Dimensional Analysis of the Earthquake Response of a Pounding Oscillator

In this paper, the dynamic response of a pounding oscillator subjected to pulse type excitations is revisited with dimensional analysis. The study adopts the concept of the energetic length scale which is a measure of the persistence of the distinguishable pulse of strong ground motions and subsequently presents the dimensionless Π products that govern the response of the pounding oscillator. The introduction of Buckingham’s Π theorem reduces the number of variables that govern the response of the system from 7 to 5. The proposed dimensionless Π products are liberated from the response of an oscillator without impact and most importantly reveal remarkable order in the response. It is shown that, regardless the acceleration level and duration of the pulse, all response spectra become self-similar and, when expressed in the dimensionless Π products, follow a single master curve. This is true despite the realization of contacts with increasing durations as the excitation level increases. All physically realizable contacts (impacts, continuous contacts, and detachment) are captured via a linear complementarity approach. The proposed analysis stresses the appreciable differences in the response due to the directivity of the excitation (toward or away the stationary wall) and confirms the existence of three spectral regions where the response of the pounding oscillator amplifies, deamplifies, and equals the response of the oscillator without pounding.     

Distributed radio access technology selection for adaptive networks in high‐speed,B3G infrastructures

Wireless systems migrate towards the era of ‘Beyond the 3rd Generation’ (B3G). A fundamental facilitator of this vision is the evolution of high speed, adaptive networks, needed for better handling the offered demand and improving resource utilization. Adaptive networks dynamically select their configuration, in order to optimally adapt to the changing environment requirements and conditions. This paper presents optimization functionality that can be used to support network adaptability (cognition‐reconfigurability) in a B3G context. The paper starts from the business case that justifies the need for placing research onto adaptive networks and then continues with the management functionality for (re)configuration decisions, which is targeted to the dynamic selection of the appropriate radio access technologies (RATs). RAT selection is modelled through an optimization problem called (RAT, Demand and QoS‐Assignment problem—RDQ‐A), the solution of which assigns in a distributed manner the available RATs to adaptive Base Station transceivers and the demand (users) to these transceivers and to QoS levels, respectively. The RDQ‐A optimization problem is decoupled in several sub‐problems and is implemented in phases corresponding to the aforementioned assignments, while efficient custom greedy algorithms are mobilized in each phase for obtaining the optimum assignment. Finally, indicative results from the application of the proposed functionality to a simulated network are presented. Copyright © 2006 John Wiley & Sons, Ltd.     

Simultaneous stochastic optimisation of an open-pit gold mining complex with waste management

ABSTRACT

Simultaneous stochastic optimisation manages risk and capitalises on the unique interactions that occur in a mining complex, where materials are transferred between mines, processors, stockpiles, and waste facilities to achieve a marketable product. Typically, when optimising the production schedule, the primary focus is to deliver valuable products to the market. However, this tends to ignore the environmental and economic impact of simplifying waste management requirements, including the storage and disposal of waste material. Stricter regulations and engineering requirements are transforming past mining practices to develop more sustainable operations. These transformations increase the financial cost of waste management and identify the requirement to integrate waste management into the production schedule. Additionally, misrepresenting the material uncertainty and variability associated with the amount of waste produced can impact, both, the stakeholders and the profitability of a mining complex. In this case study, a simultaneous stochastic optimisation approach is applied in a gold mining complex that integrates waste management into the long-term production schedule. The resulting schedule leads to a 6% increase in the net present value when compared to a conventional approach, while minimising the likelihood of deviating from production targets and ensuring permit constraints are satisfied.     

Flammability Assessment of Mediterranean Forest Fuels

The time-to-ignition of various dominant Mediterranean forest fuels was measured during laboratory tests, in order to develop a relative flammability classification and determine the moisture of extinction of these fuels. The tests were performed with an ignition apparatus manufactured according to the ISO standards (ISO 5657-1986E) and under a wide gradient of fuel moisture contents, ranging from air-dry to fresh foliage. Moisture content was the single most significant factor that affected fuel flammability. Regression models were developed between the time-to-ignition and the moisture content values of all the fuels tested, and subsequently, were used for the relative flammability ranking of these fuels. Fuel moisture of extinction was assessed to have a threshold value ranging from 40% to more than 140% o.d.w. for the species tested. The flammability ranking of natural fuels can be useful in fuel hazard assessment and fire danger rating, thus facilitating the judicial fire management planning in wildlands and at the rural-urban interface.     

Multi-fractal conditional simulation of fault populations in coal seams using analogues: Method and application

The modelling of fault populations and quantification of fault risk is a challenge for earth science and engineering applications, including minerals and coal mining, tunnel construction, forecasting of petroleum reservoir production, and selection of subterranean repositories for the disposal of toxic waste. This paper discusses a new advance in the use of stochastic fault simulation methods for the quantification of the risk of fault presence. The multi-fractal properties of a fully known fault population are used as an analogue of the properties of an undiscovered fault population. The approach is elucidated through the quantification of fault risk in a prospective coalfield at Wyong, New South Wales, Australia, and incorporates spatial patterns of available ‘hard’ and ‘soft’ geological data. The method does not find faults unequivocally; rather, the output is a map of fault probability. Simulations are found to be consistent with the available information and are statistically and spatially reasonable in geological terms. Significantly, the analogue approach provides a robust, quantified assessment of fault risk using limited exploration information.     

A conditional fractal (fBm) simulation approach for orebody modelling

Abstract

Fractal methods provide scale-invariant approaches to modelling the in-situ variability of orebody attributes. Fractional Brownian motion (fBm) models are a specific class of random fractals that offer a fast and efficient way to generate simulations of pertinent orebody characteristics. fBm models are constructed using the Weierstrass-Mandelbrodt equation and a fractal co-dimension inferred from the available data. Unconditional fBm simulations can be further constrained to existing data to generate conditional simulations reproducing the (i) available data, (ii) data statistics, and (iii) power law variogram and fractal co-dimension. The computational efficiency of the conditioning process is improved based on dual kriging. Testing conditional fBm simulation in a controlled environment indicates that it is an efficient approach to generating multiple realisations, when the available conditioning data is in the order of several hundred. The efficiency and performance of the approach suggest that it may be particularly suitable for delineating ore/waste boundaries and grade control.     

Stochastic long-term production scheduling of iron ore deposits: Integrating joint multi-element geological uncertainty

Meeting production targets in terms of ore quantity and quality is critical for a successful mining operation. In-situ grade variability and uncertainty about the spatial distribution of ore and quality parameter cause both deviations from production targets and general financial deficits. A stochastic integer programming formulation (SIP) is developed herein to integrate geological uncertainty described by sets of equally possible scenarios of the unknown orebody. The SIP formulation accounts not only for discounted cashflows and deviations from production targets, discounts geological risk, while accounting for practical mining. Application at an iron ore deposit in Western Australia shows the ability of the approach to control a risk of deviating from production targets over time. Comparison shows that the stochastically generated mine plan exhibits less risk in deviating from quality targets than the traditional mine planning approach based on a single interpolated orebody model.     

Analytical Description of Self-Interference in Direct Sequence Spread Spectrum Communications

A new analytical statistical model has been developed in order to describe the self-interference that results due to multipath propagation in a BPSK spread spectrum system. Motivation for this work was the fact that the well-known Gaussian approximation that has been extensively used in the past in order to describe such terms is not satisfactory, especially when the probability of error is smaller than10^–2 or 10^–3. The existence of two paths inthe radio channel has been assumed. The amplitude of each path has been assumed as Rayleigh distributed while the arrival time of the second path with respect to the arrival time of the first one has been assumed to be uniformly distributed. The validity of the developed model has been examined by comparing analytical results with results obtained by simulation using Monte Carlo techniques. Results taken from the Gaussian approximation were also compared with the simulation results. It was shown that the developed model describes with great accuracy the pre-mentioned undesirable term. On the contrary, the Gaussian approximation exhibits significant differences when compared to the simulation results. This is also evident by the use of a comparison parameter which is several orders of magnitude smaller for the developed model than for the Gaussian approximation (the smaller this parameter is the better the fit between two groups of data).