Improving accuracy of stochastic collocation in ODE/PDE systems via data-driven factorization of the model dynamics
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De, Saibal↗
SEARCH · Engineering Papers
Results for “Stochastic modelling”
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Improving accuracy of stochastic collocation in ODE/PDE systems via data-driven factorization of the model dynamics
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De, Saibal↗
Improving Accuracy of Stochastic Collocation Surrogates for ODE/PDE Systems via Data-Driven Factorization of Model Dynamics
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De, Saibal↗
Stochastic mixing method for sensitivity analysis of temperature variations in the MCNP model of the ACRR Fuel during operations.
Abstract not provided.
Moreno, Melissa Andrea↗
Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids
The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Techno-economic uncertainty analysis of wet waste-to-biocrude via hydrothermal liquefaction
Not Available
Li, Shuyun↗
Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module
MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.
97 MATHEMATICS AND COMPUTING↗
Convergence of Hyperbolic Neural Networks Under Riemannian Stochastic Gradient Descent
Abstract We prove, under mild conditions, the convergence of a Riemannian gradient descent method for a hyperbolic neural network regression model, both in batch gradient descent and stochastic gradient descent. We also discuss a Riemannian version of the Adam algorithm. We show numerical simulations of these algorithms on various benchmarks.
Whiting, Wes (ORCID:0000000247505060)↗
Fractional Modeling in Action: A Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials
Modeling of phenomena such as anomalous transport via fractional-order differential equations has been established as an effective alternative to partial differential equations, due to the inherent ability to describe large-scale behavior with greater efficiency than fully-resolved classical models. In this review article, we first provide a broad overview of fractional-order derivatives with a clear emphasis on the stochastic processes that underlie their use. We then survey three exemplary application areas – subsurface transport, turbulence, and anomalous materials – in which fractional-order differential equations provide accurate and predictive models. For each area, we report on the evidence of anomalous behavior that justifies the use of fractional-order models, and survey both foundational models as well as more expressive state-of-the-art models. We also propose avenues for future research, including more advanced and physically sound models, as well as tools for calibration and discovery of fractional-order models.
97 MATHEMATICS AND COMPUTING↗
Wake-Resolving Acoustic Tomography: Advances through Numerical Covariance Methods
Acoustic tomography offers path-integrated measurements of atmospheric velocity and temperature fluctuations with high spatial resolution. Classical implementations of time-dependent stochastic inversion rely on homogeneous, isotropic covariance models that are poorly suited to the anisotropic structure of wind turbine wakes. By directly estimating heterogeneous covariances from large-eddy simulations (LESs) into the time-dependent stochastic inversion operator, we relax implicit assumptions in the analytical models used historically. Retrievals using these LES-informed models improve agreement with true fields in variance, turbulent kinetic energy, and spectral content compared to analytical and precursor-based covariance models. The results indicate that LES-informed covariance models can enhance the accuracy of acoustic tomography retrievals in complex, anisotropic flows such as wind turbine wakes in some cases and highlight instances where analytical models still offer competitive performance, despite their simplifying assumptions.
17 WIND ENERGY↗
Stochastic room temperature creep of 316 L stainless steel
The creep behavior of 316 L stainless steel at room temperature was evaluated as a function of time and applied stress using a new high-throughput approach. Several common creep models were evaluated against the observations, leading to deeper analysis of a stress-dependent modified logarithmic creep model. Within this model, multiple sources of uncertainty were compared. Aleatoric stochastic variation between samples under nominally identical conditions was identified as the primary contributor to uncertainty in creep response. Under any particular set of conditions, the sample-to-sample variability in creep strain was as high as a factor of two, highlighting the engineering importance of characterizing large statistical datasets. The model's extrapolation capabilities were assessed by comparing predictions derived from calibration on partial, shorter-duration subsets of the data. In conclusion, these findings underscore the importance of accounting for stochastic effects in predictive modeling of aging phenomena.
High-throughput↗
Why is it still too warm or cold in my house? Examining the relationships between energy efficient capital and household energy insecurity
Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A simple way to integrate distributed storage into a wholesale electricity market
Abstract Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator.
Lamadrid, Alberto J.↗
Subsurface microbial communities as a tool for characterizing regional-scale groundwater flow
Subsurface microbial community distribution patterns are influenced by biogeochemical and groundwater fluxes and may inform hydraulic connections along groundwater-flow paths. This study examined the regional-scale microbial community of the Death Valley Regional Flow System and evaluated whether subsurface communities can be used to identify groundwater-flow paths between recharge and discharge areas. Samples were collected from 36 sites in three groundwater basins: Pahute Mesa–Oasis Valley (PMOV), Ash Meadows (AM), and Alkali Flat–Furnace Creek Ranch (AFFCR). Microbial diversity within and between communities varied by location, and communities were separated into two overall groups that affiliated with the AM and PMOV/AFFCR basins. Network analysis revealed patterns between clusters of common microbes that represented groundwaters with similar geochemical conditions and largely corroborated hydraulic connections between recharge and discharge areas. Null model analyses identified deterministic and stochastic ecological processes contributing to microbial community assemblages. Most communities were more different than expected and governed by dispersal limitation, geochemical differences, or undominating processes. However, certain communities from sites located within or near the Nevada National Security Site were more similar than expected and dominated by homogeneous dispersal or selection. Overall, the (dis)similarities between the microbial communities of DVRFS recharge and discharge areas supported previously documented hydraulic connections between: (1) Spring Mountains and Ash Meadows; (2) Frenchman and Yucca Flat and Amargosa Desert; and (3) Amargosa Desert and Death Valley. However, only a portion of the flow path between Pahute Mesa and Oasis Valley could be supported by microbial community analyses, likely due to well-associated artifacts in samples from the two Oasis Valley sites. This study demonstrates the utility of combining microbial data with hydrologic, geologic, and water-chemistry information to comprehensively characterize groundwater systems, highlighting both strengths and limitations of this approach.
54 ENVIRONMENTAL SCIENCES↗
Assessing the potential of composite confining systems for secure and long-term CO 2 retention in geosequestration
A potential geologic target for CO 2 storage should ensure secure containment of injected CO 2 . Traditionally, this objective has been achieved by targeting reservoirs with overlying seals-regionally extensive, low permeability units that have been proven capable of retaining buoyant fluid accumulations over geologic time. However, considering that the amount of CO 2 is limited by a decadal injection period, vertical migration of CO 2 can be effectively halted by a composite system of discontinuous shale/silt/mudstone barriers in bedded sedimentary rocks. Here, we studied the impact of depositional architectures in a composite confining system on CO 2 migration and confinement at reservoir scale. We stochastically generated lithologically heterogeneous reservoir models containing discontinuous barriers consistent with statistical distributions of net-sand-to-gross-shale ratio (NTG) and horizontal correlation lengths derived from well log data and observations of producing hydrocarbon fields in Southern Louisiana. We then performed an extensive suite of reservoir simulations of CO 2 injection and post-injection to evaluate the sensitivity of CO 2 plume migration and pressure response of the composite system to a series of geologic and fluid parameters including the lateral continuity of barriers, NTG, permeability anisotropy within the sand body, and capillary pressure contrast between the sand and shale facies. The results indicate that lateral continuity of barriers and NTG are the dominant controls on CO 2 plume geometry and pressure build-up in the reservoir, while the impact of NTG is particularly pronounced. The significance of intraformational barriers becomes apparent as they facilitate the local capillary trapping of CO 2 . Those barriers improve the pore space occupancy by promoting a more dispersed shape of the plume and ultimately retard the buoyancy-driven upward migration of the plume post injection.
58 GEOSCIENCES↗
Differentiable stochastic halo occupation distribution
ABSTRACT In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep reinforcement learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of halo occupation distribution (HOD) models that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalogue generated from the Bolshoi simulation using a standard HOD model and find near-identical posteriors as standard Markov chain Monte Carlo techniques with an increase of ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.
79 ASTRONOMY AND ASTROPHYSICS↗
A Distributionally Robust Resilience Enhancement Strategy for Distribution Networks Considering Decision-Dependent Contingencies
When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). Here, to address these two challenges, this paper develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured for each possible EWE scenario. Then, a two-stage tri-level decision-dependent distributionally robust resilient enhancement (DD-DRRE) model is formulated, whose outputs include the optimal line hardening, distributed generation (DG) allocation, and proactive network reconfiguration strategy under the worst-case distributions in SWDD-ASs. Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer linear programming (MILP)-based master problem and multiple scenario-wise subproblems, facilitating the adoption of a customized column-and-constraint generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable improvement in the out-of-sample performance of our model, compared to its prevailing stochastic and robust counterparts. Moreover, the potential values of incorporating the ambiguity and distributional information are quantitatively estimated, providing a useful reference for planners with different budgets and risk-aversion levels.
decision-dependent uncertainty↗
Application of Scalar Filtered Density Function to Turbulent Flows Under Supercritical Condition
Abstract The scalar filtered density function (FDF) methodology is extended and employed for large eddy simulation (LES) of turbulent flows under supercritical condition. To describe real fluid behavior, the extended methodology incorporates the generalized heat and mass diffusion models along with real fluid thermodynamic relations which are derived using the cubic Peng–Robinson equation of state. These models are implemented within the stochastic differential equations comprising the scalar FDF transport. Simulations are conducted of a temporally developing mixing layer under supercritical condition and the results are assessed by comparing with data generated by direct numerical simulation (DNS) of the same layer. The consistency of the proposed FDF methodology is assessed. The LES-FDF predictions are shown to agree favorably with the DNS data and exhibit several key features pertaining to supercritical turbulent flows.
Energy & Fuels↗