Transportation

Massachusetts Institute of Technology (MIT) is using solar-derived hydrogen and common soil bacteria called Ralstonia eutropha to turn carbon dioxide (CO2) directly into biofuel. This bacteria already has the natural ability to use hydrogen and CO2 for growth. MIT is engineering the bacteria to use hydrogen to convert CO2 directly into liquid transportation fuels. Hydrogen is a flammable gas, so the MIT team is building an innovative reactor system that will safely house the bacteria and gas mixture during the fuel-creation process.

The Ohio State University is genetically modifying bacteria to efficiently convert carbon dioxide directly into butanol, an alcohol that can be used directly as a fuel blend or converted to a hydrocarbon, which closely resembles gasoline. Bacteria are typically capable of producing a certain amount of butanol before it becomes too toxic for the bacteria to survive. Ohio State is engineering a new strain of the bacteria that could produce up to 50% more butanol before it becomes too toxic for the bacteria to survive.

Pennsylvania State University (Penn State) is genetically engineering bacteria called Rhodobacter to use electricity or electrically generated hydrogen to convert carbon dioxide into liquid fuels. In collaboration with the University of Kentucky, Penn State is taking genes from oil-producing algae called Botryococcus braunii and putting them into Rhodobacter to produce hydrocarbon molecules, which closely resemble gasoline.

Carnegie Mellon University will use deep reinforcement learning and atomistic machine learning potentials to predict catalyst surface stability under reaction conditions. Current methods for determining the metastability of bifunctional and complex surfaces undergoing reaction are difficult and expensive. Carnegie Mellon’s technology will enable stability analysis in both traditional catalysts and new classes of materials, including those used in tribology (friction), corrosion-resistant alloys, additive manufacturing, and battery materials.

The University of Missouri will develop deep learning methods to predict inter-protein amino acid interactions and build three-dimensional structures of protein complexes, which are useful for designing and engineering protein molecules important for renewable bioenergy production. Proteins in cells interact and form complexes to carry out various biological functions such as catalyzing biochemical reactions. The team will use the deep learning methods it develops to construct green algae protein complexes that play important roles in biomass and biodiesel production.

The University of Texas at Austin proposes to create efficient, accurate, and scalable deep neural network (DNN) representations of design optimization problem solutions. The inputs to these DNN representations will be the vector of design requirement parameters, the outputs will be the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team will focus on the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using Reynolds-Average Navier Stokes models for minimal drag and energy savings.

The University of Maryland (UMD) will create inverse design tools for the development of enhanced heat transfer surfaces at reduced computational cost. Heat transfer surfaces are used to increase the efficiency of many energy conversion systems, but they are currently designed in a slow, iterative fashion. UMD will use a direct inverse design method map from given environments and performance metrics to design variables or materials.

GE Research will develop design optimization tools for the laser powder bed fusion based additive manufacturing of turbomachinery components. The team will integrate the latest advances in multi-physics topology optimization with fast machine learning-based producibility evaluations extracted from large training datasets comprising high-fidelity physics-based simulations and experimental validation studies. The integrated methodology will be used to demonstrate simultaneous improvements in the producibility and thermodynamic efficiency of a multi-physics turbomachinery component.

Carnegie Mellon University (CMU) and team will develop an integrated machine learning-accelerated design and optimization workflow that will reduce the time and cost required to develop functional energy materials in devices. The core innovation pairs machine learning based filtering of candidate materials with accelerated high-fidelity modeling to efficiently search a large design space for high-performance materials under realistic operating conditions.

The National Renewable Energy Laboratory (NREL) will develop a novel wind turbine design capability that enables designers to explore advanced technology concepts at a lower cost. This capability will harness the power of a deep neural network (DNN)-based inverse design methodology. To overcome challenges with the use of traditional DNNs in this application, NREL will develop innovative techniques to sparsify the neural network using active subspaces that will ensure that the model is invertible and can quickly zoom in on relevant designs at minimal cost.