Vibration-assisted bone-graft compaction in impaction bone grafting of the femur

The complications of impaction bone grafting in revision hip replacement includes fracture of he femur and subsidence of the prosthesis. In this in vitro study we aimed to investigate whether the use of vibration, combined with a perforated tamp during the compaction of morsellised allograft would reduce peak loads and hoop strains in the femur as a surrogate marker of the risk of fracture and whether it would also improve graft compaction and prosthetic stability. We found that the peak loads and hoop strains transmitted to the femoral cortex during graft compaction and subsidence of the stem in subsequent mechanical testing were reduced. This innovative technique has the potential to reduce the risk of intra-operative fracture and to improve graft compaction and therefore prosthetic stability. © 2007 British Editorial Society of Bone and Joint Surgery.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Multiply conditioned analyses of stratification in highly swirling methane/air flames

The effects of stratification on a series of highly swirling turbulent flames under globally lean conditions (φg=0.75) are investigated using a new high-spatial resolution multi-scalar dataset. This dataset features two key properties: high spatial resolution which approaches the 60 micron optical limit of the measurement system, and a wavelet oversampling methodology which significantly reduces the influence of noise. Furthermore, the very large number of realizations (30,000) acquired in the stratified cases permits statistically significant results to be obtained even after aggressive conditioning is applied. Data are doubly conditioned on equivalence ratio and the degree of stratification across the flame in each instantaneous realization. The influence of stoichiometry is limited by conditioning on the equivalence ratio at the location of peak CO mass fraction, which is shown to be a good surrogate for the location of peak heat release rate, while the stratification is quantified using a linear gradient in equivalence ratio across the instantaneous flame front. This advanced conditioning enables robust comparisons with the baseline lean premixed flame. Species mass fractions of both carbon monoxide and hydrogen are increased in temperature space under stratified conditions. Stratification is also shown to significantly increase thermal gradients, yet the derived three-dimensional flame surface density is shown to be relatively insensitive to stratification. Whilst the presence of instantaneous stratification broadens the curvature distribution relative to the premixed case, the degree of broadening is not significantly influenced by the range of global stratification ratios examined in this study. © 2012 The Combustion Institute.

Cortical thickness estimation of the proximal femur from multi-view Dual-Energy X-ray Absorptiometry (DXA)

Hip fracture is the leading cause of acute orthopaedic hospital admission amongst the elderly, with around a third of patients not surviving one year post-fracture. Although various preventative therapies are available, patient selection is difficult. The current state-of-the-art risk assessment tool (FRAX) ignores focal structural defects, such as cortical bone thinning, a critical component in characterizing hip fragility. Cortical thickness can be measured using CT, but this is expensive and involves a significant radiation dose. Instead, Dual-Energy X-ray Absorptiometry (DXA) is currently the preferred imaging modality for assessing hip fracture risk and is used routinely in clinical practice. Our ambition is to develop a tool to measure cortical thickness using multi-view DXA instead of CT. In this initial study, we work with digitally reconstructed radiographs (DRRs) derived from CT data as a surrogate for DXA scans: this enables us to compare directly the thickness estimates with the gold standard CT results. Our approach involves a model-based femoral shape reconstruction followed by a data-driven algorithm to extract numerous cortical thickness point estimates. In a series of experiments on the shaft and trochanteric regions of 48 proximal femurs, we validated our algorithm and established its performance limits using 20 views in the range 0°-171°: estimation errors were 0:19 ± 0:53mm (mean +/- one standard deviation). In a more clinically viable protocol using four views in the range 0°-51°, where no other bony structures obstruct the projection of the femur, measurement errors were -0:07 ± 0:79 mm. © 2013 SPIE.

Discrete multicomponent model for biodiesel spray combustion simulation

An important first step in spray combustion simulation is an accurate determination of the fuel properties which affects the modelling of spray formation and reaction. In a practical combustion simulation, the implementation of a multicomponent model is important in capturing the relative volatility of different fuel components. A Discrete Multicomponent (DM) model is deemed to be an appropriate candidate to model a composite fuel like biodiesel which consists of four components of fatty acid methyl esters (FAME). In this paper, the DM model is compared with the traditional Continuous Thermodynamics (CTM) model for both diesel and biodiesel. The CTM model is formulated based on mixing rules that incorporate the physical and thermophysical properties of pure components into a single continuous surrogate for the composite fuel. The models are implemented within the open-source CFD code OpenFOAM, and a semi-quantitative comparison is made between the predicted spray-combustion characteristics and optical measurements of a swirl-stabilised flame of diesel and biodiesel. The DM model performs better than the CTM model in predicting a higher magnitude of heat release rate in the top flame brush region of the biodiesel flame compared to that of the diesel flame. Using both the DM and CTM models, the simulation successfully reproduces the droplet size, volume flux, and droplet density profiles of diesel and biodiesel. The DM model predicts a longer spray penetration length for biodiesel compared to that of diesel, as seen in the experimental data. Also, the DM model reproduces a segregated biodiesel fuel vapour field and spray in which the most abundant FAME component has the longest vapour penetration. In the biodiesel flame, the relative abundance of each fuel component is found to dominate over the relative volatility in terms of the vapour species distribution and vice versa in the liquid species distribution. © 2014 Elsevier Ltd. All rights reserved.