Traditional Deterministic Methods Research Articles

Development of random geometry capability in RMC code for stochastic media analysis

Simulation of particle transport in random media poses a challenge for traditional deterministic transport methods, due to the significant effects of spatial and energy self-shielding. Monte Carlo method plays an important role in accurate simulation of random media, owing to its flexible geometry modeling and the use of continuous-energy nuclear cross sections. Three stochastic geometry modeling methods including Random Lattice Method, Chord Length Sampling and explicit modeling approach with mesh acceleration technique, have been developed in RMC to simulate the particle transport in the dispersed fuels. The verifications of the accuracy and the investigations of the calculation efficiency have been carried out. The stochastic effects of the randomly dispersed fuel particles are also analyzed. The results show that all three stochastic geometry modeling methods can account for the effects of the random dispersion of fuel particles, and the explicit modeling method can be regarded as the best choice.

Ground heat exchanger design subject to uncertainties arising from thermal response test parameter estimation

This paper presents a new design paradigm of ground heat exchanger (GHE), which takes account of uncertainty in the estimation of geothermal properties, including ground thermal conductivity and borehole thermal resistance from a thermal response test (TRT). Some challenges during the TRT parameter estimation process are discussed: A sensitivity analysis to a more accurate solution of the infinite line source model is introduced to identify the parameters to be estimated; a nonlinear least-square method is employed to estimate the selected parameters; and then validated by a traversing method. Afterwards, a case study is conducted to illustrate how the proposed method can be employed to a practical engineering application. The proposed uncertainty design method that provides a quantified margin of design output can be an alternative to traditional deterministic methods that simply add safety factor to expected design value.

A Multifaceted Approach for Safety Design and Probabilistic Optimization

Safety design and probabilistic optimization are fields that are widely subject to uncertainty, thus making traditional deterministic methods highly unreliable for these two fields. Popular design optimizations methods widely used for safety design and probabilistic optimization are the performance measure approach (PMA) and the performance measure approach (RIA). In a problem where the analysis is performed from an infeasible design space, a modified reliability index approach (MRIA) is employed to address some inefficiency of the traditional RIA to be able to find the optimal solutions. The PMA uses an inverse reliability analysis, which is more computationally efficient at finding the most probable design points but has been reported to have numerical instabilities on some cases. In this paper, three benchmark examples were thoroughly studied with various initial points to examine the stability and efficiency of MRIA and PMA. A hybrid reliability approach (HRA) was then presented after determining a selection factor from the optimum conditions. The proposed HRA aims to determine which of the two optimization methods would be more appropriate during the optimization processes.

A Hybrid Method for Stochastic Performance Modeling and Optimization of Chemical Engineering Processes

As chemical engineers seek to improve plant safety, reliability, and financial performance, a wide range of uncertaintyladen decisions need to be made. It is widely agreed that probabilistic approaches provide a rational framework to quantify such uncertainties and can result in improved decision making and performance when compared with deterministic approaches. This article proposes a novel method for design and performance analysis of chemical engineering processes under uncertainty. The framework combines process simulation tools, response surface techniques, and numerical integration schemes applied in structural reliability problems to determine the probability of a process achieving a performance function of interest. The approach can be used to model processes in the presence or absence of performance function(s), with or without parameter interactions, at both design and operational phases. With this, process behavior can be quantified in terms of stochastic performance measures such as reliability indices and the associated most probable process design/operating conditions, providing a simple way to analyze a wide range of decisions. To validate the applicability of the proposed framework, three case study systems are considered: a plug flow reactor, a heat exchanger, and finally a pump system. In each case, performance criteria based on the original physical model and the surrogate model are set up. Reliability analysis is then carried out based on these two models and the results are assessed. The results show that the proposed framework can be successfully applied in chemical engineering analysis with additional benefits over the traditional deterministic methods.

Achieving ‘best value’ for the community by deployment of risk based cost estimation using Monte-Carlo Simulation to rate-payer-funded capital intensive road projects.

This paper presents the application and validation of a new tool developed by the first author for accurate risk-based estimation of project budgets. Typical capital intensive projects to which this tool can be applied include road reconstruction, road resheet and road rehabilitation projects. Quantitative risk analysis and stochastic modeling using Monte -Carlo simulation is embedded in the algorithms of the computer code. The tool forecasts a range of possible project costs and the probability of the occurrence of those costs by taking into account uncertainties and associated risks. Application of the tool to capital intensive road projects designed by the second author and constructed in 2011 & 2012 demonstrates its validity and utility. Comparisons of forecasted estimates using this tool with actual costs and with traditional deterministic methods of cost estimation (such as --point base-case estimates inclusive of contingency) provide valuable insights that can aid management in evaluating alternatives and in making informed decisions when estimating and allocating budgets to a portfolio of road projects.

Competency 6. Participate in identifying system errors and implementing potential systems solutions

The enormous complexity of modern medical care has made error detection and management extremely difficult. Traditional deterministic methods of solving the “error issue” cannot cope with the huge number of potential errors that are possible. Systems thinking and approach to error reduction provides a different avenue for tackling this challenging dilemma. 1 Weiman T.J. Weiman E.A. A systems approach to error prevention in medicine. J Surg Oncol. 2004; 88: 115-121 Crossref PubMed Scopus (18) Google Scholar

A discrete particle swarm algorithm for geometric image inpainting

Reconstructing image structure can be accomplished using a geometric image inpainting algorithm. This is done following three main steps: the first step consists of locating the damaged region. In the second step, the structure of the corrupted contours is reconstructed by matching each damaged contour with its corresponding one in a way to obtain a visually plausible image. In the third step, the found couples are joined using a curve fitting technique. The search space of candidate solutions dramatically increases as the number of contours increases, which makes the search of the optimal solution by a traditional deterministic method infeasible. A good choice to solve this problem is the use of meta-heuristics. In this paper, a discrete particle swarm optimisation (DPSO) is used to find the best correspondence between contours using their curvature values as a quality metric. Additional information is used, which is the mean ordinate of each contour. It adds spatial information of the location of damaged contours. The experimental results and comparisons with the genetic algorithm show the efficiency of the DPSO for geometric image inpainting.

Power System Risk Assessment Software Design under Impact of Disaster Conditions

With the Influence of disaster factor on power system reliability become more and more significant, traditional deterministic risk analysis method has been difficult to adapt to power system risk assessment in changing disaster conditions. This paper puts disaster factor into power system risk assessment, and use improved Monte Carlo method to analyze the risk of all possible states of the system. Combined with online short-term assessment technology, based on the integration of multiple data sources, it uses several typical risk indexes for power system risk assessment under the impact of disaster conditions, then shows power grid operation risk, security warning information and weakness information. The system has been applied to practical power system to verify its scientific and practical design.

Bayesian Statistic Forecasting Model for Middle-Term and Long-Term Runoff of a Hydropower Station

AbstractThe middle-term and long-term runoff forecasting model of the hydropower station reservoir is established with the Bayesian statistic forecasting theory; uncertainty of the hydrological forecasting is quantitatively described in the form of a probability distribution to explore the statistic forecasting theory and its application value. The uncertainty of the input factor is processed with the forecasting model of grey correlation of meteorological factors, and real-time weather data are effectively combined with the historical hydrological data to break through the restriction of traditional deterministic forecasting methods in the aspects of information utilization and sample study to improve the precision of hydrological forecasting. The established model has been assessed by the example of the reservoir of the Fengman hydropower plant. It is indicated by the analog computation result that this model, compared with the deterministic runoff forecasting method, has advantages not only in quantita...

The Practical Probability Analysis Methods for Stability of Landslide

As an important part of landslide risk assessment, hazard analysis which is based on the probability analysis of landslide stability have caught more and more attentions from researchers and engineers. While in landslide engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties are complex and different, so a single Factor of Safety calculated by traditional deterministic analyses methods can not represent the landslide stability exactly. To provide a more rational mathematical framework to incorporate different types of uncertainties in the landslide stability estimation, some practical probability analysis methods for stability of landslide were proposed, then the user-friendly implementing softwares were recommended. Finally, an application case was described to illustrate the approaches adopted and proved the correctness and feasibility of the proposed methods. The results show the proposed techniques can be applied widely.

Effect of customer worth of interrupted supply on the optimal design of small isolated power systems with increased renewable energy penetration

The analysis and design of a small isolated power system (SIPS) with renewable energy sources (RES) and storage can be challenging, because of the large number of design options and the uncertainty in key parameters. RES add further complexity because their power output may be intermittent, seasonal and non-dispatchable. Owing to this characteristic, reliability evaluation of a RES-based SIPS cannot be implemented using the traditional deterministic and analytical methods. Moreover, this evaluation has to be done within a cost-benefit framework. This study models and investigates the effect of customer worth of interrupted supply (customer damage cost) on the optimal design of SIPS with storage and increased RES penetration. The SIPS optimal design is implemented with a genetic algorithm combined with local search procedure. In addition, this study examines the effect of the forced outage rate of SIPS components on SIPS optimal design via Monte Carlo simulation. The performance of the proposed hybrid optimisation methodology is investigated for a large number of alternative scenarios via sensitivity analysis, which study the effect on the results because of the uncertainty on weather data, components efficiency and cost data. The results show that the optimal design of a RES-based SIPS depends largely on the consideration of customer damage cost as well as the inclusion of components forced outage rate. The method and results presented in this article should be valuable in planning and operating SIPS with RES.

Using support vector regression to estimate sonic log distributions: A case study from the Anadarko Basin, Oklahoma

Abstract In petroleum industry, the compressional acoustic or sonic log (DT) is commonly used as a predictor because its capabilities respond to changes in porosity or compaction which, in turn, are further used to estimate formation (sonic) porosity, to map abnormal pore-fluid pressure, or to carry out petrophysical studies. Despite its intrinsic capabilities, the sonic log is not routinely recorded in during well logging. We propose using a method belonging to the class of supervised machine learning algorithms — Support Vector Regression (SVR) — to synthesize missing compressional acoustic or sonic (DT) logs when only common logs (e.g., natural gamma ray—GR, or deep resistivity—REID) are available. Our approach involves three steps: (1) supervised training of the model; (2) confirmation and validation of the model by blind-testing the results in wells containing both the predictor (GR, REID) and the target (DT) values used in the supervised training; and (3) application of the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT log. SVR methodology offers two advantages over traditional deterministic methods: strong nonlinear approximation capabilities and good generalization effectiveness. These result from the use of kernel functions and from the structural risk minimization principle behind SVR. Unlike linear regression techniques, SVR does not overpredict mean values and thereby preserves original data variability. SVR also deals greatly with uncertainty associated with the data, the immense size of the data and the diversity of the data type. A case study from the Anadarko Basin, Oklahoma, about estimating the presence of abnormally pressurized pore-fluid zones by using synthesized DT values, is presented. The results are promising and encouraging.

Quantification of gains and risks of static thermal rating based on typical meteorological year

The growing demand for electricity and the restructuring of power markets is forcing the power industry to change the way that power systems are planned and operated. Traditionally, transmission lines have been operated based on fixed deterministic thermal ratings, causing underutilization of their potential capacity. Efforts to overcome this limitation led to the development of alternative rating strategies based on probabilistic and dynamic methods.In this paper, a probabilistic static thermal rating method based on typical weather conditions along a transmission line is described and analyzed. The results of load and energy throughput analyses show that the use of this rating approach can significantly increase line throughput compared to traditional deterministic rating methods. However, this approach can also substantially increase the risk of thermal overload.To identify the problems associated with the use of a probabilistic static thermal rating method, we performed a sensitivity study. Statistical analysis of weather parameters shows that line ratings calculated from typical weather data are inflated. Additional results confirm that values of risk tolerance and wind direction incorporated into the rating method significantly affect the resulting rating values. We suggest values for these parameters that minimize the risk of line overloading.

A Novel Two-Level Optimization Framework Based on Constrained Ordinal Optimization and Evolutionary Algorithms for Scheduling of Multipipeline Crude Oil Blending

This paper introduces a practical scheduling of multipipeline crude oil blending (SMCOB) problem. It is formulated as a complex mixed integer nonlinear programming (MINLP) model, taking the charging sequence and flow rates of oil tanks as decision variables, which cannot be efficiently solved by traditional deterministic methods and solvers. Then, a novel two-level optimization framework based on constrained ordinal optimization (COO) and evolutionary algorithms (EA) is proposed. The solution methodology has two stages based on the main procedures of COO. At the crude evaluation stage, discrete EA are used to search for sequence solutions in the outer level. It evolves the sequence solutions on the basis of their rough evaluation of the feasibility and objective value obtained from the inner level and keeps certain number of probably best sequence solutions. At the accurate evaluation stage, the probably best sequence solutions kept by the crude evaluation stage are accurately evaluated by inner-level continuous EA. The COO approach ensures that some true good enough sequence and flow rate solutions can be obtained from the accurate evaluation stage with high probability. COO-based EA are compared with mixed-coding EA to verify the framework’s efficiency and robustness.

Thermal-to-fusion neutron convertor and Monte Carlo coupled simulation of deuteron/triton transport and secondary products generation

Thermal-to-fusion neutron convertor has being studied in China Academy of Engineering Physics (CAEP). Current Monte Carlo codes, such as MCNP and GEANT, are inadequate when applied in this multi-step reactions problems. A Monte Carlo tool RSMC (Reaction Sequence Monte Carlo) has been developed to simulate such coupled problem, from neutron absorption, to charged particle ionization and secondary neutron generation. “Forced particle production” variance reduction technique has been implemented to improve the calculation speed distinctly by making deuteron/triton induced secondary product plays a major role. Nuclear data is handled from ENDF or TENDL, and stopping power from SRIM, which described better for low energy deuteron/triton interactions. As a validation, accelerator driven mono-energy 14MeV fusion neutron source is employed, which has been deeply studied and includes deuteron transport and secondary neutron generation. Various parameters, including fusion neutron angle distribution, average neutron energy at different emission directions, differential and integral energy distributions, are calculated with our tool and traditional deterministic method as references. As a result, we present the calculation results of convertor with RSMC, including conversion ratio of 1mm 6LiD with a typical thermal neutron (Maxwell spectrum) incidence, and fusion neutron spectrum, which will be used for our experiment.

SCHEDULING IN HETEROGENEOUS COMPUTING AND GRID ENVIRONMENTS USING A PARALLEL CHC EVOLUTIONARY ALGORITHM

Scheduling is a capital problem when using distributed heterogeneous computing (HC) and grid environments to solve complex problems. The scheduling problem in heterogeneous environments is NP‐hard, so a significant effort has been made to develop efficient methods for solving the problem. However, few works have faced realistic grid‐sized problem instances. This work presents a parallel CHC (pCHC) evolutionary algorithm codified over MALLBA, a general‐purpose library for combinatorial optimization, for solving the scheduling problem in HC and grid environments. Efficient numerical results are reported in the experimental analysis performed on both a standard benchmark and a set of large‐sized problem instances specially designed in this work. The comparative study shows that pCHC is able to achieve high problem solving efficacy, significantly improving over traditional deterministic scheduling methods, while also showing a good scalability behavior when solving large problem instances.

The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty

Aggressive development of renewable electricity sources will require significant expansions in transmission infrastructure. We present a stochastic two-stage optimisation model that captures the multistage nature of transmission planning under uncertainty and use it to evaluate interregional grid reinforcements in Great Britain (GB). In our model, a proactive transmission planner makes investment decisions in two time periods, each time followed by a market response. Uncertainty is represented by economic, technology, and regulatory scenarios, and first-stage investments must be made before it is known which scenario will occur. The model allows us to identify expected cost-minimising first-stage investments, as well as estimate the value of information, the cost of ignoring uncertainty, and the value of flexibility. Our results show that ignoring risk in planning transmission for renewables has quantifiable economic consequences, and that considering uncertainty can yield decisions that have lower expected costs than traditional deterministic planning methods. In the GB case, the value of information and cost of disregarding uncertainty in transmission planning were of the same order of magnitude (approximately £100M, in present worth terms). Further, the best plan under a risk-neutral decision criterion can differ from the best under risk-aversion. Finally, a traditional sensitivity analysis-based robustness analysis also yields different results than the stochastic model, although the former's expected cost is not much higher.

Monte Carlo Simulation for Particle and γ-Ray Emissions in Statistical Hauser-Feshbach Model

Monte Carlo simulations for particle and γ-ray emissions from a compound nucleus based on the Hauser-Feshbach statistical theory with pre-equilibrium emission are performed. The simulation yields reliable nuclear-reaction-wise energy spectra, or so-called exclusive spectra, for emitted neutrons and γ-rays, which are required in particle transport calculations for nuclear applications. The Monte Carlo method is applied to neutron-induced nuclear reactions on 56Fe, and the results are compared with a traditional deterministic method. The neutron and γ-ray emission correlation is examined by gating on an 847 keV γ-ray that is produced by an inelastic scattering process. The partial γ-ray energy spectra for different γ-ray multiplicities are inferred using this Monte Carlo method. In addition, we investigate a correlation between two neutrons in the (n,2n) reaction.

Monte Carlo simulation for statistical Hauser-Feshbach theory

Monte Carlo simulations for particle andγ-ray emissions from a compound nucleus based on the Hauser-Feshbach statistical theory are performed. The Monte Carlo method is applied to the neutron induced nuclear reactions on 56 Fe, and the results are compared with a traditional deterministic method. The neutron and γ-ray emission correlation is examined by gating on an 847 keVγ-ray that is produced by an inelastic scattering process. The partial γ-ray energy spectra for differentγ-ray multiplicities is inferred using this Monte Carlo method.

Using Gene Expression Programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin, Oklahoma

In the oil and gas industry, characterization of pore-fluid pressures and rock lithology, along with estimation of porosity, permeability, fluid saturation and other physical properties is of crucial importance for successful exploration and exploitation. Along with other well logging methods, the compressional acoustic (sonic) log (DT) is often used as a predictor because it responds to changes in porosity or compaction and, in turn, DT data are used to estimate formation porosity, to map abnormal pore-fluid pressure, or to perform petrophysical studies. However, despite its intrinsic value, the sonic log is not routinely recorded during well logging. Here we propose the use of a soft computing method — Gene Expression Programming (GEP) — to synthesize missing DT logs when only common logs (such as natural gamma ray — GR, or deep resistivity — REID) are present. The Gene Expression Programming approach can be divided into three steps: (1) supervised training of the model; (2) confirmation and validation of the model by blind-testing the results in wells containing both the predictor (GR, REID) and the target (DT) values used in the supervised training; and (3) applying the predicted model to wells containing the predictor data and obtaining the synthetic (simulated) DT log. GEP methodology offers significant advantages over traditional deterministic methods. It does not require a precise mathematical model equation describing the dependency between the predictor values and the target values. Unlike linear regression techniques, GEP does not overpredict mean values and thereby preserves original data variability. GEP also deals greatly with uncertainty associated with the data, the immense size of the data and the diversity of the data type. A case study from the Anadarko Basin, Oklahoma, involving estimating the presence of overpressured zones, is presented. The results are promising and encouraging.