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Physics–Informed Neural Networks of the Saint–Venant Equations for Downscaling a Large–Scale River Model
Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes.
Interpretation of large-scale spiral structure
Large scale spiral galaxy structure interpretation, examining density wave theory, Milky Way System and spiral form origin
Interaction of a cumulus cloud ensemble with the large-scale environment
Large-scale modification of the environment by cumulus clouds is discussed in terms of entrainment, detrainment, evaporation, and subsidence. Drying, warming, and condensation by vertical displacement of air are considered as well as budget equations for mass, static energy, water vapor, and liquid water.
On large-scale system stability.
A large-scale system is considered as a system constituted of subsystems which may be connected or disconnected from each other during operation. A new notion of connective stability is introduced by which a large-scale system is regarded as stable if it remains stable (in the sense of Liapunov) under structural perturbations produced by the on-off participation of the subsystems. Algebraic conditions are developed that guarantee exponential connective stability in large-scale systems which may be composed of linear and nonlinear subsystems coupled by linear or nonlinear connections.
Large Scale Flows through the Solar Cycle
Large scale flows within the solar convection zone are the primary drivers of the Sun's magnetic activity cycle. Differential rotation amplifies the magnetic field and converts poloidal fields into toroidal fields. Poleward meridional flow near the surface carries magnetic flux that reverses the magnetic poles and converts toroidal fields into poloidal fields. The deeper, equatorward meridional flow carries magnetic flux toward the equator where it must reconnect with oppositely directed fields in the other hemisphere. These axisymmetric flows are themselves driven by large scale convective motions. Given these intimate connections between the large scale flows and solar activity, it would be surprising if there weren't solar cycle variations in the flow characteristics. Some variations, namely the torsional oscillations, are well established. Other variations, namely changes in the meridional flow and in the convective motions themselves, are more controversial. In this presentation I will describe the observed characteristics of the large scale flows and discuss possible variations associated with the solar cycle.
Ocean Mesoscale and Frontal-Scale Ocean–Atmosphere Interactions and Influence on Large-Scale Climate: A Review
Two decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research.
Sound production due to large-scale coherent structures
The acoustic pressure fluctuations due to large-scale finite amplitude disturbances in a free turbulent shear flow are calculated. The flow is decomposed into three component scales; the mean motion, the large-scale wave-like disturbance, and the small-scale random turbulence. The effect of the large-scale structure on the flow is isolated by applying both a spatial and phase average on the governing differential equations and by initially taking the small-scale turbulence to be in energetic equilibrium with the mean flow. The subsequent temporal evolution of the flow is computed from global energetic rate equations for the different component scales. Lighthill's theory is then applied to the region with the flowfield as the source and an observer located outside the flowfield in a region of uniform velocity. Since the time history of all flow variables is known, a minimum of simplifying assumptions for the Lighthill stress tensor is required, including no far-field approximations. A phase average is used to isolate the pressure fluctuations due to the large-scale structure, and also to isolate the dynamic process responsible. Variation of mean square pressure with distance from the source is computed to determine the acoustic far-field location and decay rate, and, in addition, spectra at various acoustic field locations are computed and analyzed. Also included are the effects of varying the growth and decay of the large-scale disturbance on the sound produced.
Radar backscattering from a sea having an anisotropic large-scale surface, part 2
A two scale scattering model was derived that combines specular reflections from sea waves and Bragg scattering in a manner consistent with energy conservation. The effect of the tilting of the small scale roughness by the large scale roughness was included, which accounted for the reduction of reflected power. The special case of backscattering for which the transmitted polarization equaled the received polarization was considered. An anisotropic large scale surface was used to specify the probability density function of the large scale surface normal. In order to isolate the azimuthal variation of the normalized radar cross section produced by the anisotropic probability density function, an isotropical small scale spectrum was assumed.
Lessons Learned from Large-Scale Aerospace Structural Testing
Large-scale testing of aerospace structures is frequently the final step in a development project to validate the structural performance, and that typically involves a large cost and time investment. In order to ensure that the testing provides the required data, avoiding errors that can result in an unsuccessful test and failure to meet objectives is critical. Presented herein are five lessons learned to provide insight to those conducting tests in order to help them avoid known pitfalls that may result in an unsuccessful test. Five subject large-scale tests are described, and include two composite wing tests, a composite hybrid-wing body center section test, a full-scale 27.5-ft diameter metallic barrel test, and an 8-ft diameter metallic barrel test. Problems identified during the testing and mitigation approaches to solve the problems are presented, then the lessons learned are summarized.
Lessons Learned from Large-Scale Aerospace Structural Testing
Large-scale testing of aerospace structures is frequently the final step in a development project to validate the structural performance, and that typically involves a large cost and time investment. In order to ensure that the testing provides the required data, avoiding errors that can result in an unsuccessful test and failure to meet objectives is critical. Presented herein are five lessons learned to provide insight to those conducting tests in order to help them avoid known pitfalls that may result in an unsuccessful test. Five subject large-scale tests are described, and include two composite wing tests, a composite hybrid-wing body center section test, a full-scale 27.5-ft diameter metallic barrel test, and an 8-ft diameter metallic barrel test. Problems identified during the testing and mitigation approaches to solve the problems are presented, then the lessons learned are summarized.
Lessons Learned from Large-Scale Aerospace Structural Testing
Large-scale testing of aerospace structures is frequently the final step in a development project to validate the structural performance, and that step typically involves a large cost and time investment. To ensure that the testing provides the required data, avoiding errors that can result in an unsuccessful test and failure to meet objectives is critical. Five lessons learned are presented herein to provide insight to those conducting tests in order to help them avoid known pitfalls that may result in an unsuccessful test. Five large-scale tests are described, and include two composite wing tests, a composite hybrid-wing body center section test, a full-scale 27.5-ft diameter metallic barrel test, and an 8-ft diameter metallic barrel test. Problems identified during the testing and mitigation approaches to solve the problems are presented, then the lessons learned are identified and discussed.
Solar@Scale: Improving the Local Rules of the Game for Large Scale Solar
This final report summarizes the Solar@Scale Project including project goals, milestones, tasks and deliverables. Solar@Scale, led by ICMA in partnership with the American Planning Association, took place from 2020-2025. The initiative addressed large-scale solar soft costs by developing tools and resources related to planning, siting, permitting and inspection in support of local governments, special districts, and other authorities that have jurisdiction over large-scale solar projects.
Large-scale V/STOL testing
Several facets of large-scale testing of V/STOL aircraft configurations are discussed with particular emphasis on test experience in the Ames 40- by 80-foot wind tunnel. Examples of powered-lift test programs are presented in order to illustrate tradeoffs confronting the planner of V/STOL test programs. It is indicated that large-scale V/STOL wind-tunnel testing can sometimes compete with small-scale testing in the effort required (overall test time) and program costs because of the possibility of conducting a number of different tests with a single large-scale model where several small-scale models would be required. The benefits of both high- and full-scale Reynolds numbers, more detailed configuration simulation, and number and type of onboard measurements increase rapidly with scale. Planning must be more detailed at large scale in order to balance the trade-offs between the increased costs, as number of measurements and model configuration variables increase and the benefits of larger amounts of information coming out of one test.
Large-scale white noise and cosmology
The generation of white noise on large scales is a generic property of the dynamics of physical systems described by local nonlinear partial differential equations. Nonlinearities prevent the small-scale dynamics from being erased by smoothing. Unresolved small-scale dynamics act as an uncorrelated (white or Poissonian) noise (seemingly stochastic but actually deterministic) contribution to large-scale dynamics. This white noise exists even when the dynamics is very nearly linear. In cases where the power spectrum is sub-Poissonian on large scales, this noise will dominate on the largest scale power no matter the amplitude of the inhomogeneities. Such is the case in the standard model of cosmology, where the primordial density power spectrum is expected to have an almost Harrison-Zel’dovich, P [ k ] ∼ k , spectrum on a much broader range of scales than can be observed. Even though linear gravitational evolution dominates nonlinear corrections by a factor of ∼ 10 5 , the nonobservation of white noise on the Hubble scale precludes the extrapolation of this power law below the comoving 1 pc scale. More generally, observation or nonobservation of large-scale white noise provides a powerful probe of the Universe on very small scales in the early Universe. Gravitational radiation, phase transitions, vorticity, and running of the spectral index are all phenomena that can be probed with large-scale white noise. Large-scale white noise is a nonoptional feature of all cosmological models but one which has not heretofore been appreciated.
A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model
Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.
Joining technique for in-oven/autoclave molds manufactured by large scale polymer additive manufacturing
Large scale additive manufacturing (AM) of fiber-reinforced polymer composites has gained traction in the mold and die community over the last decade, largely due to reduced material cost and lead time. However, limitations in mold size remain present due to printer volume constraints and costs associated with large format machines. Here, this paper describes a method for producing additively manufactured polymer molds in segments with an O-ring joint design, which can be machined and subsequently assembled for autoclave applications. The developed joining mechanism allows for maintaining the vacuum integrity during the cure cycle while accounting for the anisotropic nature of the additively manufactured polymer molds. Over a range of tested temperatures, 20–200 °C, the multi-part mold joining design was able to achieve full vacuum of ∼73.7 cmHg (29 inchHg) and maintain a vacuum integrity with vacuum losses less than 1.5 cmHg/5min (0.6 inchHg).
A bibliographical survey of large-scale systems
Bibliography on large scale systems such as large societal, communication, vehicular traffic control, and management systems