Hybridizing existing hydropower plants with energy storage or other generation can expand the flexibility, reliability services, and economic value provided by existing hydropower assets. However, permitting delays and direct regulatory costs can create barriers. So far in the US, nine hydro-hybrids have received Federal Energy Regulatory Commission (FERC) approval and thirteen total hydro-hybrids have been developed. Analyzing these nine cases along with similar projects shows that regulatory delays have increased recently. While hydro-hybrids have not yet been affected by the worst of the delays, the risk of large delays (>500 days) has increased significantly. The worst delay observed was 762 days, which effectively increased the project cost by up to 28%. FERC could reduce this barrier by lowering regulatory costs and the risk of large delays with a stronger and broader categorical exclusion under the National Environmental Policy Act (NEPA) and by narrowly defining FERC’s jurisdiction over hybrid assets. A broader categorical exclusion for typical hybridization projects would reduce repeated work across projects and streamline the part of the process that carries the most timeline risk. Narrowly defining FERC’s jurisdiction over secondary storage or generation assets would increase certainty about the process and streamline many projects. For example, hybridization projects that do not affect hydropower operations and already go through environmental reviews at the state or local level may not need federal regulation to ensure responsible development and operation. This would significantly reduce the cost and risk of large delays to licensees without negative environmental or social outcomes.
Mechanical screening of elongated biomass is not solely governed by aperture size: particles may be narrow enough to pass yet long enough to cause bridging, blockage, or unstable transport. This study evaluated length-sensitive separation of milled miscanthus in a pilot-scale Forest Concepts Orbital Screen using an experiment-informed framework integrating physical screening tests, 3D particle-shape characterization, shape-resolved discrete element method (DEM) simulations, and machine-learning surrogate modeling. In experiments, all feed particles passed a 12.7-mm sieve-shaker reference, whereas 9.21% of recovered material was retained on the Orbital Screen upper deck as the over-length/oversize stream, demonstrating length-sensitive retention beyond conventional sieve classification. However, elongated particles with lengths up to 20.9 mm also entered the on-spec stream, indicating incomplete partitioning. DEM simulations clarified this behavior by isolating particle width and length in monodisperse cases. For a representative 1.2 × 2.4 × 19.2 mm particle, the passage fraction was 58.5% in the 12.7-mm Orbital Screen upper-deck model, compared with 98.2% in the same-aperture sieve-shaker reference. Surrogate maps showed a broader probabilistic passage-retention transition zone in the Orbital Screen (39.27% of width-length space) than in the sieve-shaker reference (13.98%). Particle-scale analysis linked this broader transition mainly to orientation dynamics: particles in the Orbital Screen remained nearly flat, whereas those in the confined sieve-shaker reference more often adopted upright, passage-prone orientations. These results show that length-sensitive biomass screening depends on particle geometry, deck configuration, and transient orientation, providing a basis for improving over-length rejection while preserving on-spec recovery.
Autonomous driving applications demand large fields-of-view (FoVs) and high scan rates from microelectromechanical system (MEMS)-based light detection and ranging (LiDAR) systems. To meet these requirements, a multispot system is proposed using a single beam source and an MEMS structure for simultaneous beam steering and splitting. The system integrates an MEMS scanner with a diffractive optical element (DOE), achieving an 86 degrees H & times; 13 degrees V FoV at a 20-Hz frame rate. The electrostatic MEMS mirror is based on a gimbal architecture, with a reflective surface relief Dammann grating (1550-nm wavelength targeted) on the mirror surface to split the incident beam into five equally distributed beams, increasing five times the probing FoV for the same vertical mechanical deflection. Vertically asymmetric electrodes actuate the inner axis (horizontal FoV) at resonance, while the outer axis (vertical FoV) was optimized for quasi-static operation using staggered actuators. The device, fabricated on a 50-& micro;m-thick SOI wafer using a multilevel, self-aligned, dicing-free process, achieves an angular separation of the diffracted spots of 2.57 degrees, with a beam uniformity of 68%. This work demonstrates a combined beam steering and beam splitting MEMS mirror to simultaneously increase the FoV and scan line resolution, motivating their integration in next-generation multispot LiDAR.
This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems—Sampler, MultiApp, Reporter, and Surrogate—in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.
This study provides a combined experimental and computational investigation into the structure and impact of the cation interdiffusion layer that appears at the gadolinium doped ceria (GDC)/yttria stabilized zirconia (YSZ) interface in solid oxide electrolysis cells (SOECs). Scanning transmission electron microscopy (STEM) illustrates that a similar to 0.4 mu m interdiffusion layer (IDL) with an intermixed cation distribution and fine grain size forms upon sintering. STEM identifies that the interdiffusion layer exists in the cubic fluorite structure despite changes in cation composition. The interdiffusion layer microstructure formed during sintering does not change during SOEC testing at either 1.3V or heightened voltage pulse testing. Modeling predicts that ionic conductivity may decrease in the interdiffusion layer due to Coulombic trapping between mobile oxygen vacancies and excess Gd3+ acceptor dopants. Yet, the density and continuous nature of the layer should benefit cell stability by substantially reducing the formation of SrZrO3, which is corroborated by STEM and Synchrotron X-ray diffraction (XRD). We conclude that the interdiffusion layer acts as a beneficial barrier to Sr diffusion, when operating in a regime where electrolyte void formation is not observed.
