Latin Hypercube Sampling Method Research Articles

Is biomass burning always a dominant contributor of fine aerosols in upper northern Thailand?

Biomass burning (BB) is an important contributor to the air pollution in Southeast Asia (SEA), but the emission sources remain great uncertainty. In this study, PM2.5 samples were collected from an urban (Chiang Mai University, CMU) and a rural (Nong Tao village, NT) site in Chiang Mai, Thailand from February to April (high BB season, HBB) and from June to September (low BB season, LBB) in 2018. Source apportionment of carbonaceous aerosols was carried out by Latin Hypercube Sampling (LHS) method incorporating the radiocarbon (14C) and organic markers (e.g., dehydrated sugars, aromatic acids, etc.). Thereby, carbonaceous aerosols were divided into the fossil-derived elemental carbon (ECf), BB-derived EC (ECbb), fossil-derived primary and secondary organic carbon (POCf, SOCf), BB-derived OC (OCbb) and the remaining OC (OCnf, other). The fractions of ECbb generally prevailed over ECf throughout the year. OCbb was the dominant contributor to total carbon with a clear seasonal trend (65.5±5.8% at CMU and 79.9±7.6% at NT in HBB, and 39.1±7.9% and 42.8±4.6% in LBB). The distribution of POCf showed a spatial difference with a higher contribution at CMU, while SOCf displayed a temporal variation with a greater fraction in LBB. OCnf, other was originated from biogenic secondary aerosols, cooking emissions and bioaerosols as resolved by the principal component analysis with multiple liner regression model. The OCnf, other contributed within a narrow range of 6.6%-14.4%, despite 34.9±7.9% at NT in LBB. Our results highlight the dominance of BB-derived fractions in carbonaceous aerosols in HBB, and call the attention to the higher production of SOC in LBB.

Process window estimation in manufacturing through Entropy-Sigma active learning

In manufacturing, there exist boundary identification problems for defining parameter spaces that meet desired thresholds on outcomes. This paper presents an Entropy-Sigma acquisition function for active learning of the process window/map in manufacturing using a Gaussian Process surrogate. The method is applied to identify the stability boundary for the stability process map in machining using time-domain simulations with a periodic sampling stability metric. Results show that the proposed Entropy-Sigma method significantly outperforms Latin hypercube sampling or grid-based methods. The described method can be applied to identify the process window/map for any manufacturing application using a quantitative process outcome metric.

Energy-saving oriented optimization design of the impeller and volute of a multi-stage double-suction centrifugal pump using artificial neural network

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Q d, 1.0Q d, and 1.2Q d. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Q d, 1.0Q d, and 1.2Q d, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN).

Blade shape optimization of Savonius wind turbine using radial based function model and marine predator algorithm

In order to overcome the problems of low efficiency and poor results of classical optimization methods, an efficient, intelligent, and reliable method for optimizing the shape of the blades of the Savonius wind turbine is proposed in this paper. Firstly, the shape of the blade is parameterized by the third-order Bezier curve, and then a certain number of design schemes in the design space are obtained by the Latin hypercube sampling method and the torque coefficient of each design scheme is evaluated based on CFD simulation, and next a radial basis function surrogate model is constructed based on these design schemes and their torque coefficients, and finally the optimal design scheme is obtained by solving the surrogate model using the marine predator algorithm, and is verified using the CFD simulation. The results show that when the tip speed ratio is 1, the average torque coefficient of the optimized blade obtained by the above method is about 7% higher than that of the classical semicircular blade, and under other tip speed ratios, the average torque coefficients of the optimized blade is also significantly higher than that of the classical blade. Furthermore, compared with the classical semicircular blade, about 11% materials can be saved in the manufacturing of the optimized blade.

Optimal design of Savonius wind turbine blade based on support vector regression surrogate model and modified flower pollination algorithm

Blade shape has a significant effect on the wind-capturing ability of the Savonius wind turbine. This paper proposes an intelligent optimization method for optimizing the shape of the blade of the Savonius wind turbine. This method firstly uses the cubic Bezier curve with four design parameters (x1, y1, x2, y2) to characterize the complex blade shape, and then uses the Latin hypercube sampling method to sample some design schemes throughout the design space, and uses computational fluid dynamics simulation to evaluate the response value, i.e. the moment coefficient, of each scheme, and then uses the support vector regression surrogate model to describe the relationship between the design parameters and their response values, finally uses modified flower pollination algorithm to solve the surrogate model to obtain the optimal blade shape. After comparing and analyzing the optimized blade and the classical semicircular blade, it is found that compared with the classical semicircular blade, the optimized blade has a better wind-capturing ability. When the wind speed is 7 m/s and the tip speed ratio is 1, its average power coefficient Cp is significantly increased from 0.260027 to 0.277902 (about 6.87% higher). In addition, the aerodynamic performance of the optimized blade is also better than the classical semicircular blade at other tip speed ratios (TSRs = 0.6–1.2). It is shown that the wind turbine with the optimized blade has great potential in a practical application environment.

Robust optimal operation of continuous catalytic reforming process under feedstock uncertainty

The continuous catalytic regenerative (CCR) reforming process is one of the most significant sources of hydrogen production in the petroleum refining process. However, the fluctuations in feedstock composition and flow rate could significantly affect both product distribution and energy consumption. In this study, a robust deviation criterion based multi-objective optimization approach is proposed to perform the optimal operation of CCR reformer under feedstock uncertainty, with simultaneous maximization of product yields and minimization of energy consumption. Minimax approach is adopted to handle these uncertain objectives, and the Latin hypercube sampling method is then used to calculate these robust deviation criteria. Multi-objective surrogate-based optimization methods are next introduced to effectively solve the robust operational problem with high computational cost. The level diagram method is finally utilized to assist in multi-criteria decision-making. Two robust operational optimization problems with different objectives are solved to demonstrate the effectiveness of the proposed method for robust optimal operation of the CCR reforming process under feedstock uncertainty.

Analysis of prediction and clustering for uncertainty of EV charging station behavior on V2G platform

The charging behavior of electric vehicles has uncertainty, which leads to uncertainty in the load of the V2G platform. In this paper, a clustering model of charging load at the charging station of the V2G platform containing uncertainty factors is put forward, and analyze the uncertain charging behavior and charging power characteristics. To ensure the reliability of the uncertainty quantification results, an efficient Latin Hypercube Sampling method is chosen to make the extracted samples comprehensive and homogeneous. The load prediction of electric vehicles charging behavior with uncertainty is carried out by Monte Carlo method. Finally, the typical load data of electric vehicles charging stations in Chongqing V2G platform is used as an example to verify the superiority of the uncertainty charging load clustering and prediction method proposed in this paper.

In Silico Modeling for Ex Vivo Placental Transfer of Morphine.

Morphine may be administered in pregnant women as an analgesic agent. The transplacental pharmacokinetics (PK) of morphine varies during pregnancy because of physiological and metabolic changes. In this work, we use a multi‐compartment model to simulate ex vivo human placental transfer studies of morphine. The computational model is based on a recently published model for metformin with both passive and active transport kinetics. Modifications were made to incorporate morphine‐specific transfer parameters. Parameters for the PK models were determined via the nonlinear regression method. In addition, the Latin hypercube sampling (LHS) method was used for the global parameter analysis of the model. Simulation results show good agreement between the model and observed fetal and maternal morphine concentrations. In addition, the lower efflux of morphine from fetal to maternal plasma reflects reduced P‐glycoprotein (P‐gp) transport as pregnancy progresses, which leads to slower clearance of morphine in the maternal plasma. The LHS analysis also indicates the more significant roles played by the passive diffusion parameters than the active transport parameter on the fetal/maternal morphine concentrations. In conclusion, we used an in silico model to investigate the transplacental properties of morphine and to predict the in vivo transplacental properties of morphine when PK parameters change.

Multi-objective optimization of a novel vortex finder for performance improvement of cyclone separator

This study proposed a multi-objective optimization of novel vortex finder, called Slotted Vortex Finder (SVF), applied in the Stairmand cyclone separator, to obtain a most appropriate geometric configuration for performance improvement as the Stairmand cyclone separator had been widely used in industry for dust removal since last century. The Optimal Latin Hypercube Sampling (OLHS) method was utilized to conduct design of experiment for four significant design variables, including slot number (Ls), slot length (N), span angle (α) and inclination angle (β). Computational Fluid Dynamics (CFD) and Response Surface Model (RSM) were adopted to get precise output response and construct accurate fitness functions between design variables and objectives. Multi-objective Genetic algorithm was employed to find out the best solution with maximum collection efficiency and minimum Euler number based on the well-constructed fitness functions. The finally selected optimal model provided Euler number reduction of 34.51%, collection efficiency increasing of 4.32% and cut off size reduction of 5.53%, compared with original model. The obtained optimal geometry shape of novel vortex finder could be used as an effective reference for industrial factories to gain enough considerable cyclone performance improvement and high economic benefits by making only small modifications on the in-using cyclone separator. • A novel and efficient structure of slotted vortex finder was demonstrated and discussed. • Surrogate model was utilized to evaluate the effect of geometric design variables. • Multi-objective optimization of slotted vortex finder was conducted. • The optimal solution of slotted vortex finder is meaningful and promising for industrial application.

Alternator noise reduction based on claw-pole optimization

The high-frequency aerodynamic noise of claw-pole alternator presents a harsh sound in human subjective perception, which has an important impact on the car passengers’ comfort. Therefore, experimental and optimization studies have been conducted to investigate the high-frequency aerodynamic noise during the high-speed operation of claw-pole alternator. In this study, a claw-pole alternator was first tested under no-load conditions. The experimental results revealed that the 6th, 9th, 12th, 18th, 30th and 36th orders are the main noise orders. Meanwhile, the dipole was the main noise source at a speed range of 8000–14,000 rpm. Subsequently, a hybrid simulation method was applied to solve the acoustics field problem. The simulation and experimental results of the acoustics field characteristics satisfactorily agreed for all main orders of the aerodynamic noise at both speeds of 8000 and 12,000 rpm. The minimum error for the 9th order was 0.35dBA at 8000 rpm, whereas the minimum error of the 8th order reached 0.51 dBA at a speed of 12,000 rpm. Thus, the proposed simulation model could be considered a reliable approach. Additionally, the pressure amplitude distribution on the rotor surface for different orders was analyzed. The higher-pressure amplitudes at higher orders, such as the 30th and 36th orders, are mainly distributed at the claw-pole near the stator and root of the claw-pole, indicating that higher acoustics levels corresponding to the higher orders are mainly associated with the claw-pole. Finally, the claw-pole notching was parametrically modeled, and the eigenvalues of the radial, the notch inclination angle and the axial stretch length were optimized by using Latin hypercube sampling method. After notching, the air mass flow through the front and rear blades exhibited small changes, which slightly affected the cooling performance of the alternator. The claw-pole notching yielded the highest optimization, when the eigenvalues of radial, notch inclination angle and axial stretch length were 46.82 mm, 63.3° and 14.65 mm respectively.

Research on Optimization of Structural Parameters of Equipment Cabin Bottom Cover

Since the railway vehicle structure has lots of parameters and several complex constraints, this study establishes a method for structural parameter optimization based on sensitivity analysis and surrogate models. Fatigue crack problem of the equipment cabin bottom cover of the EMU is taken as an example to optimize its structural parameters. First, establish the finite element (FE) model of the bottom cover and compare it with the bench test results to verify the accuracy of the load and restraint conditions. The sensitivity analysis method is used to determine the main parameters. The input samples are obtained by Latin hypercube sampling method, and the output samples are obtained by the method jointly developed by ABAQUS+Python and the surrogate model between the input and output samples is obtained by fitting, and its accuracy is verified. According to the design requirements, the optimization objective function and constraint conditions are established, and the optimization result is obtained by optimization algorithm. The results were substituted into the FE model for verification. The results show that the maximum equivalent stress of the bottom cover is reduced from 126.7 MPa to 78.9 MPa under a cyclic aerodynamic load of ±4 kPa, which is 37.7% optimized, and the effect is significant. This method avoids the iterative optimization of the FE model and improves the optimization efficiency.

Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service

With the development of bridge structures towards being light weight and having a large span , the overall flexibility, and, hence, wind sensitivity, of the bridge increases. Flutter is one of the pivotal factors considered in the design and operation stage for long-span cable-stayed bridges due to its devastating impact, often intrigued by relatively low instability caused by wind speed. This paper presents a reliability theory-based numerical analysis on bridge flutter stability and its influence law of key parameters using a real bridge, the Xiangshan Harbor highway cable-stayed bridge in China. The analysis starts with creating a full scale of finite element model for the bridge in service to calculate the flutter derivative and time-dominated combining rational function in order to obtain the critical-flutter wind speed, and then the aerodynamic self-excited forces on the bridge and flutter time-history response are calculated to identify the flutter critical wind speed. Further, the influence of key parameters for flutter reliability, including the stiffness of the main girder, wire breaking rate, damping ratio and cable breakage location are analyzed comprehensively to achieve the change law of critical flutter wind speed with these parameters. Considering the uncertainty of the actual parameters, these parameters are taken as random variables, and the reliability index and failure probability of bridge flutter are calculated according to their probability distribution and the Latin hypercube sampling method. On this basis, a few suggestions are put forward for flutter risk-control during the service of this cable-stayed bridge, which can further enhance the design theory for long-span flexible bridges.

Introducing Non-Hierarchical RSM and MIGA for Performance Prediction and Optimization of a Centrifugal Pump under the Nominal Condition

In order to improve the operation performance of the multi-stage double-suction centrifugal pump and reduce the internal energy loss of the pump, this paper proposes a single-objective optimization design method based on the non-hierarchical response surface methodology (RSM) and the multi-island genetic algorithm (MIGA). Nine parameters, such as the blade outlet width and blade wrap angle, were used as design variables, and the optimization objective was efficiency under design conditions. In total, 149 sets of valid data were obtained under the Latin hypercube sampling method (LHS), the corresponding thresholds were set for efficiency and head, and 99 sets of valid data were obtained. A cross-validation analysis of the sieved data was carried out based on non-hierarchical RSM, global optimization of the efficiency was carried out using MIGA, and numerical verification was carried out via CFD. The research results show that compared with hierarchical RSM, non-hierarchical RSM can approximate the nonlinear relationship between the objective function and the design variables with higher accuracy, and the model fitting R2 value was 0.919. The efficiency was improved by 3.717% after optimization. The overall prewhirl of the impeller inlet after optimization decreased, the internal speed of the volute significantly improved, the large-area vortex at the volute and the outlet pipe was eliminated, the impact loss at the volute separating tongue disappeared, and the overall hydraulic performance of the pump was improved. The total entropy output value of the optimized pump was reduced by 4.79 (W/K), mainly concentrated on the reduction in the entropy output value of the double volute, and the overall energy dissipation of the pump was reduced.

Improving measurement accuracy of indoor positioning system of a Mecanum wheeled mobile robot using Monte Carlo - Latin hypercube sampling based machine learning algorithm

In this study, an efficient ultra-wideband technology based indoor positioning system is developed to improve the measurement accuracy of a mecanum wheeled mobile robot. Data processing structure of the system is created using Monte Carlo - Latin Hypercube Sampling based machine learning algorithm. Monte Carlo - Latin Hypercube Sampling method can be used to perform the position estimation since it enables the range of each variable is fully accessible. This makes the algorithm outputs more accurate and reliable and thus increases correctness in measurements. An experimental setup including hardware, software and mathematical model is developed. A number of experiments are conducted to validate the indoor positioning system proposed. Efficiency, accuracy and precision of methodology are also tested in the experimental studies. The adaptation of the algorithm in a real system and the field tests are also conducted using a four-mecanum-wheeled mobile robot. The experimental studies show that the position estimation is improved by more than 20% using the proposed algorithm compared to the methods presented in the literature.

Multi-Objective Optimization of Deep-Sea Mining Pump Based on CFD, GABP Neural Network and NSGA-III Algorithm

In order to improve the hydraulic performance of a deep-sea mining pump, this research proposed a multi-objective optimization strategy based on the computational fluid dynamics (CFD) numerical simulation, genetic algorithm back propagation (GABP) neural network, and non-dominated sorting genetic algorithm-III (NSGA-III). Significance analysis of the impeller and diffuser parameters was conducted using the Plackett–Burman experiment to filter out the design variables. The optimum Latin hypercube sampling method was used to produce sixty sample cases. The GABP neural network was then utilized to establish an approximate model between the pump’s hydraulic performance and design variables. Finally, the NSGA-III was utilized to solve the approximation model to determine the optimum parameters for the impeller and diffuser. The results demonstrate that the GABP neural network can accurately forecast the deep-sea mining pump’s hydraulic performance, and the NSGA-III global optimization is effective. On the rated clear water conditions, the optimized pump has a 14.65% decrease in shaft power and a 6.04% increase in efficiency while still meeting the design requirements for the head. Under rated solid-liquid two-phase flow conditions, the head still meets the design requirements, the shaft power is decreased by 15.64%, and the efficiency is increased by 6.00%.

Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive

Three machine learning techniques (multilayer perceptron, random forest and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modelling and real-time control applications. The machine learning models use a database of more than 16 000 GENRAY/CQL3D simulations for training, validation and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters ($n_{e0}$,$T_{e0}$,$I_p$,$B_t$,$R_0$,$n_{\|}$,$Z_{{\rm eff}}$,$V_{{\rm loop}}$and$P_{{\rm LHCD}}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to$\sim$ms with high accuracy across the input parameter space.

Machine learning based design optimization of centrifugal impellers

Big data and machine learning are developing rapidly, and their applications in the aerodynamic design of centrifugal impellers and other turbomachinery have attracted wide attention. In this paper, centrifugal impellers with large flow coefficient (0.18–0.22) are taken as research objects. Firstly, through one-dimensional design and optimization, main one-dimensional geometric parameters of those centrifugal impellers are obtained. Subsequently, hundreds of samples of centrifugal impellers are obtained by using an in-house parameterization program and Latin hypercube sampling method. The NUMECA software is used for CFD calculations to build a sample library of centrifugal impellers. Then, applying the artificial neural network (ANN) to deal with the data in the sample library, a nonlinear model between the flow coefficients, the geometric parameters of these centrifugal impellers and the aerodynamic performance is constructed, which can replace CFD calculations. Lastly with the help of the multi-objective genetic algorithm, a global optimization is carried out to fulfull a rapid design optimization for centrifugal impellers with flow coefficients in the range of 0.18–0.22. Three examples provided in the paper show that the design and optimization method described above is faster and more reliable compared with the traditional design method. This method provides a new way for the rapid design of centrifugal impellers.

A multi-grid sampling multi-scale method for crack initiation and propagation

In this study, a multi-grid sampling multi-scale (MGSMS) method is proposed by coupling with finite element (FEM), extended finite element (XFEM) and molecular dynamics (MD) methods. Crack is studied comprehensively from microscopic initiations to macroscopic propagation by MGSMS method. In order to establish the coupling relationship between macroscopic and microscopic model, the multi-grid FEM is used to transmit the macroscopic displacement boundary conditions to the atomic model and the multi-grid XFEM is used to feedback the microscopic crack initiations to the macroscopic model. Moreover, an image recognition based crack extracting method is proposed to extract the crack coordinate from the MD result files efficiently and the Latin hypercube sampling method is used to reduce the computational cost of MD. Numerical results show that MGSMS method can be used to calculate micro-crack initiations and transmit it to the macro-crack model. The crack initiation and propagation simulation of plate under mode I loading is completed.

Multimodal critical-scenarios search method for test of autonomous vehicles

Purpose The purpose of this paper is to search for the critical-scenarios of autonomous vehicles (AVs) quickly and comprehensively, which is essential for verification and validation (V&V). Design/methodology/approach The author adopted the index F1 to quantitative critical-scenarios' coverage of the search space and proposed the improved particle swarm optimization (IPSO) to enhance exploration ability for higher coverage. Compared with the particle swarm optimization (PSO), there were three improvements. In the initial phase, the Latin hypercube sampling method was introduced for a uniform distribution of particles. In the iteration phase, the neighborhood operator was adapted to explore more modals with the particles divided into groups. In the convergence phase, the convergence judgment and restart strategy were used to explore the search space by avoiding local convergence. Compared with the Monte Carlo method (MC) and PSO, experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the method. Findings Results show that IPSO can search for multimodal critical-scenarios comprehensively, with a stricter threshold and fewer samples in the experiment on critical-scenario search, the coverage of IPSO is 14% higher than PSO and 40% higher than MC. Originality/value The critical-scenarios' coverage of the search space is firstly quantified by the index F1, and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs, which shows application potential for V&V.

FARKLI ÖRNEKLEME YÖNTEMLERİNİN YAPAY SİNİR AĞLARINDA PERFORMANSININ KARŞILAŞTIRILMASI

Optimizasyon modelleme problemlerinde, kullanılacak verinin seçimi büyük önem arz etmektedir. Bu çalışmada farklı örnekleme yöntemlerinin Yapay Sinir Ağlarının başarısındaki etkisini, anten tasarımlarında sıkça kullanılan model olan Çok Katmanlı Algılayıcı kullanılarak mikroşerit yama anteni üzerinde performans karşılaştırılması yapılmaktadır. Seçilen Yapay Sinir Ağı kara kutu modeli 5 giriş ve 1 çıkış parametrelerinden oluşmaktadır. Mikroşerit yama anten modellenmesinde kullanılacak veri seçiminde Latin Hiperküp ve Monte Carlo örnekleme yönteminin uygulanması incelenmiştir. Öncelikle örnek sayısı (eğitim ve doğrulama veri setleri) giriş parametrelerinin belirlenen değer aralıklarında Latin Hiperküp ve Monte Carlo örnekleri kendilerine özgün oluşturulma yöntemine göre temin edilmiştir. Akabinde oluşturulan 2 farklı örnek sayısına sahip problemler kendi içlerinde %50 ve %33 oranında rastgele ayrılarak eğitim ve doğrulama verileri oluşturulmuştur. Performans karşılaştırmasında, 3 algoritma ile 4 farklı mimari yapıya sahip toplamda 12 farklı ağ ile birlikte 4 farklı sayıda eğitim ve doğrulama veri setleri kullanılmaktadır. Çıkan sonuçlar birbirleri ile mukayese edildiğinde, düşük veya yüksek eğitim örnek sayısına sahip modellemede de Monte Carlo örnekleme yönteminin performans olarak daha başarılı sonuçlar verdiği görülmektedir. Buna karşın kendi içinde Latin Hiperküp örnekleme yönteminin ise eğitim örnek sayısında artışa gidilmesi kısmi iyileşmeye neden olduğu görülmektedir. Fakat yine de daha az örnek sayısına sahip olan diğer Monte Carlo örnekleme yönteminin performans olarak gerisinde kalmıştır. Dolayısı ile Monte Carlo örnekleme yönteminin bu ve benzer problemler için daha uygulanabilir olduğuna kanaat getirilmiştir.