Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Angel Yanguas-Gil Project Name: madman Division: ES Project title: Modeling the advanced manufacturing of energy materials Associated funding: This effort is supported through LDRD and EERE funds Other Systems: None Science: This project aims at developing multiscale predictive models for advanced manufacturing applications, specifically nanomanufacturing and additive manufacturing. Our research has two goals: 1) improve the current understanding of the fundamental processes controlling the synthesis and microstructure of materials, 2) develop models that can predict the scale up of manufacturing process as well as the performance of the resulting materials. Project description: Goals ===== The goal of this project is to develop predictive models for advanced manufacturing applications, specifically nanomanufacturing and additive manufacturing as part of three funded activities supported by DOE's EERE office (Sunshot program) and through Argonne's LDRD program. Project overview ================ The scope of this project covers the following three research lines: Multiscale simulations of ALD on high surface area materials ------------------------------------------------------------ ### Main approach One of the main applications of thin film deposition techniques such as ALD is the coating of high surface area materials. We have developed a simulation tool to couple reactive transport at a reactor scale and inside high surface area materials for the coating and functionalization of high surface area materials. Our reactor scale component of our simulation code is based on OpenFOAM, the coupling between reactor scale and the nanoscale taking place at the boundary condition. Reactive transport inside high surface area materials is carried out using a combination of approaches: - We are using LAMMPS to model the growth of high surface area materials based on nanoparticle spheres, using a molecular dynamic approach to model the settling of nanoparticles in solution through a combination of interatomic potentials, viscous drag and an external force that forces the sedimentation. Typical number of particles involve ranges between 20000 - 100000 spheres. - For nanoparticle based substrates, we then calculate the view factors using kinetic Monte Carlo simulations. This is an embarrasingly parallel problem that can take advantage of multiple cores available in the cluster, and our prototype simulation takes advantage of the parallel programming capabilities built-in in julia to share the load between cores. - The resulting view factors are then incorporated into a model that treats the reactive transport of species as an absorbing Markov Chain. This provides the probability that particles react at a certain depth of our high surface area material. - For deterministic structures such as circular pores or rectangular trenches, view factors are computed analytically and directly fed into the Markov chain model. All these components were tested in Blues during the prior cycle. ### Applications We are currently applying this model to two separate cases: #### Predictive scale up of the synthesis of nanostructured materials using roll-to-roll compatible gas-phase manufacturing techniques. In particular, we are looking at the impact of microstructure and reactivity on the spatial ALD of high surface area materials. #### Conformal coating of large size tubes using self-limited and non self-limited processes for nuclear engineering applications. Models of additive manufacturing -------------------------------- ### Main approach We are developing physics-based models to simulate the material and energy flow during part processing through additive manufacturing. Our focus here is in developing coarse-scale models in the time and length scale of the actual printing of parts. Our approach combines finite volume approach through OpenFOAM with the development of custom coarse-scale simulations that model that incorporate the rastering of the nozzle or laser in a similar way as it is carried out in a 3D printing process. This will allow us to understand the impact of rastering sequence on the heat load seen at every point of the part as it gets printed and correlate these values with the microstructure of 3D printed parts. These models will then be used as an input for reduced order models that will pair down the complexity of the model even further as part of our approach to bring more advanced simulations of the 3D printing of materials to industry. Project members =============== We expect three people working on this project. Allocation ========== We are asking for 60,000 core hours for the next fiscal years. Of these, we expect that 10% will be used in runs involving single-core processes running concurrently, 30% will involve single node calculations, with the remaining extending through multiple nodes. Industry partnership: Project URL: Current FY Hours Used: undetermined amount New FY Requested allocation: 60000 Q1: 15000 Q2: 15000 Q3: 15000 Q4: 15000 Justification: Storage requirements: Thank You, The LCRC Accounts System