Developing an integrated hydrograph separation and lumped modelling approach to quantifying hydrological pathways in Irish river catchments

An appreciation of the quantity of streamflow derived from the main hydrological pathways involved in transporting diffuse contaminants is critical when addressing a wide range of water resource management issues. In order to assess hydrological pathway contributions to streams, it is necessary to provide feasible upper and lower bounds for flows in each pathway. An important first step in this process is to provide reliable estimates of the slower responding groundwater pathways and subsequently the quicker overland and interflow pathways. This paper investigates the effectiveness of a multi-faceted approach applying different hydrograph separation techniques, supplemented by lumped hydrological modelling, for calculating the Baseflow Index (BFI), for the development of an integrated approach to hydrograph separation. A semi-distributed, lumped and deterministic rainfall runoff model known as NAM has been applied to ten catchments (ranging from 5 to 699 km2). While this modelling approach is useful as a validation method, NAM itself is also an important tool for investigation. These separation techniques provide a large variation in BFI, a difference of 0.741 predicted for BFI in a catchment with the less reliable fixed and sliding interval methods and local minima turning point methods included. This variation is reduced to 0.167 with these methods omitted. The Boughton and Eckhardt algorithms, while quite subjective in their use, provide quick and easily implemented approaches for obtaining physically realistic hydrograph separations. It is observed that while the different separation techniques give varying BFI values for each of the catchments, a recharge coefficient approach developed in Ireland, when applied in conjunction with the Master recession Curve Tabulation method, predict estimates in agreement with those obtained using the NAM model, and these estimates are also consistent with the study catchments’ geology. These two separation methods, in conjunction with the NAM model, were selected to form an integrated approach to assessing BFI in catchments.

Understanding macroalgal dispersal in a complex hydrodynamic environment: a combined population genetic and physical modelling approach

Gene flow in macroalgal populations can be strongly influenced by spore or gamete dispersal. This, in turn, is influenced by a convolution of the effects of current flow and specific plant reproductive strategies. Although several studies have demonstrated genetic variability in macroalgal populations over a wide range of spatial scales, the associated current data have generally been poorly resolved spatially and temporally. In this study, we used a combination of population genetic analyses and high-resolution hydrodynamic modelling to investigate potential connectivity between populations of the kelp Laminaria digitata in the Strangford Narrows, a narrow channel characterized by strong currents linking the large semi-enclosed sea lough, Strangford Lough, to the Irish Sea. Levels of genetic structuring based on six microsatellite markers were very low, indicating high levels of gene flow and a pattern of isolation-by-distance, where populations are more likely to exchange migrants with geographically proximal populations, but with occasional long-distance dispersal. This was confirmed by the particle tracking model, which showed that, while the majority of spores settle near the release site, there is potential for dispersal over several kilometres. This combined population genetic and modelling approach suggests that the complex hydrodynamic environment at the entrance to Strangford Lough can facilitate dispersal on a scale exceeding that proposed for L. digitata in particular, and the majority of macroalgae in general. The study demonstrates the potential of integrated physical–biological approaches for the prediction of ecological changes resulting from factors such as anthropogenically induced coastal zone changes.

Modelling the performance of self-compacting SIFCON of cement slurries containing limestone powder using genetic programming technique

The paper explores the potential of applicability of Genetic programming approach (GP), adopted in this investigation, to model the combined effects of five independent variables to predict the mini-slump, the plate cohesion meter, the induced bleeding test, the J-fiber penetration value, and the compressive strength at 7 and 28 days of self-compacting slurry infiltrated fiber concrete (SIFCON). The variables investigated were the proportions of limestone powder (LSP) and sand, the dosage rates of superplasticiser (SP) and viscosity modifying agent (VMA), and water-to-binder ratio (W/B). Twenty eight mixtures were made with 10-50% LSP as replacement of cement, 0.02-0.06% VMA by mass of cement, 0.6-1.2% SP and 50-150% sand (% mass of binder) and 0.42-0.48 W/B. The proposed genetic models of the self-compacting SIFCON offer useful modelling approach regarding the mix optimisation in predicting the fluidity, the cohesion, the bleeding, the penetration, and the compressive strength.

Estimation of Clarinet Reed Parameters by Inverse Modelling

Analysis of the acoustical functioning of musical instruments invariably involves the estimation of model parameters. The broad aim of this paper is to develop methods for estimation of clarinet reed parameters that are representative of actual playing conditions. This presents various challenges because of the di?culties of measuring the directly relevant variables without interfering with the control of the instrument. An inverse modelling approach is therefore proposed, in which the equations governing the sound generation mechanism of the clarinet
are employed in an optimisation procedure to determine the reed parameters from the mouthpiece pressure and volume ?ow signals. The underlying physical model captures most of the reed dynamics and is simple enough to be used in an inversion process. The optimisation procedure is ?rst tested by applying it to numerically synthesised signals, and then applied to mouthpiece signals acquired during notes blown by a human player. The proposed inverse modelling approach raises the possibility of revealing information about the way in which the embouchure-related reed parameters are controlled by the player, and also facilitates physics-based re-synthesis of clarinet sounds.

Modelling the control of interceptive actions

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics.

Multibody modelling of a TB31 and a TB32 crash test with vertical portable concrete barriers: model verification and sensitivity analysis.

In this article the multibody simulation software package MADYMO for analysing and optimizing occupant safety design was used to model crash tests for Normal Containment barriers in accordance with EN 1317. The verification process was carried out by simulating a TB31 and a TB32 crash test performed on vertical portable concrete barriers and by comparing the numerical results to those obtained experimentally. The same modelling approach was applied to both tests to evaluate the predictive capacity of the modelling at two different impact speeds. A sensitivity analysis of the vehicle stiffness was also carried out. The capacity to predict all of the principal EN1317 criteria was assessed for the first time: the acceleration severity index, the theoretical head impact velocity, the barrier working width and the vehicle exit box. Results showed a maximum error of 6% for the acceleration severity index and 21% for theoretical head impact velocity for the numerical simulation in comparison to the recorded data. The exit box position was predicted with a maximum error of 4°. For the working width, a large percentage difference was observed for test TB31 due to the small absolute value of the barrier deflection but the results were well within the limit value from the standard for both tests. The sensitivity analysis showed the robustness of the modelling with respect to contact stiffness increase of ±20% and ±40%. This is the first multibody model of portable concrete barriers that can reproduce not only the acceleration severity index but all the test criteria of EN 1317 and is therefore a valuable tool for new product development and for injury biomechanics research.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Modelling Future Coronary Heart Disease Mortality to 2030 in the British Isles

OBJECTIVE: Despite rapid declines over the last two decades, coronary heart disease (CHD) mortality rates in the British Isles are still amongst the highest in Europe. This study uses a modelling approach to compare the potential impact of future risk factor scenarios relating to smoking and physical activity levels, dietary salt and saturated fat intakes on future CHD mortality in three countries: Northern Ireland (NI), Republic of Ireland (RoI) and Scotland.

METHODS: CHD mortality models previously developed and validated in each country were extended to predict potential reductions in CHD mortality from 2010 (baseline year) to 2030. Risk factor trends data from recent surveys at baseline were used to model alternative future risk factor scenarios: Absolute decreases in (i) smoking prevalence and (ii) physical inactivity rates of up to 15% by 2030; relative decreases in (iii) dietary salt intake of up to 30% by 2030 and (iv) dietary saturated fat of up to 6% by 2030. Probabilistic sensitivity analyses were then conducted.

RESULTS: Projected populations in 2030 were 1.3, 3.4 and 3.9 million in NI, RoI and Scotland respectively (adults aged 25-84). In 2030: assuming recent declining mortality trends continue: 15% absolute reductions in smoking could decrease CHD deaths by 5.8-7.2%. 15% absolute reductions in physical inactivity levels could decrease CHD deaths by 3.1-3.6%. Relative reductions in salt intake of 30% could decrease CHD deaths by 5.2-5.6% and a 6% reduction in saturated fat intake might decrease CHD deaths by some 7.8-9.0%. These projections remained stable under a wide range of sensitivity analyses.

CONCLUSIONS: Feasible reductions in four cardiovascular risk factors (already achieved elsewhere) could substantially reduce future coronary deaths. More aggressive polices are therefore needed in the British Isles to control tobacco, promote healthy food and increase physical activity.

Efficient Non-Linear Idealisations of Aircraft Fuselage Panels in Compression

Aircraft fuselages are complex assemblies of thousands of components and as a result simulation models are highly idealised. In the typical design process, a coarse FE model is used to determine loads within the structure. The size of the model and number of load cases necessitates that only linear static behaviour is considered. This paper reports on the development of a modelling approach to increase the accuracy of the global model, accounting for variations in stiffness due to non-linear structural behaviour. The strategy is based on representing a fuselage sub-section with a single non-linear element. Large portions of fuselage structure are represented by connecting these non-linear elements together to form a framework. The non-linear models are very efficient, reducing computational time significantly