Bedrock Inversion and Hyper Differential Sensitivity Analysis for the Shallow Ice Model.
Abstract not provided.
Reese, William↗
SEARCH · Engineering Papers
Results for “model inversion”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Inversion of multiwavelength radiometer measurements by three-dimensional filtering
Remote sensing data from satellites typically have three dimensions: scan position, spacecraft position, and wavelength. Inversion of the radiometric data to infer geophysical parameters is a filtering problem in which the dimension of wavelength (or channel number) is transformed into a dimension of geophysical parameters, and the most general solution is a three-dimensional filter. Linear filters have the advantages of computational speed and easily described transfer functions; but often the measurements are nonlinear functions of the parameters to be inferred. To the extent that the nonlinear inversion problem is overdetermined, it can be modeled by a critically determined linear problem. As an example, inversion of Scanning Multichannel Microwave Radiometer (SMMR) data by means of a three-dimensional Wiener Filter is described. Atmospheric water vapor content, rain liquid water content, surface wind speed and surface temperature are the parameters inferred from the measurements. Nonprecipitating liquid water and water vapor scale height are also modeled but not retrieved. The a priori statistics on which the filter is trained have the effect of governing the selection of a trade-off point of noise as a function of resolution (in all three retrieval dimensions).
Rosenkranz, P. W.↗
Evidence for active hotspots on Venus from analysis of Magellan gravity data
The 500-Myr average crater retention age for Venus has raised questions about the present-day level of tectonic activity. In this study we examine the relationship between the gravity and topography of four large volcanic swells, Beta, Atla, Bell, and Western Eistla Regiones, for clues about their stage evolution. The Magellan line-of-sight gravity data are inverted using a point mass model of the anomalous mass to solve for the local vertical gravity field. Spectral admittance calculated from both the local gravity inversions and a spherical harmonic model is compared to three models of compensation: local compensation, a 'flexural' model with local and regional compensation of surface and subsurface loads, and a 'hotspot' model of compensation that includes top loading by volcanoes and subsurface loading due to a deep, low density mass anomaly. The coherence is also calculated in each region, but yields an elastic thickness estimate only at Bell Regio. In all models, the long wavelengths are compensated locally. Our results may indicate a relatively old, possibly inactive plume.
Smrekar, Suzanne E.↗
Modeling a simple coronal streamer during whole sun month
The solar minimum streamer structure observed during the whole sun month was modeled. The Van de Hulst inversion was used in order to determine the coronal electron density profiles and scale-height temperature profiles. The axisymmetric magnetostatic model of Gibson, Bagenal and Low was also used. The density, temperature, and magnetic field distribution were quantified using both coronal white light data and photospheric magnetic field data from the Wilcox Solar Observatory. The densities and temperatures obtained by the Van de Hulst and magnetostatic models are compared to the magnetic field predicted by the magnetostatic model to a potential field extrapolated from the photosphere.
Gibson, S. E.↗
Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗
Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.
42 ENGINEERING↗
Temperature of the atmosphere of Jupiter from Pioneer 10/11 radio occultations
Radio-occultation data from the Pioneer 10 and 11 flybys are analyzed using an integral inversion technique combined with a model for Jupiter's shape based on gravity data in order to account for the oblateness of the planet's atmosphere. The inversion technique defines the center of refraction in terms of the radius of curvature and the normal to the equipotential surface at the closest approach point of the ray. An Abelian inversion of the data obtained during the Pioneer 10 entry, the Pioneer 10 exit, and the Pioneer 11 exit is performed by fixing the center of refraction at some average value for each occultation event. The results for all three measurements are found to be consistent and to show a temperature inversion between the 10-mb and the 100-mb levels, with temperatures of 130 to 170 K at 10 mb and 80 to 100 K at 100 mb. Major sources of error are discussed, and it is concluded that the range of reasonable temperature uncertainties at these levels does not exceed 40 K. It is noted that the temperature inversion was also inferred from Pioneer 10 IR radiometric data.
Kliore, A. J.↗
Effect of Stability on Mixing in Open Canopies
In open canopies, the within-canopy flux from the ground surface and understory can account for a significant fraction of the total flux above the canopy. This study incorporates the important influence of within-canopy stability on turbulent mixing and subcanopy fluxes into a first-order closure scheme. Toward this goal, we analyze within-canopy eddy-correlation data from the old aspen site in the Boreal Ecosystem - Atmosphere Study (BOREAS) and a mature ponderosa pine site in Central Oregon, USA. A formulation of within-canopy transport is framed in terms of a stability- dependent mixing length, which approaches Monin-Obukhov similarity theory above the canopy roughness sublayer. The new simple formulation is an improvement upon the usual neglect of the influence of within-canopy stability in simple models. However, frequent well-defined cold air drainage within the pine subcanopy inversion reduces the utility of simple models for nocturnal transport. Other shortcomings of the formulation are discussed.
Lee, Young-Hee↗
Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches
The increasing use of tristructural isotropic (TRISO) particle fuel in both advanced and existing reactors necessitates a thorough evaluation of uncertainties and shortcomings in TRISO fission product release models. These inadequacies arise from the simplifications made in computational models compared to experimental data. Utilizing the BISON fuel performance code and experimental data from the Advanced Gas Reactor (AGR) program provides a unique chance to rigorously assess these inadequacies within a Bayesian uncertainty quantification (UQ) framework. This study contrasts the standard Bayesian framework with the Kennedy-O'Hagan (KOH) framework, which explicitly accounts for modeling inadequacies, in the context of UQ for TRISO silver release models. It examines both the traditional Arrhenius equation and a more advanced lower-length-scale (LLS)-informed model that incorporates microstructure information. The inverse UQ process applied to AGR-2 and AGR-3/4 datasets identified modeling inadequacy as the primary source of uncertainty, with experimental noise also being significant, while model parameter uncertainty was minimal. Both the Arrhenius and LLS-informed models showed similar levels of modeling inadequacy. For forward predictive UQ using the AGR-1 dataset, the KOH framework enhanced the accuracy and quality of quantified uncertainties by approximately 30% and 40%, respectively, compared to the standard Bayesian framework. This improvement was observed for both the Arrhenius and LLS-informed models. At the engineering scale, both models performed similarly, but the LLS-informed model outperformed the Arrhenius equation at the mesoscale. These findings underscore the importance of explicitly considering modeling inadequacy in the UQ process and highlight the need for ongoing refinement of physics-based models to address these shortcomings.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates
The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.
Kuang-Yu Chang↗
Modeling-Error-Driven Performance-Seeking Direct Adaptive Control
This paper presents a stable discrete-time adaptive law that targets modeling errors in a direct adaptive control framework. The update law was developed in our previous work for the adaptive disturbance rejection application. The approach is based on the philosophy that without modeling errors, the original control design has been tuned to achieve the desired performance. The adaptive control should, therefore, work towards getting this performance even in the face of modeling uncertainties/errors. In this work, the baseline controller uses dynamic inversion with proportional-integral augmentation. Dynamic inversion is carried out using the assumed system model. On-line adaptation of this control law is achieved by providing a parameterized augmentation signal to the dynamic inversion block. The parameters of this augmentation signal are updated to achieve the nominal desired error dynamics. Contrary to the typical Lyapunov-based adaptive approaches that guarantee only stability, the current approach investigates conditions for stability as well as performance. A high-fidelity F-15 model is used to illustrate the overall approach.
Kulkarni, Nilesh V.↗
Modeling arson - An exercise in qualitative model building
A detailed example is given of the role of von Neumann and Morgenstern's 1944 'expected utility theorem' (in the theory of games and economic behavior) in qualitative model building. Specifically, an arsonist's decision as to the amount of time to allocate to arson and related activities is modeled, and the responsiveness of this time allocation to changes in various policy parameters is examined. Both the activity modeled and the method of presentation are intended to provide an introduction to the scope and power of the expected utility theorem in modeling situations of 'choice under uncertainty'. The robustness of such a model is shown to vary inversely with the number of preference restrictions used in the analysis. The fewer the restrictions, the wider is the class of agents to which the model is applicable, and accordingly more confidence is put in the derived results. A methodological discussion on modeling human behavior is included.
Heineke, J. M.↗
The magnetic field of Neptune
A model is given of the planetary magnetic field of Neptune based on a spherical harmonic analysis of the observations obtained by the Voyager 2. Generalized inverse techniques are used to partially solve a severely underdetermined inverse problem, and the resulting model is nonunique since the observations are limited in spatial distribution. Dipole, quadrupole, and octupole coefficients are estimated independently of other terms, and the parameters are shown to be well constrained by the measurement data. The large-scale features of the magnetic field including dipole tilt, offset, and harmonic content are found to characterize a magnetic field that is similar to that of Uranus. The traits of Neptune's magnetic field are theorized to relate to the 'ice' interior of the planet, and the dynamo-field generation reflects this poorly conducting planet.
Connerney, J. E. P.↗
Consensus Equilibrium for Subsurface Delineation
Heterogeneity and insufficient site characterization limit our knowledge of the subsurface. Inversion techniques, which minimize the mismatch between observations and model predictions, have become an essential tool of subsurface characterization. Most optimization-based approaches fail to incorporate various implicit priors and capture the geological complexity. We overcome these limitations by deploying the plug-and-play and consensus equilibrium (CE) strategies, which provide a flexible framework for image reconstruction. Our CE methodology for spatial delineation of geologic formations consists of an image denoiser and a variational auto-encoder (deep learning-based emulator). The former ameliorates the reconstruction noise, yielding well-defined geological structures; its mathematical equivalence with the proximal operator allows the deployment of advanced denoisers (e.g., CNN-based denoiser) that do not correspond to a regularization objective. The latter defines a geology prior that imposes a geological constraint, for example, continuity and shape of geological features, onto the reconstructed image. Here, we conduct a series of numerical experiments dealing with transient two-dimensional flow driven by a pumping well and natural hydraulic head gradient. They demonstrate the CE framework's ability to delineate, both probabilistically and deterministically, complex subsurface environments with sufficient quality.
42 ENGINEERING↗
Model Evaluation of Methods for Estimating Surface Emissions and Chemical Lifetimes from Satellite Data
Column densities from satellite retrievals can provide valuable information for estimating emissions and chemical lifetimes objectively across the globe. To better understand the uncertainties associated with these estimates, we test four methods using simulated column densities from a point source: a box model approach, a 2D Gaussian fit, an Inverse Radius fit and an Exponentially-Modified Gaussian fit. The model results were simulated using the WRF and CAMx models for the year 2005, for a single point source outside Atlanta in Georgia, USA with specified emissions and three chemical scenarios: no chemical reactions, 12 h chemical lifetime and 1 h chemical lifetime. No other sources were included in the simulations. We find that the box model provides reliable estimates irrespective of plume speed and plume direction, if the plume speed and the chemical lifetime are known accurately. The 2D Gaussian fit was found to be sensitive to plume speed and direction, and requires omnidirectional dispersion in order to have a decent fit. However, the 2D Gaussian fit is only an approximate fit to the data, and the discrepancies mean that the results are dependent on the geographical domain used for the optimization. An Inverse Radius fit is introduced to correct this issue, which is found to provide improved emissions and lifetime estimates. The Exponentially-Modified Gaussian fit also gave improved estimates. It is however dependent on accurate plume rotation such that reported chemical lifetimes with this method could be significantly underestimated.
Emission inventory↗
Modeling Atmospheric CO2 Processes to Constrain the Missing Sink
We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.
Kawa, S. R.↗
The Relaxation Matrix for Symmetric Tops with Inversion Symmetry: Line Mixing Effects in the V1 Band of NH3 - II
Line mixing effects have been calculated in the ν1 parallel band of self-broadened NH3. The theoretical approach is an extension of a semi-classical model to symmetric-top molecules with inversion symmetry developed in the companion paper [Q. Ma and C. Boulet, J. Chem. Phys. 144, 224303 (2016)]. This model takes into account line coupling effects and hence enables the calculation of the entire relaxation matrix. A detailed analysis of the various coupling mechanisms is carried out for Q and R inversion doublets. The model has been applied to the calculation of the shape of the Q branch and of some R manifolds for which an obvious signature of line mixing effects has been experimentally demonstrated. Comparisons with measurements show that the present formalism leads to an accurate prediction of the available experimental line shapes. Discrepancies between the experimental and theoretical sets of first order mixing parameters are discussed as well as some extensions of both theory and experiment.
Boulet, C.↗
Study of a Simulation Tool to Determine Achievable Control Dynamics and Control Power Requirements with Perfect Tracking
This paper contains a study of two methods for use in a generic nonlinear simulation tool that could be used to determine achievable control dynamics and control power requirements while performing perfect tracking maneuvers over the entire flight envelope. The two methods are NDI (nonlinear dynamic inversion) and the SOFFT(Stochastic Optimal Feedforward and Feedback Technology) feedforward control structure. Equivalent discrete and continuous SOFFT feedforward controllers have been developed. These equivalent forms clearly show that the closed-loop plant model loop is a plant inversion and is the same as the NDI formulation. The main difference is that the NDI formulation has a closed-loop controller structure whereas SOFFT uses an open-loop command model. Continuous, discrete, and hybrid controller structures have been developed and integrated into the formulation. Linear simulation results show that seven different configurations all give essentially the same response, with the NDI hybrid being slightly different. The SOFFT controller gave better tracking performance compared to the NDI controller when a nonlinear saturation element was added. Future plans include evaluation using a nonlinear simulation.
Ostroff, Aaron J.↗
Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics
Advances in theoretical modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials emerging as a result of complex non-local interactions. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress–strain curves, ferroelectric hysteresis loops, etc. Here, we suggest and implement Gaussian process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. In this work, we discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. Additionally, this approach is illustrated via the hysteresis loop engineering in ferroelectric materials but can be adapted to other functionalities and generative models.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗