Two-stage Simulation Research Articles

Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.

Rethinking public service facility distribution and management strategies with the consideration of carbon peak – Insights from Suzhou, China

Existing practices have primarily focused on the accessibility and per capita indices of public service facilities (PSFs) in decision-making processes aimed at promoting social equity. However, the national commitment to achieving a carbon peak may induce additional considerations into the planning and policy decisions of PSFs for local governments. By conducting a two-stage simulation of carbon emissions (CEs) and correlation analysis, we identified uncertainties and confirmed the nonnegligible impact of land use structure, particularly PSF distribution, on achieving the urban transport-related carbon peak. Subsequently, we elaborated on the imperative of optimizing PSF distribution for carbon reduction, rather than solely depending on transport management strategies, through a case study of individual medical care travel characteristics. Moreover, a further discussion on the potential trade-offs and synergies between current and low-carbon-oriented PSF planning and policy decisions underscores the necessity of collaboration among decision-makers across pertinent administrative sectors.

Fatigue crack growth in wheel-rail rolling-sliding contact: A perspective of elastic-plastic fracture mechanics criterion

The presence of fatigue crack is a common damage pattern in railway wheel-rail system, which will reduce service life and increase the maintenance costs of railway transportation. In this study, a two-stage simulation strategy was proposed to investigate the elastic-plastic fatigue crack growth behaviour of the wheel and rail, where a three-dimensional wheel-rail rolling contact finite element model was first built to solve the elastic-plastic wheel-rail contact pressure and tangential traction modification, and then a simplified three-dimensional finite element model of the wheel and rail inserted with vertical cracks under the equivalent moving load was built to explore the fatigue crack growth. Taking the J-integral at the crack tip as an elastic-plastic crack growth parameter, the influences of crack face friction, crack depth and traction coefficient on the crack growth were elucidated. Meanwhile, the competing mechanisms of wheel/rail wear and fatigue crack growth under rolling-sliding contact condition was illuminated based on the Archard wear model. The simulation results indicate that two critical locations for crack growth appear during the contact load approaching and departing from the crack. The Mises stress near the deepest crack tip exceeds the yield stress, causing a small plastic zone with the greatest crack growth rate. The reduction in the crack face friction, and/or the increase in crack depth and traction coefficient will accelerate the propagation of fatigue crack. In the case of only normal contact load or rolling-sliding contact (μ ≤ 0.375), the crack growth on the wheel and rail is dominant compared to wear and crack will continue to grow.

Study on quantitative analysis of bracket-induced nonlinear response of labio-cheek soft tissue during the orthodontic process

In the orthodontics process, intervention and sliding of an orthodontic bracket during the orthodontic process can arise large response of the labio-cheek soft tissue. Soft tissue damage and ulcers frequently happen at the early stage of orthodontic treatment. In the field of orthodontic medicine, qualitative analysis is always carried out through statistics of clinical cases, while quantitative explanation of bio-mechanical mechanism is lacking. For this purpose, finite element analysis of a three-dimensional labio-cheek-bracket-tooth model is conducted to quantify the bracket-induced mechanical response of the labio-cheek soft tissue, which involves complex coupling of contact nonlinearity, material nonlinearity and geometric nonlinearity. Firstly, based on the biological composition characteristics of labio-cheek, a second-order Ogden model is optimally selected to describe the adipose-like material of the labio-cheek soft tissue. Secondly, according to the characteristics of oral activity, a two-stage simulation model of bracket intervention and orthogonal sliding is established, and the key contact parameters are optimally set. Finally, the two-level analysis method of overall model and submodel is used to achieve efficient solution of high-precision strains in submodels based on the displacement boundary obtained from the overall model calculation. Calculation results with four typical tooth morphologies during orthodontic treatment show that: ① the maximum strain of soft tissue is distributed along the sharp edges of the bracket, consistent with the clinically observed profile of soft tissue deformation; ② the maximum strain of soft tissue is reduced as the teeth align, consistent with the clinical manifestation of common damage and ulcers at the beginning of orthodontic treatment and reduced patient discomfort at the end of treatment. The method in this paper can provide reference for relevant quantitative analysis studies in the field of orthodontic medical treatment at home and abroad, and further benefit to the product development analysis of new orthodontic devices.

Rock neutron backgrounds from FNAL neutrino beamlines in the νBDX−DRIFT detector

The $\ensuremath{\nu}\mathrm{BDX}\text{\ensuremath{-}}\mathrm{DRIFT}$ Collaboration seeks to detect low-energy nuclear recoils from $\mathrm{CE}\ensuremath{\nu}\mathrm{NS}$ or BSM interactions at FNAL. Backgrounds due to rock neutrons are an important concern. We present a genie and geant4 based model to estimate backgrounds from rock neutrons produced in neutrino-nucleus interactions within the rock walls surrounding the underground halls. This model was bench-marked against the 2009 COUPP experiment performed in the MINOS hall in the NuMI neutrino beam, and agreement is found between experimental results and the modeled result to within 30%. Working from this validated model, a similar two-stage simulation was performed to estimate recoil backgrounds in the $\ensuremath{\nu}\mathrm{BDX}\text{\ensuremath{-}}\mathrm{DRIFT}$ detector across several beamlines. In the first stage utilizing geant4, neutrons were tallied exiting the walls of a rectangular underground hall utilizing four different neutrino beam configurations. These results are presented for use by other underground experiments requiring estimations of their rock neutron backgrounds. For $\ensuremath{\nu}\mathrm{BDX}\text{\ensuremath{-}}\mathrm{DRIFT}$, the second stage propagated neutrons from the walls and recorded energy deposited within a scintillator veto surrounding the detector and nuclear recoils within the detector's fiducial volume. The directional signal from the $\ensuremath{\nu}\mathrm{BDX}\text{\ensuremath{-}}\mathrm{DRIFT}$ detector allows additional background subtraction. A sample calculation of a $10\text{ }\text{ }{\mathrm{m}}^{3}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{yr}$ exposure to the NuMI low-energy beam configuration shows a $\mathrm{CE}\ensuremath{\nu}\mathrm{NS}$ signal-to-noise ratio of $\ensuremath{\sim}2.5$.

An Interactive Two-Stage Framework for Simultaneous Machine Selection and Buffer Allocation

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.

Two-stage nested simulation of tail risk measurement: A likelihood ratio approach

Estimating tail risk measures is an important task in many financial and actuarial applications and often requires nested simulations, with the outer simulations representing real world scenarios, and the inner simulations typically used for risk neutral pricing or conditional risk measurement. The standard nested simulation method is highly flexible, able to incorporate complex asset models and exotic payoff structures, but it is also computationally highly burdensome, particularly in a multi-period setting, when inner simulation paths are required at each time step of each outer level scenario. In this study, we propose and analyze a two-stage simulation procedure that efficiently estimates the conditional tail expectation of cost of a dynamic hedging program for a Variable Annuity Guaranteed Minimum Withdrawal Benefit (GMWB), under a multi-period nested simulation. In each of the two stages, the method re-uses the same set of inner level simulation paths for each outer scenario at each time point, using a likelihood ratio method to re-weight the probabilities of each individual path for the different outer scenarios. Our numerical study shows that our two-stage, likelihood ratio weighted procedure can offer a very significant improvement in efficiency, of the order of 95% as measured by the RMSE, compared with a standard nested simulation with the same computational cost.

Numerical modeling of rolling contact fatigue cracks in the railhead

Rolling contact fatigue (RCF) crack is one of dominating damages on the rail surface due to repeated wheel and rail contact. RCF defects, including squats and head checks, can potentially cause rail fracture failure if not treated in time. It is critical to accurately predict the pertinent damage for different operational conditions. A two-stage simulation strategy was proposed to investigate the mechanisms of RCF-induced cracks on the rail, consisting of a dynamic FEM for wheel and rail interaction and a coupled static FEM/BEM for crack growth. It was found that there are two critical wheel positions for crack propagation during wheel approaching to and departing from the pre-existed crack. The different cracking mechanisms at these two critical wheel positions are correlated with the local physical phenomena, i.e., the traction at the wheel/rail interface, the relative distance between the contact patch and crack, and the sliding at crack faces. The twisting of the cracks from the transverse plane to the longitudinal plane at the railhead significantly reduces crack growth in mode I, enhances crack propagation in modes II and III, and shifts the dominant crack growth from the crack front center to the crack corners. The increased incline of the crack in the rolling direction promotes the crack to grow downward in opening mode I.

Optimal Water Resources Allocation in the Yinma River Basin in Jilin Province, China, Using Fuzzy Programming

Being one of the most important sources of water in the Jilin Province in China, the Yinma River Basin (YRB) is facing problems of water scarcity in low economic areas and low utilization in richer areas mainly caused by the irrational allocation of water, excessive pursuit of economic benefits, and neglect of environmental problems. Restricting watershed development involves potential decision-making risks. Some scholars have used the interval two-stage stochastic planning method to adjust water resource allocation in the Drinking Horse River Basin, but the method uses historical statistics for projection and does not take into account the ambiguity and uncertainty in real planning situations. Therefore, this study addresses the problems prevalent in the allocation of water resources in the YRB through optimization using stochastic programming methods, interval and two-stage, and introduces the fuzzy mathematical programming method, with the aim of coordinating the water balance of various water-consuming sectors in the YRB, so as to reconfigure the water allocation. The goal is to solve the existing problems of irrational water allocation, reduce system risks posed by excessive economic development, mitigate water shortages in the water-consuming sectors, and alleviate potential decision-making risks and vague uncertainties associated with the allocation of water resources. Additionally, optimization of the pollution-holding capacity improvement project was carried out. The interval fuzzy two-stage model simulation developed in this study shows that the distribution of water across the different administrative regions can be reduced by up to 30% compared with the original model, effectively reducing the problem of water wastage. Post-optimization, the impact of water shortage in the water resources allocation scheme is alleviated to a significant degree, and there is no water shortage in some areas. At the same time, the eco-environmental sector has gradually taken the leading role in the distribution of water reuse among the different water-consuming sectors. The pollution-holding capacity has been enhanced, and the discharge and river entry chemical oxygen demand (COD) and ammonia nitrogen, two typical pollutants, have been reduced. The membership interval in the interval fuzzy two-stage model reflects the relationship between the possible level of the target value and the risk level. This study provides a guideline for decision makers for balancing the relationship between benefits and risks and proposes a planning scheme that is more conducive to the development of the river basin.

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (R mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (RMSE) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the RMSE of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error (MAPE)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (MAPE: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.

Efficient estimation of a risk measure requiring two-stage simulation optimization

This paper is concerned with the efficient estimation of the risk measure of a system where the estimation requires solving a two-stage simulation optimization problem. The first stage samples risk factors that specify a second stage simulation optimization problem. The second stage solves a simulation optimization problem and outputs the best performance of the system under the realized risk factors, which are then aggregated across all first stage samples to produce an estimate of the risk measure. Applications of such an estimation scheme arise frequently in important industries such as financial, healthcare, logistics, and manufacturing. Because a large number of first stage samples are typically needed, each of which requires solving a computationally expensive simulation optimization problem, the two-stage simulation optimization approach faces a major computational efficiency challenge. In response to this challenge, this paper proposes a sequential simulation budget allocation procedure that determines the allocation of simulation budget based on a score known as revised probability of sign change for each decision under each scenario. The consistency of the proposed procedure is proved and the computational efficiency gain of the proposed is demonstrated using both benchmark test functions and two test cases in the context of financial portfolio risk estimation and healthcare system resilience estimation.

Combined effect of corrosion and strain rate on the bond behavior: A two-stage simulation

The objective of the study is to investigate the bond performance between corroded steel bar and concrete subjected to dynamic loadings. A 3D refined simulation model is established, in which the geometrical shape of corroded rebar can be explicitly described. In addition, the shape variation of steel bars due to corrosion and the materials strain rate effect are also considered. In the simulations, a two-stage analysis (stage I: corrosion induced expansion, and stage II: dynamic relative slip) approach is utilized to explore the influences of corrosion and strain rate on the dynamic bond performance. Firstly, the corrosion expansion displacement is applied on concrete surface in contact with rebar to simulate the corrosion expansive behavior. After obtaining the damage pattern of concrete caused by corrosion and then taking this state as the initial condition, we apply a dynamic load on the loading end of corroded rebar to evaluate its dynamic bond behavior between the corroded rebar and concrete. A good agreement is obtained between the simulated results and the available experimental results on the failure patterns and corroded bond stress-slip curves. The combined effects and quantitative relationship between corrosion level and strain rate on the bond behavior are discussed. Also, the simulated bonding strength and peak slip present good consistency with the available test data. Furthermore, a modified bond stress-slip model considering the influence of corrosion level and strain rate on the bond strength as well as the peak slip is proposed.

Oxygen-Induced Elemental Mercury Oxidation in Chemical Looping Combustion of Coal.

Mercury emission is an important issue during chemical looping combustion (CLC) of coal. The aim of this work is to explore the effects of different flue gas components (e.g., HCl, NO, SO2, and CO2) on mercury transformation in the flue gas cooling process. A two-stage simulation method is used to reveal the reaction mechanism of these gases affecting elemental mercury (Hg0) oxidation. Furthermore, using this method, Hg0 oxidation by eight oxygen carriers (Co3O4, CaSO4, CeO2, Fe2O3, Al2O3, Mn2O3, SiO2, and CuO) commonly used in CLC are investigated and their Hg0 oxidation efficiencies were compared with the existing experimental results. The results show that HCl, NO, and CO2 promote Hg0 oxidation during flue gas cooling, while SO2 inhibits Hg0 oxidation. The stronger the oxygen release capacity of oxygen carriers, the higher the oxidation efficiency of Hg0 becomes. The order of Hg0 removal efficiency from high to low is Co3O4, CuO, Mn2O3, CaSO4, Fe2O3, CeO2, Al2O3, and SiO2, and this sequence is in good agreement with the existing experimental results. Different flue gas components directly or indirectly affect the O2 content, thus affecting the content of gaseous oxidized mercury (Hg2+). Different oxygen carriers have different oxygen release capacities and different Hg0 oxidation efficiencies. Therefore, O2 is the core species affecting the mercury transformation in CLC.

A simulation task partition method based on cloud computing resource prediction using ensemble learning

The calculation processing and interactions between complex system simulation entities in a cloud-computing environment exhibit dynamic change. However, parallelized task partitioning methods often ignore these dynamic characteristics, which makes it difficult to improve execution efficiency for complex system simulations. Therefore, we propose a two-stage simulation task partition method that computes the resource prediction information. In the first stage, the features of collected data are analyzed to select the optimal feature dimensions. A stacking ensemble learning method is used to predict the simulation runtime under the condition of allocating various computing resources, and the number of resources required for the shortest runtime is obtained by sorting the predicted results. In the second stage, a multi-weight graph structure is designed to represent the dynamic interaction of simulation entities, with the goal of minimizing the load imbalance on each computing node, as well as the maximum number of communications between nodes. Subsequently, the simulated annealing optimization algorithm is used to partition multiple weight graphs and map simulation tasks to resources. We compared our multi-weight graph partition method with the Metis and Chacos models. The experimental results demonstrate that, while all three methods can achieve the shortest runtime with the predicted number of computing resources, our algorithm can further reduce the runtime by up to 26%.

A two-stage simulation analysis of uncertain road damage on the urban emergency delivery network.

When a city encounters a natural disaster, the traffic capacity of the road will change uncertainly over time as the disaster spreads. At this time, it will affect the overall distribution of the urban road network. Therefore, in order to ensure the normal operation of the city, evaluate the objective regularities of impact is of great significance and urgency to emergency decision-makers. The extent and scope of road damaged in the disaster-stricken area varies with time due to the impact of natural calamities. To reveal the regularities impact, this paper provides a two-stage analysis method based on the distribution path of the road network, offering basic data analysis and nonlinear fitting regression analysis on distribution costs, spatial accessibility and distribution efficiency. This study uses the degree of road network damage and the double randomness of road damaged to establish a transportation model for dynamic simulation analysis. The research results show that the delivery regularity of costs, spatial accessibility, and efficiency present the s-curve changes obviously. There are obvious inflection points when the damaged road percentage reaches about 10%-15% and 30%-40%. Therefore, the most suitable delivery route and time can be selected to maximize efficiency and reduce losses.

Infrastructure enabled eco-approach for transit system: A simulation approach

This paper develops an infrastructure-enabled eco-approach specifically for transit systems, where a two-stage simulation approach is proposed to include the impact of bus stops, and can further quantify the effects of infrastructure-enabled eco-approach on transit operating and service. The developed system is then embedded into a well-developed simulation environment, i.e., Simulation of Urban Mobility (SUMO), and a series of real-world-based cases are designed to compare the proposed system with traditional method and non-control case at segment-, line-, and network-level service respectively. Results reveal that the proposed method outperforms the traditional eco approach and can significantly improve the energy efficiency and reduce CO2 emission by up to 54.33%, where the environmental benefits are more clearly seen when more transit services are involved. In particular, it has the capability of marginalizing the speed fluctuations around the bus stop.

A Study on Minimizing Welding Deformation of Joints for the Sealing of Emission After-Treatment Structure.

As the environmental pollution issue has recently become significant, environmental regulations in Europe and the United States are being strengthened. Thus, there is a demand for the quality improvement of emission after-treatment systems to satisfy the strengthened environmental regulations. Reducing the amount of welding heat distortion by optimization of the welding order of each part could be a solution for quality improvement since the emission after-treatment system consists of many parts and each assembly is produced by welding individual ones. In this research, a method to derive a welding sequence that effectively minimizes welding deformation was proposed. A two-stage simulation was performed to obtain the optimal welding sequence. In the first stage, the welding sequence was derived by analyzing the number of welding groups in each assembly of a structure. The derived welding sequence was verified by performing a thermal elasto-plastic analysis and comparing it with the experimental results.

Application of the four-dimensional lattice spring model in direct shear testing of intact rock

Rock shear strength and its influencing parameters, such as cohesion and friction angle, are critical for both surface and underground project rock engineering designs. The application of direct shear testing is more commonly employed for rock joints than for intact rock due to several limitations, such as the nonuniform normal stress distribution. Hence, triaxial tests are more frequently used to determine the cohesion and friction angle of intact rock specimens. However, compared to the basic tools of the direct shear test, these tests require more complex experimental equipment, making the triaxial testing less accessible to laboratories in general. Therefore, it is important to develop a method for making intact rock direct shear test results more accurate for engineering applications. To evaluate and enhance the accuracy of intact direct shear tests, laboratory experiments on intact cubic marble specimens and consequent numerical simulation were performed using the four-dimensional lattice spring model (4D-LSM). The 4D-LSM is arguably more efficient than the conventional discrete element method since it uses direct calibration of microscale parameters from macroscale parameters while maintaining the ability to reproduce material fracture. In this study, a two-stage simulation was introduced in the 4D-LSM to represent the stages when the normal and shear loads are applied. To determine the failure state of the specimen, the Mohr– Coulomb failure criterion was implemented into the 4D-LSM. A bilinear stiffness model was used to reproduce the shear force-displacement curve from the experiments. Our results show that this method was able to reproduce numerical results that are similar to those obtained in experimental direct shear tests.

ASCOT orbit-following simulations of ion cyclotron heating with synthetic fast ion loss diagnostic: a first application to ASDEX Upgrade

The orbit-following Monte Carlo code ASCOT interfaced with the radiofrequency Monte Carlo library RFOF can simulate radiofrequency heating of ion populations using wave solutions from full-wave solvers such as TORIC. It is applied to the fundamental mode ion cyclotron resonant heating of hydrogen and 2nd harmonic frequency heating of neutral beam injected deuterium in ASDEX Upgrade discharge #33147 to validate the model against fast ion loss detector (FILD) measurements. In addition, for FILD signal simulations requiring enhanced resolution or scanning the effect of various perturbations such as magnetohydrodynamic phenomena near the plasma edge, a two-stage simulation scheme is presented where the fast ion population is first created by a full ASCOT-RFOF ion cyclotron heating simulation, and the resulting distribution is then used as an input to ASCOT’s distribution-sampling marker source module for more efficient simulation of the ICRH ion wall load and FILD signal. A satisfactory agreement with the experimentally observed FILD signal is found.

A Two-stage Simulation Assisted Differential Evolution Algorithm for Reliable Chance Constrained Programming with Minimum Risk Level

Recently the literature of Simulation Optimization for solving stochastic constrained optimization problems has been substantially increasing. From the optimization perspective, the population based meta-heuristics algorithms, such as Differential Evolutionary (DE), has shown tremendous success in solving continuous constrained optimization problems. While from the simulation and stochastic perspectives, Monte-Carlo Simulation alongside Chance Constrained Programming (CCP), is one of the most successful combined approaches to handle constraint uncertainty, also called chance constraints. Risk levels are used in CCP to bound the stochastic constraints infeasibility. However, the determination of the risk levels is not precise and is based on subjective personal judgments; due to the lack of a scientific approach to determine their values especially in critical problems that require the minimum possible risk levels. Furthermore, reliability in such problems is a vital requirement, especially for studies under worst-case scenarios. Therefore, we propose a novel approach, called Two-stage simulation assisted Differential Evolutionary for Chance constrained programming algorithm (Two-stage DE-CCP), with two major contributions. The first contribution aims to obtain minimum boundaries for the chance constrains’ risk levels, by utilizing the meta-heuristics DE implicit capabilities with a two stages modification. While the second contribution proposed as a Reliable Chance Constrained Programming (RCCP) with a novel scheme for chance constraints’ feasibility checking, that is called Extreme Scenario Feasibility Checking (ESFC). This name reveals that ESFC aims to obtain more reliable solutions, through comparing the extreme/worst-case scenarios of the chance constraints. The proposed algorithm is applied under normal CCP feasibility checking and ESFC for RCCP, while a comparison between them is performed and an empirical Reliability ratio (Rr) is proposed. The experimental results on benchmark problems and real application show that the proposed algorithm is able to provide decision makers with the minimum possible risk levels. While a substantially high Rr is obtained from the proposed RCCP, as compared to traditional CCP.