Quantifying Timing Leaks and Cost Optimisation

We develop a new notion of security against timing attacks where the attacker is able to simultaneously observe the execution time of a program and the probability of the values of low variables. We then show how to measure the security of a program with respect to this notion via a computable estimate of the timing leakage and use this estimate for cost optimisation.Comment: 16 pages, 2 figures, 4 tables. A shorter version is included in the proceedings of ICICS'08 - 10th International Conference on Information and Communications Security, 20-22 October, 2008 Birmingham, U

Program Synthesis and Linear Operator Semantics

For deterministic and probabilistic programs we investigate the problem of program synthesis and program optimisation (with respect to non-functional properties) in the general setting of global optimisation. This approach is based on the representation of the semantics of programs and program fragments in terms of linear operators, i.e. as matrices. We exploit in particular the fact that we can automatically generate the representation of the semantics of elementary blocks. These can then can be used in order to compositionally assemble the semantics of a whole program, i.e. the generator of the corresponding Discrete Time Markov Chain (DTMC). We also utilise a generalised version of Abstract Interpretation suitable for this linear algebraic or functional analytical framework in order to formulate semantical constraints (invariants) and optimisation objectives (for example performance requirements).Comment: In Proceedings SYNT 2014, arXiv:1407.493

Selection of Failure Frequency and its Impact on Risk Assessment – A Case Study from Plot Plan Optimisation

PresentationFacility Siting is an important phase of project development. A critical stage is plot plan optimisation, where significant potential hazards are eliminated due to equipment spacing. In addition to ensuring appropriate compliance with minimum spacing requirements, occupied building studies to achieve compliance with the requirements of API 752 and API 753 could also be undertaken to optimise safety outcomes. The studies are done in three stages, where the first stage is hazard identification, second stage is consequence assessment and the third stage is risk assessment. Third stage assessments are only carried, if the consequence based siting recommendations are not practical to implement. This paper presents the challenges in estimating risk due to process hazards with a focus on selecting right event likelihood data. A comparison is presented on the variation in predicted risk levels based on equipment failure rates and leak frequencies. Case study of a plot plan optimisation study is undertaken with DNVGL Phast Risk and the variation in risk levels up to two orders of magnitude are recorded. Challenges such as adaption of data for local conditions, consistent definitions of failure, sample size of data, applicability of data play a significant role in identifying and correctly quantifying the risk levels. Such challenges and its impact on risk quantification are presented in this paper as well as its impact on facility siting

Probabilistic timing covert channels: to close or not to close?

We develop a new notion of security against timing attacks where the attacker is able to simultaneously observe the execution time of a program and the probability of the values of low variables. We then propose an algorithm which computes an estimate of the security of a program with respect to this notion in terms of timing leakage and show how to use this estimate for cost optimization

Best Environmental Management Practice for the Car Manufacturing Sector Learning from frontrunners

The European automotive industry is one of the EU's largest manufacturing sectors, and the automotive value chain covers many activities largely carried out within the EU, such as design and engineering, manufacturing, maintenance and repair, and end-of-life vehicle (ELV) handling. This Best Practice report describes Best Environmental Management Practices (BEMPs), i.e. techniques, measures or actions that are implemented by the organisations within the sector which are most advanced in terms of environmental performance in areas such as energy and resource efficiency, emissions, or supply chain management. The BEMPs provide inspirational examples for any organisation within the sector to improve its environmental performance. The report firstly outlines technical information on the contribution of car manufacturing and end-of-life vehicle (ELV) handling to key environmental burdens in the EU, alongside data on the economic relevance of the sector. The second chapter presents best environmental management practice of interest primarily for manufacturing companies (car manufacturers and associated manufacturers in the supply chain) covering cross-cutting issues related to key environmental impacts (such as energy, waste, water management, or biodiversity) before exploring best practice linked to specific topics, such as supply chain management. Subsequently, specific information concerning actors in the treatment of end-of-life vehicles is presented in the third chapter, focussing in particular on best practice applicable to processers of ELVs. This Best Practice Report was developed with support from a Technical Working Group of experts from the car manufacturing and ELV sector and associated fields. The report gives a wide range of information (environmental benefits, economics, indicators, benchmarks, references, etc.) for each of the proposed best practices in order to be a source of inspiration and guidance for any company of the sector wishing to improve environmental performance. In addition, it will be the technical basis for a Sectoral Reference Document on the car manufacturing sector, to be produced by the European Commission according to the EMAS Regulation.JRC.B.5-Circular Economy and Industrial Leadershi

Development of the Next Generation of Water Distribution Network Modelling Tools Using Inverse Methods

The application of optimisation to Water Distribution Network (WDN) Modelling involves the use of computer-based techniques to many different problems, such as leakage detection and localisation. The success in the application of any model-based methodology for finding leaks highly depends on the availability of a well-calibrated model. Both leak detection and localisation, as well as model calibration are procedures that constitute the field of inverse problems in WDN modelling. The procedures are interlinked and dependent as when a leak is found and the model is updated its quality improves, while when a model is calibrated its ability to detect and localise leaks also improves. This is because both inverse problems are solved with the aim to mimic the behaviour of the real system as closely as possible using field measurements. In this research, both inverse problems are formulated as constrained optimisation problems. Evolutionary Optimisation techniques, of which Genetic Algorithms are the best-known examples, are search methods that are increasingly applied in WDN modelling with the aim to improve the quality of a solution for a given problem. This, ultimately, aids practitioners in these facets of management and operation of WDNs. Evolutionary Optimisation employs processes that mimic the biological process of natural selection and “survival of the fittest” in an artificial framework. Based on this philosophy a population of individual solutions to the problem is manipulated and, over time, “evolves” towards optimal solutions. However, such algorithms are characterised by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of WDNs incurs significant computational burden, can limit the applicability and scalability of this technology across the Water Industry. In addition, the inverse problem is often “ill-posed”. In practice, the ill-posed condition is typically manifested by the non-uniqueness of the problem solution and it is usually a consequence of inadequate quantity and/or quality of field observations. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to solve leakage related inverse problems in WDN Modelling. A number of new procedures are presented for improving the performance of such algorithms when applied to the complex inverse problems of leak detection and localisation, as well as model calibration. A novel reformulation of the inverse problem is developed as part of a decision support framework that minimizes the impact of the inherent computational complexity and dimensionality of these problems. A search space reduction technique is proposed, i.e., a reduction in the number of possible solution combinations to the inverse problem, to improve its condition considering the accuracy of the available measurements. Eventually, this corresponds to a targeted starting point for initiating the search process and therefore more robust stochastic optimisations. The ultimate purpose is to increase the reliability of the WDN hydraulic model in localising leaks in real District Metered Areas, i.e., to reduce the number false positives. In addition, to speed up the leak search process (both computationally and physically) and, improve the overall model accuracy. A calibrated model of the WDN is not always available for supporting work at distribution mains level. Consequently, two separate problem-specific methods are proposed to meet the abovementioned purpose: (a) a Leak Inspection Method used for the detection and localisation of leaks and; (b) a Calibration Method for producing an accurate average day model that is fit for the purpose of leak detection and localisation. Both methods integrate a three-step Search Space Reduction stage, which is implemented before solving the inverse problem. The aim is to minimize the number of decision variables and the range of possible values, while trying to preserve the optimum solution, i.e., reduce the inverse problem dimensionality. The search space reduction technique is established to generate a reduced set of highly sensitive decision variables. Eventually this is done to provide a viable, scalable technique for accelerating evolutionary optimisation applications in inverse problems being worthwhile on both academic and practical grounds. The novel methodologies presented here for leak detection and localisation, as well as for model calibration are verified successfully on four case studies. The case studies include two real WDN examples with artificially generated data, which investigate the limits of each method separately. The other two case studies implement both methods on real District Metered Areas in the United Kingdom, firstly to calibrate the hydraulic network model and, then, to detect and localise a single leak event that has actually happened. The research results suggest that leaks and unknown closed or open throttle valves that cause a hydraulic impact larger than the sensor data error can be detected and localised with the proposed framework which solves the inverse problem after search space reduction. Moreover, the quality of solutions can dramatically improve for given runtime of the algorithm, as 99.99% of infeasible solution combinations are removed, compared to the case where no search space reduction is performed. The outcomes of the real cases show that the presented search space reduction technique can reduce the search area for finding the leak to within 10% of the WDN (by length). The framework can also contribute to more timely detection and localisation of leakage hotspots, thus reducing economic and environmental impacts. The optimisation model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow field tests

Reducing the environmental impact of hydraulic fracturing pumps

This thesis was previously held under moratorium from 01/12/16 to 01/12/21The current approach to hydraulic fracturing requires large amounts of industrial hard-ware to be transported, installed and operated in temporary locations. Typically 70% of the mass of this equipment is comprised of the fleet of truck-mounted pumps required to provide the high pressures and flows necessary for well stimulation. The established design of these pumps were developed for the shale gas extraction industry in North America, where the environmental, geological, regulatory and social constraints are very different from Europe. Consequently the engineering choices made in the current pump designs did not focus on minimising the physical and environmental footprint of the operation. These aspects are of paramount importance for the emerging hydraulic fracturing industry in Europe, so it is timely to address these factors when considering the design of future high-pressure pumps for European shale resources. This thesis develops and applies a methodology for environmental optimisation of the key mechanical design parameters for the high-pressure pumps that are central to hydraulic fracturing operations. Before describing the optimisation methodology the thesis provides an overview of the industrial plant required to carry out a hydraulic fracturing operation, and an estimate of the functional requirements (i.e. pressure and flow) of the equipment. The computational model, central to the optimisation process, is validated by using field data from a hydraulic fracturing site in North America and an experimental test rig. The optimisation analysis concludes that reducing the plunger diameter and running the pump at higher angular velocity, with lower forces, can increase pump efficiency by up to 4.6%. Furthermore the modification of the pump’s parameters would result in several environmental benefits beyond the obvious economic gains of lower fuel con-sumption. Previous studies have shown that over 90% of the emissions of CO2 and other pollutants that occur during a hydraulic fracturing operation are associated with the pumps and their prime movers. Consequently, any increase in pumping efficiency will also reduce the greenhouse gas emissions and improve local air quality (CO2, NOx and other pollutants). Additionaly, the reduction in plunger diameter will reduce the amplitude of fatigue stresses and so increase the life of the units and allow their overall mass to be reduced. More reliable pumps could decrease the number of standby (i.e. backup) units necessary, and so reduce procurement costs and site traffic, including the overall site footprint. The concluding system optimisation study suggests that the highest level of direct on-site emission is due to the inefficient and asynchronous operation of multiple frac-truck assemblies. Reducing the number of frac-truck assemblies subsequently affects pump traffic lowering the nuisance effects to the local community such as noise, road damage and road traffic risk.The current approach to hydraulic fracturing requires large amounts of industrial hard-ware to be transported, installed and operated in temporary locations. Typically 70% of the mass of this equipment is comprised of the fleet of truck-mounted pumps required to provide the high pressures and flows necessary for well stimulation. The established design of these pumps were developed for the shale gas extraction industry in North America, where the environmental, geological, regulatory and social constraints are very different from Europe. Consequently the engineering choices made in the current pump designs did not focus on minimising the physical and environmental footprint of the operation. These aspects are of paramount importance for the emerging hydraulic fracturing industry in Europe, so it is timely to address these factors when considering the design of future high-pressure pumps for European shale resources. This thesis develops and applies a methodology for environmental optimisation of the key mechanical design parameters for the high-pressure pumps that are central to hydraulic fracturing operations. Before describing the optimisation methodology the thesis provides an overview of the industrial plant required to carry out a hydraulic fracturing operation, and an estimate of the functional requirements (i.e. pressure and flow) of the equipment. The computational model, central to the optimisation process, is validated by using field data from a hydraulic fracturing site in North America and an experimental test rig. The optimisation analysis concludes that reducing the plunger diameter and running the pump at higher angular velocity, with lower forces, can increase pump efficiency by up to 4.6%. Furthermore the modification of the pump’s parameters would result in several environmental benefits beyond the obvious economic gains of lower fuel con-sumption. Previous studies have shown that over 90% of the emissions of CO2 and other pollutants that occur during a hydraulic fracturing operation are associated with the pumps and their prime movers. Consequently, any increase in pumping efficiency will also reduce the greenhouse gas emissions and improve local air quality (CO2, NOx and other pollutants). Additionaly, the reduction in plunger diameter will reduce the amplitude of fatigue stresses and so increase the life of the units and allow their overall mass to be reduced. More reliable pumps could decrease the number of standby (i.e. backup) units necessary, and so reduce procurement costs and site traffic, including the overall site footprint. The concluding system optimisation study suggests that the highest level of direct on-site emission is due to the inefficient and asynchronous operation of multiple frac-truck assemblies. Reducing the number of frac-truck assemblies subsequently affects pump traffic lowering the nuisance effects to the local community such as noise, road damage and road traffic risk