Regular Model Research Articles

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Analysis of the Implementation of the Inclusive Early Childhood Education Curriculum Model : Case Study at Bright Kiddie Kindergarten Ponorogo, Indonesia

This research aims to analyze the implementation of the inclusive Early Childhood Education curriculum model at Bright Kiddie Kindergarten Ponorogo, Indonesia. This research used a qualitative approach with a case study method. The informants for this research were the school principal, class teacher and teacher accompanying children with special needs. Data collection techniques used were interviews, observation, and documentation. Next, data analysis used interactive model analysis, and for the validity of the research data, the triangulation method was used. The research results showed that Bright Kiddie Kindergarten had children with special needs with different characteristics, needs and abilities. In implementing inclusive learning, the school used regular and pull-out class models with duplication, simplification, substitution and omission curriculum models according to the needs of each student with special needs. Children with special needs study with other children in regular classes. However, at certain times, they will be moved to another room to receive additional learning from special accompanying teachers. The curriculum model was also adjusted based on the identification and assessment results of each child with special needs as a basis for appropriate learning planning. Some duplicate the same curriculum as normal children (duplication), some simplify it without eliminating the substance (simplification), some replace it with equivalent material (substitution), and there are those who change it completely to suit the needs of each child.

Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures.

An azeotrope is a constant boiling point mixture, and its behavior is important for fluid separation processes. Predicting azeotropes from atomistic simulations is difficult due to the complexities and convergence problems of Monte Carlo and free-energy perturbation techniques. Here, we present a methodology for predicting the azeotropes of binary mixtures, which computes the compositional dependence of chemical potentials from molecular dynamics simulations using the S0 method and employs experimental boiling point and vaporization enthalpy data. Using this methodology, we reproduce the azeotropes, or lack thereof, in five case studies, including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and acetone/chloroform mixtures. We find that it is crucial to use the experimental boiling point and vaporization enthalpy for reliable azeotrope predictions, as empirical force fields are not accurate enough for these quantities. Finally, we use regular solution models to rationalize the azeotropes and reveal that they tend to form when the mixture components have similar boiling points and strong interactions.

FUSION3D: Multimodal data fusion for 3D shape reconstruction: A soft-sensing approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g., dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing three-dimensional (3D) shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Sparse coding-based transfer learning for energy disaggregation

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a technique that predicts the consumption levels of individual appliances from only the main signal in the building. Various methods have been proposed to solve this problem, including sparse coding (SC), which offers great advantages due to its ability to capture complex patterns in data. However, a challenging aspect of NILM is that data containing appliance-level information is scarce. Moreover, the houses that the models are tested on might be from a different population than the training data, thus resulting in a domain shift. Therefore, we need to develop approaches that are adapted to training data scarcity through the use of transfer learning (TL). In this paper, we test several TL approaches on SC models with the aim of discriminative energy disaggregation (DD). We propose four TL SC models: “Transfer SC+DD”, “SC source, DD target”, “Transfer Deep SC” and “Transfer Variational SC”. We compare these models with 4 models that do not apply domain adaptation. We show that TL-based models achieve similar and sometimes better results than the regular models. We also show that further training on only the aggregate signal of some target domain houses improves the performance of a SC model trained only on the source.

Modeling Fallout from Nuclear Weapon Detonations: Efficient Activity and Dose Calculation of Radionuclides and Their Progeny.

The purpose of this paper is to present a practical method for quick determination of potential radiological doses and contaminations by fallout from nuclear detonations, or other releases, that includes the contributions from all nuclides. We precalculate individual (total) activities of all radionuclides from any initial cocktail and all their ingrowing progeny at a set of pinpoints in time with a logarithmic time-spacing. This is combined with the set of dose conversion factors (DCC) for any exposure pathway to obtain a time-dependent cocktail for the whole release as if it is one substance. An atmospheric dispersion model then provides the thinning coefficient of the released material to give local concentrations and dose rates. Progeny ingrowth is illustrated for pure 238 U and for a nuclear reactor that has been shut down. Efficient dose assessment is demonstrated for fallout from nuclear detonations and compared with the traditional approach of preselecting nuclides for specific endpoints and periods-of-interest. The compound cocktail DCC reduces the assessment of contaminations and potential dose-effects from fallout after a nuclear detonation to (the atmospheric dispersion of) only one tracer substance, representing any cocktail of nuclides and their progeny. This removes the need to follow all separate nuclides or an endpoint-specific preselection of "most important nuclides." As the cocktail DCCs can be precalculated and the atmospheric dispersion of only one tracer substance has to be modelled, our method is fast. The model for calculating cocktail DCCs is freely available, easily coupled to any regular atmospheric dispersion model, and therefore ready for operational use by others.

Intersection matrices for the minimal regular model of X0(N)${X}_0(N)$ and applications to the Arakelov canonical sheaf

Intersection matrices for the minimal regular model of X0(N)${X}_0(N)$ and applications to the Arakelov canonical sheaf

Time-variant reliability assessment for bridge structures based on deep learning and regular vine copula models

This study proposes a novel method for assessing the time-variant reliability of bridge structures by combining deep learning and regular vine (R-vine) copula models. The sample convolution and interaction network (SCINet) model, which is a deep learning algorithm, is established to predict future structural responses at different control-monitoring points. The R-vine copula model captures the complex correlations of the failure modes associated with different control-monitoring points and models their dependence structure. By integrating the SCINet and R-vine copula models, the SCINet-pair-copula model (SCINet-PCM) is established, and a framework based on SCINet-PCM is proposed to forecast the time-variant reliability of bridge structures. The results for a practical bridge reveal that the calculated results obtained using the proposed SCINet-PCMs satisfy the quality-inspection results of the bridge and are closer to the benchmark results of importance sampling, indicating that the proposed method is accurate and reasonable.

Transformation of significant wave height and set-up due to entrained air bubbles effect in breaking waves

The transformation of wave height is of paramount significance in coastal engineering and the design of coastal structures. Considering the influence of air bubbles, this study devised an optimal dissipation model for accurately calculating changes in significant wave height (Hm0) and wave set-up for irregular waves undergoing breaking. Existing regular wave breaking models, which consider the effects of air bubbles, were adapted for direct application to irregular waves by deriving novel formulations. The proposed models leverage the probability of the fraction of broken waves. Hm0 was computed using the energy balance equation, while the wave set-up was calculated based on the momentum balance equation. A wide range of test scenarios, incorporating diverse scales (small and large) and experimental field data, was considered for validation. One of the proposed models, namely model-I (M-I), particularly demonstrated superior performance, manifesting lower error indices (P20), root-mean-square relative error (RMSRE), and Brier skill score (BSS) values in computing both Hm0 and wave set-up. Therefore, utilising M-I is strongly recommended for the precise estimation of Hm0 and set-up transformation.

Simulation of flow and heat transfer in high‐temperature and high‐pressure reservoir based on multi‐physical field coupling model at pore scale

AbstractThe use of irregular pore‐scale models to study heavy oil reservoirs with high‐temperature, high‐pressure, and high‐stress characteristics is effective. Previous studies have typically focused on regular models and conventional environmental reservoirs, with limited exploration of irregular models and reservoirs in extreme environments. In investigating the process of water displacing heavy oil within reservoirs under high‐temperature, high‐pressure, and high‐stress conditions at the pore scale, the utilization of the four‐parameter method creates a micro‐scale irregular porous media model. The model systematically considers the variation of physical properties of rocks and heavy oil with temperature. The results indicate that an appropriate increase in water injection rate or a decrease in reservoir contact angle will increase the recovery rate, temperature, and stress of the reservoir. At a displacement time of 0.3 s, with the water injection rate increasing from 0.004 to 0.01 m ∙ s−1, the reservoir's recovery degree experiences an increase of 0.091. Simultaneously, the average temperature and average stress of the reservoir increase by 29.66 K and 1.9464 × 109 N · m−2, respectively. At a displacement time of 0.3 s and with the contact angle decreasing from 2π/3 to π/3, the reservoir's recovery degree increases by 0.44537, and the average temperature and average stress of the reservoir increase by 2.87 K and 1.86 × 108 N · m−2, respectively.

On solving a rank regularized minimization problem via equivalent factorized column-sparse regularized models

On solving a rank regularized minimization problem via equivalent factorized column-sparse regularized models

Investigation of meso-mechanical properties of Jinping dolomitic marble based on flat-joint model

Brittle rock has the mechanical characteristics of a high ratio of uniaxial compressive strength to tensile strength, brittle-ductile transition, and so on. The mineral meso-structure of brittle rock shows the interlocking behavior and there are some microcracks between the mineral grains, so it is difficult to analyze the mechanical characteristics accurately with a regular constitutive model. The previous numerical models have some limitations when applied to brittle rock, that they cannot provide sufficient internal interlocking and are incapable of considering the microcracks. These limitations are addressed by using the updated flat-joint contact model to join notional polygonal particles with an initial gap and increasing the particle coordination number by adding flat-joint contacts to more particles that are within some non-zero distance of one another via the range coefficient contact identification method. A comparison of experimental and numerical results confirms that the updated flat-jointed model provides a good match with the mechanical properties of brittle rock. In this paper, we investigate the influence of mineral meso-structure (micro-cracks, range coefficient, and radius multiplier) on the meso-mechanical properties of Jinping dolomitic marble via the updated flat-joint contact model under direct-tension tests and compression tests. It is found that the updated model can reproduce the microstructure effect of brittle rock and can better simulate the mechanical characteristics of the brittle rock than conventional models, such as high strength ratio, brittle-ductile transition, initial nonlinearity, and different modulus in compression and tension.

지역사회 통합돌봄 맞춤형 인재 양성 방안 연구

Korea has experienced a rapid increase in demand for care due to its aging population at an unprecedented pace. However, it is not able to adequately respond to the rapidly increasing demand and needs of the elderly due to its facilities, hospital-oriented care systems, and segmental service delivery systems. In response to these challenges, specific programs have been developed to address the increasing number of individuals in need of care. These programs aim to develop a curriculum in universities and establish a network to cultivate professional manpower with the necessary knowledge and skills. Additionally, they seek to establish win-win cooperation between local governments and universities to revitalize the care economy. To achieve this, the latest information on the necessity and problems of integrated care in the community has been extracted using various research methods such as scope-grasping literature review, systematic literature review, and government policy data review, commonly used in the field of social science. This information has been used to develop a regular curriculum model for universities, specialized multi-job-linked education, a practical model of small-scale degree courses, and a strategy for establishing the role and governance of universities and local governments. These initiatives aim to supplement the blind spots in existing care services and provide care services that are suitable for the unique characteristics of the Gwangju area.

Density Based Regularization Model for Effective Forecasting of Stage Transition in Chronic Kidney Disease

Density Based Regularization Model for Effective Forecasting of Stage Transition in Chronic Kidney Disease

Regular Tetrahedron Model for the Assessment of High-Resolution Mass Spectrometry Data of Four-Way Fractionated Dissolved Organic Matter.

A regular tetrahedron model was established to pierce the fractionation of dissolved organic matter (DOM) among quaternary components by using high-resolution mass spectrometry. The model can stereoscopically visualize molecular formulas of DOM to show the preference to each component according to the position in a regular tetrahedron. A classification method was subsequently developed to divide molecular formulas into 15 categories related to fractionation ratios, the relative change of which was demonstrated to be convergent with the uncertainty of mass peak area. The practicality of the regular tetrahedron model was verified by seven kinds of sludge from waste leachate treatment and sewage wastewater treatment plants by using stratification of extracellular polymeric substances coupled with Orbitrap MS as an example, presenting the DOM chemodiversity in stratified sludge flocs. Sensitivity analysis proved that classification results were relatively stable with the perturbation of four model parameters. Multinomial logistic regression analysis could further help identify the effect of molecular properties on the fractionation of DOM based on the classification results of the regular tetrahedron model. This model offers a methodology for the assessment of specificity of sequential extraction on DOM from solid or semisolid components and simplifies the complex mathematical expression of fractionation coefficients for quaternary components.

Exploring spatiotemporal patterns of algal cell density in lake Dianchi with explainable machine learning

The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water quality parameters and both the delayed and instantaneous effects of meteorological factors, ensemble learning, and quasi-Monte Carlo methods were employed to predict daily algal cell density (AD) between January 2021 and January 2024. Consistently, superior predictive accuracy across all three clusters was exhibited by the Stacking-Elastic-Net regularization model. Furthermore, the minimum combination of drivers that achieved near-optimal accuracy for each cluster was identified, striking a balance between accuracy and cost. The ranking of the effect of drivers on AD varied by cluster, while the delayed effect of meteorological factors on AD generally outweighed their instantaneous effect for all clusters. Additionally, the heterogeneous or homogeneous thresholds and responses between drivers and AD were explored. These findings could serve as a scientific and cost-effective basis for government agencies to develop regional sustainable strategies for managing water quality.

Solubility of oxazepam in supercritical carbon dioxide: Experimental and modeling

This study thoroughly investigates the solubility of oxazepam in supercritical carbon dioxide (SC-CO2) at temperatures of 308, 318, 328, and 338 K and pressures ranging from 12 to 30 MPa. The solubility measurements revealed a mole fraction of oxazepam ranging from 2.50 × 10−6 to 7.13 × 10−5, with the highest solubility observed at 338 K and 30 MPa. The solubility data were effectively modeled using semi-empirical density-based models (Mendez-Santiago and Teja (MST), Chrastil, Bartle et al., Kumar and Johnston (K-J), and Alwi-Garlapati), two equations of state (Peng-Robinson and modified-Pazuki) and regular solution models. The K-J model emerged as the best fit for the experimental data, boasting the lowest Average Absolute Relative Deviation (AARD) value of 7.73 %. The Peng-Robinson equation of state outperformed the modified-Pazuki equation, with AARD values of 15.71 % and 18.15 %, respectively. The study's key finding is the low solubility of oxazepam in SC-CO2, which suggests that supercritical antisolvent techniques could be effectively employed for synthesizing nanoparticles of this pharmaceutical compound. These findings have practical implications as they provide valuable insights for optimizing drug formulation processes and demonstrate the potential of SC-CO2 in pharmaceutical applications.

Automatic classification of fetal heart rate based on a multi-scale LSTM network.

Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates. Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models. The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility. Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women's clinical characteristics and disregard the potential impact of varying gestational weeks.

Image reconstruction based on TV-L1 model for planar array capacitance defect detection

Planar array capacitive imaging is an emerging nondestructive testing (NDT) technology with broad application prospects. The severe ill-posed inverse problem of planar array capacitive imaging reduces quality of reconstructed images. In this paper, a total variation regularization model with an L1-norm (TV-L1 model) of 3 × 4 planar array capacitance imaging is established for imaging the defects detected in the inner layers of multi-layer composite components. Considering the solution instability caused by the non-differentiability of the TV-L1 model, a variable splitting self-adaptive augmented Lagrange alternating direction algorithm (VS-SALAD) is proposed. An augmented Lagrange method enhances model splitability, and a series of subproblems are obtained by variable splitting operation. The subproblems are solved iteratively using alternating minimization method with self-adaptive penalty parameter. Experimental results demonstrate that TV-L1 model effectively alleviates the ill-posed problem, and the proposed algorithm significantly improves both the quality of reconstructed images and solving speed of the TV-L1 model.

High Performance CNN Accelerators based on Hardware and Algorithm Co-Optimization

The efficacy of convolutional neural networks (CNNs) has led to their extensive application in picture recognition and categorization. Nevertheless, embedded systems face challenges when it comes to storing the massive volumes of weight data required by CNNs in their on-chip memory. Pruning a convolutional neural network (CNN) model can reduce its size with no impact on accuracy; however, when used with a parallel architecture, the resulting model will be slower to execute. A CNN compression method that is hardware-centric is introduced in this paper. A model of a deep neural network (DNN) will include "pruning layers (P-layers)" and "no-pruning layers (NP-layers)" attached to it. For effective and parallel processing, an NP-layer employs a regular distribution for its weights. Pruning causes a P-layer to have an irregular form, yet the high compression ratio it yields makes the shape undesirable. Using uniform and gradual quantization methods, we can achieve a processing efficiency/compression ratio trade-off with a small loss of precision. Integrating multiple parallel finite impulse response (FIR) filters into an NP-layer distributed convolutional architecture, we further improve the regular model. The P-layer irregular sparse model is suggested to use an ADF-based processing element. The suggested compression strategy and hardware architecture can be utilised by a hardware/algorithm co-optimization (HACO) method to run an NP→P hybrid compressed CNN model on FPGAs. The VGG-16 network achieves a compression ratio of 27.5× with a top-5 accuracy loss of 0.44% by using a hardware accelerator on a single FPGA chip without off-chip memory. A result of 83.0 FPS, or 1.8 times quicker, was achieved when the compressed VGG-16 model was implemented on a Xilinx VCU118 evaluation board for image applications. Index Terms - CNN, Accelerators, Co-Optimization.