Latin Hypercube Sampling Method Research Articles

Inverse design of TPMS piezoelectric metamaterial based on deep learning

Piezoelectric metamaterials are a class of composite structural materials with a piezoelectric effect that achieve efficient coupling between electrical and mechanical energy through their unique microscopic design. Typically, the tailoring of cellular architectures paves the way for unlocking the potential to achieve unprecedentedly designed materials with specific properties. We have developed three different inverse design frameworks for metamaterials, using deep learning (DL) techniques and underpinned by Bayesian optimization. These frameworks reveal precise one-to-many correlations between material properties and geometric parameters within the triple periodic minimal surface (TPMS) piezoelectric metamaterial. For the first time, we have introduced the Progressive Latin Hypercube Sampling (PLHS) method to the inverse design process of metamaterials, highlighting the effectiveness of uniform sampling to improve the efficiency of deep learning models. The conditional variational autoencoder (cVAE), the conditional generative adversarial network (cGAN) and the multilayer perceptron (MLP) are studied for comparative analysis. All three models incorporate an inverse design network and a feature predictor to construct a continuous latent representation of a large number of hybrid TPMS (H-TPMS). After extensive training, the models can generate various piezoelectric porous structures that accurately meet the required material property parameters. Our results demonstrate the high accuracy of cVAE in inverse design, with 98.92% agreement for piezoelectric values and 96.44% agreement for mechanical properties. The three inverse design frameworks show different generalization capabilities, with cVAE performing best. This study thoroughly analyses the strengths and limitations of each model, providing valuable insights for researchers seeking inverse design recommendations.

A New Latin Hypercube Sampling with Maximum Diversity Factor for Reliability-Based Design Optimization of HLM

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

Reliability assessment for pipelines corroded by longitudinally aligned defects

Internal corrosion poses a significant threat to offshore pipeline services. Toward offshore pipeline integrity management, this paper aims to use Finite Element Method (FEM) to assess the reliability of corroded pipelines. The FEM analysis was conducted using ABAQUS software, facilitated through Python scripts, to optimize the modeling process on a large scale. Additionally, the input database was generated through a random sampling method using the Latin Hypercube Sampling (LHS) method. The reliability of 32” oil and gas offshore internally corroded pipelines has been evaluated by fragility curve assessment along with Incremental Pressure Analysis (IPA) which is a new method inspired by Incremental Dynamic Analysis (IDA). Multiple corrosion defects pose a higher risk to offshore pipelines during service, with potentially more severe consequences than a single defect alone. Therefore, the interaction of corrosion defects has been considered in this study. Following the reliability assessment the most appropriate Probability Density Function (PDF) for the Maximum Von Mises Stress (MVMS) and failure probability at various Internal Pressure (IP) and longitudinal spacing (Sl) levels has been evaluated.

Hydrodynamic shape optimization of an autonomous and remotely-operated vehicle via a multi-surrogate model

The design requirements for the hydrodynamic performance of underwater vehicles vary significantly depending on the application. Optimization without an initial model results in challenges such as large design domains, nonlinear complexity, and high data requirements. To optimize the hydrodynamic performance during the design process, in this paper, a multi-surrogate model was employed to progressively design the shape of an autonomous and remotely-operated vehicle. Based on the data characteristics of different stages, this approach strikes a balance among data quantity, prediction accuracy, and multi-objective requirements. An artificial neural network prediction surrogate model was constructed based on the principle of the minimum prediction factor using the optimal Latin hypercube sampling method. During the optimization of the design domain, the optimum objective was to minimize the dimensionless force (G(x)). During the multi-objective optimization stage, a regression kriging surrogate model was constructed based on a support vector product. The optimization objectives were to maximize volume and minimize dimensionless forces. This enabled the overall design process to attain optimal Pareto solutions within the design domain while simultaneously ensuring high prediction accuracy and minimum data requirements. The results obtained are consistent with the simulation comparison, thus verifying the reliability of the entire optimization process.

Multihazard resilience and economic loss evaluation method for cable-stayed bridges under the combined effects of scour and earthquakes

Cable-stayed bridges (CSBs) commonly experience scour-induced foundation issues during their service life, impacting both structural seismic resilience and economic losses. Nonetheless, these aspects remain inadequately addressed in existing seismic codes. This paper presents a comprehensive probabilistic multihazard fragility, resilience, and economic loss evaluation method for CSBs influenced by scour and earthquakes. Numerical models with various scour depths considering epistemic uncertainties in loads, structural properties, and soil conditions are first established using the Latin-hypercube sampling (LHS) method. Incremental dynamic analyses (IDAs) are then conducted on these models in cases with near-fault and far-field bidirectional ground motions, yielding probabilistic multihazard demand models and component- and system-level multihazard fragility surfaces for diverse limit states. Subsequently, resilience and economic losses are comparatively evaluated by integrating damage probabilities with corresponding uncertain functional recovery functions. Eventually, a rapid bridge traffic capacity evaluation method based on the critical traffic capacity surface concept is devised to effectively assess CSB open patterns under various peak ground accelerations (PGAs), scour depths, and repair times. The numerical results show apparent shifts in the nonmonotonic seismic fragility, resilience, and economic loss of the case CSB due to scour effects, with the rates of change reaching 31.4 %, 34 %, and 24.8 %, respectively, associated with a critical scour depth of 12 m at IM = 0.8 g. The developed analysis framework provides a reference for the life-cycle design and maintenance of CSBs affected by multihazard scour and earthquakes.

Active learning for efficient data-driven aerodynamic modeling in spaceplane design

Data-driven surrogate model is extensively applied in the aerodynamics prediction for flight vehicle design. However, for three-dimensional problems, modeling costs hugely in acquiring adequate data. Adaptive and efficient sampling represents a promising approach, so we employ Active Learning (AL) to assess the sampling space. An aerodynamics prediction framework based on the Loss for Active Learning (LLAL) is proposed for capturing the sample insufficient space. The LLAL-based method refines the model by estimating the biases between the ground truths and the predictions as a measure of the high-value samples, then selecting top-K samples for infilling. We first validate our proposed method using an analytical benchmark two-dimensional function, followed by its application to aerodynamics prediction of spaceplane with the number of 60 and the deformation dimensions of 18. Through using both the Latin Hypercube Sampling and LLAL-based methods to infill samples, we observed the R2 of lift-to-drag ratio improves from 0.82 to 0.85. The AL method can enhance the accuracy of models with a limited number of samples, thereby reducing sampling costs and improving the efficiency of aerodynamic design.

Improving tuberculosis control: assessing the value of medical masks and case detection-a multi-country study with cost-effectiveness analysis.

Tuberculosis (TB) remains a significant global health concern, necessitating effective control strategies. This article presents a mathematical model to evaluate the comparative effectiveness of medical mask usage and case detection in TB control. The model is constructed as a system of ordinary differential equations and incorporates crucial aspects of TB dynamics, including slow-fast progression, medical mask use, case detection, treatment interventions and differentiation between symptomatic and asymptomatic cases. A key objective of TB control is to ensure that the reproduction number, , remains below unity to achieve TB elimination or persistence if exceeds 1. Our mathematical analysis reveals the presence of a transcritical bifurcation when the signifies a critical juncture in TB control strategies. These results confirm that the effectiveness of case detection in diminishing the endemic population of symptomatic individuals within a TB-endemic equilibrium depends on exceeding a critical threshold value. Furthermore, our model is calibrated using TB yearly case incidence data per 100 000 population from Indonesia, India, Lesotho and Angola. We employed the bootstrap resampling residual approach to assess the uncertainty inherent in our parameter estimates which provides a comprehensive distribution of the parameter values. Despite a declining trend in new incidence, these four countries exhibit a reproduction number greater than 1, indicating persistent TB cases in the presence of ongoing TB control programmes. We employ the partial rank correlation coefficient in conjunction with the Latin hypercube sampling method to conduct a global sensitivity analysis of the parameter for each fitted parameter in every country. We find that the medical mask use is more sensitive to reduce compared with the case detection implementation. To further gain insight into the necessary control strategy, we formulated an optimal control and studied the cost-effectiveness analysis of our model to investigate the impact of case detection and medical mask use as control measures in TB spread. Cost-effectiveness analysis demonstrates that combining these interventions emerges as the most cost-effective strategy for TB control. Our findings highlight the critical importance of medical masks and their efficacy coupled with case detection in shaping TB control dynamics, elucidating the primary parameter of concern for managing the control reproduction number. We envisage our findings to have implications and be vital for TB control if implemented by policymakers and healthcare practitioners involved in TB control efforts.

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.

Study on optimal scheduling of energy storage participation in power market considering source-load uncertainty

With the increasing penetration of new energy sources such as wind and light in the power grid, the selection of new energy-storage joint scheduling and operation methods can reduce the phenomenon of wind and light abandonment, but the new energy-storage joint scheduling and operation methods need to take into account the market factors, characteristics, and other aspects of a variety of problems. Given these difficulties, this paper takes into account the uncertainty of wind power, adopts the Latin hypercube sampling method to generate and consider the probabilistic distance fast reduction method, selects the typical scenarios, establishes the energy storage market operation model by minimizing the comprehensive operation cost, and responds to the source-load demand through the integrated response of the energy storage and the power grid. Finally, the effectiveness of the proposed method is demonstrated through simulation examples.

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.