Discrete Model Research Articles

Development and calibration of discrete element model for layered gravel soils in forested areas

In this paper, a Discrete Element Method (DEM) model is developed to accurately simulate the interaction between forestry machinery vehicles and forest-layered gravel soils. The soil grains in the DEM model are simulated as spherical particles, while the gravel is represented by nine types of non-spherical particle clusters based on the actual gravel content, size, and shape. The DEM model parameters are calibrated through the indoor angle of repose (AOR) and triaxial shear tests, followed by statistical calibration using Plackett-Burman, Steepest Ascent, and Box-Behnken experiments. The values of the optimal DEM model parameters are determined based on the obtained quadratic regression analysis. Finally, the MED model accuracy is verified by comparing indoor test results with the DEM simulation outputs and mathematical predictions. The results revealed that the DEM model simulated curves closely match the measured values, indicating good agreement.

The Discrete Grey Power Model Based on Aging Accumulation and its Application

Abstract Accumulation generation is one of the key methods to reduce the randomness of data series, which significantly enhances the predictive performance of the grey prediction model. By integrating a degenerate operator, a novel accumulating discrete grey power model is introduced, capable of substantially enhancing the accuracy of predictions. This method allows for the flexible adjustment of the accumulation coefficient in the modeling data, mitigating the fluctuations in model parameters due to data perturbations, and effectively minimizing the loss of differential information. The outcomes of practical numerical examples demonstrate that the proposed aging accumulation discrete grey power model exhibits superior performance.

Spinal Apophyses Localization in Discretized Models of Human Backs by Shape Index Analysis.

The paper proposes a non-invasive original methodology to directly and automatically identify the spine line from the external position of vertebral apophyses, which are key anatomical landmarks. Apophyses are detected directly on discrete high-density geometric models of human backs acquired by a 3D scanner. The methodology is inspired by the posturologist's approach that detects the spine line through the identification, by manual palpation, of the spinal apophyses. For this purpose, an appropriate shape index is used to identify vertebral positions. The shape index estimates the local differential geometric properties of the back surface. This index is very discriminating in locating both pronounced and blurred apophyses. To validate the method, the research involved the analysis of 21 healthy human backs acquired in both standing and asymmetric postures. For each of them, a skilled operator detected the spinal apophyses by tactile investigation and located them through cutaneous marking. Markers have been used as the reference for spinal apophyses' positions. A comparison of the proposed approach with state-of-the-art methods has been conducted. This study evidences the high accuracy of the methodology proposed here and the capability to recognize also blurred apophyses. The method automatically performs the spine line identification and accurately locates apophyses along both vertical and coronal directions. The proposed inexpensive and easy-to-use approach significantly advances over other non-invasive methods. Its ability to detect the apophyses' location potentially offers new capabilities in detecting, diagnosing, and monitoring spinal disorders.

Statistical study of mechanical responses of ordinary refractory ceramics under cold crushing condition with discrete element modelling

Statistical study of mechanical responses of ordinary refractory ceramics under cold crushing condition with discrete element modelling

An integrated workflow of history matching and production prediction for fractured horizontal wells

This paper presents a novel method for history matching and production prediction for fractured horizontal wells by combining the data space inversion method (DSI) with the embedded discrete fracture model (EDFM), referred to as DSI-EDFM. In this approach, several initial numerical models with varying reservoir geological and fracture geometry parameters, but identical production schedules, are generated through random sampling and then run using the EDFM. The DSI method is subsequently employed to process the production data, creating a proxy model that matches actual historical data and predicts production performance by solving a quadratic optimization problem. A key improvement over the original DSI method is introduced, providing and proving the conditions under which the optimization problem derived from DSI is a positive definite quadratic optimization problem. With these conditions, the optimal solution can be directly obtained using the Newton method without any iterations. Furthermore, it is identified that overfitting issues frequently arise when using the Newton method for DSI. However, the simultaneous perturbation stochastic approximation algorithm effectively mitigates this problem, allowing the proposed DSI-EDFM to handle real reservoirs and uncertainty parameters efficiently. Three numerical examples are implemented to validate the method, including depleting development, water flooding operations, and the flush stage of fractured horizontal wells. The results demonstrate that the proposed DSI-EDFM achieves high accuracy in conducting history matching and performance prediction for fractured horizontal wells, even under complex flow model conditions and with a limited number of initial models. Additionally, the accuracy improves as the number of initial models increases.

Analysis and mitigation of erosion wear of transfer ducts in a falling particle CSP system

Solid particles are effective heat capture and transfer media for next-generation (Gen3) concentrating solar power (CSP) plants that offer high-temperature operability, lower levelized cost, and higher efficiency. However, the relative motion of the solid particles with respect to the CSP system components causes significant wear of the wall materials in the form of erosion and abrasion. The transfer duct is one of the prevalent components in the falling particle CSP system that experiences erosion wear due to the impact of particles on its surface, which depends on several factors, such as duct design, material, and particle flow rate, among others. In this work, erosion wear in the transfer duct and its mitigation were studied computationally through different duct designs and particle flow parameters, including transfer duct length, diameter, slanted angle of the duct, and entrance velocity. Computational simulations based on particle tracking and discrete element modeling were used to understand erosion behavior systematically and, in turn, identify areas most prone to erosive wear. It was found that the use of erosion-resistant coatings in selected locations determined from the simulations was sufficient to mitigate overall erosion wear in the component. This study provides fundamental insight into the erosion wear of a key component of the falling particle CSP system for the first time and the design approaches to mitigate wear.

A reinforcement learning-based algorithm for discrete dynamic stochastic recognition of speech dialog emotions

Abstract At present, the most common speech dialogue emotion discrete dynamic random recognition calculation natural language processing is mostly independent processing of object data, and the recognition efficiency is low, resulting in the infinite increase of the final FRP value. According to the current recognition requirements, speech data resource collection and speech recognition are carried out first, and a multi-level method is adopted to improve the recognition efficiency and realize multi-level processing and sentiment analysis of natural speech. Based on this, a discrete dynamic random recognition calculation model of reinforcement learning speech dialogue emotion was constructed, and the multi-cycle automatic synchronous correction method was used to realize the random recognition processing. The test results show that for the six randomly selected test periods, compared with the improved emotion model random recognition method and the artificial intelligence emotion random recognition method, the final FRP value of the reinforcement learning emotion random recognition method designed this time is well controlled 15%, which indicates that with the assistance of reinforcement learning technology, the designed calculation method is more flexible and changeable. Furthermore, its inherent random recognition mechanism is more comprehensive, efficient, and targeted, thereby rendering it highly valuable and significant for applications under complex background conditions.

The impact of street and building configurations on respiratory health risks in an urban environment considering street behavior heterogeneity

The rapid urbanization has led to serious air pollution issues, making it crucial to effectively reduce respiratory health risks for the health of urban residents. This paper proposes an approach for adopting the PM2.5 personal intake fraction (PIF) in neighborhood street to assess respiratory health risks by considering pollutant concentrations along with the behavior patterns of three subgroups (i.e., pedestrians, cyclists, and shop vendors). PIF was indirectly predicted by combining computer vision and computational fluid dynamics simulations based on the discrete phase model. Fifty-one configurations with different building densities, porosity (dimensions of setbacks and podiums), and height variability were examined. This study found significant differences in exposure levels and behavior heterogeneity among subgroups, with risk assessment results varying under different street cross-section scenarios. From the perspective of respiratory health, the recommendations involved modify the street cross-section through setting business interface for vendors, excessive sidewalk expansion for pedestrian and cyclists, and the building configurations of the adjacent street blocks according to the street orientation to minimizing the PIF. This study provides valuable insights into the impact of street and building configurations on respiratory health risks in neighborhood street, and this assessment framework can be partially applied to other types of streets.

Assessing the impact of technology partners on the level of cyberattack damage in hospitals

ObjectiveReliable performance of medical devices is crucial for hospitals. However, these devices are increasingly connected with the internet and, thus, prone to cyberattacks resulting in risks for patient safety and financial loss. As the number of specialists in this field is limited in most hospitals, technology partnerships with medical technology manufacturers can be a suitable concept for increasing the level of security or limiting damage in the event of a cyberattack. MethodsBased on a discrete event simulation model (DES), the effects of security incidents with different degrees of impact on downtime costs, length of stay, staff utilization and lost arrivals in an emergency department of a general hospital were modelled and simulated. The effects of a technology partnership were simulated using what-if scenarios in order to be able to draw conclusions about the benefits by comparing the avoidable damage effects and the investment costs incurred for a technology partnership. ResultsDepending on the scenario, the resulting savings range from €245,579 to €315,768, with a cost-benefit ratio between 4 and 5 over a 21-day period. Non-financial benefit (e.g. shorter lengths of stay or reduction IT resources) can also be achieved. ConclusionOur analysis demonstrates that the level of security for hospitals and their medical devices as well as the operational functionality in the event of damage can be increased if such a concept it applied, i.e., patient safety can be increased while costs can be cut.

Form-Finding and Structural Analysis of Deployable Rigid Folding Structures using Synchronized Vector-based Graphic Statics and Finite Element Method

This paper presents a computational framework for designing and analysing deployable rigid folding plate structures by integrating Vector-based Graphic Statics (VGS) and Finite Element Method (FEM). To ensure consistent data flow from crease pattern generating to structural analysis and form-finding, a matrix-based data structure is utilized to represent the topology and geometry of the folding structure. An algorithm applied to this data structure enables simulation of the folding kinematics within a particle-based physical system, and solving for static equilibrium in the folded states to construct the discrete stress field model with VGS. This model provides a concise representation of edge and in-plane static equilibrium in a folded state. Additionally, the data structure maintains uniform detail levels between VGS and FEM by aligning mesh subdivision in FEM with the discrete stress field in VGS. Two case studies demonstrate the effectiveness and intuitiveness of the proposed method in limit state analysis by evaluating the multiple folded states of a continuous folding procedure, and in topological navigating and optimizing the folding crease pattern related to the performance of the deployable form. The research highlights advancements in the rapid, visually informed design process and concludes that this integrated approach serves as both a tool for engineers and a collaborative framework for architects and engineers in architectural design.

Step-by-step time discrete Physics-Informed Neural Networks with application to a sustainability PDE model

The use of Artificial Neural Networks (ANNs) has spread massively in several research fields. Among the various applications, ANNs have been exploited for the solution of Partial Differential Equations (PDEs). In this context, the so-called Physics-Informed Neural Networks (PINNs) are considered, i.e. neural networks generally constructed in such a way as to compute a continuous approximation in time and space of the exact solution of a PDE.In this manuscript, we propose a new step-by-step approach that allows to define PINNs capable of providing numerical solutions of PDEs that are discrete in time and continuous in space. This is done by establishing connections between the network outputs and the numerical approximations computed by a classical one-stage method for stiff Initial Value Problems (IVPs). Links are also highlighted between the step-by-step PINNs derived here, and the time discrete models based on Runge–Kutta (RK) methods proposed so far in literature. To evaluate the efficiency of the new approach, we build such PINNs to solve a nonlinear diffusion-reaction PDE model describing the process of production of renewable energy through dye-sensitized solar cells. The numerical experiments show that not only the new step-by-step PINNs are able to well reproduce the model solution, but also highlight that the proposed approach can constitute an improvement over existing continuous and time discrete models.

Discrete element modelling and mechanical properties and cutting experiments of Caragana korshinskii Kom. stems.

The forage crop Caragana korshinskii Kom. is of high quality, and the biomechanical properties of its plant system are of great significance for the development of harvesting equipment and the comprehensive utilisation of crop resources. However, the extant research on the biomechanical properties of Caragana korshinskii Kom. is inadequate to enhance and refine the theoretical techniques for mechanised harvesting. In this study, we established a discrete element model of CKS based on the Hertz-Mindlin bonding contact model. By combining physical experiments and numerical simulations, we calibrated and validated the intrinsic and contact parameters. The Plackett-Burman design test was employed to identify the significant factors influencing bending force, and the optimal parameter combination for these factors was determined through response surface analysis. When the shear stiffness per unit area was 3.56×109 Pa, the bonded disk scale was 0.93 mm, the normal stiffness per unit area was 9.68×109 Pa, the normal strength was 5.62×107 Pa, the shear strength was 4.27×107 Pa, the discrete element numerical simulation results for three-point bending, radial compression, axial tension, and shear fracture exhibited a maximum failure force error of 3.32%, 4.37%, 4.87% and 3.74% in comparison to the physical experiments. In the cutting experiments, a smaller radial angle between the tool edge and the stem resulted in less damage to the cutting section, which was beneficial for the smoothness of the stubble after harvesting and the subsequent growth of the stem. The discrepancy in cutting force between the physical and numerical simulations was 3.89%, and the F-x (force versus displacement) trend was consistent. The multi-angle experimental validation demonstrated that the discrete element model of CKS is an accurate representation of the real biomechanical properties of CKS. The findings offer valuable insights into the mechanisms underlying crop-machine interactions.

Comparative Analysis and Application of Mass and Heat Transfer Simulation in Fractured Reservoirs Based on Two Fracture Models

The projection-based embedded discrete fracture model (pEDFM) and its counterpart, the embedded discrete fracture model (EDFM), have become standard tools for the depiction of the fractures in reservoir simulations. Despite their widespread use, there are still some unclear areas in modeling the complex processes of mass and heat transfer within fractured reservoirs, particularly in both single-phase and multiphase flow scenarios. Our research introduces a numerical methodology for simulating the mass and heat transfer in fractured reservoirs which is developed by extending the framework of the pEDFM and EDFM. To gauge the effectiveness of these models, we devised two cases which were designed to evaluate the adaptability of the pEDFM and EDFM in scenarios involving an ultra-low permeability fracture or a high permeability fracture under single-phase and multiphase conditions. By using local grid refinement (LGR) as a reference, the results of the pEDFM were in reasonably good agreement with the LGR in terms of pressure, temperature, and saturation distributions. This comparison suggests that the pEDFM has a significant advantage in depicting the mass and heat transfer at the matrix–fracture interface compared to the EDFM. Furthermore, a comprehensive analysis of the flow trajectories in both the pEDFM and EDFM provided a reasonable explanation for their differences. Furthermore, the numerical applications involving the heat extraction of Enhanced Geothermal Systems (EGSs) and the water flooding in fractured reservoirs illustrate the adaptability of the pEDFM in the numerical simulation for complex geological conditions. The insights and conclusions obtained in this paper can enhance our understanding of the distinctions between the pEDFM and EDFM, aiding in the selection of the most suitable method for characterizing the fractures in numerical simulations.

Changes in the association between educational achievement, attainment and subsequent mental health: A survival analysis of 21 Swedish graduation cohorts

BackgroundLow academic achievement and low educational attainment in adolescence is associated with higher risks of internalizing disorders later in life. However, less is known regarding if these associations vary over time across cohorts. The aim of this study was to investigate temporal changes in the association between academic achievement or educational attainment and subsequent inpatient treatment for internalizing disorders among Swedish youths.MethodsRegister data on all students graduating from compulsory school in Sweden between 1990 and 2010 (N = 2 252 703) were used. Students were followed for a maximum of 8 years using discrete time proportional hazard models. Internalized disorders were measured by specialized inpatient psychiatric care for depression or anxiety disorders. Academic achievement was measured by grades at the end of compulsory school, and educational attainment by completion of upper secondary school.ResultsThe positive association between inpatient treatment for internalizing disorders and both low compulsory school achievement and non-completion of upper secondary school became stronger in more recent cohorts. The results were completely driven by girls and native-born youth.ConclusionsLow compulsory school achievements and failure to complete upper secondary school has become more important risk factors for inpatient treatment for internalizing disorders, particularly in native-born youth and girls. More research is needed to establish whether youth with internalizing disorders increasingly fail in school or whether low achievement has become more harmful for mental health.

RSM applied to lattice patterns for stiffness optimization

PurposeIn this study, response surface methodology (RSM) is applied to a three-point bending stiffness analysis of low-cost material (PLA) specimens printed using FDM technology to analyze the performance of different internal lattice structures (Octet and IsoTruss principally). The purpose of this study is to extend the definition from a discrete (lattice) model to an analytical one for its use in subsequent design phases, capable of optimizing the type of cell to be used and its defining parameters to find the best stiffness-to-weight ratio.Design/methodology/approachThe representative function of their mechanical behavior is extrapolated through a two-variable polynomial model based on the cell size and the thickness of the beam elements characterizing it. The polynomial is obtained thanks to several tests performed according to the scheme of RSM. An analysis on the estimation errors due to discontinuities in the physical specimens is also conducted. Physical tests applied to the specimens showed some divergences from the virtual (ideal) behavior of the specimens.FindingsThe study allowed to validate the RSM models proposed to predict the behavior of the system as the size, thickness and type of cells vary. Changes in stiffness and weight of specimens follow linear and quadratic models, respectively. This generally allows to find optimal design points where the stiffness-to-weight ratio is at its highest.Originality/valueAlthough the literature provides numerous references to studies characterizing and parameterizing lattice structures, the industrial/practical applications concerning lattice structures are often still detached from theoretical research and limited to achieving functioning models rather than optimal ones. The approach here described is also aimed at overcoming this limitation. The software used for the design is nTop. Subsequent three-point bending tests have validated the reliability of the model derived from the method’s application.

Exergy Efficiency and Energy Efficiency of Double Air Pass Solar Tunnel Air Dryer: A CFD Analysis

This work presents, the computational fluid dynamics simulation of a forced convection double air pass solar tunnel drying system to study the temperature distribution, the flow behaviour of the air stream, and the exergy performance. Realizable k-epsilon non-equilibrium wall function turbulence model, discrete ordinate radiation model, and species transport were used. The average temperature, thermal efficiency, and exergy efficiency were found to 59.2 oC, 30%, and 9.41%, respectively. The result revealed that the uniform temperature and velocity distribution throughout the heating and drying chamber. Therefore, the newly developed double air pass solar tunnel dryer enhances the air temperature and the exergy performance leads to the increment of drying rate and reduction of drying period.

UTILIZING DISCRETE HIDDEN MARKOV MODELS TO ANALYZE TETRAPLOID PLANT BREEDING

In plant heredity, the phenotype is the result of observation that can be directly observed, while the genotype is the underlying hidden factor that underlies the expression of the phenotype. The genotype is an important aspect that needs to be understood to explain the pattern of trait inheritance and predict trait inheritance in subsequent generations. The discrete hidden Markov model is a model generated by pair of an unobserved Markov chain and an observation process. This model can be applied to tetraploid plant crosses by modeling genotypes as hidden state and phenotypes as the obeservation process. The probability of dominant phenotype in monohybrid, dihybrid and trihybrid crosses occurring over ten generations during that period is as follows 61,305%, 37,583%, and 23,041%. Furthermore, as more traits are crossed, the probability of dominant phenotype appearing within ten generations decreases. When the dominant phenotype occurs over ten generations, the same genotype can be obtained in monohybrid, dihybrid, and trihybrid crosses, which is heterozygous in the first and second generations, while from the third to the tenth generation it is homozygous dominant.

Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.

Electrophysiology lab efficiency using cryoballoon for pulmonary vein isolation in central and eastern Europe: A sub-analysis of the cryo global registry study.

Cryoballoon ablation for treatment of atrial fibrillation (AF) reduces procedure times, but limited data is available about its impact on electrophysiology (EP) lab efficiency in Central and Eastern Europe (CEE). Using CEE-specific procedure data, the present study modeled cryoballoon ablation procedures on EP lab resource consumption to improve efficiency. A discrete event simulation model was developed to assess EP efficiency with cryoballoon ablation. Model inputs were taken from CEE sites within the Cryo Global Registry, namely Czech Republic, Hungary, Poland, Serbia, and Slovakia. The main endpoints were percentage of days that resulted in overtime and percentage of days with time for one extra simple EP procedure. Use of the 'figure of 8' (Fo8) closure technique to reduce procedure time was also examined. The mean lab occupancy time across all CEE sites was 133 ± 47 minutes (min: 104 minutes, max:181 minutes). Cryoballoon ablation in the base-case scenario resulted in 14.6% of days with overtime and 64.8% of days with time for an extra simple EP procedure. Use of the Fo8 closure technique enhanced these values to 5.5% and 85.3%, respectively. Model endpoints were most sensitive to changes in lab occupancy times and overtime start time. In this CEE-specific analysis of EP lab efficiency it was found that 3 cryoballoon ablation procedures could be performed in 1 lab day, leaving time for a 4th simple EP procedure on most days. As such, use cryoballoon ablation for PVI is an effective way to improve EP lab efficiency.

Financial Fraud Detection Study - Based on Logit Model

The problem of financial fraud has always been a key issue of concern for researchers at home and abroad, and financial fraud of listed companies occurs from time to time at home and abroad, which produces a huge loss of interest for investors as well as the downturn and instability of the capital market, for this reason, it is necessary to carry out a study on the detection of financial fraud. This paper analyzes the financial fraud detection of Chinese listed companies by establishing a Logit model, firstly, obtain the financial statement sample data of Chinese listed companies from CSMAR, and divide the sample into training set and test set with the ratio of 9:1 to preprocess the sample data and fill in the missing values; secondly, this paper selects the financial statement data, and based on the previous research, selects the characteristics that are related to the risk of financial fraud and constitute the risk of financial fraud. high features, and constitute the feature indicators for financial fraud detection; again, since detecting financial fraud is a binary classification problem, the sample is divided into fraudulent companies and normal companies, so this paper studies the financial fraud problem through the discrete choice model in the econometric model, constructs a Logit model, conducts a goodness-of-fit test on the sample data of CSMAR, and estimates the parameters using the method of maximum likelihood estimation ; finally, the test set data is used to test the model's ability to predict financial fraud.