Investigation of Compaction Process of Graphene Nano-Platelet Modified Asphalt Mixtures
Principal Investigator(s):Jialiang Le, Associate Professor, Civil, Environmental and Geo-Engineering
This study involves a series of experiments and simulations aimed at building a framework for modeling the graphene nanoplatelet (GNP)-influenced, improved compactivity of asphalt mixtures. To do so, researchers consider asphalt mixtures in their most common form: a combination of gravel and other macroscopic particles in a crude oil residue (comprised of a viscoelastic matrix with dispersed hard mineral colloidal particles). The work over the course of the year involves three research tasks:
Task 1: Researchers are performing rheological measurements to determine how the properties of the binder change with percentage of GNP added using a temperature-controlled Brookfield rheometer. The results are compared with previous theoretical predictions and experiments on the behavior of similar but more idealized viscoelastic fluids with particle dispersions.
Task 2: Researchers are performing computational simulations of the compaction process using the measured rheological properties of the binder to help investigate how the GNP influences the compaction behavior of asphalt mixtures (e.g., through binder rheology or effective interparticle friction). The modeling component is primarily based on an in-house discrete element method (DEM) code where the macroscopic particles are simulated directly and the effect of the GNP on binder rheology and friction coefficients are parameterized. The form of the parameterization is twofold: 1) a lubrication-like modification of the interparticle forces based on measured viscoelastic binder rheology and 2) the less-well-understood interparticle frictional modification based on measurements of other unbound materials. The influence of the small amount of air in the binder is being incorporated using the mixture theory. Researchers are setting up boundary conditions similar to those run in laboratory compaction tests and field installations so they can validate and improve the model through comparisons with previously published data and new experiments performed over the year of the project (Task 3). To maximize the generalizability and thus future use of the model for practical applications, researchers are also using the simulations to build an understanding of the physics that governs the influence of the GNP-modified binder on the asphalt compactivitity. These include particle-scale effects such as mesoscopic interparticle force structures (e.g., force chains) and system scale effects such as the evolution of the constitutive behavior. Over the course of the year, the plan is to develop a more fully coupled CFD/DEM model, likely based on CFDEM--an open source parallelizable code for coupled computational fluid dynamics (CFD) and DEM simulations. CFDEM can be used to enable the development of protocols that use GNP for improved performance in specific applications, including large-scale field installations.
Task 3: To validate the code, researchers are performing compaction experiments using the binder mixtures measured in Task 1 with particles of similar size distributions simulated in Task 2. The study is comparing the measured compaction with those predicted by the simulations to improve the parameterizations in both generations of the simulations. The validated model is being used to perform a series of parametric studies from which researchers can determine the influence of aggregate size distribution on the manner in which GNP affects the compaction performance of asphalt mixtures in terms of the final air void ratio for the given compaction temperature and the number of compaction gyrations.