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Engineering design and dimensional measurement tend to occur at opposite ends of the product creation process. As a result, the dialogue may sometimes be poor, and can lead to the production of features that are difficult to measure, adding unnecessary cost and risk to the business. In response, the EngD research aims to identify ways in which the relationship between these activities can be strengthened. An emphasis was placed on product lifecycle management (PLM), due to the sponsor’s desire to maximise the value of their existing investments in this area. Since the problem is complex, and seemingly intractable, a mixed methods approach was adopted in which laboratory experiments were interwoven with small-scale interventions within industry. The research began with the development of a theoretical framework, labelled ‘PLM-integrated dimensional measurement’ (PiDM). The framework builds on existing literature, whilst incorporating issues identified by stakeholders. Test cases were structured and executed against the framework in order to identify technology gaps; key amongst which was the need to improve measurement planning for coordinate measuring machines by incorporating uncertainty evaluation techniques. Four interconnected investigations were then carried out in an industrial setting to explore measurement capability in practice. The findings from these investigations informed subsequent development of an uncertainty-based measurement planning system. The system brings together commercially available simulation software and measurement programming software into a PLM environment. It allows features to be categorised according to their ‘measurability’, providing quantitative data for verification planning and engineering design. The EngD concludes with an industrial case study, investigating potential routes to deployment. This case study provided the data needed to commission a further two year programme of research into the topic, formally engaging the sponsor organisation’s strategic metrology and PLM solution providers. This new research programme is structured around the PiDM framework.

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Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

The best estimate codes are used for licensing of Nuclear Power Plants (NPP), but with conservative assumptions. It is claimed that the uncertainties are covered by the conservatism of the calculation. Nowadays, it is possible to estimate certain parameters using non-conservative data with the complement of uncertainty evaluation, and these calculations can also be used for licensing. As NPPs are applying for power up-rates and life extension, new licensing calculations need to be performed. In this case, evaluation of the uncertainties could help improve the performance, while staying below the limit of the safety margins. Given the problem of unstable behavior of Boiling Water Reactors (BWR), which is known to occur at certain power and flow conditions, it could cause SCRAM and decrease the economic performance of the plant. Performing an uncertainty analysis for BWR stability would give better understating of the phenomenon and it would help to verify and validate (V&amp;V) the codes used to predict the NPP behavior. This thesis, reports a sensitivity/uncertainty study of numerical, neutronics, and thermal-hydraulics parameters on the prediction of the BWR stability within the framework of OECD Ringhals-1 (R1 stable reactor) and OECD Oskarshamn-2 (O2 unstable reactor) stability benchmarks. The time domain code TRACE/PARCS was used in the analyses. This thesis is divided in three parts: space-time convergence; uncertainty; sensitivity. A space-time convergence study was done for the numerical parameters (nodalization and time step). This was done by refining nodalization of all components and time step until obtaining space-time converged solution, i.e. further refinement doesn’t change the solution. When the space-time converged solutions were compared to the initial models, much better solution accuracy has been obtained for the stability measures (decay ratio and frequency), for both stable (R1) and unstable (O2) reactors with the space-time converged models. Further on, important neutronics and thermal-hydraulics parameters were identified and an uncertainty calculation was performed using the Propagation of Input Errors (PIE) methodology. This methodology, also known as the GRS method, has been used because it has been extensively tested and verified by the industry, and because it allows identifying the most influential parameters using the spearman rank correlation method. Using the uncertainty method’s results, an attempt has been done to identify the most influential parameters affecting the stability. A methodology using the spearman rank correlation coefficient has been implemented, which helps to identify the most influential parameters on the stability (decay ratio and frequency). Additional sensitivity calculations have been performed for better understanding of BWR stability and parameters that affect it. / <p>This work has been preformed thanks to the support of the Swedish Radiation Safety Authority (SSM) and EU project NURISP. QC 20121129</p>

Natural gas and petroleum are non-renewable and scarce energy sources. Although, it is well known that hydrocarbon reserves are depleting through the years, oil and gas remain the principal source of energy upon which our society is strongly dependent. Hence, optimization and accurate planning of hydrocarbon production are the main keys to making it safer, more efficient, and cheaper. One of the tools commonly used to evaluate the optimization of oil/gas production system is the process simulation modeling. A hydrocarbon production system typically consists of at least one underground reservoir where several wells have been drilled into the hydrocarbon-bearing rock to form a fixed topology network. Wells are interconnected with manifolds to transport the gas or oil to a storage or sale location. The process simulation consists of calculating the total hydrocarbon production for the given production system. The pressure in the wellbore is the main variable in determining the hydrocarbon production process. When oil/gas is produced, the pressure decreases until production cannot be sustained. If the well is shut down, the pressure at the wellbore increases because of the natural gas flow coming from the reservoir. In addition, artificial lift techniques, such as water injection, gas lift and pump systems can be incorporated into the simulation program. The oil/gas production has been also modeled as a multi-period optimization case to incorporate the possibility of different demands, cost and overall time behavior. The current field optimization approaches take in account the availability in a general way, adding to the planning a lot of uncertainty. The proposed study includes a suitable analysis of the likelihood of equipment failure, which will predict the availability of the equipment in a certain period of time to perform a more accurate planning. In this work, we have integrated the availability analysis to the model described above. The availability of a system is analyzed by Monte Carlo simulation, which involves the modeling of the probabilities of failure, the type of failure, the time to repair associated with each failure, and time of occurrence for a field system. The availability model performed reduces significantly the uncertainties on a multi-period planning production of either oil or gas, predicting the probability of failure and the downtime related to the hydrocarbon production through its lifetime. In this study, the unavailability of the equipment was quantified, reporting a subsea equipment downtime of approximately 7%. As a result, new production planning is accomplished in the effective work period, which will be beneficial in financial risk decisions such as a government’s deliverability contracts.

Because of the essentially stochastic nature of power systems behavior, mainly due to random equipment outages and load variations, it is very important to consider uncertainties of power systems to promote the accuracy of Available Transfer Capability (ATC) calculations. This paper proposed a probabilistic technique "Bootstrap" to provide an accuracy criteria for ATC estimation that uses historical data and a future postulated condition. The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. Test results showed that when the uncertainties of loads and line outages were considered, using the proposed probabilistic approach the confidence intervals could reflect the accuracy of the posted ATC values and it will be more flexible in bidding ATC to further aid transmission customers in their evaluation of transaction biddings and risk analysis.

This study proposes a new, easily applied method to quantify uncertainty in production forecasts based on reservoir simulation. The new method uses only observed data and mismatches between simulated values and observed values as history matches of observations progress to a final "best" match. The method is applicable even when only limited information is available from a field. Previous methods suggested in the literature require more information than our new method. Quantifying uncertainty in production forecasts (i.e., reserve estimates) is becoming increasingly important in the petroleum industry. Many current investment opportunities in reservoir development require large investments, many in harsh exploration environments, with intensive technology requirements and possibly marginal investment indicators. Our method of quantifying uncertainty uses a set of history-match runs and includes a method to determine the probability density function (pdf) of future oil production (reserves) while the history match is evolving. We applied our method to the lower-Pleistocene 8-Sand reservoir in the Green Canyon 18 field, Gulf of Mexico. This field was a challenge to model because of its complicated geometry and stratigraphy. iv We objectively computed the mismatch between observed and simulated data using an objective function and developed quantitative matching criteria that we used during history matching. We developed a method based on errors in the mismatches to assign likelihood to each run, and from these results, we determined the pdf of reservoir reserves and thus quantified the uncertainty in the forecast. In our approach, we assigned no preconceived likelihoods to the distribution of variables. Only the production data and history matching errors were used to assess uncertainty. Thus, our simple method enabled us to estimate uncertainty during the history-matching process using only dynamic behavior of a reservoir.

<p>Assessments of chemical health risks are performed by scientific experts. Their intended use is as bases for decisions. This thesis tries to answer the questions of how uncertainty is, and should be, communicated in such risk assessments. The thesis consists of two articles and an introductory essay.</p><p>Article I focuses on the linguistic aspect of the communication of uncertainty in risk assessments. The aim of the article is to elucidate how risk assessors actually indicate uncertainty in risk assessment reports. Because of the prevalent uncertainty in risk assessment, deriving from several sources, uncertainty is communicated in verbal, rather than numerical terms. A typology of uncertainty indicators – phrases used to express uncertainty – is proposed and applied to the reviewed reports. It is found that the use of such phrases is not transparent, and the article concludes by a number of recommendations for improving the practice.</p><p>Article II mainly deals with the content of the communication. The overall question treated is what a characterization of uncertainty should include if a decision made on the basis of the risk assessment information is to be as well-founded as possible. A set of conditions is put forward to be fulfilled by a characterization of uncertainty if it is to be adequate from a decision-making point of view.</p><p>The greater part of the introductory essay is devoted to the concept of uncertainty which, at the conceptual level, does not appear to have been much discussed by philosophers</p>