Latin Hypercube Sampling Method Research Articles

Improvement the resiliency of distribution network with routing, scheduling, and prepositioning mobile energy resources with limited fuel

Mobile energy resources (MERs) are equipment which are used to improve the resiliency of distribution networks after natural disasters. An important point about using these resources is optimal routing and scheduling them to supply essential loads. In this paper, to make the problem more actual, a new formulation considering limited fuel for the trunks that move these energy resources is presented. Then, refueling vehicle is used to lessen the effect of limited fuel. Therefore, an optimization algorithm is presented for optimal allocating refueling vehicle, simultaneously optimal routing and scheduling of limited-fuel MERs. In the next stage, to achieve a better result and reducing the restoration time, a new formulation is presented for prepositioning MERs considering the stochastic occurrence of damages in various points of the network. Latin hypercube sampling method is used as scenario reduction method to implement the stochastic problem formulation. To illustrate the efficiency of the proposed method considering limited fuel for MERs, refueling vehicles, and MERs prepositioning, simulations are carried out on IEEE 33-bus test system and the results are compared with the previous studies’ results.

An integrated uncertainty analysis method for the risk assessment of hydrogen refueling stations

In order to assess more rigorously the risks of hydrogen refueling stations (HRSs) with consideration of uncertainties, an uncertainty analysis method integrating the Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+), Monte Carlo simulation, and Latin Hypercube Sampling (LHS) method is proposed in this paper. In order to discuss and demonstrate the potential ability of the proposed method, the leak risk of a 35 MPa dispenser at a HRS is assessed. The results indicate that the proposed method can effectively quantify the leak risk uncertainty of the dispenser at a HRS. These uncertainties considered have significant effects on the leak risks of the dispenser, and the risk contribution of jet fire is much larger than that from explosion. Moreover, the leak risk of the dispenser is underestimated by the HyRAM+, so it should be used by combining a reliable safety factor. Current work can provide a more accurate method for the risk assessment and performance-based design of HRSs.

Cooperative economic dispatch of EV-HV coupled electric-hydrogen integrated energy system considering V2G response and carbon trading

With the increasing penetration of renewable energy, the Power-to-hydrogen technology accelerates the adoption of new energy vehicles, including both hydrogen vehicles (HV) and electric vehicles (EV). Simultaneously, the integration of Vehicle-to-Grid (V2G) technology further promotes the development of electric-hydrogen integrated energy systems coupled with transportation systems. In this study, an optimal scheduling model is developed for an EV-HV coupled electric-hydrogen integrated energy system, taking into account V2G response and carbon trading. Firstly, to address the randomness of renewable energy and loads, the Latin hypercube sampling method in conjunction with the Kantorovich scenario reduction method, is employed for scenario generation and simplification. Secondly, an optimization model is developed for the operation of hydrogen-blended gas turbines and the energy management of EVs and HVs. To encourage the participation of EVs and HVs, a reward mechanism for V2G responses is introduced, complemented by a time-of-use tariff, to guide orderly charging and discharging. Furthermore, the influence of the operating modes of EVs and HVs, as well as the hydrogen blending ratio, on the total system cost and carbon emissions is examined. An industrial park is selected as an illustrative example to showcase the effectiveness of the proposed method, demonstrating satisfied economic and environmental performances.

Global Sensitivity Analysis of A Rabies Epidemic Model involving Dog Vaccination and Dog Population Management

Rabies is a zoonotic disease which is spread by animals mostly carnivores. Rabies regards as tropic disease. In this article, we construct a mathematical model for rabies involving dog vaccination and dog population management. The model has two equilibrium points, namely rabies-free equilibrium point and endemic equilibrium point. We determine the effective reproduction ratio using next generation matrix. Our dynamical analysis shows that rabies-free equilibrium point is conditionally stable. A global sensitivity analysis is performed to investigate which intervention is the most crucial among the two interventions considered in the model. We use Latin hypercube sampling method to generate parameter space. To investigate the parameter sensitivity, we calculate the partial rank correlation coefficient. We provide numerical experimental results related to stability and global sensitivity analysis. Our results show that the effective reproduction ratio is more sensitive to dog population management than vaccination intervention. This suggests that dog population management intervention, such as sterilization and monitoring of dog movements significantly reduces the effective reproduction ratio compared to vaccination programs. In addition, the number of infectious dogs has a strong correlation with dog culling actions.

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Impact of Component Correlation on Probabilistic Seismic Responses of Steel Frames by Modifying Non-Positive Definite Correlation Matrices

ABSTRACT This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix.

A probabilistic performance-based analysis approach for a vibrator-ground interaction system

There is an increasing interest in investigating the effects of input uncertainties on dynamic systems. The probabilistic analyses for a vibrator-ground (VG) interaction system are rare and the effects of system uncertainties need to be revealed. This study aims to present an approach for the probabilistic performance-based analysis of the VG system under multi-source uncertainties. The probabilistic model of the VG system is constructed on the basis of the Monte Carlo (MC) simulation combined with the Latin Hypercube Sampling (LHS) method, while the artificial neural networks optimized by the genetic algorithms are employed to reduce the large computational expenses in the MC simulation. Then, a multi-criteria sensitivity analysis is presented by using a technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the effects of input uncertainties on the dynamic performance of the vibrator. Finally, a probabilistic simulation analysis of the VG system is conducted by implementing the presented approach. The results demonstrate the effectiveness of the presented probabilistic performance-based analysis approach for the VG system and evaluate the effects of input uncertainties on the dynamic performance of the system.

Best Estimate Plus Uncertainty Analysis of Station Blackout Transient in Pool-Type Research Reactor

Simulation of a station blackout (SBO) scenario was carried out for an open pool–type nuclear research reactor. The SBO transient was analyzed using the best estimate (BE) thermal-hydraulic code RELAP5/MOD3.2 to evaluate the performance of safety systems and inherent thermal inertia provided by the reactor pool in ensuring adequate core cooling during a prolonged SBO condition lasting up to 7 days. This encompasses assessment of cooling provided by battery-operated auxiliary pumps in the initial phase followed by setup of the natural convection cooling mode for the extended period. Best Estimate Plus Uncertainty (BEPU) methodology was applied for assessment of safety margins. This involved estimation of required simulations using the Wilks first-order formulation to achieve results within the tolerance limit of 95/95. Identification of relevant uncertainties and their propagation was carried out; subsequently, a case matrix for 59 simulation runs was generated using the Latin hypercube sampling method. The upper/lower bounds of uncertainty results were analyzed and compared with the BE code results. Later, sensitivity analysis was carried out using sensitivity coefficients generated using the Pearson and Spearman coefficient. The results show that the values of the crucial thermal-hydraulic parameters obtained with the tolerance limit of 95/95 met the acceptance criteria, with adequate safety margins.

An investigation on implementation of generating adversarial network-based surrogate models for prediction of turbine endwall film cooling effectiveness

Film cooling is an essential cooling technique for turbine blade, which guides coolant to cover the turbine blades surface by arranging discrete film holes on it. For the newly designed cooling units, the process of predicting film cooling effectiveness is really complex and time-consuming. For the purpose of accelerating the design procedure, the U-Net which is a very popular algorithm in the field of deep learning was improved with the basic layer structure, then it was adopted as the proxy function for the film cooling effectiveness prediction. The basic samples were generated by Latin hypercube sampling method and calculated by commercial software. The results were verified by experiment in a linear cascade. The training and testing samples were calculated at experimental conditions (inlet Mach number values 0.12). The results showed that comparing with the basic U-Net network, the novel U-Net network has a great better accuracy on the prediction of contour results. Both of the improved U-Net networks had higher prediction accuracy than original models. The correlation index reached more than 99.5% and the first order error was lower than 0.3%. The number of basic neural layers is not positively correlated with the network prediction accuracy which exists an optimal ratio. The experimental verification shows that the generating adversarial network prediction results are consistent with the experimental results. The final prediction results showed that the U-Net model can be used as a surrogate model and meets the requirements for accuracy of film cooling effectiveness prediction.

Structural optimization design of ice abrasive water jet nozzle based on multi-objective algorithm

To enhance the cleaning efficiency of the ice abrasive water jet (IAWJ), the nozzle in the jet structure was optimized. The cleaning effect of nozzle with different structural design is different. The design variables include the geometric parameters of the nozzle's structure and the flow process parameters of the jet. The maximum wall static pressure, outlet velocity and wash width are the optimization objectives. Based on the Optimal Latin Hypercube Sampling (OLHS) method, 50 sets of sample capacity were designed for simulation calculation. The parameter functions associated with the three objectives were fitted by MATLAB. The Competitive mechanism multi-objective particle swarm optimizer (CMOPSO) algorithm was used to optimize the nozzle to improve the cleaning effect, which was verified with the test results. The inlet pressure and outlet diameter are critical factors influencing the cleaning effect of the IAWJ. The maximum wall static pressure produced by the optimized nozzle is about 4.5% higher than that of the ordinary cosine nozzle. The cleaning effect of the optimized nozzle is 10% higher than that of the ordinary cosine nozzle, which is verified by the experiments. The optimization of the nozzle serves as a valuable reference for the nozzle structure design of the cleaning machine.

Structural analysis and optimal design of a spherical thin-walled stainless steel water tank without reinforced tie ribs

A spherical thin-walled stainless steel water tank without reinforced tie ribs has been designed to address the issues of easy fracture and corrosion of the ribs, difficulty in maintenance and cleaning, and short service life exposed during the use of thin-walled stainless steel water tanks with reinforced tie ribs. Firstly, an analytical model of a flat steel thin-walled water tank without reinforced tie ribs was established and subjected to static analysis under water pressure. The deformation and stress distribution patterns of the side molded plate of the flat box were obtained. Secondly, a spherical non ribbed thin-walled stainless steel water tank structure was designed with circular cross-section under different box bulge parameters, and its mechanical response characteristics under water pressure load were analyzed. A strengthening scheme was designed for the bottom box molded plate. Once again, optimize the combination design of the box scheme and the reinforcement scheme, and analyze their static, thermodynamic, and thermal solid coupling performance. Finally, the Latin Hypercube Sampling method was used to generate experimental design samples, and a response surface approximation model of a spherical thin-walled stainless steel water tank without reinforced tie ribs was constructed. The wall thickness of the box molded plate, skeleton, and reinforcement were used as design variables, and the maximum deformation and maximum equivalent stress were used as constraints. The lightweight design was carried out with the goal of minimizing mass. The research results indicate that the design program and parameter selection method for spherical thin-walled stainless steel water tanks without reinforced tie ribs proposed in the article are efficient and feasible, and can provide technical reference and theoretical support for the layout and overall optimization design of non ribbed thin-walled stainless steel water tank structures.

On the estimation of interior walls in the district-scale Life Cycle Assessment of buildings

The large-scale Life Cycle Assessment (LCA) of buildings supports decision-making processes in reaching the climate goals of the building sector. Yet, the considerable amount of data required for detailed assessments hinders the method. This is particularly the case for buildings’ interior, which oftentimes surpasses the normative cutoff criteria’s negligibility threshold, and thus has to be considered in building LCA. In light of interior walls’ contribution to buildings’ overall environmental impacts, and a lack of cohesive determination methods thereof, this article proposes a parametric approach to complement an existing district-scale LCA workflow for residential buildings. The approach is applied to an exemplary residential district, using a Latin Hypercube Sampling (LHS) method, to establish a range of interior wall areas and resulting effects on LCA calculations. Thereafter, the outcome is compared with the results of a preexisting approach and validated project data. The novel method is capable of significantly reducing the tool’s overestimation of interior wall areas from 400% to a value between 7% and 53%. However, it is evident that the area estimation in terms of Life Cycle Inventory (LCI) needs to be complemented with a more accurate material setup, which could be differentiated by building archetypes.

A methodology to quantify effects of constitutive equations on safety analysis using integral effect test data

To improve the predictive capability of a nuclear thermal hydraulic safety analysis code by developing a better constitutive equation for individual phenomenon has been the general research direction until now. This paper proposes a new method to directly use complex experimental data obtained from integral effect test (IET) to improve constitutive models holistically and simultaneously. The method relies on the sensitivity of a simulation result of IET data to the multiple constitutive equations utilized during the simulation, and the sensitivity of individual model determines the direction of modification for the constitutive model. To develop a robust and generalized method, a clustering algorithm using an artificial neural network, sample space size determination using non-parametric statistics, and sampling method of Latin hypercube sampling are used in a combined manner. The value of the proposed methodology is demonstrated by applying the method to the ATLAS DSP-05 IET experiment. A sensitivity of each observation parameter to the constitutive models is analyzed. The new methodology suggested in the study can be used to improve the code prediction results of complex IET data by identifying the direction for constitutive equations to be modified.

Application of data-driven model reduction techniques in reactor neutron field calculations

High-order harmonic techniques can be used to recreate neutron flux distributions in reactor cores using the neutron diffusion equation. However, traditional source iteration and source correction iteration techniques have sluggish convergence rates and protracted calculation periods. The correctness of the implicitly restarted Arnoldi method (IRAM) in resolving the eigenvalue problems of the one-dimensional and two-dimensional neutron diffusion equations was confirmed by computing the benchmark problems SLAB_1D_1G and two-dimensional steady-state TWIGL using IRAM. By integrating Galerkin projection with Proper Orthogonal Decomposition (POD) techniques, a POD-Galerkin reduced-order model was developed and the IRAM model was used as the full-order model. For 14 macroscopic cross-section values, the TWIGL benchmark problem was perturbed within a 20% range. We extracted 100 sample points using the Latin hypercube sampling method, and 70% of the samples were used as the testing set to assess the performance of the reduced-order model The remaining 30% were utilized as the training set to develop the reduced-order model, which was employed to rebuild the TWIGL benchmark problem. The reduced-order model demonstrates good flexibility and can efficiently and accurately forecast the effective multiplication factor and neutron flux distribution in the core. The reduced-order model predicts keff and neutron flux distribution with a high degree of agreement compared to the full-order model. Additionally, the reduced-order model's computation time is only 10.18% of that required by the full-order model.The neutron flux distribution of the steady-state TWIGL benchmark was recreated using the reduced-order model. The obtained results indicate that the reduced-order model can accurately predict the keff and neutron flux distribution of the steady-state TWIGL benchmark.Overall, the proposed technique not only has the potential to accurately project neutron flux distributions in transient settings, but is also relevant for reconstructing neutron flux distributions in steady-state conditions; thus, its applicability is bound to increase in the future.

CFD simulation of cannabidiol delivery through microneedle patches

This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered via the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.

Two-stage low-carbon optimal dispatch of the power system considering demand response to defend large uncertainties and risks

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper.Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs.Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan.Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation.

Optimization design on cooling structure of High-Temperature magnetic fluid dynamic seal device

In order to better sealing performance of magnetic fluid at high temperature, structural optimization design of cooling component in magnetic fluid dynamic sealing device at high temperature is studied in this paper. The magnetic field model of magnetic fluid sealing device at high-temperature is established to analyze the influence of temperature on the sealing performance of magnetic fluid, and magnetic field strength distribution is obtained. The simulated results show both magnetic induction strength difference and theoretical sealing pressure decrease gradually with the increase of the operating temperature. To reduce the temperature having great influence on sealing performance of magnetic fluid dynamic seal device, the temperature field model is constructed. Based on the Latin hypercube sampling method, the design matrix between cooling structural parameters and temperature is determined. In consideration of the neural network approach, the approximate agent model is built to analyze the correlation relationship between cooling structure parameters and cooling performance. The multi-island genetic algorithm is conducted on searching the optimal cooling structural parameters under global design space. The optimized results show that the average temperature of the permanent magnet is decreased by 27 °C, and the average temperature of the seal gap is reduced by 28 °C. The theoretical sealing differential pressure of the high-temperature magnetic fluid is improved by 49 KPa.

Comprehensive Assessment and Empirical Research on Green and Low-Carbon Technologies in the Steel Industry

The iron and steel industry is the leading industry supporting China’s industrial sector. Currently, there is less assessment work on green and low-carbon technologies for the iron and steel industry. This study clarifies the overall strategy of technology assessment by researching the relevant theories and methods of technology assessment. The study further establishes a scientific and reasonable comprehensive assessment index system of green and low-carbon technologies for the iron and steel industry from the aspects of technology index, economy and promotion, and application, including factors such as 11 indexes, the amount of energy saving, carbon dioxide emission reduction, and the resource recovery rate by utilising analytical and comprehensive methods and combining with the characteristics of the technologies. By analysing and comparing the advantages and disadvantages of the commonly used assessment methods, the entropy weighting method, grey correlation analysis method, and TOPSIS (technique for order preference by similarity to an ideal solution) method are combined and optimised to construct a comprehensive assessment model. The Latin hypercube sampling method is also introduced to analyse the technical parameters in combination with the evaluation model. Finally, fourteen iron and steel green and low-carbon technologies were selected for case assessment and uncertainty analysis of technical parameters, and it was found that the comprehensive assessment result of gas combined cycle power generation technology was optimal. After determining the weights of each assessment indicator through the entropy weighting method, it is concluded that the technical performance indicator > economic indicator > promotional indicator. A comparative analysis of the results under the three preference decisions concludes that technical performance is the main obstacle to improving the comprehensive assessment score of the technology, followed by the economics of the technology. Finally, the uncertainty analysis of the technical parameters shows that the fluctuation of the technical parameters not only affects the performance of the technology, but also affects the weights of the indicators and the comprehensive evaluation results of the technology.

Modelling of tuberculosis dynamics incorporating indirect transmission of contaminated environment and infectivity of smear-negative individuals: A case study for Xinjiang, China

Xinjiang has been one of the high incidence areas of pulmonary tuberculosis (PTB) in China. Besides being infected by direct contacting with active PTB individuals (direct infection), the susceptible would be infected because of the exposure to the environment contaminated by Mycobacterium tuberculosis (indirect infection). Active PTB individuals include not only the smear-positive PTB (PTB+) but also the smear-negative PTB (PTB–) who are infectious due to their ability to release tiny Mycobacterium tuberculosis particles even in the absence of visible Mycobacterium tuberculosis in sputum. By taking account of direct/indirect infection and the difference between PTB+ and PTB– individuals in transmission capability, a periodic dynamical PTB transmission model is proposed. The model is fitted to the newly monthly PTB+ and PTB– cases in Xinjiang from 2008 to 2017 by Markov Chain Monte Carlo algorithm. Moreover, global sensitivity analysis is constructed to address the uncertainty of some key parameters by using Latin hypercube sampling and partial rank correlation coefficient methods. Basic reproduction number R0 for PTB transmission in Xinjiang is estimated to be 2.447 (95% CrI:(1.203, 3.844)), indicating that PTB has been prevalent in Xinjiang over the study period. Our results suggest that reducing the direct/indirect transmission rates, early screening, isolating and treating the latent, PTB+ and PTB– individuals, and enhancing the clearance of Mycobacterium tuberculosis in the environment could more effectively control PTB transmission in Xinjiang. The model fits the reported PTB data well and achieves acceptable prediction accuracy. We believe that our model can provide heuristic support for controlling PTB transmission in Xinjiang.

Modeling and digital mapping of soil quality indicators in different land uses (a case study: Ravansar-Sanjabi Plain, Kermanshah).

Identifying the spatial variability of soil quality indices is necessary to manage soil resources on a watershed scale. This study aimed to identify suitable indices for soil quality assessment at the watershed scale using various soil characteristics and modeling approaches. Another objective was to map soil quality variability in a representative area in the Qarasu watershed in Kermanshah province, west of Iran. Latin hypercube sampling method using the auxiliary variables used to select 163 sampling points based on land use, soil, and topographical variability in an area of about 57 thousand hectares. In the field operations, soil profiles were described, and samples were taken from different soil profile horizons. Soil properties such as texture, pH, salinity, available water, equivalent calcium carbonate, saturation percentage, soil organic carbon, nitrogen, available phosphorous, potassium, Fe, Zn, Cu and Mn, and soil aggregate stability (mean weight diameter (MWD), geometric mean diametric (GMD), and stable aggregates larger than 0.25 mm (WAS)) measured in the laboratory. Soil quality indices (productivity index (PI), soil quality index (SQI) and reduced dimension soil quality index using principal component analysis (SQI-PCA)) were calculated for each point using the measured soil properties. Soil quality indices were simulated using modeling with the random forest and support vector machine methods and auxiliary variables. Results showed that the range of soil characteristics and integrated soil quality indices was very high across the study area. Soil organic carbon percent varied from about 0.19 to 8.44%. The range of changes in PI in the study area was more than SQI and SQI-PCA indices. Quantities of all soil quality indices were higher in forest and rangeland than in agricultural lands. The spatial estimation accuracy of the SQI-PCA was higher than other soil quality indices and converged well with land use changes compared to PI and SQI.