System identification from noise-corrupted measurements.
System identification from noise corrupted measurement, discussing optimum mean square approximation to unknown systems using stochastic method
Holmes, J. K.↗
SEARCH · Engineering Papers
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Focusing of waves in turbulent inhomogeneous media
A stochastic method using geometrical acoustics is employed to investigate the growth of a large fluctuation in the amplitude of high-frequency waves or shocks propagating through turbulent, inhomogeneous media. Nonlinear terms are retained in the analysis to correctly model focusing and growth of singular fluctuations in the amplitude. A two-dimensional analysis reveals that fluctuations in the ray-tube area grow exponentially and every ray displays caustics. Probability densities for the appearance of caustics are provided, and moments of the ray-tube area distribution and amplitude-related statistics are formulated for distances far into the region of caustic formation. Finally, a relationship is defined between theoretical predictions and measurements on an image plane.
Kulkarny, V. A.↗
Heat and mass transfer in flames
Heat- and mass-transfer processes in turbulent diffusion flames are discussed, considering turbulent mixing and the structure of single-phase flames, drop processes in spray flames, and nonluminous and luminous flame radiation. Interactions between turbulence and other phenomena are emphasized, concentrating on past work of the author and his associates. The conserved-scalar formalism, along with the laminar-flamelet approximation, is shown to provide reasonable estimates of the structure of gas flames, with modest levels of empiricism. Extending this approach to spray flames has highlighted the importance of drop/turbulence interactions; e.g., turbulent dispersion of drops, modification of turbulence by drops, etc. Stochastic methods being developed to treat these phenomena are yielding encouraging results.
Faeth, G. M.↗
Simulation of proton-induced energy deposition in integrated circuits
A time-efficient simulation technique was developed for modeling the energy deposition by incident protons in modern integrated circuits. To avoid the excessive computer time required by many proton-effects simulators, a stochastic method was chosen to model the various physical effects responsible for energy deposition by incident protons. Using probability density functions to describe the nuclear reactions responsible for most proton-induced memory upsets, the simulator determines the probability of a proton hit depositing the energy necessary for circuit destabilization. This factor is combined with various circuit parameters to determine the expected error-rate in a given proton environment. An analysis of transient or dose-rate effects is also performed. A comparison to experimental energy-disposition data proves the simulator to be quite accurate for predicting the expected number of events in certain integrated circuits.
Fernald, Kenneth W.↗
Random search optimization based on genetic algorithm and discriminant function
The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.
Kiciman, M. O.↗
Simulated orbiter lift-off acceleration - A stochastic model
A simple procedure for generating synthetic orbiter liftoff acceleration is described. This stochastic method is based on a filtered white noise model which accounts for the nonstationary time evolution of amplitude and frequency content of orbiter lift-off accelerations. The example of Space Transportation System STS-41 Z-acceleration is carefully examined, and the corresponding synthetic orbiter lift-off accelerations model was developed. It is shown that the resulting simulated accelerations preserve significant properties of the original record. Statistically based acceleration response spectra curves for the simulated orbiter lift-off excitations are also evaluated and the results are compared with those for the actual flight data.
Lee-Glauser, Gina↗
Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms
Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments
Prognostics↗
Hiereachical Bayesian Model for Combining Geochemical and Geophysical Data for Environmental Applications Software
Development of a hierarchical Bayesian model to estimate the spatiotemporal distribution of aqueous geochemical parameters associated with in-situ bioremediation using surface spectral induced polarization (SIP) data and borehole geochemical measurements collected during a bioremediation experiment at a uranium-contaminated site near Rifle, Colorado. The SIP data are first inverted for Cole-Cole parameters including chargeability, time constant, resistivity at the DC frequency and dependence factor, at each pixel of two-dimensional grids using a previously developed stochastic method. Correlations between the inverted Cole-Cole parameters and the wellbore-based groundwater chemistry measurements indicative of key metabolic processes within the aquifer (e.g. ferrous iron, sulfate, uranium) were established and used as a basis for petrophysical model development. The developed Bayesian model consists of three levels of statistical sub-models: 1) data model, providing links between geochemical and geophysical attributes, 2) process model, describing the spatial and temporal variability of geochemical properties in the subsurface system, and 3) parameter model, describing prior distributions of various parameters and initial conditions. The unknown parameters are estimated using Markov chain Monte Carlo methods. By combining the temporally distributed geochemical data with the spatially distributed geophysical data, we obtain the spatio-temporal distribution of ferrous iron, sulfate and sulfide, and their associated uncertainity information. The obtained results can be used to assess the efficacy of the bioremediation treatment over space and time and to constrain reactive transport models.
Chen, Jinsong↗
Overview of Energy Life Cycle Analysis at NETL - LCAXVIII
This work was presented at the American Center for Life Cycle Assessment (ACLCA) LCAXVIII Conference in Fort Collins, CO on September 25, 2018. At NETL, LCA is used as a tool and framework for undertaking a broad range of evaluations. This overview describes the LCA process at NETL, including unique application of stochastic methods to environmental and economic analyses, and shows highlights from several recent studies.
Skone, Timothy J.↗
Overview of Energy Life Cycle Analysis at NETL - LCAXVI
This work was presented at the American Center for Life Cycle Assessment (ACLCA) LCAXVI Conference in Charleston, SC on September 27, 2016. At NETL, LCA is used as a tool and framework for undertaking a broad range of evaluations. This overview describes the LCA process at NETL, including unique application of stochastic methods to environmental and economic analyses, and shows highlights from several recent studies.
Skone, Timothy J.↗
Overview of Energy Life Cycle Analysis at NETL - LCAXVII
This work was presented at the American Center for Life Cycle Assessment (ACLCA) LCAXVIII Conference in Portsmouth, NH on October 5, 2017. At NETL, LCA is used as a tool and framework for undertaking a broad range of these types of evaluations. This overview describes the LCA process at NETL, including unique application of stochastic methods to environmental and economic analyses, and show highlights from several recent studies.
Skone, Timothy J.↗
List-Mode source injection algorithm for detectors with arbitrary pose and trajectory
environments, introducing actual sources is usually neither feasible nor economical. It is more practical to move the detector (whether airborne, vehicle-borne, or human-portable) through the test environment and then later artificially superimpose either measured or simulated source signatures on the recorded background data. We present a source injection (SI) algorithm that inserts source events into list-mode background data. It is designed to accommodate cases where “injection data” are available only at a limited set of locations. Stochastic methods are employed both to select source events from a measured dataset containing both source and background events, and to scale the number of selected events to match the field conditions of detector-source distance, air attenuation, acquisition duration, and the relative strength of the measured and injected sources. The algorithm is deployed as part of the Airborne Radiation Enhanced-sensor System (ARES) Advanced Technology Demonstration, and is applied to field data obtained using the ARES detector system. For this helicopter-borne detection system, we describe a practical ground-based measurement scheme for acquiring data for later injection. These data are then injected into background data acquired during flight, where arbitrary detector poses and trajectories are possible. Similar flight paths are traversed with and without actual 137Cs and 60Co sources present. SI is applied to the source-absent datasets, and the results are compared to the source-present datasets. SI output is found to be consistent with measured activity within the estimated uncertainties in GPS position and source strength.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Managing Uncertainty and Flexibility in Day-Ahead Electricity Markets
Net load imbalances from day ahead forecasts can lead to significant grid operations costs and are expected to increase as variable renewable energy adoption grows. We propose a new wholesale market product to manage the risk of net load imbalances called Flexibility Options. This product relies on probabilistic forecasts to estimate flexibility demand and would be co-optimized in the day-ahead market. We also propose stochastic methods that enable DER and flexible load aggregators to participate in flexibility markets while considering the uncertainty in weather and occupant behavior.
day-ahead market↗
Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2023 Circulation Test Data
Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.
58 GEOSCIENCES↗
Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2024 Circulation Test Data
Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.
15 GEOTHERMAL ENERGY↗
Stochastic linear multistep methods for the simulation of chemical kinetics
In this paper, we introduce the Stochastic Adams-Bashforth (SAB) and Stochastic Adams-Moulton (SAM) methods as an extension of the τ-leaping framework to past information. Using the Θ-trapezoidal τ-leap method of weak order two as a starting procedure, we show that the k-step SAB method with k ≥ 3 is order three in the mean and correlation, while a predictor-corrector implementation of the SAM method is weak order three in the mean but only order one in the correlation. These convergence results have been derived analytically for linear problems and successfully tested numerically for both linear and non-linear systems. A series of additional examples have been implemented in order to demonstrate the efficacy of this approach.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Simulation of Osmotic Swelling by the Stochastic Immersed Boundary Method
We develop here computational methods for the simulation of osmotic swelling phenomena relevant to microscopic vesicles containing transformable solute molecules. We introduce stochastic immersed boundary methods (SIBMs) that can capture osmotically driven fluid transport through semipermeable elastic membranes subject to thermal fluctuations. We further develop numerical methods to handle within SIBMs an elastic shell model for a neo-Hookean material. Our extended SIBM allows for capturing osmotic swelling phenomena driven by concentration changes and interactions between a discrete collection of confined particles while accounting for the thermal fluctuations of the semipermeable membrane and the hydrodynamic transport of solvent. We use our computational methods to investigate osmotic phenomena in regimes that go beyond the classical Van't Hoff theory. We develop statistical mechanics theories for osmotic swelling of vesicles when there are significant interactions between particles that can transform over time. We validate our theoretical results against detailed computational simulations. Our methods are expected to be useful for a wide class of applications allowing for the simulation of osmotically driven flows, thermally fluctuating semipermeable elastic structures, and solute interactions.
97 MATHEMATICS AND COMPUTING↗
Primal and Dual Integrated Force Methods Used for Stochastic Analysis
At the NASA Glenn Research Center, the primal and dual integrated force methods are being extended for the stochastic analysis of structures. The stochastic simulation can be used to quantify the consequence of scatter in stress and displacement response because of a specified variation in input parameters such as load (mechanical, thermal, and support settling loads), material properties (strength, modulus, density, etc.), and sizing design variables (depth, thickness, etc.). All the parameters are modeled as random variables with given probability distributions, means, and covariances. The stochastic response is formulated through a quadratic perturbation theory, and it is verified through a Monte Carlo simulation.
Patnaik, Surya N.↗