Improving Energy Efficiency and Environmental Sustainability of Commercial Insulation

With increasingly stringent energy standards set in place by the Department of Energy, energy efficiency is becoming a paramount concern to manufacturers of appliances. Additionally, the production and disposal of the voluminous amount of polyurethane foam commonly utilized as insulation in refrigeration units poses a significant environmental challenge. In this context, this study investigated an alternative insulation for use in commercial refrigerator/freezer units. A prototype exploring the use of evacuated packets of pyrogenic silica substituting for conventional insulation was assessed. Assessment criteria included experimental comparison of heat transfer characteristics and the energy efficiency of the new insulation as well as its life cycle as it is related to environmental sustainability. Results indicate that in the new insulation design applied to the unit’s cover, heat flux decreased by an average of 36%, and energy efficiency improved by 5.1% over a 24 hour period. The new insulation design also resulted in improved environmental sustainability, resulting in a savings of 0.257 metric tons of CO2e over 20 years for a single unit. Results provide an alternative insulation design for use in commercial refrigerator and freezers, and a framework by which to assess the efficiency and environmental performance of similar products

THE USE OF FUZZY SETS MATHEMATICS IN PAVEMENT EVALUATION AND MANAGEMENT (UNCERTAINTY, FUZZIFICATION, PSR)

A methodology for ranking pavement sections according to the maintenance urgency has been developed for the state of Indiana using fuzzy sets theory. Concepts of fuzzy sets theory are shown to supplement the existing procedures by methodically handling the human and system uncertainty in the pavement management system. Pavement sections in the highway network are grouped according to their maintenance needs, using the fuzzy PSI obtained from a PSR--RR (Roadmeter reading) correlation. This aids in scheduling the relevant tests for further evaluation and ranking for each pavement category. The concept of a fuzzy PSR which accounts for both the uncertainty inherent in each rating and the relative perceptiveness of each rater is introduced. Two novel approaches have been laid out for the correlation of PSR with RR. In one approach, RR is treated as a fuzzy number due to the imprecision and variability associated with it and a fuzzy relation is used for correlation. In the second approach the current notion of random Roadmeter variability is retained and correlation is done through fuzzy regression analysis. Variability inherent in the Skid-tester and the Dynaflect is also incorporated by considering the respective readings as fuzzy numbers. This is achieved by formulating a direct but an efficient fuzzification technique. It is shown how fuzzification can also be applied to methodically account for the subjectivity in the evaluation of distress manifestation of pavements. The resulting fuzzy pavement condition rating (PCR) is certainly a step towards the need of improving the condition surveys. The final ranking scheme is formulated using fuzzy multiattribute decision making concepts. The attributes relevant to each category of maintenance are identified and an expert knowledge base containing priority values for known attribute value combinations is formed in collaboration with decision makers. The ranking scheme is capable of handling possible fuzziness in the expert priorities as well. Finally it is shown how the fuzzy attribute values for each section can interact with the expert knowledge base to produce a unique set of rankings for the pavement sections

A non linear incremental finite element program for the analysis of shafts and tunnels in oilsands

A method for analysing the deformation behaviour of oilsand adjacent to shafts and tunnels is presented. Oilsand is comprised of a dense sand matrix with its pore spaces filled with bitumen, water and free or dissolved gases. The Engineering behaviour of oilsand is governed by the stresses in the sand matrix. The bitumen does not contribute directly to the strength of the sand. However, indirectly the presence of bitumen may greatly affect its behaviour. This is because the presence of bitumen reduces the effective permeability of the oilsand and very often undrained conditions occur. Then the pressure of the pore gases remain high reducing the effective stresses for unloading conditions. A nonlinear incremental finite element model is used to analyse the oilsand skeleton behaviour. Dilation or shear induced volume change is an important characteristic of a dense sand and this is included in the analysis using a modified form of Rowe's stress dilatancy theory. The unloading condition at the face of a tunnel or shaft can lead to a violation of the failure criterion and this condition is rectified by a stress redistribution technique. The compressibilities of the oil and water phases are neglected in comparison with that of the gas phase and pore pressure changes are predicted by the ideal gas laws. Under undrained conditions the pore pressure is coupled into the skeleton stresses by maintaining volumetric strain compatibility between the skeleton and pore fluid phases. The results have been checked with drained and undrained closed form solutions. The solution for the unloading of a tunnel in oilsand is presented and it shows that the limiting support pressures can be reduced by venting elements to a reasonable distance from the tunnel. It is also found that the effects of shear dilation are significant only when the limiting support pressure is approached.Applied Science, Faculty ofCivil Engineering, Department ofGraduat

Analysis and Design of Pavement Surface Mixtures for Traffic Noise Reduction

Project DescriptionRoad traffic noise pollutes the living environment and has adverse effects on public health, but it can be reduced at the source of one of its major components, the tire-pavement noise, by a porous pavement surface. This research project investigated the relationship between design parameters of porous asphalt mixtures placed at the pavement surface and the pavement acoustic performance. A mechanistic-empirical model was developed based on a microstructural model of the acoustic absorption of porous media and regression analysis of model parameters as functions of mixture design parameters, using a set of experimental data covering a range of porous asphalt mixture designs. This model may be used to predict the acoustic absorption of porous asphalt concrete, particularly at high frequencies. Regression models were developed to estimate the effect of mixture design, primarily aggregate gradation, on tire pavement noise at low frequencies. The impact of porous mixture design on pavement friction, in terms of skid resistance and hydroplaning speed, was also evaluated. Based on the research outputs (i.e., the mechanistic-empirical model and the regression models), a procedure was recommended to include the consideration of acoustic performance in the design of porous asphalt mixtures for road pavement surfaces. It is recommended that within the ranges of aggregate gradation allowed by current design methods for open-graded friction course mixtures, gradation selection may go towards a smaller nominal maximum aggregate size or a lower percentage passing the 2.36-mm sieve for lower tire-pavement noise.U.S. Department of Transportation 69A355174711

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated

An Improved Data-Driven Approach for the Prediction of Rainfall-Triggered Soil Slides Using Downscaled Remotely Sensed Soil Moisture

The infiltration of rainwater into soil slopes leads to an increase of porewater pressure and destruction of matric suction, which causes a reduction in soil shear strength and slope instability. Hence, surface moisture and infiltration properties must be direct inputs in reliable landslide hazard assessment methods. Since the in situ measurement of pore pressure is expensive, the use of remotely sensed soil moisture is practically feasible. Downscaling improves the spatial resolution of soil moisture for a better representation of specific local conditions. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a better accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. This model provides a good classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil slide hazard. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can provide the threshold probability for identifying locations with a high soil slide hazard is proposed

Using the astm e 274 skid trailer data to characterize pavement friction behavior with respect to the traveling speed and the wheel slip ratio

Knowing the 3-D behavior of friction coefficient (m) vs. traveling speed (v) and wheel slip ratio (s) on runway and highway pavements can facilitate the modern pavement engineers' job to a great extent. However, current methodology is limited to measuring m at desired v and predicting it at different s values (at the same measured v) using 2-dimensional models. The paper presents a study carried out with friction data collected using Locked Wheel Skid Trailer (LWST) (ASTM E 274 Standard Test Method), to obtain the 3-D behavior of m vs. v and s. An available 3-D friction model which is a combination of two well known 2-D friction models; Pennsylvania State University (PSU) model and Rado model, was used with LWST data collected in a field test on a wet asphalt pavement. The findings suggest that this method can provide reasonable predictions of m for pavement management purposesUna visión tridimensional de la resistencia al deslizamiento de superficies de pavimento respecto a variables como la velocidad y la razón de deslizamiento permiten caracterizar apropiadamente su comportamiento y facilitan el entendimiento del fenómeno. Sin embargo, los modelos utilizados actualmente para caracterizar la resistencia al deslizamiento solo evalúan un comportamiento bidimensional. La presente investigación utiliza los datos obtenidos por el deslizómetro ASTM E 274 para realizar una caracterización 3D del comportamiento de la resistencia al deslizamiento por medio de la fusión de dos reconocidos modelos bidimensionales de fricción (Modelo de Penn State y el Modelo de Rado). Los resultados de la investigación sugieren que la metodología propuesta presenta apropiados niveles de predicción para ser implementados en sistemas de gestión de infraestructura via