Transfer learning of Gaussian processes to capture unmodeled physics: application to control of nonlinear dynamical systems.
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Abstract. Simple climate models (SCMs) are numerical representations of the Earth's gas cycles and climate system. SCMs are easy to use and computationally inexpensive, making them an ideal tool in both scientific and decision-making contexts (e.g., complex climate model emulation, parameter estimation experiments, climate metric calculations, and probabilistic analyses). Despite their prolific use, the fundamental responses of SCMs are often not directly characterized. In this study, we use fundamental impulse tests of three chemical species (CO2, CH4, and black carbon – BC) to understand the fundamental gas cycle and climate system responses of several comprehensive (Hector v2.0, MAGICC 5.3, MAGICC 6.0) and idealized (FAIR v1.0, AR5-IR) SCMs. We find that while idealized SCMs are widely used, they fail to capture the magnitude and timescales of global mean climate responses under emissions perturbations, which can produce biased temperature results. Comprehensive SCMs, which have physically based nonlinear forcing and carbon cycle representations, show improved responses compared to idealized SCMs. Even the comprehensive SCMs, however, fail to capture the response timescales to BC emission perturbations seen recently in two general circulation models. Some comprehensive SCMs also generally respond faster than more complex models to a 4×CO2 concentration perturbation, although this was not evident for lower perturbation levels. These results suggest where improvements should be made to SCMs. Further, we demonstrate here a set of fundamental tests that we recommend as a standard evaluation suite for any SCM. Fundamental impulse tests allow users to understand differences in model responses and the impact of model selection on results.
Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. We introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.
Geologic carbon storage (GCS) is likely to play a key part of the global effort to dramatically reduce CO2 emissions and perhaps even reduce atmospheric CO2 concentrations through carbon negative operations. A critical part of effort to commercialize and widely deploy this technology is developing the capability to rapidly assimilate real-time monitoring data into a form that will enable site operators to make decisions to manage the safe and efficient operations. Two of the risks associate with GCS are the risk of inducing fractures in the sealing formations that can create leakage pathways and the risk of inducing earthquakes of sufficient magnitude to cause public concern, property damage, or safety risks. To properly manage these risks the site operator needs to know the initial state of stress, the change in stress induced by injection, and the relationship between operational parameters such as injection rate and pressure and the change in stress. Current methods of estimating the change in stress require choosing the type of constitutive model and the model parameters based on core, log, and geophysical data during the characterization phase, with little feedback from operational observations to validate or refine these choices. These characterization methods interrogate the geologic formations using length scales, loading rates or magnitudes that are quite different from those encountered by the actual storage system. It is shown that errors in the assumed constitutive response, even when informed by laboratory tests on core samples, are likely to be common, large, and underestimate the magnitude of stress change caused by injection. Recent advances in borehole-based strain instruments and borehole and surface-based tilt and displacement instruments have now enabled monitoring of the deformation of the storage system throughout its operational lifespan. This data can enable validation and refinement of the knowledge of the geomechanical properties and state of the system, but brings with it a challenge to transform the raw data into actionable knowledge. We demonstrate a method that uses automatic differentiation and a finite-element based geomechanical model perform a gradient-based deterministic inversion of geomechanical monitoring data. This approach allows autonomous integration of the instrument data without the need for time consuming manual interpretation and selection of updated model parameters. Furthermore, only isotropic linear elasticity is considered in this paper, the approach presented is very flexible as to what type of geomechanical constitutive response can be used. The approach is easily adaptable to nonlinear physics-based constitutive models to account for common rock behaviors such as creep and plasticity. The approach also enables training of machine learning-based constitutive models by allowing back propagation of errors through the finite element calculations. This enables strongly enforcing known physics, such as conservation of momentum and continuity, while allowing data-driven models to learn the truly unknown physics such as the constitutive or petrophysical responses.
Limit criteria for determining the dynamic performance capabilities of high performance, fast-response (greater than 100 Hz) electrohydraulic servosystems are presented. A detailed analysis of the maximum load locus of these systems is used as a basis for the derivation of improved limit criteria. These criteria predict the maximum performance limits caused by system nonlinearities and physical limitations. The criteria are applied to experimental data to verify their validity. Design criteria which assist in the selection of system components for optimal performance are also discussed.
The importance of the quasi-evanescent mode of acoustic-gravity waves (AGW) was recently stressed to elaborate on the daytime dispersion characteristics of horizontal velocity of medium-scale traveling ionospheric disturbances (MS-TID) which were observed by a high frequency Doppler (HFD) sounder array in central Japan. Observed MS-TIDs were classified into two categories: the internal mode and the quasi-evanescent mode as regards physical implication. Nonlinear wave-wave interaction is proposed in an attempt to explain salient features of the latter-class TID.
This is an essay on the general concept of covariance, and its connection with the structure of the nested set of higher frame bundles over a differentiable manifold. Examples of covariant geometric objects include not only linear tensor fields, densities and forms, but affinity fields, sectors and sector forms, higher order frame fields, etc., often having nonlinear transformation rules and Lie derivatives. The intrinsic, or invariant, sets of forms that arise on frame bundles satisfy the graded Cartan-Maurer structure equations of an infinite Lie algebra. Reduction of these gives invariant structure equations for Lie pseudogroups, and for G-structures of various orders. Some new results are introduced for prolongation of structure equations, and for treatment of Riemannian geometry with higher-order moving frames. The use of invariant form equations for nonlinear field physics is implicitly advocated.
An analogue of the magnetosphere developed on the basis of Shaw's (1984) dripping faucet model was used to model the mechanisms of the magnetospheric response to energy transfer from the solar wind. It is demonstrated that geomagnetic activity results from nonlinearly coupled physical processes and that the strength and the nature of the coupling changes dramatically as the magnetosphere is driven harder and harder by increasing energy input. Based on initial results obtained from the model, is is suggested that a chaotic transition takes place in the analogue system as the loading rate is increased beyond a critical value. This model is able to explain many of the features in the results of linear prediction filtering techniques.
The project objectives are to develop process models to investigate the interaction of planetary and synoptic-scale waves including the effects of latent heat release (precipitation), nonlinear dynamics, physical and boundary-layer processes, and large-scale topography; to determine the importance of latent heat release for temporal variability and time-mean behavior of planetary and synoptic-scale waves; to compare the model results with available observations of planetary and synoptic wave variability; and to assess the implications of the results for monitoring precipitation in oceanic-storm tracks by satellite observing systems. Researchers have utilized two different models for this project: a two-level quasi-geostrophic model to study intraseasonal variability, anomalous circulations and the seasonal cycle, and a 10-level, multi-wave primitive equation model to validate the two-level Q-G model and examine effects of convection, surface processes, and spherical geometry. It explicitly resolves several planetary and synoptic waves and includes specific humidity (as a predicted variable), moist convection, and large-scale precipitation. In the past year researchers have concentrated on experiments with the multi-level primitive equation model. The dynamical part of that model is similar to the spectral model used by the National Meteorological Center for medium-range forecasts. The model includes parameterizations of large-scale condensation and moist convection. To test the validity of results regarding the influence of convective precipitation, researchers can use either one of two different convective schemes in the model, a Kuo convective scheme or a modified Arakawa-Schubert scheme which includes downdrafts. By choosing one or the other scheme, they can evaluate the impact of the convective parameterization on the circulation. In the past year researchers performed a variety of initial-value experiments with the primitive-equation model. Using initial conditions typical of climatological winter conditions, they examined the behavior of synoptic and planetary waves growing in moist and dry environments. Surface conditions were representative of a zonally averaged ocean. They found that moist convection associated with baroclinic wave development was confined to the subtropics.
NASA technical memorandum discusses research on use of data from network of ground-based magnetic direction-finding ground-strike lightning sensors to diagnose and predict occurrence and evolution of thunderstorms. Purposes of study to explore applicability and limitations of extrapolation techniques used to generate forecasts from data; to examine physically-based, nonlinear mathematical models for applicability to lightning-forecast problem; and to determine valid extrapolation ranges of such models for various weather scenarios.
Aerodynamic shape design has long persisted as a difficult scientific challenge due its highly nonlinear flow physics and daunting geometric complexity. However, with the emergence of Computational Fluid Dynamics (CFD) it has become possible to make accurate predictions of flows which are not dominated by viscous effects. It is thus worthwhile to explore the extension of CFD methods for flow analysis to the treatment of aerodynamic shape design. Two new aerodynamic shape design methods are developed which combine existing CFD technology, optimal control theory, and numerical optimization techniques. Flow analysis methods for the potential flow equation and the Euler equations form the basis of the two respective design methods. In each case, optimal control theory is used to derive the adjoint differential equations, the solution of which provides the necessary gradient information to a numerical optimization method much more efficiently then by conventional finite differencing. Each technique uses a quasi-Newton numerical optimization algorithm to drive an aerodynamic objective function toward a minimum. An analytic grid perturbation method is developed to modify body fitted meshes to accommodate shape changes during the design process. Both Hicks-Henne perturbation functions and B-spline control points are explored as suitable design variables. The new methods prove to be computationally efficient and robust, and can be used for practical airfoil design including geometric and aerodynamic constraints. Objective functions are chosen to allow both inverse design to a target pressure distribution and wave drag minimization. Several design cases are presented for each method illustrating its practicality and efficiency. These include non-lifting and lifting airfoils operating at both subsonic and transonic conditions.
Asymptotic methods are used to describe the nonlinear self-interaction between pairs of oblique instability modes that eventually develops when initially linear spatially growing instability waves evolve downstream in nominally two-dimensional laminar boundary layers. The first nonlinear reaction takes place locally within a so-called 'critical layer', with the flow outside this layer consisting of a locally parallel mean flow plus a pair of oblique instability waves - which may or may not be accompanied by an associated plane wave. The amplitudes of these waves, which are completely determined by nonlinear effects within the critical layer, satisfy either a single integro-differential equation or a pair of integro-differential equations with quadratic to quartic-type nonlinearities. The physical implications of these equations are discussed.
This report presents the testing and analyses of a foam projectile impacting onto thirteen 6x6 inch flat panels at a 90 degrees incidence angle. The panels tested in this investigation were fabricated of Reinforced-Carbon-Carbon material and were used to aid in the validation of an existing material model, MAT58. The computational analyses were performed using LS-DYNA, which is a physics-based, nonlinear, transient, finite element code used for analyzing material responses subjected to high impact forces and other dynamic conditions. The test results were used to validate LS-DYNA predictions and to determine the threshold of damage generated by the MAT58 cumulative damage material model. The threshold of damage parameter represents any external or internal visible RCC damage detectable by nondestructive evaluation techniques.
The simulation of changes in the Earth's climate due to solar and thermal radiative processes with global climate models (GCMs) is highly complex, depending on the parameterization of a multitude of nonlinearly coupled physical processes. In contrast, the germ of global climate change, the radiative forcing from enhanced abundances of greenhouse gases, is relatively well understood. The impressive agreement between detailed radiation calculations and highly resolved spectral radiation measurements in the thermal infrared under cloudless conditions (see, for example, Fig. 1) instills confidence in our knowledge of the sources of gaseous absorption. That the agreement spans a broad range of temperature and humidity regimes using instruments mounted on surface, aircraft, and satellite platforms not only attests to our capability to accurately calculate radiative fluxes under present conditions, but also provides confidence in the spectroscopic basis for computation of fluxes under conditions that might characterize future global climate (e.g., radiative forcing). Alas, the computational costs of highly resolved spectral radiation calculations cannot be afforded presently in GCMs. Such calculations have instead been used as the foundation for approximations implemented in fast but generally less accurate algorithms performing the needed radiative transfer (RT) calculations in GCMs. Credible climate simulations by GCMs cannot be ensured without accurate solar and thermal radiative flux calculations under all types of sky conditions: pristine cloudless, aerosol-laden, and cloudy. The need for accuracy in RT calculations is not only important for greenhouse gas forcing scenarios, but is also profoundly needed for the robust simulation of many other atmospheric phenomena, such as convective processes.
The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.
Understanding the interactions between the atmosphere, the land, and the subsurface is fundamental to hydrology and is critical for a better assessment of the impacts of climate change and human management on hydrological systems. However, many land surface models simplify the subsurface hydrology and thereby these interactions. In this study, we couple the land surface model Noah-MP included in the NASA Land Information System (LIS) with the integrated hydrologic model ParFlow (ParFlow-LIS) using the Earth System Modeling Framework (ESMF) and the National United Operational Prediction Capability (NUOPC). This coupling improves the simulation of water and energy cycle processes by adding the three-dimensional variably saturated and heterogeneous flow in the subsurface using sophisticated and nonlinear physics-based equations as well as the advances in satellite remote sensing-based data assimilation of the land surface, thereby benefiting the integrated hydrologic modeling and data assimilation community. We use the High Plains aquifer, located in the central United States, as a testbed to evaluate the coupled ParFlow-LIS system. The new ParFlow-LIS system accounts for the effects of topographically driven flows on the land surface, producing more fine-scale patterns of land surface states and fluxes than standalone LIS. In addition, ParFlow-LIS enables the consideration of the effect of subsurface water storage on evapotranspiration. This is particularly important in areas and times with dry soils, such as during drought conditions or in the presence of a cone of depression due to pumping.
LOKI is a simulation code that models weakly-coupled plasma evolution using Eulerian kinetic models in four phase space dimensions. The code is useful to understand basic plasma nonlinear plasma physics phenomena - in particular, wave-particle and wave-wave resonances that occur in laser plasma interactions. LOKI can simulate fundamental physical process in low-density plasmas on a scale of hundreds of light wavelengths. It does so by solving the Vlasov and Valsov-Fokker-Planck differential transport equations describing the collision less and low-collisionality evolution of charged particles in electromagnetic fields in two configuration space and two velocity space dimensions. Two principle electromagnetic models are provided: electrostatic (Poisson's Equation) and electromagnetic (Maxwell's equations).
The physics of bounded free shear flows is studied at supersonic Mach numbers. Wall locations are at 1500 momentum thicknesses away from the shear layer center line. Intrinsic to this flow are high multiplicities of supersonic modes not present in the unbounded case. It is seen that these supersonic modes are resistant to subharmonic growth, a necessary precursor for efficient mixing. Previous results (Eberhardt et al., 1988) with wall locations at less than one half momentum thickness away from the shear layer center indicate that modes resembling channel modes (maximum amplification at the walls) did not roll up in numerical simulations. The similarity between these two cases is pointed out.