This new research provides transformative marine energy technology to effectively power the blue economy. Harmonizing the energy capture and power from Wave Energy Converter (WEC) arrays require innovative designs for the buoy, electric machines, energy storage systems (ESS), and coordinated onshore electric power grid (EPG) integration. This paper introduces two innovative elements that are co-designed to extract the maximum power from; i) individual WEC buoys with a multi-resonance controller design and ii) synchronized with power packet network phase control through the physical placement of the WEC arrays reducing ESS requirements. MATLAB/Simulink models were created for the WEC array dynamics and control systems with Bretschneider irregular wave spectrum as inputs. The numerical simulation results show that for ideal physical WEC buoy array phasing of 60 degrees the ESS peak power and energy capacity requirements are minimized while the multi-resonant controllers optimize EPG power output for each WEC buoy.
Particle heat exchangers are a critical enabling technology for next generation concentrating solar power (CSP) plants that use supercritical carbon dioxide (sCO2) as a working fluid. This report covers the design, manufacturing and testing of a prototype particle-to-sCO2 heat exchanger targeting thermal performance levels required to meet commercial scale cost targets. In addition, the the design and assembly of integrated particle and sCO2 flow loops for heat exchanger performance testing are detailed. The prototype heat exchanger was tested to particle inlet temperatures of 500 °C at 17 MPa which resulted in overall heat transfer coefficients of approximately 300 W/m2-K at the design point and cases using high approach temperature with peak values as high as 400 W/m2-K
Tracer gases, whether they are chemical or isotopic in nature, are useful tools in examining the flow and transport of gaseous or volatile species in the underground. One application is using detection of short-lived argon and xenon radionuclides to monitor for underground nuclear explosions. However, even chemically inert species, such as the noble gases, have bene observed to exhibit non-conservative behavior when flowing through porous media containing certain materials, such as zeolites, due to gas adsorption processes. This report details the model developed, implemented, and tested in the open source and massively parallel subsurface flow and transport simulator PFLOTRAN for future use in modeling the transport of adsorbing tracer gases.
This project created and demonstrated a framework for the efficient and accurate prediction of complex systems with only a limited amount of highly trusted data. These next generation computational multi-fidelity tools fuse multiple information sources of varying cost and accuracy to reduce the computational and experimental resources needed for designing and assessing complex multi-physics/scale/component systems. These tools have already been used to substantially improve the computational efficiency of simulation aided modeling activities from assessing thermal battery performance to predicting material deformation. This report summarizes the work carried out during a two year LDRD project. Specifically we present our technical accomplishments; project outputs such as publications, presentations and professional leadership activities; and the project’s legacy.
Solar Thermal Ammonia Production has the potential to synthesize ammonia in a green, renewable process that can greatly reduce the carbon footprint left by conventional Haber-Bosch reaction. Ternary nitrides in the family A3BxN (A=Co, Ni, Fe; B=Mo; x=2,3) have been identified as a potential candidate for NH3 production. Experiments with Co3Mo3N in Ammonia Synthesis Reactor demonstrate cyclable NH3 production from bulk nitride under pure H2. Production rates were fairly flat in all the reduction steps with no evident dependence on the consumed solid-state nitrogen, as would be expected from catalytic Mars-van Krevelen mechanism. Material can be re-nitridized under pure N2. Bulk nitrogen per reduction step average between 25 – 40% of the total solid-state nitrogen. Selectivity to NH3 stabilized at 55 – 60% per cycle. Production rates (NH3 and N2) become apparent above 600 °C at P(H2) = 0.5 – 2 bar. Optimal point of operation to keep selectivity high without compromising NH3 rates currently estimated at 650 °C and 1.5 - 2 bar. The next steps are to optimize production rates, examine effect of N2 addition in NH3 synthesis reaction, and test additional ternary nitrides.
Reno, Matthew J.; Blakely, Logan; Trevizan, Rodrigo D.; Pena, Bethany; Lave, Matthew S.; Azzolini, Joseph A.; Yusuf, Jubair Y.; Jones, Christian B.; Furlani Bastos, Alvaro F.; Chalamala, Rohit; Korkali, Mert; Sun, Chih-Che; Donadee, Jonathan; Stewart, Emma M.; Donde, Vaibhav; Peppanen, Jouni; Hernandez, Miguel; Deboever, Jeremiah; Rocha, Celso; Rylander, Matthew; Siratarnsophon, Piyapath; Grijalva, Santiago; Talkington, Samuel; Mason, Karl; Vejdan, Sadegh; Khan, Ahmad U.; Mbeleg, Jordan S.; Ashok, Kavya; Divan, Deepak; Li, Feng; Therrien, Francis; Jacques, Patrick; Rao, Vittal; Francis, Cody; Zaragoza, Nicholas; Nordy, David; Glass, Jim; Holman, Derek; Mannon, Tim; Pinney, David
This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.
Simulating molecules and atomic systems at quantum accuracy is a grand challenge for science in the 21st century. Quantum-accurate simulations would enable the design of new medicines and the discovery of new materials. The defining problem in this challenge is that quantum calculations on large molecules, like proteins or DNA, are fundamentally impossible with current algorithms. In this work, we explore a range of different methods that aim to make large, quantum-accurate simulations possible. We show that using advanced classical models, we can accurately simulate ion channels, an important biomolecular system. We show how advanced classical models can be implemented in an exascale-ready software package. Lastly, we show how machine learning can learn the laws of quantum mechanics from data and enable quantum electronic structure calculations on thousands of atoms, a feat that is impossible for current algorithms. Altogether, this work shows that combining advances in physics models, computing, and machine learning, we are moving closer to the reality of accurately simulating our molecular world.