian polynomials. The proposed decomposition method can be applied to fractional-order PWL systems composed of nth PWL functions, where each PWL function may have any number of affine segments. In particular, we demonstrate various examples of how to solve fractional-order systems with 1D 2-scroll, 4-scroll, and 4×4-grid scroll chaotic attractors by applying the proposed approach. From the theoretical and implementation results, we found the proposed approach eliminates the unneeded terms, has a low computational cost, and permits a straightforward physical implementation of multi-scroll chaotic attractors on ARMs and FPGAs digital platforms.

Highly efficient inverse lumped modeling for the pre-strained circular dielectric elastomer

The equi-biaxially pre-strained circular dielectric elastomer actuator (PCDEA) is a classical actuator configuration to demonstrate the electromechanical deformation mechanism of dielectric elastomer. Till now, there still lacks an effective method to analyze the non-uniform voltage-induced deformation of PCDEA with both high accuracy and low computational cost. Besides, the inverse problem that determines the voltage input for a desired deformation is also a hard problem for the current modeling approaches. In this work, a novel shape function approximation method is proposed to model the non-uniform deformation of PCDEA in a highly accurate lumped-parameter way. The method can solve both the inverse problem and forward problem of the PCDEA, making it possible to position the onset of electromechanical instability (EMI) of this configuration. Detailed comparisons are made between this method and high-resolution finite element analysis to verify its accuracy. This method also features low computational cost, providing opportunities for developing model-based real-time control and proprioception techniques. Further investigation is also carried out on how material types and configurational parameters interact with PCDEAs’ performance, along with checking the findings with experiment observation. Guidelines for DE material development and actuator design are also summarized accordingly.

Underestimation of extremes in sea level surge reconstruction

Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underestimate extreme surges. Here, we explore how to reduce biases on extremes using two methods—multiple linear regressions and neural networks—for surge reconstructions. Models with different configurations are tested at 14 long-term tide gauges in the North-East Atlantic. We found that (1) using the wind stress rather than the wind speed as predictor reduces the bias on extremes. (2) Adding the significant wave height as a predictor can reduce biases on extremes at a few locations tested. (3) Building on these statistical models, we show that atmospheric reanalyses likely underestimate extremes over the 19th century. Finally, it is demonstrated that neural networks can effectively predict extreme surges without wind information, but considering the atmospheric pressure input extracted over a sufficiently large area around a given station. This last point may offer new insights into air-sea interaction studies and wind stress parametrization.

A numerical approach for solving nonlinear fractional Klein–Gordon equation with applications in quantum mechanics

Abstract In this paper, we propose a numerical approach for solving the nonlinear fractional Klein–Gordon equation (FKGE), a model of significant importance in simulating nonlinear waves in quantum mechanics. Our method combines the Bernoulli wavelet collocation scheme with a functional integration matrix to obtain approximate solutions for the proposed model. Initially, we transform the main problem into a system of algebraic equations, which we solve using the Newton–Raphson method to extract the unknown coefficients and achieve the desired approximate solution. To theoretically validate our method, we conduct a comprehensive convergence analysis, demonstrating its uniform convergence. We perform numerical experiments on various examples with different parameters, presenting the results through tables and figures. Our findings indicate that employing more terms in the utilized techniques enhances accuracy. Furthermore, we compare our approach with existing methods from the literature, showcasing its performance in terms of computational cost, convergence rate, and solution accuracy. These examples illustrate how our techniques yield better approximate solutions for the nonlinear model at a low computational cost, as evidenced by the calculated CPU time and absolute error. Additionally, our method consistently provides better accuracy than other methods from the literature, suggesting its potential for solving more complex problems in physics and other scientific disciplines.

Low-cost IoT and computer vision-based aquaculture monitoring system

Low-cost IoT and computer vision-based aquaculture monitoring system

MFFC-SDN: multi-level feature fusion codec-based ship detection network in SAR images

ABSTRACT In recent years, with the development of deep learning, SAR image-based ship target-detection technology has become a current research hotspot. SAR images are characterized by complex backgrounds, significant speckle noise interference, and poor interpretability. To address this challenge, this study proposes a new Gaussian circle radius determination strategy and a multi-level feature fusion codec-based ship detection network (MFFC-SDN) for SAR images. Differing from CenterNet’s corner-oriented Gaussian circle strategy, the Gaussian circle strategy proposed in this paper takes the centre point coordinates of the real target as the origin to define a circular region. This approach allows for more precise supervision of the training model. Meanwhile, we propose MFFC-SDN to solve the problem of the poor performance of CenterNet in SAR ship detection. MFFC-SDN is an anchor-free, single-stage target-detection method with a low computation cost. In MFFC-SDN, we introduce a Multilevel Feature Recapture Codec Module (MFR-CM) to enhance the feature extraction capability of the network by using the features of the encoding stage twice because of the easy loss of image features in the lengthy codec process of the CenterNet network backbone. A Residual Attention Pyramid Feature Fusion Module (RA-PFFM) is designed to enhance the feature map and obtain multi-scale features. Experimental results on the SSDD and SAR-ship-dataset show that MFFC-SDN significantly enhances SAR image target-detection performance, effectively addressing detection challenges related to large target size variations and complex environmental conditions. Compared with existing algorithms, MFFC-SDN achieves the highest A P 50 .

Prior-free 3D tracking of a fast-moving object at 6667 frames per second with single-pixel detectors.

Real-time tracking and 3D trajectory computation of fast-moving objects is a promising technology, especially in the field of autonomous driving. However, existing image-based tracking methods face significant challenges when it comes to real-time tracking, primarily due to the limitation of storage space and computational resources. Here, we propose a novel approach that enables real-time 3D tracking of a fast-moving object without any prior motion information and at a very low computational cost. To enable 3D coordinate synthesis with a space-efficient optical setup, geometric moment patterns are projected on two non-orthogonal planes with a spatial resolution of 125 μm. Our experiment demonstrates an impressive tracking speed of 6667 frames per second (FPS) with a 20 kHz digital micromirror device (DMD), which is more than 200 times faster than the widely adopted video-based tracking methods. To the best of our knowledge, this is the highest tracking speed record in the field of single-pixel 3D trajectory tracking. This method promotes the development of real-time tracking techniques with single-pixel imaging (SPI).

Clothing-invariant contrastive learning for unsupervised person re-identification

Clothing change person re-identification (CC-ReID) aims to match images of the same person wearing different clothes across diverse scenes. Leveraging biological features or clothing labels, existing CC-ReID methods have demonstrated promising performance. However, current research primarily focuses on supervised CC-ReID methods, which require a substantial number of manually annotated labels. To tackle this challenge, we propose a novel clothing-invariant contrastive learning (CICL) framework for unsupervised CC-ReID task. Firstly, to obtain clothing change positive pairs at a low computational cost, we propose a random clothing augmentation (RCA) method. RCA initially partitions clothing regions based on parsing images, then applies random augmentation to different clothing regions, ultimately generating clothing change positive pairs to facilitate clothing-invariant learning. Secondly, to generate pseudo-labels strongly correlated with identity in an unsupervised manner, we design semantic fusion clustering (SFC), which enhances identity-related information through semantic fusion. Additionally, we develop a semantic alignment contrastive loss (SAC loss) to encourages the model to learn features strongly correlated with identity and enhances the model’s robustness to clothing changes. Unlike existing optimization methods that forcibly bring closer clusters with different pseudo-labels, SAC loss aligns the clustering results of real image features with those generated by SFC, forming a mutually reinforcing scheme with SFC. Experimental results on multiple CC-ReID datasets demonstrate that the proposed CICL not only outperforms existing unsupervised methods but can even achieves competitive performance with supervised CC-ReID methods. Code is made available at https://github.com/zqpang/CICL.

Non-intrusive reduced order models for partitioned fluid–structure interactions

The main goal of this work is to develop a data-driven Reduced Order Model (ROM) strategy from high-fidelity simulation result data of a Full Order Model (FOM). The goal is to predict at lower computational cost the time evolution of solutions of Fluid–Structure Interaction (FSI) problems. For some FSI applications, the elastic solid FOM (often chosen as quasi-static) can take far more computational time than the fluid one. In this context, for the sake of performance one could only derive a ROM for the structure and try to achieve a partitioned FOM fluid solver coupled with a ROM solid one. In this paper, we present a data-driven partitioned ROM on two study cases: (i) a simplified 1D-1D FSI problem representing an axisymmetric elastic model of an arterial vessel, coupled with an incompressible fluid flow; (ii) an incompressible 2D wake flow over a cylinder facing an elastic solid with two flaps. We evaluate the accuracy and performance of the proposed ROM-FOM strategy on these cases while investigating the effects of the model’s hyperparameters. We demonstrate a high prediction accuracy and significant speedup achievements using this strategy.

Highly accurate ab initio electronic stopping power results for protons in Al material: a Lindhard stopping theory investigation

The main goal of this work is to enhance theoretical precision evaluations for the random electronic stopping power (RESP) of protons in solid targets across a wide range of energies, including low, intermediate, and high energies. The RESP of protons is investigated in different crystalline forms of Al material: FCC and two theoretical build structures (hexagonal and tetragonal). Initially, we divide the RESP calculations of protons in Al material into two individual contributions: one for the valence electrons and the other for the core electrons. Using this approach, we introduce a combination method that defines the total RESP as the sum of these contributions. We estimate the core electrons’ contribution to the RESP results within the local density approximation (LDA) based on the Lindhard stopping theory, where we calculate the density of Al material within the density functional theory (DFT) framework. We employ the RESP contribution of valence electrons as determined in our previous study within the linear response time-dependent density functional theory. We produce high-quality RESP results with the assistance of the combination method with low computational cost compared to other theoretical works. We test the accuracy of using the LDA based on Lindhard’s stopping model in calculating the RESP of protons in the Al target. In addition, within this approach, we investigate the influence of the crystal structure on core electrons’ contribution to the RESP.

An Enhanced Modeling Framework for Bearing Fault Simulation and Machine Learning-based Identification with Bayesian-optimized Hyperparameter Tuning

Abstract Condition monitoring of rotating machinery offers a salient tool for predictive maintenance on rolling elements, subjected to continuous working loads, wear, fatigue, and degradation. In this study, an enhanced computational tool for bearing fault simulation and feature extraction is proposed. A subsequent identification scheme is realized, through Bayesian optimization of hyperparameters, including support vector classifier (SVC), gradient boosting (GBoost), random forest (RF), extreme gradient boosting (XBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The proposed hyperparameter optimization technique stands out from traditional methods by offering a more informed and efficient pathway to optimal performance in predictive maintenance. Utilizing Bayesian optimization for hyperparameter tuning of machine learning models, which has not been extensively explored in this field, our approach demonstrates significant advancements. Typical instances of bearing faults, like inner race, outer race, and ball faults are considered. The analysis relies on extraction of statistical and engineering features form collected response signals, including kurtosis, root mean square, peak, and ridge factor. Highly influential variables are highlighted based on feature selection and importance algorithms, allowing bearing fault classification. We demonstrate that SVC and LightGBM produce over 97% of accuracy at low computational cost. This approach constitutes a scalable and robust framework for similar applications in engineering diagnostics.

Application of Funnel Metadynamics to the Platelet Integrin αIIbβ3 in Complex with an RGD Peptide.

Integrin αIIbβ3 mediates platelet aggregation by binding the Arginyl-Glycyl-Aspartic acid (RGD) sequence of fibrinogen. RGD binding occurs at a site topographically proximal to the αIIb and β3 subunits, promoting the conformational activation of the receptor from bent to extended states. While several experimental approaches have characterized RGD binding to αIIbβ3 integrin, applying computational methods has been significantly more challenging due to limited sampling and the need for a priori information regarding the interactions between the RGD peptide and integrin. In this study, we employed all-atom simulations using funnel metadynamics (FM) to evaluate the interactions of an RGD peptide with the αIIb and β3 subunits of integrin. FM incorporates an external history-dependent potential on selected degrees of freedom while applying a funnel-shaped restraint potential to limit RGD exploration of the unbound state. Furthermore, it does not require a priori information about the interactions, enhancing the sampling at a low computational cost. Our FM simulations reveal significant molecular changes in the β3 subunit of integrin upon RGD binding and provide a free-energy landscape with a low-energy binding mode surrounded by higher-energy prebinding states. The strong agreement between previous experimental and computational data and our results highlights the reliability of FM as a method for studying dynamic interactions of complex systems such as integrin.

New Hybrid Conjugate Gradient Method as a Convex Combination of PRP and RMIL+ Methods

The Conjugate Gradient (CG) method is a powerful iterative approach for solving large-scale minimization problems, characterized by its simplicity, low computation cost and good convergence. In this paper, a new hybrid conjugate gradient HLB method (HLB: Hadji-Laskri-Bechouat) is proposed and analysed for unconstrained optimization. By comparing numerically CGHLB with PRP and RMIL+ and by using the Dolan and More CPU performance, we deduce that CGHLB is more efficient. Keywords: Unconstrained optimization, hybrid conjugate gradient method, line search, descent property, global convergence.

Flow regime prediction of a self-oscillatory flow induced by a vertical jet in a heated cavity: Computational and analytical approach

Interaction effects between the inlet Reynolds number (250≤Rei≤3000) and the heat flux of the impingement wall (0.5≤q″≤20kW/m2) was investigated in a vertical self-excited oscillating jet confined by a heated cavity. The results revealed that some high amounts of q″ stop oscillating flow. Thus, this study developed a new prediction method based on an analytical approach to identify the threshold of flow regime transition from oscillatory to non-oscillatory based on Rei. To this aim, a new dimensionless number named Non-dimensional Effective Number (NEN) was introduced. The proposed NEN index refers to the ratio of the thermal power applied on the impingement wall to the effective momentum power of the incoming jet. The performance of the proposed index was examined and compared in vertical upward and downward-facing chambers for different heat flux values. Investigations indicated that the proposed analytical method and the new NEN index have a much lower computational cost, despite the very high accuracy (about 95 %) compared to the numerical results extracted from the OpenFOAM code. The results also demonstrated that vertical downward self-excited jets maintain oscillatory behavior regardless of the amount of q″. In this case, the frequency of oscillations varied linearly with Rei for Rei >2000.

Effects of steady-state fluid-structure interactions on air-supported membrane structures subjected to wind actions

With the perspective of static aeroelasticity, steady-state influences of fluid-structure interaction (FSI) on wind loads and responses of rectangular-planed air-supported membrane structure (ASMS) are investigated in this study. Steady-state FSI simulations are performed by coupling Reynolds-averaged computational fluid dynamics solving wind loads and static finite element analysis solving structural deformations. The feasibility of these simulations is validated with wind tunnel tests concerning time-averaged results. In contrast to analysis without static aeroelasticity, significant variations in wind pressure distributions and amplifications on structural wind responses due to steady-state FSI effects are observed. Subsequently, influencing factors and mechanisms of steady-state FSI effects are analyzed. These time-averaged effects are more significant with the increasing magnitude of structural wind actions, lower internal pressures and less membrane tensile stiffness. Though unlikely to induce irreversible effects as shell structures, the buckling of ASMS can make steady-state FSI more pronounced because of the coupling between stronger flow separation and larger membrane deformations. Accompanied with steady-state FSI, such buckling effect usually contributes to varying locations of the structural maximum responses and noticeable increases in response amplification factors, which deserves attentions in practice. Practically, it is realizable to evaluate these steady-state FSI effects above with simulations because of the much lower computational cost and reliable accuracy.

A novelty detection method for efficient data storage in smart grids

This work presents a new novelty detection method for efficiently storing electrical power system data. The proposed method makes innovative use of the Cycle-by-Cycle Difference (CCD) and the Mathematical Morphology (MM) techniques to identify novelties in the voltage of an electrical power system. Once the novelties are identified, the frames where they occur are extracted from the signal, and the other frames are discarded. Among the extracted frames, those containing power quality (PQ) disturbances are fully stored, while the frames that represent stationary states have only their main parameters stored, contributing to efficient storage. The proposed method exhibited low computational complexity in the operational phase, which was evaluated via a field-programmable gate array (FPGA) platform implementation using a customized embedded processor. The method was evaluated with real and simulated signals. The results indicate that the developed method attained high detection probabilities alongside low false alarm probabilities, resulting in a competitive Area Under the Curve (AUC) exceeding 0.976 (except for interharmonics). Comparison with other methods revealed that our approach yielded competitive results with the lowest computational cost. We concluded that the proposed method may be useful for monitoring PQ disturbances in smart grids, where the data volumes are too large.

MWformer: a novel low computational cost image restoration algorithm

MWformer: a novel low computational cost image restoration algorithm