SUMMARY Reduced order modeling plays an indispensible role for most real‐world complex models. The objective of this manuscript is to hybridize local and global sensitivity analysis methods to enable the application of reduced order modeling to complex nonlinear models, often encountered in real system design and analysis calculations, for example, nuclear reactors. This is achieved by first employing local variational methods to identify important nonlinear features of the original model that are required to reach a user‐defined accuracy for the reduced model. This information is obtained by sampling local first‐order derivatives of a pseudoresponse utilizing a modified representation of an infinite series expansion around some reference point. The resulting derivative information is aggregated in a subspace of dimension much less than the dimension of the input parameter space. The accuracy of the reduced model can be mathematically quantified using a bounding norm. Next, global sensitivity methods are employed to exhaustively search the reduced subspace for sensitivity information. The theory and implementation details of the proposed method are exposed in this manuscript. Numerical tests based on prototype nonlinear functions and radiation transport models with many input parameters and many responses are conducted as proof of principle. Copyright © 2012 John Wiley & Sons, Ltd.
The Real-Time Optimization Technical Interest Group (RTO TIG) has endeavored to clarify the value of real-time optimization projects. RTO projects involve three critical components: People, Process, and Technology. Understanding these components will help to establish a framework for determining the value of RTO efforts. In this paper, the Technology component is closely examined and categorized. Levels within each Technology category are illustrated using spider diagrams, which help decision-makers understand the current status of operations and the impact of future RTO projects. Uncertain value perception in our industry has been one of the critical issues in adopting RTO systems. Therefore, case histories are reviewed to demonstrate the impact of RTO projects. To assist RTO project promotion, we list lessons learned through case histories, suggest a justification process, and present a simple economic example. Introduction Industry case histories demonstrate many types of benefits from RTO, such as volume increase, ROI increase, decision quality, HSE improvement, and opex reduction. However, they have lacked systematic project evaluation methods or processes for justification. Today, promoting RTO is in essence a competition for capital within producing companies. The project teams that recognize this fact and then clearly outline the purpose, benefits, costs (direct or indirect), and strategic business alignment of their proposals will be in an advantageous position to secure funding. Because RTO is still an emerging discipline, classifying projects of this nature is still dependent on an individual's point of view. This paper is intended to enable classification of RTO in an objective manner and to help provide a common vocabulary to address issues. Three Cornerstones in Adopting New Technology In adopting any new technology, TIG members realize that there are three major factors: People, Process, and Technology, as shown in Fig. 1. New RTO technology can achieve the benefits we seek, but it is not likely without corresponding changes in the way we work with others and in the processes or workflow in which we perform tasks. This challenge is common to the implementation of any new technology, whether RTO or not. Engineers tend to emphasize the technology aspect because we are most familiar with it, but the other aspects are equally important. For example, the lack of workflow modification, which requires training and possible organizational changes, is tends to result in unsustainable efforts and ultimately underperformance of the investment in RTO. People People issues manifest themselves in several ways1: corporate culture, organizational structure, and training. Corporate culture is the set of tacit understandings and beliefs that form the foundation of how an organization works. It is a mental model that people have about the nature of an organization and how it sees itself. Within an organization, culture is "how things are done around here." The culture of an organization can be appropriate and supportive to an organization's goals and strategies, or it can hinder its initiatives and projects. Usually any major change in an organization, such as deployment of new technology, radical strategic shifts, or new initiatives, is countercultural. That is, the change breaks existing cultural rules and assumptions, and the change is automatically resisted and thereby impeded.
In this investigation, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 m × 180 m block in an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Due to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms comprised of mixed optimization techniques. For global optimization, we consider Simulated Annealing (SA), Particle Swarm (PS) and Genetic Algorithm (GA), which rely solely on objective function evaluations; i.e., they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic Implicit Filtering method (IF), which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques combining global optimization and Implicit Filtering address difficulties associated with the non-smooth response, and their performances are shown to significantly decrease the computational time over the global optimization methods alone. To quantify uncertainties associated with the source location and intensity, we employ the Delayed Rejection Adaptive Metropolis (DRAM) and DiffeRential Evolution Adaptive Metropolis (DREAM) algorithms. Marginal densities of the source properties are obtained, and the means of the chains' compare accurately with the estimates produced by the hybrid algorithms.
TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractThe purpose of this paper is to call attention
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