Structural model choices regularly overshadow parametric uncertainty in agent-based simulations of household flood risk outcomes
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This report describes a formal method to quantify structural damage tolerance and reliability in the presence of a multitude of uncertainties in turbine engine components. The method is based at the material behavior level where primitive variables with their respective scatter ranges are used to describe behavior. Computational simulation is then used to propagate the uncertainties to the structural scale where damage tolerance and reliability are usually specified. Several sample cases are described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate that it is mature and that it can be used to probabilistically evaluate turbine engine structural components. It may be inferred from the results that the method is suitable for probabilistically predicting the remaining life in aging or deteriorating structures, for making strategic projections and plans, and for achieving better, cheaper, faster products that give competitive advantages in world markets.
This report describes a formal method to quantify structural damage tolerance and reliability in the presence of a multitude of uncertainties in turbine engine components. The method is based at the material behavior level where primitive variables with their respective scatter ranges are used to describe behavior. Computational simulation is then used to propagate the uncertainties to the structural scale where damage tolerance and reliability are usually specified. Several sample cases are described to illustrate the effectiveness, versatility, and maturity of the method. Typical results from this method demonstrate, that it is mature and that it can be used to probabilistically evaluate turbine engine structural components. It may be inferred from the results that the method is suitable for probabilistically predicting the remaining life in aging or in deteriorating structures, for making strategic projections and plans, and for achieving better, cheaper, faster products that give competitive advantages in world markets.
A three-bay, space, cantilever truss is probabilistically evaluated to quantify the range of uncertainties of vibration frequencies and truss free end vertical displacements due to nonuniform thermal loads, applied loads and moments (mechanical loads), and combination of both. The truss members are assumed to be made from (1) aluminum tubes or (2) high modulus graphite-fiber/intermediate modulus epoxy-matrix composite tubes. Cumulative distribution function results show that certain combination of thermal loads with mechanical loads reduce the probabilistic vibration frequencies and increase the magnitude of the truss free end vertical displacements for the aluminum truss. However, for the composite truss, the thermal effects on the probabilistic vibration frequencies are not as substantial as that for an aluminum truss. This can be attributed to the large differences in the values of coefficient of thermal expansion. Finally, the sensitivities associated with the uncertainties in the structural, material and load variables (primative variables) are investigated. Results indicate that vibration frequencies and truss free end vertical displacements are sensitive to the uncertainties in spacial (geometry) variables as well as member modulus.
The design, analysis, and simulation of a control law that guarantees a fine pointing performance under external disturbances and model uncertainties are investigated. The authors examine the achievable performance and modeling errors for the control-structure interaction (CSI) evolutionary model structure and highlight the importance of accurate knowledge of structural frequencies and damping values for very lightly damped structures. Numerical results indicate that a limited but significant line-of-sight pointing performance robustness can be attained in spite of modeled structured and unstructured uncertainties in the system. The simulation results reported demonstrate typical performance levels, types of uncertainties and their magnitudes, and design tradeoffs for a realistic laboratory model of a large flexible space structure.
Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single-model initial-condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea-ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn-to-winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.
ABSTRACT With the goal of a thorough investigation of the ionized gas and its origin in early-type group-dominant galaxies, we present archival MUSE data for 18 galaxies from the Complete Local-Volume Groups Sample (CLoGS). These data allowed us to study the spatially resolved warm gas properties, including the morphology of the ionized gas, EW(H α), and kinematics as well as the gas-phase metallicity (12 + log(O/H)) of these systems. In order to distinguish between different ionization mechanisms, we used the emission-line ratios [O iii]/H β and [N ii]/H α in the BPT diagrams and EW(H α). We find that the ionization sources in our sample have variable impacts at different radii; central regions are more influenced by low-luminosity active galactic nuclei, while extended regions of low-ionization nuclear emission-line region-like emission are ionized by other mechanisms with post-asymptotic giant branch stars photoionization likely contributing significantly. We classified our sample into three H α +[N ii] emission morphology types. We calculate the gas-phase metallicity assuming several methods and ionization sources. In general, 12 + log(O/H) decreases with radius from the centre for all galaxies, independently of nebular morphology type, indicating a metallicity gradient in the abundance profiles. Interestingly, the more extended filamentary structures and all extranuclear star-forming regions present shallow metallicity gradients. Within the uncertainties these extended structures can be considered chemically homogeneous. We suggest that group-dominant galaxies in our sample likely acquired their cold gas in the past as a consequence of one or more mechanisms, e.g. gas-clouds or satellite mergers/accretion and/or cooling flows that contribute to the growth of the ionized gas structures.
The research during the second reporting period has focused on robust control design for hypersonic vehicles. An already existing design for the Hypersonic Winged-Cone Configuration has been enhanced. Uncertainty models for the effects of propulsion system perturbations due to angle of attack variations, structural vibrations, and uncertainty in control effectiveness were developed. Using H(sub infinity) and mu-synthesis techniques, various control designs were performed in order to investigate the impact of these effects on achievable robust performance.
SUMMARY Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior covariance matrix). Even for common geophysical inverse problems its calculation is beyond the computational and storage capacities of the largest high-performance computing systems. In this study, we amend the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to perform uncertainty quantification for large-scale applications. For seismic inverse problems, the limited-memory BFGS (L-BFGS) method prevails as the most efficient quasi-Newton method. We aim to augment it further to obtain an approximate inverse Hessian for uncertainty quantification in FWI. To facilitate retrieval of the inverse Hessian, we combine BFGS (essentially a full-history L-BFGS) with randomized singular value decomposition to determine a low-rank approximation of the inverse Hessian. Setting the rank number equal to the number of iterations makes this solution efficient and memory-affordable even for large-scale problems. Furthermore, based on the Gauss–Newton method, we formulate different initial, diagonal Hessian matrices as pre-conditioners for the inverse scheme and compare their performances in elastic FWI applications. We highlight our approach with the elastic Marmousi benchmark model, demonstrating the applicability of pre-conditioned BFGS for large-scale FWI and uncertainty quantification.
A composite wing with spars and bulkheads is used to demonstrate the effectiveness of probabilistic assessment of smart composite structures to control uncertainties in distortions and stresses. Results show that a smart composite wing can be controlled to minimize distortions and to have specified stress levels in the presence of defects. Structural responses such as changes in angle of attack, vertical displacements, and stress in the control and controlled plies are probabilistically assessed to quantify their respective uncertainties. Sensitivity factors are evaluated to identify those parameters that have the greatest influence on a specific structural response. Results show that smart composite structures can be configured to control both distortions and ply stresses to satisfy specified design requirements.
This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.
This report summarizes the work done under the Laboratory Directed Research and Development (LDRD) project entitled "Learning Hidden Structure in Multi-Fidelity Information Sources for Efficient Uncertainty Quantification". In this project we investigated multi-fidelity strategies for fusing data from information sources of varying cost and accuracy. Most existing strategies exploit hierarchical relationships between models, for example that occur when different models are generated by refining a numerical discretization parameter. In this work we focused on encoding the relationships between information sources using directed acyclic graphs. The multi-fidelity networks can have general structure and represent a significantly greater variety of modeling relationships than recursive networks used in the current state literature. Numerical results show that a non-hierarchical multi-fidelity Monte Carlo strategy can reduce the cost of estimating uncertainty in predictions of a model of plasma expanding in a vacuum by almost two orders of magnitude.
A three-bay, space, cantilever truss is probabilistically evaluated to quantify the range of uncertainties of buckling loads and member forces due to nonuniform thermal loads, applied loads and moments (mechanical loads), and combination of both. The truss members are assumed to be made from Aluminum tubes or high modulus graphite-fiber/intermediate modulus epoxy-matrix composite tubes. Cumulative distribution function results show that certain combinations of thermal loads with mechanical loads reduce the probabilistic buckling loads and increase the magnitude of the member axial forces for the aluminum truss. The same trend is observed for the composite truss as well, as however, the thermal effects on the probabilistic buckling loads and member axial forces are not as substantial as that for an aluminum truss. This can be attributed to the large differences in the values of coefficient of thermal expansion. Finally, the sensitivities associated with the uncertainties in the structural, material, and load variables (primitive variables) are investigated. They show that buckling loads and member axial forces are most sensitive to the uncertainties in spacial (geometry) variables.
In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.
A three-bay, space, cantilever truss is probabilistically evaluated for adaptive/smart/intelligent behavior. For each behavior, the scatter (ranges) in buckling loads, vibration frequencies, and member axial forces are probabilistically determined. Sensitivities associated with uncertainties in the structure, material and load variables that describe the truss are determined for different probabilities. The relative magnitude for these sensitivities are used to identify significant truss variables that control/classify its behavior to respond as an adaptive/smart/intelligent structure. Results show that the probabilistic buckling loads and vibration frequencies increase for each truss classification, with a substantial increase for intelligent trusses. Similarly, the probabilistic member axial forces reduce for adaptive and intelligent trusses and increase for smart trusses.
A three-bay, space, cantilever truss is probabilistically evaluated for adaptive/smart/intelligent behavior. For each behavior, the scatter (ranges) in buckling loads, vibration frequencies, and member axial forces are probabilistically determined. Sensitivities associated with uncertainties in the structure, material and load variables that describe the truss are determined for different probabilities. The relative magnitude for these sensitivities are used to identify significant truss variables that control/classify its behavior to respond as an adaptive/smart/intelligent structure. Results show that the probabilistic buckling loads and vibration frequencies increase for each truss classification, with a substantial increase for intelligent trusses. Similarly, the probabilistic member axial forces reduce for adaptive and intelligent trusses and increase for smart trusses.
Spectroscopic and photometric infrared observations of Saturn are reviewed and compared to the expected flux from thermal structure models. Large uncertainties exist in the far-infrared measurements, but the available data indicate that the effective temperature of the disk of Saturn is 90 + or - 5 K. The thermal structure models proposed by Tokunaga and Cess and by Gautier et al. (model 'N') agree best with the observations. North-South limb scans of Saturn at 10 and 20 micrometers show that the temperature inversion is much stronger at the South polar region than at the equator.
The use of remotely sensed images to map continuous biophysical variables, such as those related to terrestrial vegetation amount, sea surface temperature, and many other targets of NASA s Earth Observing System (EOS), includes variable, parametric, positional, spatial support and structural sources of uncertainty. A complete description of uncertainty will lead to a probability distribution at each location, allowing the exploration of the spatial dimension of uncertainty, that is, where the field is not well quantified. To achieve this purpose, convenient visualization tools are required. We have produced such a tool, called PDFVis, that facilitates the display of probability density functions (pdfs) on a per-grid-cell basis. The density estimate from Monte-Carlo generated realizations is interactively displayed as well as parametric and non-parametric summaries of the pdf field (such as mean, median, quartiles, standard deviation, number of modes, and locations of modes). Shaded surface renderings of pdfs along a transect can also be projected onto a plane. This tool will become more useful as richer descriptions of spatial uncertainty become available.