scholarly journals Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft

2020 ◽  
Vol 10 (21) ◽  
pp. 7593
Author(s):  
Ju-Yeol Yun ◽  
Ho-Yon Hwang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Equations ◽  
Design Objective ◽  
Design Variables ◽  
Objective Parameter ◽  
Identification Evaluation ◽  
Design Requirements ◽  
Technology Identification ◽  
Independent Design

In this paper, sensitivity analysis and optimization of a high altitude long endurance (HALE) solar aircraft was implemented. Zephyr S was referred to for the aircraft conference configuration, and OpenVSP and XFLR5 were employed to create configuration and perform aerodynamic analysis. In the conceptual design stage of the HALE solar aircraft, technology identification, evaluation, and selection (TIES) methodology was employed. According to the design requirements, problem definition was established, and design goal, variations, and targeted values were set up to implement independent design variables to meet the design requirements. Based on the design of experiments (DOE), modeling of the relationship between design objective parameters and independent design values was implemented. The independent design variables with the largest influence were selected in the screening test. By employing the selected independent design variables, regression equations and sensitivity profiles were produced through response surface method. Inter-factor relationship was easily analyzed through the sensitivity profile. Regression equations were employed in the Monte Carlo simulation to draw design objective parameter values for 10,000 combinations of independent design variables. As a result of the Monte Carlo simulation, the design feasibility of design objective parameters was assessed. Optimization was performed using the desirability function of JMP software, and constraints were applied to each design objective parameter to derive the optimum values of independent design variables. Then, the values of optimized design independent variables were applied to the solar aircraft design framework and analyzed for the endurance flight performance. By comparing the endurance of the optimized configuration with the reference configuration, it was confirmed that the endurance could be improved by using the methodology proposed in this study.

Design Uncertainty Effects Quantification Using Monte Carlo Simulation

2012 ◽  
Author(s):  
Hesham Kamel
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Rigid Wall ◽  
Design Variables ◽  
Element Simulation ◽  
Thin Walled Tube ◽  
Final Deformation ◽  
Thin Walled ◽  
Dynamic Impact Loading

This paper presents an approach to evaluate the effect of uncontrolled and un-avoided variation within design variables on the performance of nonlinear finite element models. The approach employs Monte Carlo simulation to reveal this effect using descriptive statistics to present useful information to the designer. A case study of a thin walled tube under dynamic impact loading is used to demonstrate the proposed approach. The thin walled tube is modeled using LS-DYNA for finite element simulation. Wall thickness distributions are selected as design variables where the amount of impact energy absorbed, maximum rigid wall force and final deformation are selected as the important responses. The results clearly show that the proposed approach can provide the designer with useful information of the effect of variation within the design variables on the structure responses. Ultimately, the designer can use this helpful information in creating a design that is minimally sensitive to those uncontrolled and un-avoided variation within design variables.

Monte Carlo Simulation of the Manufacturing Tolerance of FDBs to Identify the Sensitive Design Variables Affecting the Performance of a Disk-Spindle System

2014 ◽  
Author(s):  
J. H. Lee ◽  
M. H. Lee ◽  
H. K. Jang ◽  
G. H. Jang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Journal Bearing ◽  
Fluid Dynamic ◽  
Critical Mass ◽  
Groove Depth ◽  
Friction Torque ◽  
Spindle System ◽  
Manufacturing Tolerance ◽  
Design Variables

This research investigates the Monte Carlo simulation of manufacturing tolerance of FDBs to identify the sensitive design variables for the friction torque of fluid dynamic bearings (FDBs) and the critical mass of disk-spindle system supported by FDBs. We analyze the characteristics according to design variables of FDBs and it shows that the clearance of journal bearing is most sensitive design variable of both friction torque and critical mass. Also the groove to groove and ridge ratio and groove depth of grooved journal bearing which are manufactured by ECM are also sensitive to determine the friction torque and the critical mass of the FDBs, respectively. This research can be utilized to manage manufacturing tolerance to maintain the consistent performance of FDBs and a disk-spindle system in a HDD.

scholarly journals Design of Foundations Against Differential Settlement

2021 ◽  
Author(s):  
Farzaneh Naghibi ◽  
Gordon A. Fenton
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Differential Settlement ◽  
Angular Distortion ◽  
Equivalent Stiffness ◽  
Distribution Of The Maximum ◽  
Bias Factor ◽  
Total Settlement ◽  
Design Requirements

The serviceability limit state (SLS) design of foundations typically proceeds by limiting the total settlement of individual foundations and thereby attempting to restrict the differential settlement between pairs of foundations. Due to the uncertain nature of the supporting ground, the magnitude of settlement and differential settlement are random. As it is often the differential settlement which governs serviceability, it is desirable to provide design requirements which suitably restrict differential settlements. This paper investigates, by Monte Carlo simulation, the distribution of the maximum differential settlement between pairs of foundations as a function of the spacing between foundations and the number of foundations – groups of 4, 9, or 16 foundations, arranged on a grid, are considered. The effects of the correlations between the equivalent stiffness of the ground under each foundation, as well as between the loads applied to the foundations, on the distribution of the maximum differential settlements and angular distortions are investigated. Ratios of resistance factor to resistance bias factor are presented that can be used to calibrate design requirements on the total settlement of individual foundations which also simultaneously achieve acceptable performance with respect to angular distortion.

Monte Carlo Simulation of Gas Spring in an Energy Storing Prosthetic Knee

2014 ◽  
Vol 660 ◽  
pp. 916-920
Author(s):  
Cucuk Nur Rosyidi ◽  
Rahmaniyah Dwi Astuti ◽  
Ilham Priadythama
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quality Improvement ◽  
Significant Contribution ◽  
Prosthetic Knee ◽  
Cylinder Length ◽  
Cylinder Diameter ◽  
Design Variables ◽  
Important Design ◽  
Monte Carlo Simulation Model

Gas Spring is an important component of an energy storing prosthetic knee. The spring stored energy during flexion and released the energy while in the extension. In this research, we discuss a Monte Carlo simulation model of a gas spring in an Energy Storing Prosthetic Knee (ESPK) using Oracle Crystal Ball software. The simulation is used to predict the effects of three important design variables of a gas spring which are cylinder diameter, cylinder length, and displacement to the energy storing performance of the spring. The results of simulation show that there are two design variables which have significant contribution to the variations of energy storing performance: cylinder diameter and displacement. Those design variables account for 99.3% to the total variance of energy storing. Quality improvement must be conducted to lowering the resulted energy storing variance. We proportionally decrease the variance of the design variables to lowering the energy storing variance. The simulation results show a significant quality improvement of about 50% in term of energy storing standard deviation. The results also show that cylinder diameter is more sensitive than the other two design variables in energy storing quality improvement.

Design Under Uncertainty Using Monte Carlo Simulation and Probabilistic Sufficiency Factor

2003 ◽  
Author(s):  
Xueyong Qu ◽  
Raphael T. Haftka
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Modes ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Design Under Uncertainty ◽  
Random Errors ◽  
Safety Index ◽  
Design Variables ◽  
Variable Fidelity

Monte Carlo simulation is commonly employed to evaluate system probability of failure for problems with multiple failure modes in design under uncertainty. The probability calculated from Monte Carlo simulation has random errors due to limited sample size, which create numerical noise in the dependence of the probability on design variables. This in turn may lead the design to spurious optimum. A probabilistic sufficiency factor (PSF) approach is proposed that combines safety factor and probability of failure. The PSF represents a factor of safety relative to a target probability of failure, and it can be calculated from the results of Monte Carlo simulation (MCS) with little extra computation. The paper presents the use of PSF with a design response surface (DRS), which fits it as function of design variables, filtering out the noise in the results of MCS. It is shown that the DRS for the PSF is more accurate than DRS for probability of failure or for safety index. The PSF also provides more information than probability of failure or safety index for the optimization procedure in regions of low probability of failure. Therefore, the convergence of reliability-based optimization is accelerated. The PSF gives a measure of safety that can be used more readily than probability of failure or safety index by designers to estimate the required weight increase to reach a target safety level. To reduce the computational cost of reliability-based design optimization, a variable-fidelity technique and deterministic optimization were combined with probabilistic sufficiency factor approach. Example problems were studied here to demonstrate the methodology.

A New Deterministic Approach in Probabilistic Design Optimization

2009 ◽  
Author(s):  
Hami Golbayani ◽  
Kazem Kazerounian
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simple Zero ◽  
Linear Mapping ◽  
Probabilistic Design ◽  
Engineering Systems ◽  
Deterministic Approach ◽  
Design Variables ◽  
Design Values ◽  
Zero Degree

In this paper, a simple and powerful formulation is presented for probabilistic design of engineering systems. The challenging task of optimum allocation of errors to design variables is transformed into a simple zero degree of difficulty geometric programming problem. This method is based on a known state of the design (the design values as well as the linear mapping between the input and output of the system). Uncertainties of design variables are assumed to be independent, and normally distributed. Failure is defined as a constraint in the optimization process, and has the form of the probability of divergence of outputs from their allowable bounds. Then, this constraint is simplified into a deterministic bound within six sigma spread. Having a zero DOD problem, the optimal solutions are readily available for any system regardless of the complexity. Several numerical experiments are conducted to assess the efficiency of the proposed formulation. The results are compared with more exhaustive searches using Monte Carlo simulation. For higher order and complex systems, it is demonstrated that this formulation will be %20 more conservative than the exact Monte Carlo simulation.

scholarly journals The critical period of reservoir systems considering performance indices on Malaysia rivers

2014 ◽  
Vol 3 (2) ◽  
pp. 166 ◽  
Author(s):  
Issa Saket Oskoui ◽  
Rozi Abdullah ◽  
Majid Montaseri
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stream Flow ◽  
Critical Period ◽  
Regression Equations ◽  
Performance Indices ◽  
Monte Carlo Simulation Technique ◽  
Analysis And Evaluation ◽  
Reservoir Systems ◽  
Separate Regression

The behavior of reservoir systems can be investigated using Critical Period (CP) which determines the aggregation level of the data (monthly or annual) that are required to be utilized in the reservoir analysis. Currently there are a number of methods that could approximate the behavior of reservoir systems, however the efficiency of these approaches have not been studied and verified for the Malaysia Rivers. In this study two different hypothetical reservoirs on Malaysia Rivers are selected. The stream flow data are subjected to preliminary analysis and evaluation of the fittest probability distribution function. Afterwards, the CP is estimated by applying a Monte Carlo simulation technique and considering performance indices. The CP from this study is used to determine the within-year or over-year behavior and these results are compared with those of the previous well-known equations in this area. It is observed that existing equations are incomplete and other parameters such as reliability and vulnerability should be considered to predict the behavior of reservoir systems. Consequently two separate regression equations are proposed to estimate the CP of these reservoir systems in Malaysia and some suggestions are made to generalize and extend this study. Keywords: Critical Period, Monte Carlo Simulation, Over-Year Behavior, Performance Indices, Reliability, Vulnerability, Within-Year Behavior.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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