An Approach to Quantitative Assessment of Relative Proliferation Risks from Nuclear Fuel Cycles

Feasibility of quantitative assessments of the risk of nuclear weapons proliferation is discussed in this paper. The proliferation risk is defined as a combined utility of the different fuel cycle processes or materials for the proscribed acquisition of a nuclear weapon. Based on a set of selected weighted criteria, the process utilities are calculated employing utility functions or fuzzy expectation values. The methods are compared to each other. The scheme appears feasible in relative comparisons while certain leeway must still be retained for political judgement.     

Using intervals for global sensitivity and worst-case analyses in multiattribute value trees

Sensitivity analyses have long been used to assess the impacts of uncertainties on outcomes of decision models. Several approaches have been suggested, but it has been problematic to get a quick overview of the total impact of all the uncertainties. Here we show how interval modeling can be used for global sensitivity analyses in multiattribute value trees, and a nuclear emergency case is used to illustrate the method. The approach is conceptually simple and computationally feasible. With intervals, the decision maker can include all the possible uncertainties and quickly estimate their combined impact. This is especially useful in high-risk decisions where a worst-case type of sensitivity analysis is essential. By varying the intervals one can also examine which uncertainties have the greatest impact and thus need the most consideration. Global sensitivity analysis reveals how the outcome is affected by many simultaneous variations in the model.     

Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for possibly dangerous nuclear material, such as uranium, in a fuel cycle is important because of its possible loss or diversion. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations at facilities in the fuel cycle. We develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations (\(n=3\)) that provides the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm (given the device accuracy is 100%), and (iii) determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than a fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.

     

Stakeholder Preference Mapping—seeking a way forward for the processing of spent nuclear fuel

Continuing concern in many countries about the processing of spent nuclear fuel has sparked new interest in how best to make evidence-based decisions about divisive issues. Stakeholder Preference Mapping (SPM), described here, is a way of applying multi-attribute decision analysis to structured dialogue and engagement with stakeholders. It uses the recorded views of stakeholders, supplemented where necessary by direct stakeholder contact, to understand and evidence stakeholder perspectives and to anticipate arguments for and against particular outcomes. It is illustrated in this paper through an exercise to examine competing options for the processing of spent nuclear fuel. The potential merits of SPM in terms of informing, focusing and accelerating stakeholder interactions and its relationship to other similar approaches are described and discussed.     

Coarse-Grid-CFD for the Thermal Hydraulic Investigation of Rod-Bundles

A nuclear reactor core, that is a few meters in height and diameter is composed of hundreds of fuel assemblies which are again composed of tenth of fuel rods with a diameter of about 10 mm. The relevant length scales for a Computational Fluid Dynamics (CFD) simulations range from the sub millimetre range, relevant for the sub channels up to several meters. Describing such a multi-scale situation with CFD is extremely challenging and the traditional approach is to use integral methods. These are sub channel and sub assembly analyses codes requiring closure by empirical and experimental correlations. A CFD simulation of a complete nuclear reactor set up resolving all relevant scales requires exceedingly large computational resources. However, in many cases there exists repetitive geometrical assemblies and flow patterns. Based on this observation the general approach of creating a parametrized model for a single segment and composing many of these reduced models to obtain the entire reactor simulation becomes feasible. With the Coarse-Grid-CFD (CGCFD) ( [1], [2]), we propose to replace the experimental or empirical input with proper CFD data. Application of the methodology starts with a detailed, well-resolved, and verified CFD simulation of a single representative segment. From this simulation we extract in tabular form so-called volumetric forces which upon parametrization is assigned to all coarse cells. Repeating the fine simulation for multiple flow conditions parametrized data can be obtained or interpolated for all occurring conditions to the desired degree of accuracy. Note, that parametrized data is used to close an otherwise strongly under-resolved, coarsely meshed model of a complete reactor set up. Implementation of volumetric forces are the method of choice to account for effects as long as dominant transport is still distinguishable on the coarse mesh. In cases where smaller scale effects become relevant the Anisotrop Porosity Formulation (APF) allows capturing transport phenomena occurring on the same or slightly smaller scale compared to the coarse mesh resolution. Within this work we present results of several fuel assemblies, that were investigated with our methodology. In particular, we show Coarse-Grid-CFD simulations including a 127 pin LBE cooled wire wrapped fuel assembly. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

基于DS/AHP的供应商选择方法

供应商选择方法有很多种，在众多的方法中层次分析法以能够将定性指标定量化而被广泛应用于供应商选择决策中。考虑到供应商选择问题中包含有很多的不确定性而证据理论在处理不确定问题又有着独特的优点，因此本文采用了一种由层次分析法和证据理论结合而产生的DS／AHP决策方法，并将其应用于供应商选择决策问题中，该方法综合了层次分析法和证据理论的优点，可以更科学的进行供应商选择决策，最后通过一个例子说明这种方法在供应商选择中的应用。     

Thermal regimes of high burn-up nuclear fuel rod

The temperature distribution in the nuclear fuel rods for high burn-up is studied. We use the numerical and analytical approaches. It is shown that the time taken to have the stationary thermal regime of nuclear fuel rod is less than one minute. We can make the inference that the behavior of the nuclear fuel rod can be considered as a stationary task. Exact solutions of the temperature distribution in the fuel rods in the stationary case are found. Thermal regimes of high burn-up the nuclear fuel rods are analyzed.     

Design of regional production networks for second generation synthetic bio-fuel - A case study in Northern Germany

In the medium-term, second generation synthetic bio-diesel will make an important contribution to sustainable mobility. However, attributed to political, technical, and market related uncertainties, it is still not clear which interest groups will invest in production capacities and which technologies will be used. Hence, a multi-period MIP-model is presented for integrated location, capacity and technology planning for the design of production networks for second generation synthetic bio-diesel. The approach is applied to the region of Niedersachsen, Germany. Principle network configurations are developed for this region considering different scenarios and different risk attitudes of interest groups. As results of the investigation, recommendations are drawn regarding advantageous plant concepts, as well as strategies for the capacity installation. Finally, recommendations for political decision makers as well as for potential investors are deduced.     

Constructive heuristics for the canister filling problem

The canister filling problem arises when optimizing the spent nuclear fuel repository in hard rock. It is shown the problem is NP-hard. Constructive heuristics followed by remove and reinsert local optimization are considered. Additionally, some theoretical insight on the algorithm operation is provided. Several variants of the algorithm are compared on random and realistic datasets. The obtained results have shown that constructive heuristics give satisfactory results for both random and realistic input data, although there is still some room for improvements.     

A simulation approach for handling uncertainty in the analytic hierarchy process

Uncertainty considerations are introduced into the analytic hierarchy process (AHP). The rank order of decision alternatives depends on two types of related uncertainties: (1) uncertainty regarding the future characteristics of the decision making environment described by a set of scenarios, and (2) uncertainty associated with the decision making judgment regarding each pairwise comparison. A simulation approach for handling both types of related uncertainties in the AHP is described. The example introduced by Saaty and Kearns (1985) is extended here to include uncertainty considerations.     

A stochastic model for investments in different technologies for electricity production in the long period

We present a single stage stochastic mixed integer linear model for determining the optimal mix of different technologies for electricity generation, ranging from coal, nuclear and combined cycle gas turbine to hydroelectric, wind and photovoltaic, taking into account the existing plants, the cost of investment in new plants, maintenance costs, purchase and sale of ${CO}_2$ emission trading certificates and green certificates, in order to satisfy regulatory requirements. The power producer is assumed to be a price-taker. Stochasticity of future fuel prices, which affect the generation variable costs, is included in the model by means of a set of scenarios. The main contribution of the paper, beyond considering stochasticity in the future fuel prices, is the introduction of CVaR risk measure in the objective function in order to limit the possibility of low profits in bad scenarios with a fixed confidence level.     

Modeling of multi-stage decision-making processes in multi-period energy-models

Many energy modeling efforts anticipate that the decisionmaker has complete foresight and that decision-making is a single-staged process. In addition to the many uncertainties concerning the modeling itself and the data used, these invalid assumptions prevent energy-modeling from becoming common planning practice. One step towards a better understanding of the energy system in the presence of uncertainties was the development of the so-called ‘systematic analysis of the future’ and the definition of ‘robust decisions’. These approaches define the range of outcomes given input uncertainties, and help in determining which short-term decisions to be made which are independent of the future development as long as it lies within the defined range.The aim of this paper is to give an overview of the state of the art in handling of uncertainties in energy modeling, followed by the presentation of an extended interactive optimization algorithm, which helps in handling the problems of modeling the multi-staged decision-making process with incomplete foresight.     

A fuzzy approach to value and culture assessment and an application

This paper presents a fuzzy approach to the identification of organizational values and culture. The proposed approach has been developed from crisp assessment methods in the literature and has been applied to the Industrial Engineering Department (IED) at a state university in Turkey. Highly subjective judgments and ambiguity regarding the presence of values and the culture type of the organization resulting from these values suggest the necessity of using a fuzzy approach. Where the uncertainty arises from the inability to perform adequate measurements, fuzzy sets provide a mathematical method of representing such uncertainties. Applying the fuzzy approach, organizational values which are common, and should be common in the IED, are identified and these values are organized into four generic culture types – adhocracy culture, market culture, clan culture and hierarchy culture – stating in which culture type the IED belongs. Finally the uncertainties of the culture sets are quantified by the measure of fuzzy entropy.     

Étude des paramètres hétérogènes d'une surface active simulant une couronne d'éléments combustibles

In the method of the active surfaces a whole of nuclear fuel rods is described by a surface possessing suitable properties characterized in terms of ‘heterogeneous parameters’. This study gives a calculus allowing the theoretical determination of the heterogeneous parameters concerning a cylindrical active surface. The field in which an active surface is equivalent to fuel rods is discussed with particular references to 5 different fuel arrangements. A method for reaching the heterogeneous parameters from measurements in the sub-critical assembly CACTUS is described. This method takes into account the particular energy spectrum and locations of the neutron supply sources. The values of the heterogeneous parameters theoretically obtained are in the error limits of the experimental results.     

ATLANTIS: techno-economic model of the European electricity sector

Since the nuclear accident in Fukushima the European electricity economy has been in transition. The ongoing shut down of nuclear power plants and the widespread installation of wind power and photovoltaic generation capacities, especially in Germany, has led to a high share of intermittent renewable electricity production. This high amount of generation with very little variable cost has led to a significant decline of the prices at the European energy exchange. This has meant that many thermal power plants are no longer able to work economically and have already been shut down, although they would be needed in times of high demands and as backup capacities. Therefore, a redesign of the European electricity market is needed and in order to find out the right characteristics and effects of such a redesign pre-investigations based on simulation models are reasonable. This paper introduces ATLANTIS, which is a simulation model of the European electricity economy and covers technical as well as economic and environmental issues and allows the calculation of different scenarios up to 2050 and even beyond regarding the specific characteristics of the electricity economy. After a comprehensive introduction of the model some example applications and an outlook are presented.     

FuelOpt: a decision support system for forest fuel logistics

The use of forest fuel is increasing at heating plants in Sweden. Heating plants provide energy in the form of hot water for heating houses and apartments in local municipalities. Forest fuel are products obtained from harvesting in forests that cannot be used for further processing at sawmills and pulp and paper mills. Examples of such products are tree branches, tree tops and low quality logs. The optimization of the supply chain for round-wood (logs to sawmills, pulp and paper mills) and for forest fuel is similar but involves two main differences. First, forest fuel has to be converted into chips before delivery to the customer, and second, the demand for forest fuel varies over the year due to the temperature. To balance the chipping and transportation capacities over time, it is important to manage inventory levels at terminals. The optimization model developed provides decision support for questions regarding the choice of technology for chipping, where to perform the chipping operations, and the allocation of different assortments to heating plants. The system has been tested on a large case study from a Swedish forest energy company. The results show large savings and that the system is very useful for both planning and business development.     

Selection of Alternative Fuels for Sustainable Urban Transportation under Multi-criteria Intuitionistic Fuzzy Environment

The unrestrained expansion in urbanization and increasing development of new means of transports result in major urban land use and transportation system which is socially, economically and environmentally unsustainable. Hence the major challenge for the decision makers regarding the transportation policies is to choose the alternative fuel operated vehicles resulting in a sustainable transportation system. In real life situations, it is difficult to get exact data, so to express the uncertain data, intuitionistic fuzzy data has been considered. The problem is to select the best fuel technology for land transportation subject to multiple criterions resulting in a sustainable transportation system in an uncertain environment. Here, the similarity measures of Intuitionistic fuzzy sets (IFSs) are applied for developing a methodology for identifying the best option. The weights of the attributes may be known or partially known or unknown. The unknown weights are determined by normalizing the average score functions of the intuitionistic fuzzy data for the criterion. Algorithms are given for handling different situations and numerical examples illustrate the varied cases.     

Feed-forward modelling and fuzzy logic based control strategy for powertrain efficiency improvement in a parallel hybrid electric vehicle

With the stricter limitations on both fuel consumption and air pollution, the advantages of a hybrid electric vehicle are becoming more evident than ever. In the present study, an energy management system for a hybrid electric vehicle is developed. Because the plant under consideration is nonlinear, multi-domain, time-varying, has multiple uncertainties and, in addition, the designed control strategy must be able to obey the driver's commands and achieve the par-internship for a new generation of vehicle regulations, the fuzzy logic approach is chosen. A feed-forward hybrid vehicle simulation model is used to demonstrate the validity and the convenience of the current approach and its results have been compared with the other parallel hybrid electric vehicle control strategies. Simulation results show considerable improvement in the efficiency of the internal combustion engine and, consequently, fuel consumption and acceleration performances.     

Dynamic Robust Output Min–Max Control for Discrete Uncertain Systems

Min–max control is a robust control, which guarantees stability in the presence of matched uncertainties. The basic min–max control is a static state feedback law. Recently, the applicability conditions of discrete static min–max control through the output have been derived. In this paper, the results for output static min–max control are further extended to a class of output dynamic min–max controllers, and a general parametrization of all such controllers is derived. The dynamic output min–max control is shown to exist in many circumstances under which the output static min–max control does not exist, and usually allows for broader bounds on uncertainties. Another family of robust output min–max controllers, constructed from an asymptotic observer which is insensitive to uncertainties and a state min–max control, is derived. The latter is shown to be a particular case of the dynamic min–max control when the nominal system has no zeros at the origin. In the case where the insensitive observer exists, it is shown that the observer-controller has the same stability properties as those of the full state feedback min–max control.     

Field test of nonintrusive traffic detection technologies

Accurate, low-cost methods of collecting historical traffic information are essential in making well-informed transportation planning decisions. In addition, detection of real-time traffic conditions is a key element in advanced traffic management and traveler information systems.Until the last decade, inductive loop detectors, pneumatic road tubes, and temporary manual counts were the primary methods for collecting both real-time and historical traffic data. However, technological innovations have given rise to design many different types of advanced traffic detectors. Recently developed traffic detectors use sonic, ultrasonic, microwave, or infrared energy. Most of these detectors can be mounted overhead or to the side of traffic lanes. Magnetic sensors are now being built in sizes small enough to be placed in conduits under the roadway. Artificial intelligence algorithms can process videotaped images of road scenes and output many useful traffic parameters.Even though nonintrusive technologies have been available for several years, there are still many uncertainties regarding their use. Traffic engineers lack a comprehensive comparison of the various types of traffic detection technology. A study conducted by the Minnesota Department of Transportation(Mn/DOT) and SRF Consulting Group, Inc. (SRF) and sponsored by the Federal Highway Administration (FHWA) seeks to address this need. Mn/DOT and SRF undertook a two-year effort to test a wide variety of nonintrusive traffic detection technologies. The purpose of this evaluation was to collect practical information on the performance, installation requirements, long-term maintenance requirements, and costs of various types of nonintrusive traffic detection technologies. More than a dozen devices representing magnetic, sonic, ultrasonic, microwave, infrared, and video image processing technologies were evaluated during this project. Devices were evaluated for their performance in both freeway and urban intersection monitoring situations.Testing consisted of two phases. During Phase I, which ran from November 1995 to January 1996, all participating devices measured traffic data on three lanes of Interstate 394 in Minneapolis at the Penn Avenue interchange. Phase II, which ran from February to November 1996, consisted of an all-season monitoring of the devices' performance and maintenance requirements and involved both freeway and intersection installations. The Minneapolis-St. Paul metropolitan area provided an excellent opportunity to evaluate the devices in many types of weather extremes, including very cold and very hot temperatures, rain, snow, fog, and high winds.