Dynamic Distribution Model Research Articles

Study on calculation method for dynamic load distribution of thread pair of planetary roller screw mechanism

To reveal the dynamic load distribution characteristics of planetary roller screw mechanism under the high-speed operating conditions, based on the spiral surface equation and deformation coordination relationship, a static contact force calculation method of the roller-screw and roller-nut thread at the contact point is proposed, and a formula for calculating the contact angle after bearing is derived. On this basis, further considering the inertial force generated by using high-speed operation, a dynamic load distribution model for planetary roller screw mechanism thread was established. By comparing with the finite element contact model of planetary roller screw mechansim, the error is below 5%, which verifies the correctness of the model established. Finally, the influence of the parameters such as screw speed, axial load, pitch, and tooth flank angle on the distribution of dynamic load was analyzed. The results show that the inertia force causes the roller to have a tendency to move closer to the nut and away from the screw resulting in a decrease in dynamic contact force on the roller screw side and an increase in dynamic contact force on the roller nut side. The degree of uneven distribution of dynamic load is positively correlated with the screw speed and pitch, and negatively correlated with the tooth side angle. The size of axial load has a significant impact on the distribution of dynamic load.

Predicting suitable habitats for foraging and migration in Eastern Indian Ocean pygmy blue whales from satellite tracking data

BackgroundAccurate predictions of animal occurrence in time and space are crucial for informing and implementing science-based management strategies for threatened species.MethodsWe compiled known, available satellite tracking data for pygmy blue whales in the Eastern Indian Ocean (n = 38), applied movement models to define low (foraging and reproduction) and high (migratory) move persistence underlying location estimates and matched these with environmental data. We then used machine learning models to identify the relationship between whale occurrence and environment, and predict foraging and migration habitat suitability in Australia and Southeast Asia.ResultsOur model predictions were validated by producing spatially varying accuracy metrics. We identified the shelf off the Bonney Coast, Great Australian Bight, and southern Western Australia as well as the slope off the Western Australian coast as suitable habitat for migration, with predicted foraging/reproduction suitable habitat in Southeast Asia region occurring on slope and in deep ocean waters. Suitable foraging habitat occurred primarily on slope and shelf break throughout most of Australia, with use of the continental shelf also occurring, predominanly in South West and Southern Australia. Depth of the water column (bathymetry) was consistently a top predictor of suitable habitat for most regions, however, dynamic environmental variables (sea surface temperature, surface height anomaly) influenced the probability of whale occurrence.ConclusionsOur results indicate suitable habitat is related to dynamic, localised oceanic processes that may occur at fine temporal scales or seasonally. An increase in the sample size of tagged whales is required to move towards developing more dynamic distribution models at seasonal and monthly temporal scales. Our validation metrics also indicated areas where further data collection is needed to improve model accuracy. This is of particular importance for pygmy blue whale management, since threats (e.g., shipping, underwater noise and artificial structures) from the offshore energy and shipping industries will persist or may increase with the onset of an offshore renewable energy sector in Australia.

A dynamic outbreak distribution model (DODM) for an irruptive folivore: The western spruce budworm

Forest disturbances caused by irruptive insect outbreaks have become more frequent due to the non-stationary and warming climate that favours insect development and survival, as well as forest management practices that increase host tree susceptibility. However, predicting and managing the impacts of these highly mobile pests is a challenge due to their uncertain distributions. Dynamic species distribution models (DSDM) acknowledge the non-stationary environment and species’ ecological niches and facilitate niche modeling by matching species observational data relative to environmental data, leading to better outcomes and reducing over-prediction in conservation efforts. The western spruce budworm (WSB; Choristoneura freemani Freeman) is a native forest pest with irruptive dynamics characterized by periodic landscape-scale outbreaks. WSB feeds primarily on Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) foliage and can cause extensive defoliation throughout western North American forests. Using decades-long presence and absence records of WSB outbreaks detected via aerial surveys and environmental variables, we developed dynamic outbreak distribution models (DODMs) to predict the distribution of outbreaks over time. Our results showed that DODMs provided robust temporal trends of WSB distributions while generating predictions that support documented northward shifts in epidemic populations. We found that in addition to the environmental variables known to affect WSB survival, phenological synchrony between bud burst and the emergence of larvae in spring was the most important predictor of outbreak distributions. We also identified high-risk areas for future WSB outbreaks and predicted range expansions and contractions under different climate change scenarios. Our study highlights the importance of using presence-absence data and incorporating temporal trends in environmental variables for better assessment of forest pest potential outbreak distributions. These findings have implications for proactive management strategies to prevent outbreaks and mitigate the impacts of WSB on forest ecosystems.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Numerical analytical model of metal temperature of steam boiler drum

The problem of developing and identifying a temperature distribution model in the wall of the boiler drum was solved. The numerical model is based on the analytical solution of the one-dimensional heat conduction equation with heat exchange at the boundaries at constant heat transfer coefficients. The model is implemented in a dynamic systems simulation package. Using the bounded series of eigenfunctions of the analytic solution, a representation of the plant in the state space was obtained. A universal algorithm for calculating the eigennumbers of the analytic solution was proposed. The eigenvalues dependences of the analytical solution on the values of heat transfer coefficients and thermal conductivity was investigated. Based on the results of the study, data tables were obtained to approximate the dependences of eigennumbers on heat transfer coefficients in a limited range of variation. The methodology for implementing a dynamic temperature distribution model, which takes into account changes in heat transfer coefficients during simulation, was tested. The time of change of coefficients is perceived as the beginning of a new transient from the current state of the model. Presented structural diagrams of the implementation of the model in dynamic systems simulation packages. The proposed structures allow the introduction of parametric disturbances at the time of their occurrence in the simulation. According to the experimental data of steam boiler firing, identification of the dependence of the heat transfer coefficient on the temperature of the metal wall of the boiler drum was carried out. The results showed that the heat transfer coefficients are influenced not only by the metal temperature, but also by additional parameters of the steam boiler operation. As a result of experiments, it was found that the accuracy of the model can be improved by taking into account the dependence of the heat transfer coefficient on the internal surface of the drum on the flow rate of feed water into the boiler drum. The results of the numerical experiment are presented.

Dynamic distribution modelling using a native invasive species, the mountain pine beetle

Efforts to minimize impacts of highly mobile insect pests in a warming environment are complicated by the dynamicity and uncertainty of their distributions. Tools that aid such management efforts are urgently required for successful outcomes. Dynamic species distribution models (DSDM) represent temporal variations in the environment that match the timing and location of species occurrence leading to improved predictions of species distributions while reducing over prediction. In contrast, static SDMs treat the environment and ecological niche of the species as essentially invariant, such as climatic data averaged over long reference periods, which can result in inaccurate assessments of a species' ecological niche. We modified the traditional static SDM framework such that it incorporates both dynamic biotic and abiotic predictors to account for temporal variability in the environment. We used the native-invasive mountain pine beetle (MPB; Dendroctonus ponderosae, Hopkins) as a model species. MPB is an irruptive forest insect native to western North America; however, epidemic populations have recently crossed the Rocky Mountains and invaded the province of Alberta, Canada, giving rise to concern that the beetle will spread into the transcontinental boreal forest. We compared the ability of static and dynamic SDMs to predict the potential distribution of MPB, including the impact of host tree availability, degree of native-novel host tree introgression and interannual variation in climatic variables. Evaluation results using independent test data revealed higher predictive capacity by DSDMs when compared with SDMs. DSDMs provided robust temporal trends of MPB potential distributions while generating predictions that support the known MPB distributional shifts. The DSDM framework will help species distribution modellers in understanding the distributional dynamics of mobile species in climatically variable regions.

A Contact Load Distribution Model to Capture the Influence of Structurally Compliant Rotating Ring Gear on the Dynamic Response of Epicyclic Gear Sets

AbstractEpicyclic gears, also commonly referred to as planetary gears, are power transfer components that are commonly used in several industrial applications. The structural compliance of thin-rimmed annular ring gear can significantly influence the performance of an epicyclic gear set. As powertrain components are continually being optimized to their design limits, this influence becomes prominent and can no longer be ignored. Therefore to capture the influence associated with ring gear flexibility, the current study will incorporate a finite element-based ring gear formulation into the three-dimensional planetary dynamic load distribution model (Ryali, and Talbot, 2021, “A Dynamic Load Distribution Model of Planetary Gear Sets,” Mech. Mach. Theory, 158, p. 104229). The proposed contact model employs a modified simplex algorithm to iteratively solve for the elastic gear mesh contacts in conjunction with a numerical integration scheme, which enables it to inherently capture the influence of several components and system-level design variations without the need for an empirical mesh stiffness formulation or transmission error excitation of the system. The developed formulation will be used to study the dynamic response of planetary gear sets where the ring gear is a rotating member. The discussed results demonstrate the fidelity of the developed model, thus making it an excellent tool for the design and analysis of planetary gears.

A dynamic gear load distribution model for epicyclic gear sets with a structurally compliant planet carrier

Structural deformations in a planet carrier under operating conditions results in misalignment of planet gears, which can significantly effect the gear mesh load distribution and load sharing of a planetary gear set. Therefore, to capture the influence of structural compliance associated with planet carrier, planet pin, and input/output shaft, a hybrid planetary dynamic load distribution model is developed in this paper. Finite element sub-structuring techniques are employed to ensure that the model is computational efficiency. The proposed model employs a simplex algorithm to iteratively solve for the elastic gear mesh contacts in conjunction with a numerical integration scheme, which enables it to inherently capture the influence of several component and system level design variations without the need for an empirical mesh stiffness formulation or transmission error excitation of the system. The developed formulation is used to study the effects of planet carrier flexibility on both the quasi-static and dynamic response of planetary gearsets. The discussed results not only illustrate the influence of carrier flexibility on the system response but also highlight the need for such computationally efficient models which could be used as design tools.

Optimization of emergency supplies paths based on dynamic real-time split deliver

A multi-objective dynamic demand split delivery emergency material distribution model is developed to enhance the efficiency of emergency material distribution and facilitate the smooth progress of safety rescue operations during unconventional emergencies. This model incorporates the psychological view of those affected by disasters. The issue of dynamic demand may be transformed into a static demand problem by dividing the distribution time window into time domains of equal length. The optimization process is thereafter executed in real-time with the timed batch methodology. A refined ant colony method has been developed to address the model by integrating the attributes of the mathematical model, followed by doing an arithmetic case analysis. The findings indicate that the algorithm and mathematical model suggested in this study are efficacious in addressing the emergency material distribution issue, offering valuable decision-making advice and reference.

Research on the Control Process of Mining and Combustion Disturbance Zone Based on the Three-Dimensional Dynamic Distribution Model of Void Fraction

ABSTRACT The cavities formed by the combustion of underground coal fires and the overlying rock damage caused by the combustion will cause the surface to subside or even collapse, forming crisscrossing surface cracks. On the basis of studying the fracture, subsidence and caving characteristics of overlying strata in the coal seam mining-combustion composite open space, the temporal and spatial development of the coal seam mining-combustion composite open space and the three-dimensional movement law of the induced overlying rock are studied in order to build three-dimensional dynamic theoretical distribution model of porosity in coal seam mining and combustion disturbance zone. Based on the porosity evolution model, the fire zone isotope radon method is used to detect the temperature anomalous area in the goaf. According to the location of the abnormal high-temperature area, a comprehensive treatment method is carried out, which is mainly based on the combined grouting of the upper and lower the mine, and supplemented by nitrogen injection and three-phase foam injection, thereby to solve the problem of difficult quantitative characterization of the multi-void medium in the mining-influenced underground coal fire area and targeted treatment for coal fire prevention and control in the mining-combustion disturbance area.

Multi-period dynamic multi-objective emergency material distribution model under uncertain demand

This paper proposes a multi-period dynamic emergency material distribution (EMD) model under uncertain demand by considering the disaster degree and demand of different affected areas to improve the reasonability of disaster relief material distribution in post-disaster emergencies. The objective is to minimize the unsatisfied demand, materials distribution cost, and the risk of choosing the path to distribute materials. The model also focuses on the heterogeneity of emergency materials, the dynamics of demand, and the diversity of transportation tools. In addition, the demands and transportation times are described by fuzzy numbers to better fit with the real situation. To solve the problem we propose a hybrid multi-objective salp swarm algorithm and sine cosine algorithm (HMSSA-SCA). Furthermore, numerical examples of different scenarios and scales are solved and compared with the multi-objective salp swarm algorithm (MSSA), multi-objective evolutionary decomposition algorithm for emergency resource scheduling (MOEA/D-mdERS), non-dominated sorting genetic algorithm II (NSGA-II), and strength pareto evolutionary algorithm 2 (SPEA2) to verify the performance of the HMSSA-SCA algorithm. Finally, the floods and landslides disaster event in 2011 in Rio de Janeiro state, Brazil is used to verify the effectiveness and accuracy of the model and algorithm in the real case.

Bootstrap specification tests for dynamic conditional distribution models

This paper proposes bootstrap based tests for the specification of a given parametric conditional distribution in autoregressive time series with GARCH-type disturbances. The tests are based on an estimated residual empirical process and are implemented by parametric bootstrap. We show that the proposed tests are asymptotically valid, consistent, and have nontrivial asymptotic power against a large proportion of local alternatives. Our approach relies on non-primitive regularity conditions and certain properties of exponential almost sure convergence. The regularity conditions are shown to be satisfied by GARCH(p,q); this technique of verification is applicable to other models as well. In our Monte Carlo study, the proposed tests performed well and better than several competing tests, including the information matrix test. A real data example illustrates the testing procedure.

Prediction of double-sided arc welding deformation based on dynamic heat distribution model and TEP-FE approach

Prediction of double-sided arc welding deformation based on dynamic heat distribution model and TEP-FE approach

Re-examining agricultural output-economic growth nexus in Nigeria: New insights from Dynamic ARDL and Kernel-based Regularized Least Squares

The role of agriculture in reforming social and economic framework of an economy cannot be over-emphasized. It is against this backdrop, this study re-investigate the impact of agricultural output on real output in Nigeria for the period 1981-2019, while controlling for manufacturing output, broad money supply, real effective exchange rate and inflation. We employ a dynamic simulated autoregressive distributed lag model developed by Jordan and Philips (2018) and the Kernel-based Regularized Least Squares (KRLS) proposed by Hainmueller and Hazlett (2014) with useful application in the determination of causal-effect relationship using machine-learning algorithm to implement the pointwise derivatives. Empirical result show that, all regressors except conventional money supply are insignificant in the short-run, while an increasing effect of agricultural output and decreasing effect of unconventional monetary policy on economic growth is reported in the long run. Using the KRLS, we find that, higher agricultural output, increase economic growth at a higher level to a threshold where decreasing marginal returns take place. This indicate that increase in agricultural output is not always growth stimulating. Thus, we are of the opinion that, policymakers in formulating agricultural policy should exercise caution in advocating for increase in agricultural output beyond what is normal for growth while the negative impact of unconventional monetary policy on economic growth calls for caution by the monetary authority.

Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises

As cyber attacks have become more frequent, cyber insurance premiums have increased, resulting in the need for better modeling of cyber risk. Toward this direction, Jevtić and Lanchier [Insur. Math. Econ. 91 (2020) 209–223] proposed a dynamic structural model of aggregate loss distribution for cyber risk of small and medium-sized enterprises under the assumption of a tree-based local-area-network topology that consists of the combination of a Poisson process, homogeneous random trees, bond percolation processes, and cost topology. Their model assumes that the contagion spreads through the edges of the network with the same fixed probability in both directions, thus overlooking a dynamic cyber security environment implemented in most networks, and their results give an exact expression for the mean of the aggregate loss but only a rough upper bound for the variance. In this paper, we consider a bidirectional version of their percolation model in which the contagion spreads through the edges of the network with a certain probability of moving toward the lower level assets of the network but with another probability of moving toward the higher level assets of the network, which results in a more realistic cyber security environment. In addition, our mathematical approach is quite different and leads to exact expressions for both the mean and the variance of the aggregate loss, and therefore an exact expression for the insurance premiums.

Price change prediction of Ultra high frequency financial data based on temporal convolutional network

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.

Distribution of financial resources by areas of investments in the human capital of the region

Introduction. The study has been conducted within the framework of the urgent scientific and practical task of accumulation and development of human capital of Russian regions. Under the conditions of risks and limited resources, the regional management faces the task of optimal distribution of financial resources invested in the development of human capital and improvement of the quality of life. The study aims to build and test the dynamic optimization model of financial resources distribution by areas of investment in human capital through the example of the Primorye Territory (Russian Federation). Materials and methods. The multi-period economic and mathematical model describes the influence of the volumes and structure of public and private investments on the regional human capital in the form of recurrent dependencies. The target function of the model is an integrated index of achieving the objectives for the development of human capital in the region. The model is a mathematical programming problem, the optimization variables are the shares of investment resources distributed by investment areas and years. Results. In a practical sense, the proposed model is a management tool for searching the optimal structure of investments in human capital by areas of investment and periods. Based on the annual results of modeling and numerical calculations through the example of the Primorye Territory (Russian Federation), the structure of the investments that allow advancing in the achievement of target values of strategic indicators in the field of human capital development is offered. Conclusion. In the long term, the achievement of target indicators will be facilitated by a more even structure of investments in the following areas: along with education and health care, it is advisable to increase investments in other areas, first of all, in the issues of national importance, national security, public order, and social policy.

Parallel Algorithm for Calculating the Dynamics of Tungsten Vapor Distribution

We consider an extended dynamic model of tungsten vapor distribution when the surface of a tungsten sample is heated by an electronic beam pulse. The parameters of the model are taken from the experiments on the Beam of Electrons for materials Test Applications (BETA) stand. This stand was designed in the INP SB RAS. The temperature in the sample is calculated from the two-phase Stefan problem. The temperature on the surface of the sample is taken as a boundary condition for the system of gas dynamics equations. A case of axial symmetry is considered. A parallel version of the algorithm, implemented using OpenMP technology, is presented.

Predicting the Rooting Depth, Dynamic Root Distribution and the Yield of Sunflower under Different Soil Salinity and Nitrogen Applications

Soil salinity is a major abiotic stress affecting crop production in arid and semi-arid regions, and nitrogen (N) is the main yield-limiting nutrient in most agroecosystems. To find out the effects of different soil electrical conductivity (EC) and N application rates on sunflower (Helianthus annuus L.) rooting depth, dynamic root distribution and its yield, a three-year field experiment was conducted from 2015 to 2017 in Hetao Irrigation District, China. The experiment consisted of three initial salinity (IS) levels expressed as the EC of a saturated soil extract (ECe) (S0: ECe < 3 dS m-1; S1: 3< ECe <6 dS m-1; S2: ECe> 6 dS m-1) and five N rates (45, 90, 135 180 and 270 kg N ha-1), referred as N0, N1, N2, N3 and N4 rates, respectively. The dynamics of sunflower root growth was in-situ monitored by minirhizotrons and revealed flexibility in its distribution among different treatments. Salt stress obviously decreased root penetration in the seedling stage, while it accelerated root elongation below 28.6 cm depth after bud initiation and prolonged its active development to later stages. In particular, the highest fine root length density (FRLD) of S2N1 and S2N3 were rapidly increased to 5.27 and 6.44 cm cm-3 before anthesis, respectively, which were more than three folds of those at S0 and S1 levels. Meanwhile, the N3 rate in this study obviously aggravated salt stress and accelerated sunflower root senescence at S2 level, but it could promote root vertical development after anthesis. In addition, our study proposed a dynamic root distribution model in saline fields (DRD-SF) to predict the dynamics of FRLD distribution based on IS levels, effective nitrogen application (ENA) and the observed leaf area index (LAI) at different growth time. The data in 2015 and 2017 were used to validate our model developed from 2016 data, demonstrating acceptable simulation of the flexible distribution of sunflower fine roots under the interactions of different IS levels and N rates. Moreover, this study also associated sunflower canopy development with yield production, by proposing a yield response function in the form of bivariate quadratic polynomial (r2 = 0.913, RMSE (root mean squared error) = 539.8 kg/ha) using the ENA and the maximum LAI (LAImax) as two inputs. This work will be helpful for evaluating the potential of crop root development and yield production under different N rates in saline fields.

Role of transportation network on population distribution evolution

Research on the evolution mechanism of population distribution has practical applications in transportation science, spatial economics and many other fields. However, the evolution of population distribution is influenced by numerous factors, among which transportation is a very important one. This paper proposed a dynamic model of population distribution that considers the impact of the transportation network. In the model, the human prefers to move to areas with good transportation. By considering the highway and railway network, we investigate the effects of them on the population spatial distribution. The parameters of the model can govern how many people can immigrate and emigrate in each area. It can be determined by the nonlinear least square method. Furthermore, we use the data of china’s population distribution to demonstrate our model’s ability. Moreover, this model can help us better understand the underlying mechanism of population distribution evolution.