Arghavan Louhghalam

Flügge’s Conjecture: Dissipation- versus Deflection-Induced Pavement–Vehicle Interactions

AbstractThe dissipation occurring below a moving tire in steady-state conditions in contact with a viscoelastic pavement is expressed using two different reference frames: a fixed observer attached to the pavement and a moving observer attached to the pavement–tire contact surface. The first approach is commonly referred to as dissipation-induced pavement–vehicle interaction (PVI), the second as deflection-induced PVI. Based on the principle of frame independence, it is shown that both approaches are strictly equal, from a thermodynamic point of view, and thus predict the same amount of dissipated energy. This equivalence is illustrated through application to two pavement systems: a viscoelastic beam and a viscoelastic plate both resting on an elastic foundation. The amount of dissipated energy in the pavement structure needs to be supplied by the vehicle to maintain constant speed, thus contributing to the rolling resistance, associated excess fuel consumption, and greenhouse gas emissions. The model her...

Roughness-Induced Vehicle Energy Dissipation: Statistical Analysis and Scaling

AbstractThe energy dissipated in a vehicle suspension system due to road roughness affects rolling resistance and the resulting fuel consumption and greenhouse gas emission. The key parameters driving this dissipation mechanism are identified via dimensional analysis. A mechanistic model is proposed that relates vehicle dynamic properties and road roughness statistics to vehicle dissipated energy and thus fuel consumption. A scaling relationship between the dissipated energy and the most commonly used road roughness index, the International Roughness Index (IRI), is also established. It is shown that the dissipated energy scales with IRI squared and scaling of dissipation with vehicle speed V depends on road waviness number w in the form of Vw−2. The effect of marginal probability distribution of the road roughness profile on dissipated energy is examined. It is shown that although the marginal distribution of the road profile does not affect the identified scaling relationships, the multiplicative factor...

Scaling Relationships of Dissipation-Induced Pavement–Vehicle Interactions

Rolling resistance is one of the key factors that affect the fuel efficiency of the national pavement system. In addition to pavement texture and pavement roughness, the dissipation of mechanical work provided by the vehicle because of viscous deformation within the pavement structure has been recognized as a relevant factor contributing to the environmental footprint of pavement systems. This dissipation depends on material and structural parameters that can be optimized to increase the fuel efficiency of pavements. Identifying the key material and structural parameters that drive this dissipation is the focus of this paper. This identification is achieved by a combination of dimensional analysis and model-based simulations of the dissipation of a viscoelastic beam on an elastic foundation. For linear viscoelastic systems, the dissipation is found to scale with the square of the vehicle weight and with the inverse of the viscous relaxation time, in addition to distinct power relations of top-layer stiffness, thickness, and subgrade modulus. These scaling relations can be used by pavement engineers to reduce such pavement-inherent dissipation mechanisms and increase the fuel efficiency of a pavement design. An example shows the application of these scaling relations with data extracted from FHWAs Long-Term Pavement Performance database for seven road classes. The scaling relations provide a means for evaluating the performance of the various road classes in terms of the fuel efficiency related to dissipation.

Prediction of Incipient Damage Sites in Composites using Classifiers

This paper describes a method for predicting locations in a two-phase material where effective elastic strain is concentrated above a specified threshold value by virtue of the local arrangement of phases and a specified set of boundary conditions. This prediction is made entirely based on knowledge of the material properties of the phases, their spatial arrangement, and the boundary conditions, and does not require numerical solution of the equations of elasticity. The example problem is a 2D idealization of a fiber- or particle-reinforced composite in which the fibers/particles are randomly placed in the matrix and the boundary conditions correspond to uniaxial extension. The method relies on a moving window implementation of a decision tree classifier that predicts, for all points in the material, whether the effective elastic strain will exceed a specified threshold value. The classifier operates on a set of attributes that are the coefficients of a series expansion of a discretized version of the phase geometry. The basis vectors appearing in this series expansion of the phase geometry are derived from a principal components analysis of a set of training samples for which the mechanical response is calculated using finite element analysis. These basis vectors allow the accurate representation of the phase geometry with many fewer parameters than is typical, and, because the training samples contain information regarding the mechanical response of the material, also allow prediction of the response using a classifier that takes a relatively small number of input attributes. The predictive classifier is tested on simulated two-phase material samples that are not part of the original training set, and correctly predicts whether efffective elastic strain will be elevated above a specified threshold with greater than 90% accuracy.

Model Development, Field Section Characterization, and Model Comparison for Excess Vehicle Fuel Use Attributed to Pavement Structural Response

In this study, consumption of energy attributed to pavement structural response through viscoelastic deformation of asphalt pavement materials under vehicle loading was predicted for 17 field sections in California by using three different models. Calculated dissipated energy values were converted to excess fuel consumption (EFC) to facilitate comparisons under different traffic loads (car, SUV, and truck) and speeds and different temperature conditions. The goal of the study was to compare the different modeling approaches and provide first-level estimates of EFC in preparation for simulations of annual EFC for different traffic and climate scenarios, as well as different types of pavement structures on the California state highway network. Comparison of the predicted EFC for test sections showed that all three models produced different results, which can be attributed to the differences in the three modeling approaches. However, predictions from the three models were generally of the same order of magnitude or an order of magnitude different, indicating that overall these models can be calibrated with data from field measurements, which is the next step in the research program.

Roughness-Induced Pavement–Vehicle Interactions: Key Parameters and Impact on Vehicle Fuel Consumption

Pavement roughness affects rolling resistance and thus vehicle fuel consumption. When a vehicle travels at constant speed on an uneven road surface, the mechanical work dissipated in the vehicles suspension system is compensated by vehicle engine power and results in excess fuel consumption. This dissipation depends on both road roughness and vehicle dynamic characteristics. This paper proposes, calibrates, and implements a mechanistic model for roughness-induced dissipation. The distinguishing feature of the model is its combination of a thermodynamic quantity (energy dissipation) with results from random vibration theory to identify the governing parameters that drive the excess fuel consumption caused by pavement roughness, namely, the international roughness index (IRI) and the waviness number, w (a power spectral density parameter). It is shown through sensitivity analysis that the sensitivity of model output, that is, excess fuel consumption, to the waviness number is significant and comparable to that of IRI. Thus, introducing the waviness number as a second roughness index, in addition to IRI, allows a more accurate quantification of the impact of surface characteristics on vehicle fuel consumption and the corresponding greenhouse gas emissions. This aspect is illustrated by application of the roughness–fuel consumption model to two road profiles extracted from FHWAs Long-Term Pavement Performance database.