Parametric Modeling Method Research Articles

Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

With the increasing running speed, the aerodynamic issues of high-speed trains are being raised and impact the running stability and energy efficiency. The optimization design of the head shape is significantly important in improving the aerodynamic performance of high-speed trains. Existing aerodynamic optimization methods are limited by the parametric modeling methods of train heads which are unable to accurately and completely parameterize both global shapes and local details. Due to this reason, they cannot optimize both global and local shapes of train heads. In order to tackle this problem, we propose a novel multi-objective aerodynamic optimization method of high-speed train heads based on the partial differential equation (PDE) parametric modeling. With this method, the half of a train head is parameterized with 17 PDE surface patches which describe global shapes and local details and keep the surface smooth. We take the aerodynamic drag and lift as optimization objectives; divide the optimization design process into two stages: global optimization and local optimization; and develop global and local optimization methods, respectively. In the first stage, the non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the framework of the train head with an optimized global shape. In the second stage, Latin hypercube sampling (LHS) is applied in the local shape optimization of the PDE surface patches determined by the optimized framework to improve local details. The effectiveness of our proposed method is demonstrated by better aerodynamic performance achieved from the optimization solutions in global and local optimization stages in comparison with the original high-speed train head.

Research on landscape design system based on 3D virtual reality and image processing technology

In order to ensure the rationality of landscape design, it is of great practical significance to simulate the planning effect. Aiming at the problems of two traditional landscape planning effect simulation systems, a new landscape planning effect simulation system was designed based on virtual reality technology. The overall framework of the system is composed of user layer, application layer and display layer. According to the system frame structure, hardware such as acquisition equipment, main control equipment, output and display equipment are selected; according to the system framework structure, the system software is designed to run the main program. In addition, this paper also based on 3D image processing technology landscape design system. In the design process, 3D landscape image can be preprocessed to remove noise information and redundant information, and 3D landscape image feature enhancement method is used to realize 3D landscape image feature enhancement and improve the clarity of landscape design image. Combined with the example of garden green space, according to the parametric plant modeling method, the morphological structure parameters of green plants are obtained, and the three-dimensional models of various plant modeling are established; the rules of parametric description of plant spatial layout are adopted, based on the open scene graph rendering engine, and the oculus rift virtual reality equipment is integrated to realize the virtual construction and display of three-dimensional garden vegetation landscape. The results show that the design effect of the system is very clear and of good quality. It has a high score in the design of courtyard landscape and garden landscape, and the system has low application cost, low energy consumption and high operation efficiency.

中国城投债风险溢价的及时性度量与预测:基于高维适应性算法的构建分析 (The Real-Time Measuring and Forecasting of the Chinese Chengtou Bond Risk Premia —Based on the Method of High-Dimensional Adaptive Algorithm )

Chinese abstract:债券风险溢价是衡量债券风险的晴雨表。使用参数模型进行债券风险溢价的预测不仅会受外部经济政策的冲击导致模型参数具有时变特征，同时还因经济变化的实时更迭带来解释变量的及时性选择问题。本文构建的高维适应性算法能及时探测参数与解释变量的最大化同质区间，实现在及时探测参数结构性变化的同时，也能对最佳解释变量进行实时选择。结果显示，该方法为市场提供了一种更为合理的参数建模方式以预判债券风险溢价的未来走势。我们发现：①高维适应性算法相比其他预测模型在城投债风险溢价的样本外预测与方向走势预测中具有显著的预测优势。②从同质区间识别角度来看，相比于2016与2017年，2018年与2019年参数模型的同质性区间变得更短，说明市场更加关注城投债的短期风险，在一定程度上印证和解释了城投债短期风险溢价高于长期风险溢价的“倒挂”现象。③从变量选择结果来看，不同省份在不同时间决定城投债风险溢价的作用不同。决定城投债风险溢价走势的因素集中，风险溢价较高、风险溢价波动较大以及债券发行数量较多的省份具有明显的网络溢出效应，对未来债券的风险溢价具有较大作用；而那些风险溢价较低、风险溢价波动较低以及债券发行数量较少的省份，其网络外溢效应不明显。债券平均规模与债券剩余期限的网络节点效应对债券未来风险溢价也有较大影响。从实证结果的政策含义上来看：一方面，准确、全面地披露城投债信息、管控城投债系统性风险，有助于维系债券市场价格信号的有效性，稳定债券市场；另一方面，在控制城投债风险溢价的过程中，政府需要重点关注风险溢价波动较高、债券发行数量较多的省份，同时也需要时刻关注债券的平均规模与剩余期限，在各省实行差异化的适应性金融政策，对深化金融供给侧结构性改革，维护脱贫攻坚成果具有重要意义。 English abstract: As a barometer to measure the risk, the risk premia of bonds imply the risk of the bond market. When using the parametric model to predict the risk premia, the external policies and the real-time changes of the economy not only will lead to the time-varying parameters, but also bring about the changing of the explanatory variables in the model. The high-dimensional adaptive algorithm constructed in this paper can detect the maximum homogeneous intervals of the parameter, detect the structural changes of the variables and select the best explanatory variables in time. The results show that this method provides a more reasonable parameter modeling method to predict the future trend of bond risk premia. We find that, ①in the Chinese Chengtou Bond market, compared with other prediction models, the high-dimensional adaptive algorithm has significant prediction advantages in out of sample prediction and also has the outstanding performance in the trend prediction.②From the perspective of homogeneous interval identification, compared with 2016 and 2017, the homogeneous interval of the parametric model in 2018 and 2019 becomes shorter, which indicates that the market pays more attention to the short-term risk of Chengtou bonds in 2018 and 2019. This finding can help to explain why the short-term risk premia is higher than the long-term risk premia in the Chengtou market. ③From the results of variable selection, different provinces have different roles in determining the risk premia of Chengtou bonds at different times. The factors which determine the trend of risk premia of Chengtou bonds are the provinces with higher risk premia, or the provinces with greater volatility of risk premia, or the provinces that have more issued Chengtou bonds. These factors have obvious network spillover effects. The network spillover effects are not obvious in the provinces with lower risk premia, lower volatility of risk premia and less issued Chengtou bonds. The average bond size and the residual maturity also has a great influence on the future risk premia of Chengtou bonds. From the policy implications of the empirical results: on the one hand, disclosure of the information and managing of the systematic risk will help to stabilize the Chengtou bond market and maintain the effectiveness of the price. On the other hand, in the process of managing the Chengtou bonds, the government needs to focus on the provinces with high volatility of risk premia and with large issued Chengtou bonds. At the same time, we also need to pay attention to the average scale and the residual maturity of the Chengtou bonds. The implementation of the adaptive financial policies in different provinces is of great importance in maintaining the achievements of the “Anti-Poverty-War”.

Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique.

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

Design and Implementation of User-oriented Auxiliary Treatment Instrument for Meibomian Gland Dysfunction

Dry eye is a common ophthalmic disease caused by eye maladjustment due to meibomian gland dysfunction (MGD), which is often accompanied by symptoms such as increased tear film osmotic pressure and ocular surface inflammation. In the treatment of dry eye patients, dredging gland obstruction caused by meibomian gland secretion is an effective treatment method. Based on electrothermal effect and hyperelasticity of the silicone, an auxiliary treatment instrument for MGD is designed, which can improve the blood circulation of the glands through heat compress and massage to achieve the purpose of dredging the meibomian glands. The therapy device can display the temperature and pressure during the treatment in real time, so that the surgeon can grasp the progress of the treatment in real time. The therapy device constructs a user-oriented interactive interface based on parametric modeling method, which can be customized by 3D printing according to the user's eyeball geometric parameters. The designed therapeutic device was finally tested on New Zealand white rabbits. The experimental results show that the therapeutic device has significant effectiveness and safety, as well as clinical application prospects.

Continuous Numerical Analysis of Slug Rivet Installation Process Using Parameterized Modeling Method

The slug rivet installation process is complex. A lot of parameters are included during the riveting deformation process. The workload and time cost of a traditional simulation study is very high since a traditional numerical model should be modified manually time by time when riveting parameters change. The data processing after simulation is another complex work. To improve the situation, this paper presents a parameterized modeling method. The modeling process and data processing algorithm can be developed using Python script. The parameterized model can automatically and continuously re-build without any manual intervention according to the riveting parameter auto-update condition. The post-processing analysis can be automatically conducted and saved as well. Then this paper conducts continuous analysis to illustrate the impact of riveting parameters on riveting quality. The parameterized model keeps running 41 times until the riveting parameter is out of range. The parameterized modeling method is a useful method for a simulation study. The study will pave the way for further investigations.

Selective Laser Melting Molding of Individualized Femur Implant: Design, Process, Optimization

Effective Selective Laser Melting (SLM) molding methods for femur implant design and molding can improve femur implant surgery success rates and enhance patients quality of life. In this study, an individualized femur implant and individualized biological fixed-type femur implant were designed using the parametric modeling method. The implants were then directly manufactured via SLM molding technology as the forming process was carefully analyzed. The results indicate that the proposed implant allows for favorable conjunction of the reconstructed implant model with the surrounding bone tissues under the premise of a relatively small amount of bone-cutting. The proposed implant also shows even pore distribution, good overall connectivity, relatively high bearing capacity, favorable overlapping between supports, and strong inter-pore connectivity. Only a small amount of powder adheres onto the surface and can be directly used after simple sand blasting and polishing. The comprehensive analyses of femur implant modeling and molding methods given in this paper may provide a sound foundation for the design and direct manufacture of individualized femur implants in the future.

Shape Optimization Design of a Heaving Buoy of Wave Energy Converter Based on Fully Parametric Modeling and CFD Method

Hydrodynamic shape of the heaving buoy is an important factor of the motion response in waves and thus energy conversion efficiency for point absorbers (PAs). Experience-based design considerations are currently limited by design ideas and time consumptions, hence the need for faster and smarter methods. An automated optimization method based on a fully parametric modeling method and Computational Fluid Dynamics (CFD), is proposed in the present paper. Using this method, a benchmark buoy is screen designed and then optimized for maximizing heave motion response. The geometry is described parametrically and deformed by means of the free-form deformation (FFD) method. During the optimization process, the expansion factor of control points is the basis for variations. A combination of Sobol and non-dominated sorting genetic algorithm II (NSGA-II) is used to search for the solutions. After several iterations, the heaving buoy shape with optimal heave motion response is obtained. The analyses show that the heave motion response has increased 55.3% after optimization. The developed methodology is valid and seems to be a promising way to design a novel buoy that can significantly improve the wave energy conversion efficiency of PAs in future works.

Low-Drift MEMS Thermal Wind Sensor With Symmetric Packaging Using Plastic Injection Molding Process

In this article, a novel micro-electromechanical systems (MEMS) thermal wind sensor using the plastic injection packaging method was proposed. Compared with previous devices suffering from packaging asymmetry, the presented design can relieve the drift phenomenon and improve the accuracy of wind detection. Lumped parameter model and finite-element method (FEM) simulation were developed, and the sensor's improvement was validated. By means of plastic injection molding process, the MEMS sensor chip based on ceramic substrate was encapsulated in a plastic carrier with low thermal conductivity of 0.96 W/( m ·K). In addition, individual packaging was implemented by laser cutting, which ensures that the geometric center of the chip is almost coincident with that of packaging. The sensor, operated in the constant temperature difference (CTD) mode, was tested in the wind tunnel. The experimental results show that wind speed is measured up to 30 m/s with an accuracy of ±7%, while the direction is characterized over the full range of 360° with errors less than ±5°. Compared with the device in our previous work, the measurement accuracy of the proposed prototype sensor was improved by more than two times because of effective benchmark drift suppression.

Analysis of logging parameter model of Jurassic complex reservoir in Xinjiang Shenquan Oilfield

The study discusses the characteristics of Jurassic reservoir in Shenquan Oilfield in Xinjiang. The reservoir of Qiketai Sanjianfang formation mainly develops delta front facies and shore shallow lake facies sand bodies.The lithological characteristics of the reservoir are mainly fine sandstone and siltstone, and the physical properties of the target layer are mainly medium porosity and medium permeability. The Jurassic reservoir space in Shenquan Oilfield is sandstone pore type, mainly composed of secondary pores, mainly including intergranular dissolved pores, intragranular dissolved pores and intercrystalline pores, with good connectivity between pores. According to the analysis data of well logging, core analysis, production performance and DST in Shenquan Oilfield, porosity, permeability and water saturation models are established respectively:The porosity interpretation model is established by using acoustic wave and shale content; the permeability parameter interpretation model is realized by applying graph based cluster analysis method and neural network algorithm. The oil saturation interpretation model is obtained by Archie formula. According to the logging interpretation model and interpretation results, it can meet the needs of reservoir evaluation, oil-water distribution and original geological reserve parameters. After analyzing the logging parameter model and reservoir parameter calculation method, it is considered that the Jurassic in the Shenquan Oilfield in Xinjiang has better physical properties.

Forecasting house prices in Turkey: GLM, VAR and time series approaches

Purpose- A wide range of decision-makers is interested in educated forecasts for house prices. The technical analysis introduced in this study aims to estimate future (forecasted) house prices and provide sufficient evidence in support of the adequacy of the estimated models obtained from parametric and non-parametric modeling methods for Turkey's housing market. Methodology- We employ non-parametric and various time series methods to find appropriate fits to forecast Turkey's house price index (HPI). In our modelling, we consider macroeconomic indicators related to housing markets, such as; gold, interest rate and currencies. In this study, first using the explanatory variables, we construct two Generalized Linear Models (GLM) and a Vector Auto Regressive (VAR) model. Then, we construct two univariate time series models. HPI series inherits seasonality. Even though the HPI contains seasonality, first, we neglect the seasonal effect and come up an Autoregressive Moving Average (ARMA(p,q)) model among many other alternative ARMA models. Second, we consider the seasonality effect on the housing market index and construct a seasonal Autoregressive Integrated Moving Average (ARIMA(p,d,q)(P,D,Q)) and exponential smoothing models. Findings- The analysis identifies forecasts of Turkey’s housing market index from both the seasonal ARIMA(p,d,q) (P,D,Q)_m and Holt Winter models as accurate models compared to classical time series models, namely ARIMA(p,d,q) models, based on the explanation power measure (R^2) values and out-of-sample error measures MSE, RMSE and MAE. Conclusion- The study has three main contributions: i) Our forecast shows Turkey's housing market's return will not increase in the following 12-months. ii) The seasonal ARIMA and exponential smoothing models forecast some negative returns within the given forecasting period, which should be considered a warning for Turkey's housing market for the future. iii) GLM and VAR models illustrate that Turkey's housing market shows a high dependence on gold, inflation, and foreign exchange rates than other well-known economic indicators.

Helicoids 3D modeling for additive technologies

The article provides an analysis of modern and affordable software systems for modelling shells of complex geometry and the possibilities of using these software systems in 3D printing. Such an analysis made it possible to choose software systems that most accurately allow for the implementation of the 3D modeling method proposed in the article with subsequent printing on a 3D printer. This method is considered in detail on the example of constructing several types of helicoids. The process of 3D modeling of a helicoid is described step by step and is divided into several stages: parametric modeling of a helicoid in SCAD, editing of the resulting model in AutoCAD and its export to a special format for 3D printing. The use of the method of parametric modeling is due to its accuracy and uncompromisingness. With its help, one can accurately judge the type of the built surface. Parametric modeling is the construction of a surface by compiling equations on each axis, i.e. along the x, y, z axes, and for each type of surface there are specific characteristic equations. It is not possible to implement the method of parametric modeling in all software systems; in this connection, certain difficulties arise. The article analyzes the difficulties encountered in 3D modeling of the helicoid and suggests ways to solve them.

Parametric modeling method for integrated design and manufacturing of radial compressor impeller

Radial compressor impeller (RCI) manufacturing is moving toward to improve competitiveness through smart manufacturing. Although current CAD (computer-aided design) and CAM (computer-aided manufacturing) techniques provide multiple tools to construct and optimize complicated geometries of RCI, an adjustment of blade shape often leads to nonparametric geometric reconstruction. As the key geometric elements in RCI, set of streamlines (SSL) and meridional section (MS) play vital roles in modeling and aerodynamic optimization. This paper presents a novel approach for automatic extraction of SSL and MS from a RCI 3D model or workpiece. Furthermore, the presented method can interactively construct a parameterized RCI platform, which inputs and outputs the presented RCI parameters to existing CFD and CAM systems rapidly and effectively. An integrated acquisition is employed. The straight generatrix vectors (SGVs) are identified and their boundary points are defined. After the uniform partition of SGV segments, the sequent equant points along spanwise direction are packaged to fit SSL. To manipulate the smoothness and approximation, a double-fitting method is performed to generate SSL. A parameterized system for extracting SSL and modeling RCI is developed. The validities of the presented method in different systems are demonstrated. Furthermore, the presented method breaks conventional RCI development procedure and shortens RCI product cycles, which is desirable and significative for integrated RCI design and manufacturing.

Parametric modeling of uncut chip geometry for predicting crater wear in gear skiving

In gear skiving, tool wear is one of the primary issues owing to the large thermomechanical cutting load. However, the effective rake angle and depth-of-cut can vary continuously throughout a single cut, leading to a complex uncut chip geometry that significantly hinders the understanding of the cutting characteristics and adds further difficulty to the prediction of tool wear. This study proposes a parametric modeling method for calculating the uncut chip geometry for each cutting pass. Developments in the rake angle and depth-of-cut (as subjected to an uncut chip) were comprehensively investigated with an aim to advance the understanding of the cutting characteristics during a single cut. Based on the uncut chip geometry, the crater wear was successfully predicted using the derived stress and temperature data, followed by a single-tooth skiving wear test verification. By comparing numerical and experimental results for crater wear, an error of less than 5 % was obtained in both depth and area. The proposed method provides a non-CAD/CAM-engine-software/environment-based alternative for calculating the process of an uncut chip geometry, clearly advancing the flexibility in further potential applications such as cutting parameter optimization and cutter design.

Aeroelastic analysis and structural parametric design of composite rotor blade

Based on FEM theory, a method of dynamic analysis for hingeless rotors considering anisotropic composite materials is established. A parametric modeling method of composite blade with typical profile and high simulation degree for design is proposed. Through the finite element method, the profile characteristics of rotor blade can be obtained efficiently and accurately, and the synchronization of parametric design and finite element analysis of structural characteristics can be realized. Then a 23-degrees of freedom non-linear beam element is used to simulate the extended one-dimensional beam, thereby a non-linear differential equation describing the elastic motion of the rotor is established. To obtain the cross-sectional target characteristics of the blades, an inverse design method is proposed for cross-section components based on combinatorial optimization algorithm. The calculation and validation work show that the proposed model can effectively analyze the aeroelastic characteristics of general composite rotors. Further, the influence of cross-sectional parameters on the aeroelastic stability and hub loads of hingeless rotor is analyzed and some remarkable conclusions are obtained.

The Long Tail Problem: Novel Parametric Methods Still Underestimate Conditional Long-Term Survival In Glioblastoma

The new potential for Long-Term Survival (LTS) in glioblastoma (GBM) demonstrated by the EF-14 trial, which showed significant survival benefit from adding Tumor-Treating Fields (TTFields) to maintenance temozolomide (TMZ) in newly diagnosed GBM (Stupp 2017), has made predictive modeling of LTS highly relevant for clinical and policy decision making. Reliable models must account for the evolution in prognosis over time in GBM. Previous studies (Bernard-Arnoux 2016, Connock 2019) have parametrically modeled the survival impact of TTFields with EF-14 data, but their results suffer from poor in-sample fit, clinical inconsistency, and poor fit to long-term epidemiological data. Here we evaluate LTS predictions from a novel, Responder Analysis-based method of parametric modeling with EF-14 data. Patients were classified as responders (R) or non-responders (NR) according to their Progression-Free Survival (PFS). If a patient’s PFS met or exceeded a given threshold time (TT), then they were classified as R. Otherwise they were classified as NR. Differences between R’s and NR’s Kaplan-Meier curves and resultant significance levels were studied for a range of TT’s. These significance levels are the result of the impact of longer PFS on OS and the opposing effect of smaller R-subgroup sample size with larger TT's. In the TTFields+TMZ arm, 10 months was the most significant TT, while in the TMZ-only arm, the most significant TT was 6.5 months. TT’s with high significance in both arms were selected for further study, including 10, 12, 18, and 20 months. Parametric survival distributions were separately calibrated for each R and NR subgroup in each trial arm using Maximum Likelihood Estimation. We studied results from exponential, Weibull, log-normal, log-logistic, Gamma, Gompertz, and Generalized Gamma and F distributions. The long-term conditional survival probabilities from each model were then calculated or estimated with Monte Carlo simulation and compared to epidemiological results from Porter et al. (2011) and from separate SEER database searches. For every TT and parametrization studied, the models for the R-subgroups clearly underestimated conditional LTS probabilities compared to epidemiological results beyond 5 years. Errors in these conditional probability estimates ranged from approximately -20% to -50% with models for TTFields+TMZ responders, while models for TMZ-only responders performed even worse, outputting values near 0% for conditional survival probabilities. This analysis provides further evidence that parametric models trained exclusively on relatively short-term EF-14 trial data, even with responder analysis-based enhancements, suffer from biases that limit their usefulness for LTS modeling in GBM. Long-term epidemiology data and modeling approaches that incorporate this data (Guzauskas 2018) are therefore necessary for informed decision making regarding novel treatments in glioblastoma.

Kernel-based spatial error model for analyzing spatial panel data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.

Parametric Modelling and Seismic Analysis of 220kV Power Distribution Equipment in Electrical Substation

Power grid is an important part of the earthquake lifeline project. As electrical substation is the node of the power grid, and the safety evaluation of the substation under earthquakes is essential. There are a wide variety of electrical equipment in substations, and multi pieces of equipment are interconnected to form the complex circuits. Seismic modelling of substation equipment requires engineering researchers to have an accurate understanding of the structure of electrical equipment, as well as to collect complete electrical equipment structural parameters. This process takes a lot of time and effort. This paper proposed a parametric modelling method for substation equipment, which can achieve effectiveness in seismic modelling. Parametric modelling and seismic analysis of high-voltage equipment of a 220 kV substation in Xinjiang is carried out, which verified the validity and applicability of the method.

Limbus misrepresentation in parametric eye models.

PurposeTo assess the axial, radial and tangential limbus position misrepresentation when parametric models are used to represent the cornea and the sclera.MethodsThis retrospective study included 135 subjects aged 22 to 65 years (36.5 mean ±9.8 STD), 71 females and 64 males. Topography measurements were taken using an Eye Surface Profiler topographer and processed by a custom-built MATLAB code. Eye surfaces were freed from edge-effect artefacts and fitted to spherical, conic and biconic models.ResultsWhen comparing the radial position of the limbus, average errors of -0.83±0.19mm, -0.76±0.20mm and -0.69±0.20mm were observed within the right eye population for the spherical, conic and biconic models fitted up to 5mm. For the same fitting radius, the average fitting errors were -0.86±0.23mm, -0.78±0.23mm and -0.73±0.23mm for the spherical, conic and biconic models respectively within the left eye population. For the whole cornea fit, the average errors were -0.27±0.12mm and -0.28±0.13mm for the spherical models, -0.02±0.29mm and -0.05±0.27mm for the conic models, and -0.22±0.16mm and 0.24±0.17mm for the biconic models in the right and left eye populations respectively.ConclusionsThrough the use of spherical, conic and biconic parametric modelling methods, the eye’s limbus is being mislocated. Additionally, it is evident that the magnitude of fitting error associated with the sclera may be propagating through the other components of the eye. This suggests that a corneal nonparametric model may be necessary to improve the representation of the limbus.

Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm

Existing generator parameterization methods, typically developed for large turbine generator units, are difficult to apply to small kW-level diesel generators in microgrid applications. This article presents a model parameterization method that estimates a complete set of kW-level diesel generator parameters simultaneously using only load-step-change tests with limited measurement points. This method provides a more cost-efficient and robust approach to achieve high-fidelity modeling of diesel generators for microgrid dynamic simulation. A two-stage hybrid box-constrained Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal parameter set given the parameter bounds. A heuristic algorithm, namely Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to identify proper initial estimates at the first stage, followed by a modified Levenberg-Marquardt algorithm designed to fine tune the solution based on the first-stage result. The proposed method is validated against dynamic simulation of a diesel generator model and field measurements from a 16kW diesel generator unit.