Variations In Input Parameters Research Articles

Modelling uncertainty of vehicular emissions inventory: A case study of Ireland

The vehicular emission modelling software COPERT is extensively used in generating emission levels for National Emissions Inventory in Europe and in some other countries internationally. This paper aims to study the uncertainties associated with emission estimates generated from COPERT(v5.1) outputs through sensitivity analysis of the model input parameters. The input parameters considered in this study are temperature, speed, relative humidity, trip length, and driving style (mileage share). Many of these parameters are not directly measured or country-wide average values are investigated in this study. The uncertainties of the emission estimates were obtained through varying the parameter values, within realistic limits, either individually or considering factor interactions by varying two or more parameters simultaneously. The uncertainties of the emission estimates of certain pollutants (CO, VOC and NMVOC) can be significant from −58% to +76%. Some gases, such as CO2 show almost no sensitivity to input parameter variations. The results indicate that the COPERT outputs are most sensitive to variations in trip length and speed, both of which are not directly measured. Considering the percentage of urban driving share has a huge effect on the national emission estimates, inventories were separately prepared for the Greater Dublin Area (GDA), the largest urban area of Ireland with highest population and vehicle densities. GDA has 48% and 50.37% shares of CO2 and NOx emissions respectively, compared to the national levels. The consequence of over- or underestimation of emission inventories for Ireland was calculated to be approximately +/− €45million. Considering the major implications of NEI for climate change and other related impacts it is crucial to report the pollutant emission levels appropriately stating uncertainty levels. The findings of this study provide national experts with a methodology to estimate uncertainties of COPERT outputs and to identify the key sources of these uncertainties where policy and emission reduction strategies should be targeted.

Estimating Aspergillus fumigatus exposure from outdoor composting activities in England between 2005 and 14

Bioaerosols, ubiquitous in ambient air, are released in elevated concentrations from composting facilities with open-air processing areas. However, spatial and temporal variability of bioaerosols, particularly in relation to meteorology, is not well understood. Here we model relative concentrations of Aspergillus fumigatus at each postcode-weighted centroid within 4 km of 217 composting facilities in England between 2005 and 2014. Facilities were geocoded with the aid of satellite imagery. Data from existing bioaerosol modelling literature were used to build emission profiles in ADMS. Variation in input parameters between each modelled facility was reduced to a minimum. Meteorological data for each composting facility was derived from the nearest SCAIL-Agriculture validated meteorological station. According to our results, modelled exposure risk was driven primarily by wind speed, direction and time-varying emissions factors incorporating seasonal fluctuations in compostable waste. Modelled A.fumigatus concentrations decreased rapidly from the facility boundary and plateaued beyond 1.5–2.0 km. Where multiple composting facilities were within 4 km of each other, complex exposure risk patterns were evident. More long-term bioaerosol monitoring near facilities is needed to help improve exposure estimation and therefore assessment of any health risks to local populations.

Application of Box Behnken Design to Optimize the Parameters for Kenaf-Epoxy as Noise Absorber

This journal discusses the application of Box Behnken design to plot the experiments to find the Noise Absorption Coefficient (NAC) for Kenaf/Epoxy sandwich sample with an overall objective of optimizing the density, thickness, pressure and frequency. Response Surface Methodology (RSM) has been embraced to express the yield parameters (reactions) that are chosen by the input procedure parameters. RSM likewise evaluates the connection between the variable input parameters and the comparing yield parameters. The proposed Box-Behnken configuration requires 24 trial of run for information securing and displaying the reaction surface. Software’s were utilized to plan the examination and randomize the runs. Regression model was created and its capability was checked to foresee the yield esteems at about all conditions. Facilitate the model was approved by performing tests, taking 24 sets of inputs. The yield parameters estimated through tests (actual) are in great match with the anticipated esteems utilizing the model. Utilizing Design Expert software, 2D and 3D plots were produced for the RSM evolved. This research resulted in ascertaining the optimised set of noise absorber parameters for Kenaf/Epoxy sandwich sample, to get the optimum NAC. This work gains significance in the sense with minimum number of experiments, reliable model has been generated, validated and further, the process has been optimised and mathematical equation generated.

Uncertainty Quantification in Molecular Signals Using Polynomial Chaos Expansion

Molecular signals are abundant in engineering and biological contexts, and undergo stochastic propagation in fluid dynamic channels. The received signal is sensitive to a variety of input and channel parameter variations. Currently we do not understand how uncertainty or noise in a variety of parameters affect the received signal concentration, and nor do we have an analytical framework to tackle this challenge. In this paper, we utilize Polynomial Chaos Expansion (PCE) to show to uncertainty in parameters propagates to uncertainty in the received signal. In demonstrating its applicability, we consider a Turbulent Diffusion Molecular Communication (TDMC) channel and highlight which parameters affect the received signals. This can pave the way for future information theoretic insights, as well as guide experimental design.

Sampling-based stochastic analysis of the PKN model for hydraulic fracturing

Hydraulic fracturing processes are surrounded by uncertainty, as available data is typically scant. In this work, we present a sampling-based stochastic analysis of the hydraulic fracturing process by considering various system parameters to be random. Our analysis is based on the Perkins-Kern-Nordgren (PKN) model for hydraulic fracturing. This baseline model enables computation of high fidelity solutions, which avoids pollution of our stochastic results by inaccuracies in the deterministic solution procedure. In order to obtain the desired degree of accuracy of the computed solution, we supplement the employed time-dependent moving-mesh finite element method with two new enhancements: (i) global conservation of volume is enforced through a Lagrange multiplier; (ii) the weakly singular behavior of the solution at the fracture tip is resolved by supplementing the solution space with a tip enrichment function. This tip enrichment function enables the computation of the tip speed directly from its associated solution coefficient. A novel incremental-iterative solution procedure based on a backward-Euler time-integrator with sub-iterations is employed to solve the PKN model. Direct Monte-Carlo sampling is performed based on random variable and random field input parameters. The presented stochastic results quantify the dependence of the fracture evolution process—in particular the fracture length and fracture opening—on variations in the elastic properties and leak-off coefficient of the formation, and the height of the fracture.

Adaptive Model Predictive Control for Wiener Nonlinear Systems

Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of the cascade connection as that a dynamic linear system is followed in series by a static nonlinear function. Different approaches have been developed and proposed to control this kind of systems in the last decades. In this study, a model predictive control system with online identification support has been developed. The prominent feature of online system identification may be referred to as accommodating easily to severe changes in system parameters. The combination of MPC algorithm with online identification constitutes an adaptive model predictive control algorithm that can sense the input parameter variation. To assess the performance of the proposed control system, a strong acid–strong base chemical neutralization process without buffer solution is selected, and the controller is applied to the chemical process to verify its effectiveness in acidic, alkaline and neutral regions. Results obtained from MATLAB/Simulink studies confirm the performance of the controller that serves under variable system conditions.

Stochastic modeling of expansion of starchy melts during extrusion

The objective of this study was to develop a stochastic model for flow behavior of starchy melts inside an extruder barrel and bubble growth dynamics after exiting the extruder and obtain the variability of the end product characteristics, using the variability of operating conditions (screw speed, water injection) and material properties (consistency coefficient). The model was written in Visual Basic and experimentally validated using pilot-scale twin screw extrusion of corn meal based expanded products. Process and product data was measured at different in-barrel moisture contents (19–28 g/100g dry basis) and screw speeds (250–330 rpm). The variability in input parameters was characterized using a Data Acquisition System and Monte Carlo Simulation was used to incorporate this variability into the model. The coefficient of variation (CV) of expansion ratio obtained from the model (0.24–3.11%) compared well with CV of the experimental expansion ratio (2.54–4.89%). This model was also used to conduct sensitivity analysis for understanding which raw material and process characteristics contribute most to product variability. Sensitivity analysis showed that the relative increase in CV of expansion ratio was affected more by the CV of water added in extruder (160.5%) as compared to screw speed (3.25%) and consistency index coefficient (25%).

A life-cycle approach for multi-objective optimisation in building design: methodology and application to a case study

ABSTRACTA novel approach for the identification of the optimal strategy for building retrofit is presented and applied to a case study in northern Italy. Increased envelope insulation, more efficient heat-generating systems, thermal distribution and terminal units, heating control systems, electric distribution systems, and renewable energies exploitation were considered as possible measures. A tailored multi-criteria decision analysis tool is developed to embrace the entire building life cycle and includes all the possible combinations in a discretized approach, with the aim of optimising conflicting design parameters, such as energy consumption, costs, GHG emissions, and comfort level. The proposed methodology is applied to two scenarios: nominal building parameters, and the validated model obtained using dynamic energy simulations. In both cases, most energy-efficient solutions are found to be optimal, however, large differences in overall energy consumption are found between the two scenarios resulting in different sizing of optimal renewable-energy installations and different electric distribution systems. A sensitivity analysis is also performed to estimate how results respond to the variation of input parameters. Despite a large difference between the two scenarios, the proposed methodology is found to be stable in identifying the optimal solution and offers full customisation features to adapt to different cases.

Evaluating the ENVI-met microscale model for suitability in analysis of targeted urban heat mitigation strategies

Microscale atmospheric models are increasingly being used to project the thermal benefits of urban heat mitigation strategies (e.g., tree planting programs or use of high-albedo materials). However, prior to investment in specific mitigation efforts by local governments, it is desirable to test and validate the computational models used to evaluate strategies. While some prior studies have conducted limited evaluations of the ENVI-met microscale climate model for specific case studies, there has been relatively little systematic testing of the model's sensitivity to variations in model input and control parameters. This study builds on the limited foundation of past validation efforts by addressing two questions: (1) is ENVI-met grid independent; and (2) can the model adequately represent the air temperature perturbations associated with heat mitigation strategies? To test grid independence, a “flat” domain is tested with six vertical grid resolutions ranging from 0.75 to 2.0 m. To examine the second question, a control and two mitigation strategy simulations of idealized city blocks are tested. Results show a failure of grid independence in the “flat” domain simulations. Given that the mitigation strategies result in temperature changes that are an order of magnitude larger than the errors introduced by grid dependence for the flat domain, a lack of grid independence itself does not necessarily invalidate the use of ENVI-met for heat mitigation research. However, due to limitations in grid structure of the ENVI-met model, it was not possible to test grid dependence for more complicated simulations involving domains with buildings. Furthermore, it remains unclear whether existing efforts at model validation provide any assurance that the model adequately captures vertical mixing and exchange of heat from the ground to rooftop level. Thus, there remain concerns regarding the usefulness of the model for evaluating heat mitigation strategies, particularly when applied at roof level (e.g. high albedo or vegetated roofs).

Real-Time Production Optimization of Steam-Assisted-Gravity-Drainage Reservoirs Using Adaptive and Gain-Scheduled Model-Predictive Control: An Application to a Field Model

Summary Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from deep oil-sands deposits. The efficiency of the SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool (difference in saturation and actual temperature) along the length of the horizontal well pair. Heterogeneity in reservoir properties might lead to suboptimal subcool levels without the application of closed-loop control. Recently, model-predictive control (MPC) has been proposed for real-time feedback control of SAGD well pairs based on real-time production, temperature, and pressure data along with other well and surface constraint information; however, reservoir dynamics has been represented using extremely simplified and unrealistic models. Because SAGD is a complex, spatially distributed, nonlinear process, an MPC framework with models that account for nonlinearity over an extended control period is required to achieve optimized subcool and steam conformance. In this research, two novel work flows are proposed to handle nonlinear reservoir dynamics in MPC. The first approach is adaptive MPC, and includes continuous re-estimation of the model at each control interval. This allows the evolution of the coefficients of a fixed-model structure such that the updated system-identification model in the MPC controller reflects current reservoir dynamics adequately. Another approach, gain-scheduled MPC, decomposes the subcool-control problem in a parallel manner, and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by using appropriate variations in input parameters based on the operating region. The work flows are implemented using a history-matched numerical model of a reservoir in northern Alberta. Steam-injection rates and liquid-production rate are considered input variables in MPC, constrained to available surface facilities. The well pair is divided into multiple sections, and the subcool of each section is considered an output variable. Results are compared with actual field data (in which no control algorithm is used), and are analyzed on the basis of two criteria: (1) Do all subcools track the set point while maintaining stability in input variables? and (2) Does the net present value (NPV) of oil improve with adaptive and gain-scheduled MPC? In general, we conclude that both adaptive and gain-scheduled MPC provide superior tracking of subcool set points and, hence, better steam conformance caused by adequate representation of reservoir dynamics by re-estimation of coefficients and multiple controllers, respectively. In addition, the results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69 and 10.36% in case of adaptive and gain-scheduled MPC, respectively. The proposed work flows can improve the NPV of an SAGD reservoir by optimizing the well-operational parameters while considering constraints of surface facilities and minimizing environmental footprint.

Sizing of biomass based distributed hybrid power generation systems in India

A methodology for optimum sizing of battery integrated biomass gasifier based distributed power generation systems is discussed. Typically, biomass gasifier systems are sized considering peak demand resulting into lower operational efficiency due to part load operation during low demand period. The proposed methodology using design space approach enables designer with multiple combinations of gasifier engine and battery systems for given load profile from which an appropriate combination may be identified considering specific objective or constraints if any. Two gasifier operational strategies are discussed and compared using cost of energy. The methodology is further extended for sizing of hybrid systems incorporating gasifier, solar PV and battery system. The proposed methodology is illustrated using a realistic load profile generated through a structured household survey of an un-electrified hamlet. For given load profile, battery integrated gasifier engine system in intermittent operation mode has the lowest cost of electricity followed by gasifier engine system alone, hybrid system and solar PV battery system. Sensitivity analysis is done to incorporate uncertainty related to variation in key input parameters. The proposed sizing methodology may be helpful in wider replication of gasification systems by providing cost effective sizing of such systems.

Evidence on long-term economic impact of assisted reproductive technology: a systematic review

To summarize evidence on long-term economic impact of assisted reproductive technology (ART). Systematic review of studies evaluating long-term (i.e., lifetime) economic impact of ART. Two independent reviewers conducted searches on PUBMED, MEDLINE, SCOPUS and through cross-referencing. Keywords are “ART”, “IVF”, "IUI", “infertility care”, “infertility treatment”, “economic impact”, “lifetime”, “lifetime productivity”, “generational accounting”, “net present value”, and “net tax”. We included peer-reviewed articles in English from database inception to 30 April 2018. 9 of 3576 identified papers were selected. All of them assessed long-term economic impact by calculation of net present value (NPV) based on a generational accounting (GA) model. We calculated return on investment (ROI) by dividing discounted lifetime NPV by the cost of in vitro fertilization (IVF) or intrauterine insemination (IUI). NPV was reported for 11 countries; Sweden (2008), the United States (2008), the United Kingdom (2008), Denmark (2011), Brazil (2011), Greece (2013), the Netherlands (2014), Ukraine (2015), Belarus (2015), Kazakhstan (2015) and Spain (2016). Long-term economic impact was reported for IVF-conceived children in all 11 countries and for IUI-conceived children in 1 country (Spain). The ART-conceived children had positive NPV (i.e., positive ROI) in all countries except for the Netherlands. IVF-conceived children: Discounted lifetime NPV of IVF-conceived children ranged from -119,734 USD (the Netherlands) to 215,450 USD (Denmark). Median discounted NPV was 61,429 USD (Brazil). Cost of IVF per live birth ranged from 4,147 USD (Kazakhstan) to 27,552 USD (Sweden) with the median cost of 16,159 USD (the Netherlands). ROI ranged from -740.98% (the Netherlands) to 1,598.95% (Spain) with the median ROI at 353.66% (Greece). IUI-conceived children: Discounted lifetime NPV of IUI-conceived children was 84,470 USD. Cost of IUI per live birth was 4,558 USD and the ROI was 1,853.23%. Influential factors: Influential factors based on sensitivity analyses were economic growth rate, healthcare expenditure growth rate and discount rate but not cost of IVF or IUI. Correlational analyses between NPV of IVF-conceived children and gross domestic product (GDP) per capita indicated almost no correlation in developed countries, r = -.05 but a highly correlated relationship in developing countries, r = .77. All studies used GA to calculate NPV; however, there were variations in model structures and input parameters. None of the studies included broader economic benefits that individuals could contribute to their national economy beyond tax payment. Evidence on long-term economic impact of ART showed positive NPV and ROI of approximately 354%. Therefore, ART can be perceived as a good investment from a societal perspective. However, there are limitations to the methods used to evaluate long-term economic impact of ART.

Predictive Simulation of Plastic Processing of Welded Stainless Steel Pipes

Metal forming is the most used technique to manufacture complex geometry pieces in the most efficient way, and the technological progress related to the various application fields requires increasingly higher quality standards. In order to achieve such a requirement, people are forced to perform quality and compliance tests finalized to guarantee that these standards are met; this often implies a waste of material and economic resources. In the case of welded stainless steel pipes, several critical points affecting the general trend of subsequent machining need to be taken into account. In this framework, the aim of the paper is to study the effects of different process parameters and geometrical characteristics on various members of the stainless steel family during finite elements method (FEM) simulations. The analysis of the simulation outputs, such as stress, strain, and thickness, is reported through mappings, in order to evaluate their variation, caused by the variation of the simulation input parameters. The feasibility of the simulated process is evaluated through the use of forming limit diagrams (FLD). An experimental validation of the model is performed by comparison with real cases. Major parameters that mainly guide the outcome of the simulations are highlighted.

Microstructure Analysis of Arc Welded Mild Steel Plates

In order to perform arc welding, mild steel plates of variable thickness i.e. 5 mm, 6 mm and 8mm has been chosen as parent material. Total nine numbers of experiments were conducted by considering the two factors and three levels of design according to Taguchi method. The welding current and material thickness has been selected as variable input parameters with 5mm, 6 mm, 8mm level of material thickness and 90 A, 100A, 110A level of current respectively. The microstructure of the all the nine welded material has been analysed with the help of an optical microscope which is interfaced computer. The results of microstructure obtained from the microscopic investigation have been shown. The effect of current and material on the weldment microstructure is investigated. The result also shows that the coarse structure is formed in fusion zone and fine structure is formed in Heat affected region.

A physical modeling and predictive simulation of the laser cladding process

A 3D finite element simulation model of the laser cladding process has been developed taking into account heat transfer, fluid flow, surface tension and free surface movement. All input parameters and data, which are independent of the process parameters but depend only on the material and machine properties, have been obtained from measurements. Thereby the melt pool and the resulting surface contour can be simulated without compromising assumptions or calibration, because the machine parameters are the only variable input parameters of the model. Thus, the model can easily be transferred to other material combinations or other machines. For the surface contour calculation a modified height function method is applied. The model surface follows this contour as an arbitrary Lagrangian Eulerian (ALE) method is used allowing for mesh deformations. The model was implemented using the commercial finite element software COMSOL Multiphysics and validated by comparing the simulation results with caloric measurements of the effective heat input and metallographic cross sections from experiments, where the nickel-base alloy MetcoClad® 625 in powder form was deposited on structural steel S235JRC + C and the process parameters of laser power, feed speed, laser beam spot size and powder mass flow were varied within a range of at least 50% of their mean value each. The maximum deviation of the simulation results compared to the experimental data regarding track geometry is 14% for the parameter sets without weld defects so that these parameter sets could be industrially applied, whereas the average deviation of track width and height is below 5.1%. Moreover, the maximum difference of the simulated absorptivity compared to the measurement results is 6.9%, while the average difference is only 1.7%, meaning the model shows a high predictive capability especially regarding the heat input.

Cost-effectiveness of umeclidinium compared with tiotropium and glycopyrronium as monotherapy for chronic obstructive pulmonary disease: a UK perspective

BackgroundCost-effectiveness of once-daily umeclidinium bromide (UMEC) was compared with once-daily tiotropium (TIO) and once-daily glycopyrronium (GLY) in patients with chronic obstructive pulmonary disease (COPD) from a UK National Health Service (NHS) perspective.MethodsA linked-equation model was implemented to estimate COPD progression, associated healthcare costs, exacerbations rates, life years (LY) and quality-adjusted LY (QALYs). Statistical risk equations for endpoints and resource use were derived from the ECLIPSE and TORCH studies, respectively. Treatment effects [mean (standard error)] at 12 weeks on forced expiratory volume in 1 s and St George’s Respiratory Questionnaire score were obtained from the intention-to-treat populations of two head-to-head studies [GSK study identifiers 201316 (NCT02207829) and 201315 (NCT02236611)] which compared UMEC 62.5 mcg with TIO 18 mcg and UMEC 62.5 mcg with GLY 50 mcg, respectively. Treatment costs reflect UK list prices (2016) and NHS unit costs; UMEC and GLY prices being equal and less than TIO. A lifetime horizon, discounted costs and effects at 3.5% were used. Sensitivity analyses were performed to evaluate the robustness of variations in input parameters and assumptions in the model.ResultsOver a lifetime horizon, UMEC was predicted to increase LYs (+ 0.195; 95% confidence interval [CI]: 0.069, 0.356) and QALYs (+ 0.118; 95% CI: 0.055, 0.191) and reduce the number of annual exacerbations (− 0.053; 95% CI: − 0.171, 0.028) compared with TIO, with incremental cost savings of £460/patient (95% CI: − £645, − £240). Compared with GLY, UMEC increased LYs (+ 0.124; 95% CI: 0.015, 0.281) and QALYs (+ 0.101; 95% CI: 0.043, 0.179) and reduced annual exacerbation (− 0.033; 95% CI: − 0.135, 0.017) at an additional cost of £132/patient (95% CI: £12, £330), resulting in an incremental cost-effectiveness ratio of £1310/QALY (95% CI: £284, £2060). Similar results were observed in alternative time horizons and additional sensitivity analyses.ConclusionsFor treatment of patients with COPD in the UK over a lifetime horizon, treatment with UMEC dominates treatment with TIO, providing both improved health outcomes and cost savings. In comparison with GLY, treatment with UMEC achieved improved health outcomes but was associated with a higher cost.Trial registration 201316, NCT02207829; 201315, NCT02236611

Novel Method of Sensitivity Analysis Improves the Prioritization of Research in Anticipatory Life Cycle Assessment of Emerging Technologies.

It is now common practice in environmental life cycle assessment (LCA) to conduct sensitivity analyses to identify critical parameters and prioritize further research. Typical approaches include variation of input parameters one at a time to determine the corresponding variation in characterized midpoints or normalized and weighted end points. Generally, those input parameters that cause the greatest variations in output criteria are accepted as the most important subjects of further investigation. However, in comparative LCA of emerging technologies, the typical approach to sensitivity analysis may misdirect research and development (R&D) toward addressing uncertainties that are inconsequential or counterproductive. This paper presents a novel method of sensitivity analysis for a decision-driven, anticipatory LCA of three emerging photovoltaic (PV) technologies: amorphous-Si (a-Si), CdTe and ribbon-Si. Although traditional approaches identify metal depletion as critical, a hypothetical reduction of uncertainty in metal depletion fails to improve confidence in the environmental comparison. By contrast, the novel approach directs attention toward marine eutrophication, where uncertainty reduction significantly improves decision confidence in the choice between a-Si and CdTe. The implication is that the novel method will result in better recommendations on the choice of the environmentally preferable emerging technology alternative for commercialization.

PND34 - Cost-Utility Analysis of Oral Sumatriptan Versus Oral Ergotamine/Caffeine in the Treatment of Acute Migraine Attacks

The objective of this study is to perform a cost-utility analysis to estimate the incremental cost-effectiveness ratio (ICER) of oral sumatriptan versus oral ergotamine/caffeine in patients with moderate-to-severe episodic migraine. Additionally, the ICER per block of pain freedom at 2 hours was evaluated. This study estimates the cost-effectiveness of oral sumatriptan versus oral ergotamine/caffeine using a decision tree model over a 30-day time horizon, adopting the payer perspective. The model incorporates one switch between the two therapies. The cohort reflects adults enrolled in randomized, placebo-controlled trials. Costs and utilities were obtained from previously published cost-effectiveness studies. One-way sensitivity analyses were conducted to test the robustness of the results. From the payer perspective, sumatriptan is more costly and more effective than ergotamine/caffeine, with an ICER of $10,466 per QALY gained and an ICER of $3,874 per 2-hour headache freedom achieved. After varying input parameters by 20%, the top three parameters with the greatest impact on the model were the cost of side effects (nausea/vomiting), probability of switching from ergotamine/caffeine to sumatriptan, and probability of 2-hour headache freedom on sumatriptan. Results were robust to sensitivity analyses. For individuals with moderate to severe episodic migraine, oral sumatriptan may be more cost-effective than oral ergotamine/caffeine.

A simplified model to estimate non-liquefiable NEHRP F site design spectra

The International Building Code requires a site-specific investigation for NEHRP F sites. However, established procedures for performing site-specific investigations of NEHRP F sites are limited. This study developed a simplified model to estimate design spectra for non-liquefiable NEHRP F sites to provide guidance for these cases. The model is based on the results of 4695 one-dimensional effective stress nonlinear site response analyses. It is intended to be used jointly with site-specific response analyses. The model is dependent on the period, design ground motion intensity, and site properties, and it characterizes the uncertainty of the estimate. The sensitivity of the results to reasonable variations in input parameters is investigated. The model is validated against field observations and compared with 80% NEHRP E site design spectra. The simplified model can capture the trends and magnitude of acceleration response spectra calculated from the site response analyses for all ground motions tested. Design spectra may fall below the 80% NEHRP E site design spectra for moderate to large levels of shaking intensity due to soil nonlinearity.

Integration of environmental aspects in modelling and optimisation of water supply chains

Climate change becomes increasingly more relevant in the context of water systems planning. Tools are necessary to provide the most economic investment option considering the reliability of the infrastructure from technical and environmental perspectives. Accordingly, in this work, an optimisation approach, formulated as a spatially-explicit multi-period Mixed Integer Linear Programming (MILP) model, is proposed for the design of water supply chains at regional and national scales. The optimisation framework encompasses decisions such as installation of new purification plants, capacity expansion, and raw water trading schemes. The objective is to minimise the total cost incurring from capital and operating expenditures. Assessment of available resources for withdrawal is performed based on hydrological balances, governmental rules and sustainable limits. In the light of the increasing importance of reliability of water supply, a second objective, seeking to maximise the reliability of the supply chains, is introduced. The epsilon-constraint method is used as a solution procedure for the multi-objective formulation. Nash bargaining approach is applied to investigate the fair trade-offs between the two objectives and find the Pareto optimality. The models' capability is addressed through a case study based on Australia. The impact of variability in key input parameters is tackled through the implementation of a rigorous global sensitivity analysis (GSA). The findings suggest that variations in water demand can be more disruptive for the water supply chain than scenarios in which rainfalls are reduced. The frameworks can facilitate governmental multi-aspect decision making processes for the adequate and strategic investments of regional water supply infrastructure.