Ross Gilsenan^{1}, Niall O’Murchú^{1}, Graham Fahey^{1}, Joe Hannon^{1,3}, Garrett Keane^{2}

^{1}School of Mechanical Engineering and Design, DIT

^{2}School of Civil Engineering, DIT

^{3}School of Biosystems Engineering, UCD

A coupled heat and mass transfer model was developed to simulate the chilling of a beef carcass post slaughter. The methodology followed by Mallikarjunan, P., & Mittal, G. (1994) was adopted in this study. The beef carcass was represented by five two dimensional horizontal cross sections representing five different zones in the carcass, namely the round, the sirloin, the loin, the rib and the chuck. The images of the cross sections in this paper were scanned and digitised and then cross–referenced with measurements from real carcasses to verify the dimensions. Mallikarjunan provided an equation that apportioned the total mass of the carcass to the individual sections.

The finite difference equations were formulated for Cartesian co‑ordinates and coded in Microsoft Excel VBA. The model was used to simulate the conventional chilling regime used by Mallikarjunan in which an ambient temperature of 0.44^{º}C, an average relative humidity of 85% and an air speed of 0.5 m/s were recorded. The published experimental results were averaged from five carcasses and included temperature histories at the round, sirloin and rib sections for a period of 50 hours in addition to the corresponding averaged cumulative mass loss. The average mass was 155±11kg. Constant values for the thermo-physical properties of thermal conductivity, specific heat capacity and density were determined using COSTHERM.

Energy is lost from the surface of the carcass by convection, radiation and evaporation. Kondjoyan (06) states that evaporation losses can contribute significantly to the total energy losses in the chilling of a meat carcass while radiation losses are of less significance, especially when there are a number of carcasses with similar surface temperatures hanging close together. The heat transfer coefficient of h=20W/m^{2}K, the surface mass transfer coefficient of 14.35x10^{-11}(kg m)/ (kgDMPas) and the moisture diffusivity value of D_{m}=5.83x10-10 m^{2}/s calculated used by Mallikarjunan were applied in this model.

The model predicted the temperature histories in the round, sirloin and rib sections reasonably accurately but under-predicted the total mass transfer from the carcass.

]]>The asset rating methodology developed for the certification of energy performance of buildings is the current industry standard of prediction of energy demand and post retrofit performance. The methodology is tedious and may not be viable for ESCos to send trained assessors to residential dwellings while residential customers do not possess the technical knowledge to complete the survey of their own dwelling.

This research investigates if the asset rating procedure can be simplified to permit development of simpler models, which would allow quicker and easier assessment of the energy performance of residential dwellings.

An asset rating model was created in excel. The Irish housing stock was simulated using a sample data set and Monte Carlo techniques. The model was simplified in stages by systematically removing and parameterising input variables from an initial list of 50.

The results of these test methods are presented and should be consulted by policy makers, ESCos and private bodies who wish to create a simplified asset rating type model with known error margins for the prediction of dwelling energy performance.

]]>the rate of deterioration of reinforced concrete structures as it provides the only barrier to the

ingress of water containing dissolved ionic species such as chlorides which, ultimately,

initiate corrosion of the reinforcement. In-situ monitoring of cover-zone concrete is critical in

attempting to make realistic predictions as to the in-service performance of the structure. To

this end, this paper presents developments in a remote interrogation system to allow

continuous, real-time monitoring of the cover-zone concrete from an office setting. Use is

made of a multi-electrode array [19] embedded within cover-zone concrete to acquire

discretized electrical resistivity and temperature measurements, with both parameters

monitored spatially and temporally. On-site, instrumentation, which allows remote

interrogation of concrete samples placed at a marine exposure site, is detailed, together with

data handling and processing procedures. Site-measurements highlight the influence of

temperature on electrical resistivity and an Arrhenius-based temperature correction protocol

is developed using on-site measurements to standardize resistivity data to a reference

temperature; this is an advancement over the use of laboratory-based procedures. The testing

methodology and interrogation system represents an additional technique which could be

used for intelligent monitoring of reinforced concrete structures. ]]>

A comparison of the corrosion inhibition properties of ELOTEX^{®}COPRA900 against a well known and established corrosion inhibitor on the market, namely Calcium Nitrite and a control with no corrosion inhibitor product. Calcium Nitrite works by increasing the threshold of chlorides required for corrosion to begin. ELOTEX^{®}COPRA900 on the other hand surrounds the embedded reinforcement with a secondary protective layer that is activated when the passive oxide layer also surrounding the reinforcement breaks down due to the initiation of the chloride corrosion mechanism. The aim of this project is to evaluate ELOTEX^{®}COPRA900 in concrete mixes exposed to accelerated corrosion conditions including CEM I, PFA and GGBS cements.

The results from this experimental programme have demonstrated that ELOTEX^{®}COPRA900 is an effective corrosion inhibitor. The results have shown, in every case, that concrete containing ELOTEX^{®}COPRA900 are less penetrable to chlorides than those without. The addition of ELOTEX^{®}COPRA900 has been found to alter the pore structure so it becomes discontinuous and a capillary blocking effect occurs. In the case of those concretes containing PFA and GGBS, it was found that ELOTEX^{®}COPRA900 made no significant improvement to the corrosion and durability performance.

However, one noticeable effect of the inclusion of ELOTEX^{®}COPRA900 in the concrete mix is the consistent reduction in the compression strength. This has found to be due to the hydrophobic layer that forms on the cement particles which affects the hydration and strength development. However, it is expected that this will only affect low strength concretes which are uncommon in challenging exposures. Also, the quantity of ELOTEX^{®}COPRA900 used here (4% by mass of cement) would be seen as on the higher side of what would typically be recommended. Reducing the volume of ELOTEX^{®}COPRA900 would therefore reduce these effects of loss in compressive strength.

This research has the broad objective of developing better methods of statistical analysis of highway bridge traffic loading. The work focuses on short- to medium-length (approximately 15 to 50 m), single- or two-span bridges with two opposing lanes of traffic. Dynamic interaction of the trucks on the bridge is generally not included.

Intuitively, it can be accepted that the gap between successive trucks has important implications for the amount of load that may be applied to any given bridge length. This work describes, in quantitative terms, the implications for various bridge lengths and load effects. A new method of modelling headway for this critical time-frame is presented.

When daily maximum load effects (for example) are considered as the basis for an extreme value statistical analysis of the simulation results, it is shown that although this data is independent, it is not identically distributed. Physically this is manifest as the difference in load effect between 2- and 3-truck crossing events. A method termed composite distribution statistics is presented which accounts for the different distributions of load effect caused by different event types. Exact equations are derived, as well as asymptotic expressions which facilitate the application of the method.

Due to sampling variability, the estimate of lifetime load effect varies for each sample of load effect taken. In this work, the method of predictive likelihood is used to calculate the variability of the predicted extreme for a given sample. In this manner, sources of uncertainty can be taken into account and the resulting lifetime load effect is shown to be calculated with reasonable assurance.

To calculate the total lifetime load effect static load effect plus that due to dynamic interaction), the results of dynamic simulations based on 10-years of static results are used in a multivariate extreme value analysis. This form of analysis allows for the inherent correlation between the total and static load effect that results from loading events. A distribution of dynamic amplification factor and estimates for a site dynamic allowance factor are made using parametric bootstrapping techniques. It is shown that the influence of dynamic interaction decreases with increasing static load effect.

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