Projects

EDEN is a visual analytics tool for exploratory analysis of multivariate data sets. Based on an interactive variant of parallel coordinates, EDEN includes statistical analytics that guide the user to significant associations in complex data sets without information loss.

While the term ‘innovation ecosystem’ is often utilized, the concept is rarely quantified. Oak Ridge National Lab conducted a ground-breaking application of natural language processing, link analysis and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography for the U.S. Department of Energy. The project demonstrates that a machine-assisted methodology gives the user a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems.

PISCEES is a SciDAC Earth System Modeling project with the following goals: (1) To develop and apply robust, accurate, and scalable dynamical cores for ice sheet modeling on structured and unstructured meshes with adaptive refinement, (2) To evaluate ice sheet models using new tools and data sets for verification and validation (V&V) and uncertainty quantification (UQ), (3) to integrate these models and tools into DOE's Accelerated Climate Model for Energy (ACME). Using improved estimates of ice sheet initial conditions, we will simulate decade-to-century-scale evolution of the Greenland and Antarctic ice sheets, running PISCEES ice sheet models both in standalone mode and coupled to ACME. We aim to provide useful, credible predictions, including uncertainty ranges, of future ice-sheet mass loss and resulting changes in climate and sea level.
PISCEES is jointly funded by the Office of Biological and Environmental Research (BER) and the Office of Advanced Scientific Computing Research (ASCR) of the DOE Office of Science.
Principle Investigator: Steve Price - LANL and Esmond Ng – LBNL, Kate Evans - ORNL site PI

MULTISCALE is a SciDAC Earth System Modeling project with the primary goal of producing better climate models to serve as the scientific tools and predictive tools that will address the needs of both the climate sciences and policy-oriented communities. Here are notable features of MULTISCALE: (1) Grand challenges in projecting the future of the Earth's climate result from the interactions among small-scale features and large-scale structures of the ocean and atmosphere in climate models. (2) A generation of models that capture the structure and evolution of the climate system across a broad range of spatial and temporal scales is required. MULTISCALE is an integrated team of climate and computational scientists working to accelerate the development and integration of multiscale atmospheric and oceanic parameterizations into the Community Earth System Model (CESM).
MULTISCALE is jointly funded by the Biological and Environmental Research (BER) and Advanced Scientific Computing Research (ASCR) programs in the U.S. Department of Energy Office of Science.

Principle Investigator: Bill Collins - LBNL, Jim Hack - ORNL site PI

The initial part of our research will employ atmospheric observational data and focus on (a) objectively characterizing large-scale meteorological patterns (LMPs) associated with different classes of extreme weather events (EWEs) and (b) developing simple and concise atmospheric circulation metrics for identifying such LMPs in gridded datasets. We expect that atmospheric blocking will play a critical role in several EWE classes. 
Secondly, our project consists of a detailed diagnosis of the physical mechanisms by which (a) the implicated LMPs arise, (b) LMPs enact EWEs and (c) natural modes of climate variability (i.e., planetary-scale climate modes [PCMs]) serve to modulate LMPs/EWEs. The diagnostic results will be used to construct a set of dynamical metrics and incorporate them into a formal evaluation process to be used in DOE model development efforts. 
The third part of our project is the parallel study of EWEs, LMPs and PCMs in simulations of historical and future climate. There is particular interest in studying the benefits gained through enhanced model resolution by examining coupled model simulations conducted at ORNL.

Funding Source: DOE BER RGCM program
Principle Investigator: Rob Black - Georgia Tech, Kate Evans - ORNL PI

Quantum computing promises a platform for efficiently solving certain types problems thought to be intractable for traditional computers. The number of qubits needed to be competitive with classical computers varies dramatically depending on the problem. This project seeks to determine the maximum quantum operation rate for a given cooling capacity.

Developing a ground-based, quantum-secured, authenticated time distribution system for the energy grid.

Develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation