Latin Hypercube Research Articles

Optimal condition design of cannabis drying for vacuum heat pump dryer using computational fluid dynamic method interaction with heat transfer

Utilizing low temperatures and short drying cycles, the vacuum heat pump dryer (VHPD) is ideal for cannabis drying due to the plant's sensitivity to heat and light. To achieve uniform drying and maintain cannabis quality, it is essential to design the dryer with an efficient temperature distribution throughout the drying chamber. CFD models are developed in this study to simulate various scenarios within the VHPD system. The optimization of pressure and temperature is carried out using a systematic approach with the Latin Hypercube Sampling experimental design. This method generates twenty pairs of values for the pressure and temperature variables, confined to ranges of 20–40 kPa and 30–45 °C, respectively. To confirm the simulation results, an experimental study is performed using a VHPD designed from insights gained through a detailed CFD analysis. The conditions of 20.2 kPa at 31.6 °C are considered optimal due to their more uniform temperature distribution and lower static enthalpy relative to other conditions. The temperature in the upper chamber is maintained slightly lower, within the range of 30.5–31.5 °C. Furthermore, the airflow distribution demonstrates uniform movement within the central region of the chamber, with velocities ranging from 2.5 to 3.5 m/s. The simulated temperature results demonstrated a strong correlation with a temperature difference in the range of 0–0.05 °C, evidenced by an RMSE of 0.0191. The VHPD successfully dries 18 kg of fresh cannabis in 180 min, reaching the desired moisture content range of 10–14 % on a dry basis.

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 optimisation. 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 generalisation 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 smart building energy management incorporating clustering-based tariffs in the presence of domestic solar energy, battery, and electric vehicle

Smart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clustering-based electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the real-time pricing program. Ward’s clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building’s energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building’s performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clustering-based management method has resulted in a significant reduction of 23–43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.

CPOpt: A modular framework for genetic algorithm optimization and post-optimization analysis in complex charged particle optical design

This article reports on the development of an optimization framework for charged particle optics design (CPOpt). The optimization part of CPOpt involves a custom-developed Python multi-objective genetic algorithm (MOGA), which incorporates NSGA-II to optimize for multiple objectives, such as particle transmission and beam spot size. The simultaneous tuning of different types of parameters, such as electrode voltages and geometrical parameters, is facilitated by creating isolated groups of parameters. Modeling of parameter configurations is done with the charged particle optics simulation tool SIMION and a ZeroMQ-based communication interface enables a fully automated interaction with the MOGA. The post-optimization analysis part of CPOpt provides standardized and broadly applicable methods to identify suitable solutions, based on the outcome of the optimization part. Global sensitivity analysis, based on the Delta Moment-Independent-Measure in combination with dedicated Latin hypercube sampling, allows higher-level information about parameter sensitivity and robustness to be extracted, as well as effective randomized local optimization. Demonstrated on a two-lens system with intermediate aperture, the CPOpt framework effectively optimized the two conflicting objectives of reduced beam spot size and increased transmission at the detector plane for 12 parameters, covering both electrode voltages and geometrical parameters. The optimization process was performed within less than 11h on a workstation, while all the simulations required for the post-optimization analysis took another 7h. The additional computational effort for the post-optimization analysis can contribute to deliver solutions with high performance and stability.

Study on energy-saving techniques of the lithium-ion batteries cooling system using a backup battery

The power battery in an electric vehicle is an essential part, and the damage to the battery will have a substantial impact on its thermal characteristics. In order to improve the reliability of the air-cooled lithium-ion battery packs in the high temperature environments, this paper offers a more useful and general optimization strategy for the design of the thermal management system for the batteries which have a damaged battery. To ensure the optimal heat dissipation and prolong the battery string operation, the damaged batteries should be swapped out with the backup batteries. The relationship between the target and the design variables which was established using the quadratic polynomial response function is examined. The experimental cases are selected using the optimal Latin hypercube design approach. After optimizing through the multi-island genetic approach, the error between the simulation results and the prediction results is only 0.06 %. The maximum temperature and the maximum temperature difference of the ideal structure are reduced by 4.58 % and 28.05 %, respectively. Then, the backup battery is selected as the sole battery that has the highest temperature of the ideal structure, and each broken battery is replaced by the backup battery when the damaged position is determined. Finally, a parametric equation connecting the temperature and the flow rate is created, and the inlet flow rate is tuned to match the optimal heat dissipation state of the battery pack. The results of the battery thermal management system having a damaged battery in various situations have an important significance for adjusting the operating conditions of the cooling system for the batteries.

Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications.

Knowledge graph-based representation and recommendation for surrogate modeling method

Surrogate models have been widely used in engineering design for approximating a simulation system with high computational cost. Complex system design typically is a multi-stage and multi-discipline design problem, which requires a large number of surrogate models. The choice of surrogate modeling method (SMM) is critical as it directly impacts the performance of both the surrogate models and the designed systems. With the growing variety of SMMs, designers face challenges in selecting the appropriate methods for their specific applications. To address this, we propose a representation and recommendation framework for surrogate modeling methods based on knowledge graph. Firstly, we develop an ontology to formally represent core concepts involved in the recommendation for surrogate modeling methods, including surrogate modeling method, surrogate model, and data sets,etc. Secondly, we extract 460 samples from 46 benchmark functions using Latin hypercube sampling to construct a knowledge graph with 8,343 nodes and 16,100 relationships, which involves 7,820 surrogate models generated from 17 surrogate modeling methods. Finally, we propose a knowledge graph-based recommendation method for surrogate modeling method named KGRSMM to facilitate the selection of an appropriate surrogate modeling method. We test the efficacy of KGRSMM using examples of theoretical problems and engineering problems of hot rod rolling respectively. It is shown in the results that KGRSMM is capable of recommending surrogates with appropriate accuracy, robustness, and time to satisfy designers’ preferences.

Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site

Exploring the challenges posed by uncertainties in numerical modeling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modeling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.

Generalized extended triangular designs: Construction and online generation

Method of constructing new and useful series of m-associate class partially balanced incomplete block (PBIB) designs, named as Generalized Extended Triangular (GET) designs, in m replications has been proposed along with an outline of analytical procedure. Triangular designs, one of the most popular and widely used class of PBIB(2) designs, and the extended triangular PBIB(3) designs form other two important particular cases of this generalized class of designs. To avoid the complexity involved in the construction methodology and to save time, an R package ‘‘GETdesigns’’ has been developed for the generation of the proposed designs. Further, these designs have been effectively used for obtaining mating designs and Latin hypercube designs.

A wavelet packet deep learning model for Energy-Based structural collapse assessment under Earthquake-Fire Scenarios: Framework and hybrid simulation

An energy-based framework is proposed for the dynamic stability assessment of structures subjected to earthquake-fire scenarios and verified through earthquake-fire hybrid simulation. In this framework, the wavelet packet Long Short-Term Memory (LSTM) model is used to separate the noise and residue caused by earthquake and fire within the structural response signals, guided by wavelet packet energy and power spectral density of signals. Moreover, a comparative analysis is performed with 4 previous signal processing models (empirical mode decomposition, variational mode decomposition, wavelet packet transform, and LSTM). Additionally, Latin hypercube sampling is employed to account for uncertainties in structural characteristics and hazards for dataset establishment and fragility analysis. The experimental findings suggest a decline in the structural dominant frequency with the emergence of high-frequency noise and residuals in structural responses due to the multi-hazard effect. The wavelet packet transform eliminates the high-frequency noise in the signal and avoids the oscillation of the LSTM prediction results. Post-earthquake fire increases the structural collapse possibility even under a moderate earthquake excitation. The proposed framework proves to be rational and has the potential to be further applied to other multi-hazard scenarios.

Aerodynamic Characteristics and Its Sensitivity Analysis of a Cylindrical Projectile at Different Incidences

The accurate evaluation of aerodynamic characteristics is a prerequisite and foundation for the design of high-performance aerodynamic shapes, navigation guidance, and strength of projectiles. The nonlinearity of aerodynamic calculations for a projectile is high, and the modeling and simulation are difficult, especially under the high-angle of attack flight conditions. Small variations in flight conditions, and structural parameters, etc., may cause large deviations in aerodynamic responses. Taking a small cylindrical projectile as an example, and to realize its attitude control, it is necessary to conduct aerodynamic characteristics analysis on it and analyze the main influencing factors of its aerodynamic characteristics parameters. In this paper, the finite volume method is used to solve the three-dimensional unsteady N-S equation, combined with the SST k-ω turbulence model, the overlapping grid technology, and the forced pitching vibration method, and the aerodynamic characteristics analysis model of the projectile is established, which realizes the accurate simulation of the surrounding flow field, aerodynamic coefficients, and dynamic derivative of the projectile under different flight conditions. On this basis, the Sobol global sensitivity analysis method based on the augmented radial basis function surrogate model of aerodynamics characteristics and Latin hypercube sampling is used to efficiently analyze and obtain the main influence parameters of cylindrical projectile aerodynamic characteristics. This paper provides a basic theory and fast algorithm for subsequent engineering system design, which has important theoretical and engineering value.

Every latin hypercube of order 5 has transversals

AbstractWe prove that for all every latin ‐dimensional cube of order 5 has transversals. We find all 123 paratopy classes of layer‐latin cubes of order 5 with no transversals. For each and we construct a latin ‐dimensional cuboid of order with no transversals. Moreover, we find all paratopy classes of nonextendible and noncompletable latin cuboids of order 5.

Multi-objective optimization design of rail grinding profile in Curve Section of Subway based on wear Evolution and representative worn profile

Curve rail grinding has always been one of the key points of subway maintenance and repair. The appropriate grinding can effectively reduce wear. A multi-objective optimization design method for grinding profiles in curved sections is proposed. Firstly, representative grinding profiles are selected as the initial population using the dynamic time regularization algorithm (DTW). Then, the optimal design range is determined based on wear characterization analysis, and mathematical expressions of wheel profiles are chosen as design variables to establish a parametric model. Next, the prediction model considering the evolution of wheel wear is incorporated into the multi-objective function, and objective function adaptive weight adjustment coefficient factors are introduced to establish the multi-objective optimization model for wheel profiles. The Latin hypercubic sampling method is employed to establish the RBF agent model for simulation calculation, and the optimization design of wheel profiles is carried out using the TS-NSGA-II multi-objective algorithm.

Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties

Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO2 emission cost demonstrates that when the base chiller’s capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system.

Probabilistic ductile deformation limit state prediction of monolithic exterior shear keys based on quantile regression machine learning techniques

Shear strength and ductile deformation capacity of monolithic exterior shear keys play important roles on the seismic behavior of highway bridges. Improving the ductility of the shear key through reasonable design could effectively restrict superstructure displacement and minimize damage to pier columns. However, most existing studies primarily focus on evaluating the shear strength and damage mechanisms of shear keys, with limited attention given to analytical models for assessing ductile deformation capacity. To this end, this study proposes a method that combines quantile regression and machine learning techniques to predict the ductile deformation limit states of shear keys. The proposed numerical modeling method for shear keys is validated using the experimental data on lateral load-displacement curves. On this basis, Latin hypercubic sampling and joint simulation are utilized to establish the ductile deformation limit state database. The ductile deformation limit states of shear keys are characterized by their lateral displacements in three distinct damage states. The main influencing factors of each limit states are investigated by correlation analysis. Three quantile regression machine learning models are trained and constructed to conduct point and interval prediction for the ductile deformation limit states. All three models, particularly the Gaussian process quantile regression model, are proven to demonstrate exceptional prediction accuracy and uncertainty quantification. Experimental data validation confirms that these models outperform the prediction accuracy of existing simplified models.

A Multi-Objective Non-Dominated Sorting Gravitational Search Algorithm for Assembly Flow-Shop Scheduling of Marine Prefabricated Cabins

Prefabricated cabin modular units (PMCUs) are a widespread type of intermediate products used during ship or offshore platform construction. This paper focuses on the scheduling problem of PMCU assembly flow shops, which is summarized as a multi-objective, fuzzy-blocking hybrid flow-shop-scheduling problem based on learning and fatigue effects (FB-HFSP-LF) to minimize the maximum fuzzy makespan and maximize the average fuzzy due-date agreement index. This paper proposes a multi-objective non-dominated sorting gravitational search algorithm (MONSGSA) to solve it. In the proposed MONSGSA, the ranked-order value is used to convert continuous solutions to discrete solutions. Multi-dimensional Latin hypercube sampling is used to enhance initial population diversity. Setting up an external archive to maintain non-dominated solutions while introducing an adaptive inertia factor and a trap avoidance operator to guide individual positional updates. The results of multiple sets of experiments show that Pareto solutions of MONSGSA have better distribution and convergence compared to other competitors. Finally, the instance of PMCU manufacturer is used for validation, and the results show that MONSGSA has better applicability to practical problems.

Identification of a lumped-parameter model of the intervertebral joint from experimental data.

Through predictive simulations, multibody models can aid the treatment of spinal pathologies by identifying optimal surgical procedures. Critical to achieving accurate predictions is the definition of the intervertebral joint. The joint pose is often defined by virtual palpation. Intervertebral joint stiffnesses are either derived from literature, or specimen-specific stiffnesses are calculated with optimisation methods. This study tested the feasibility of an optimisation method for determining the specimen-specific stiffnesses and investigated the influence of the assigned joint pose on the subject-specific estimated stiffness. Furthermore, the influence of the joint pose and the stiffness on the accuracy of the predicted motion was investigated. A computed tomography based model of a lumbar spine segment was created. Joints were defined from virtually palpated landmarks sampled with a Latin Hypercube technique from a possible Cartesian space. An optimisation method was used to determine specimen-specific stiffnesses for 500 models. A two-factor analysis was performed by running forward dynamic simulations for ten different stiffnesses for each successfully optimised model. The optimisations calculated a large range of stiffnesses, indicating the optimised specimen-specific stiffnesses were highly sensitive to the assigned joint pose and related uncertainties. A limited number of combinations of optimised joint stiffnesses and joint poses could accurately predict the kinematics. The two-factor analysis indicated that, for the ranges explored, the joint pose definition was more important than the stiffness. To obtain kinematic prediction errors below 1mm and 1° and suitable specimen-specific stiffnesses the precision of virtually palpated landmarks for joint definition should be better than 2.9mm.

Application of the artificial neural network and enhanced particle swarm optimization to model updating of structures

AbstractAn efficient and accurate two-stage approach, based on the artificial neural network (ANN) and an enhanced particle swarm optimization (EPSO) approach for model updating of structures using incomplete measurements, is proposed in this study. The first stage, preliminary model updating, employs the ANN to quickly learn the mapping relationship between the natural frequencies and stiffness of the structure using a few training, validation, and testing instances. The inputs and outputs of the ANN are the natural frequencies and stiffness of the structure, respectively. The ANN’s training, validation, and testing instances are extracted through Latin hypercube sampling. The ANN-predicted stiffness provides an excellent basis for determining and reducing the search space of the optimal stiffness in the second stage. The second stage, detailed model updating, searches for the optimal stiffness of the structure by using the EPSO approach. The EPSO approach improves particle swarm optimization (PSO) by employing an elite crossover strategy to avoid trapping in the local optimum and premature convergence. The feasibility and effectiveness of the proposed two-stage approach for stiffness updating of shear building structures using incomplete measurements are demonstrated by numerical and experimental examples. The results present that the proposed two-stage approach improves the computational efficiency and solution quality of the GA (Genetic Algorithm) and PSO for stiffness updating of shear building structures.

Vibration Characteristics and Structural Optimization of Pipeline Intelligent Plugging Robot under Turbulent Flow Field Excitation

Pipeline maintenance technology based on pipeline intelligent plugging robot (PIPR) has become an effective method for failure accident prevention of high-pressure subsea oil and gas pipelines. However, during the plugging operation, the vortexes and pressure fluctuation are presented under turbulent flow field excitation, which may lead to vortex-induced vibration and failure of the plugging operation. Therefore, in order to ensure the reliability of pipeline plugging, the vibration characteristics are analyzed using numerical simulation, providing guidance on the structural optimization of PIPR’s end face. Firstly, the flow field characteristics under different PIPR’s end faces are investigated. Secondly, an experimental scheme is designed based on Latin Hypercube Sampling Design (LHSD) optimized by greedy strategy. A mathematical model of the end face’s parameters and pressure gradient is established using a back propagation (BP) neural network. Then, an improved whale optimization algorithm (IWOA) is proposed to optimize the end face’s parameters to minimize the pressure gradient of the flow field. Finally, the experimental study is performed to observe the turbulent flow field and pressure fluctuation to validate the optimization results. The results demonstrate that the PIPR’s end face has a great influence on the vortex-induced vibration response. After structural optimization, the average pressure gradient of the optimal PIPR’s end face has decreased by 84.69% and 54.55% before and after the plugging process, compared to the original end face. This study can provide a reference for pipeline plugging operations, which is significant for preventing pipeline failure accidents.