Assessing the Efficacy of Universal Differential Equations to Learn Missing Dynamics from a Subset of Observable State Variables.
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In this study, we used deep learning techniques, which are a form of artificial intelligence, to create fast and effective models for predicting how fluids flow in underground geological formations. This is important for managing geological carbon storage, a method used to fight climate change by storing carbon dioxide underground. The challenge lies in the complex nature of these underground spaces and the large amount of data needed to accurately simulate them. To overcome these issues, we developed a new workflow that reduces the data’s complexity before training the deep learning model and then reconstructs the predicted results in their original form. We also proposed a unique approach to handle the specific complexities found in 3D saturation fields, a crucial aspect of fluid flow prediction. We tested our method using real-world data from the Gulf of Mexico. Our results show that our approach not only accurately predicts fluid behavior but also significantly reduces computation time. This will greatly improve real-time decision-making and risk assessment in large-scale geological carbon storage operations.
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The reliability of LED products may be affected by both luminous flux drop and color shift. Previous research on the topic focuses on either luminous maintenance or color shift. However, luminous flux degradation usually takes very long time to observe in LEDs under normal operating conditions. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphorconverted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable errorbounds (20%) after a short training of the estimator.
The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.
In the two-step method for nuclear reactor simulation, lattice physics calculations are performed to compute homogenized cross-sections for a variety of burnups and lattice configurations. A nodal code is then used to perform full-core analysis using the pre-calculated homogenized cross-sections. One source of uncertainty introduced in this method is that the lattice configuration or depletion conditions typically do not match a pre-calculated one from the lattice physics simulations. Therefore, some interpolation model must be used to estimate the homogenized cross-sections in the nodal code. This current study provides a methodology for sensitivity analysis to quantify the impact of state variables on the homogenized cross-sections. This methodology also allows for analyses of the historical effect that the state variables have on homogenized cross-sections. An application of this methodology on a lattice for the Westinghouse AP1000® reactor is presented where coolant density, fuel temperature, soluble boron concentration, and control rod insertion are the state variables of interest. The effects of considering the instantaneous values of the state variables, historical values of the state variables, and burnup-averaged values of the state variables are analyzed. Using these methods, it was found that a linear model that only considers the instantaneous and burnup-averaged values of state variables can fail to capture some variations in the homogenized cross-sections.
Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.
Fuel performance codes are critical tools for the design, certification, and safety analysis of nuclear reactors. However, their ability to predict fuel behavior under abnormal conditions is severely limited by their considerable reliance on empirical materials models correlated to burn-up (a measure of the number of fission events that have occurred, but not a unique measure of the history of the material). In this paper, we propose a different paradigm for fuel performance codes to employ mechanistic materials models that are based on the current state of the evolving microstructure rather than burn-up. In this approach, a series of state variables are stored at material points and define the current state of the microstructure. The evolution of these state variables is defined by mechanistic models that are functions of fuel conditions and other state variables. The material properties of the fuel and cladding are determined from microstructure/property relationships that are functions of the state variables and the current fuel conditions. Multiscale modeling and simulation is being used in conjunction with experimental data to inform the development of these models. Finally, this mechanistic, microstructure-based approach has the potential to provide a more predictive fuel performance capability, but will require a team of researchers to complete the required development and to validate the approach.
Based on the entropy-free thermodynamic approach, a generalized theory of thermodynamic systems with internal variables of state is being developed. For the case of nonlinear thermodynamic systems with internal variables of state and linear relaxation, the necessary and sufficient conditions have been proved for fulfillment of the second law of thermodynamics in entropy-free formulation which, according to the basic theorem of the theory, are also necessary and sufficient for the existence of a thermodynamic potential. Moreover, relations of correspondence between thermodynamic systems with memory and systems with internal variables of state have been established, as well as some useful relations in the spaces of states of both types of systems.
The present invention relates to resistive random-access memory (RRAM or ReRAM) systems, as well as methods of employing multiple state variables to form degenerate states in such memory systems. The methods herein allow for precise write and read steps to form multiple state variables, and these steps can be performed electrically. Such an approach allows for multilevel, high density memory systems with enhanced information storage capacity and simplified information retrieval.
As an important metric to tell if a nonlinear dynamic system has a singular attractor or divergent trajectory, the maximal Lyapunov exponent (MLE) can be calculated from either system models or time series of state variable measurement. However, in the real world, due to inaccurate models, measurement noise, and the fact that sometimes state variables cannot be measured directly, it is very difficult to get an accurate MLE, which limits its application in, for example, in prediction of a nonlinear physical system (e. g. power systems, etc.) behavior. To overcome these factors, this paper proposed a trajectory-estimation-based MLE calculation approach. The proposed approach addressed how to calculate the MLE when state variables cannot be accessed directly, and uncertainties in system models, as well as noise in measurements. The simulation results shows that the proposed approach is able to handle well the nonlinear measurement functions between state variables and measurements, and get better results than pure model-based approaches or measurement-based approaches in front of measurement noise and model uncertainties.
Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.
Skillful representation of tropical variability and diurnal cycle of precipitation has remained a challenge in global atmosphere models, and often improvements in the variability lead to degradation in the mean‐state. Here, we introduce a configuration of the E3SM Atmosphere Model with a new large‐scale microphysics scheme and several enhancements to the deep convective scheme that improves the variability. The new configuration improves various modes of convectively‐coupled equatorial waves, with increased strength of Kelvin waves and more coherent eastward propagation of the Madden‐Julian Oscillation from the Indian Ocean to the central Pacific Ocean. The same configuration also improves the phase of the diurnal cycle of precipitation, particularly over the continental United States in the boreal summer and over Tropical land regions. Previous studies have shown that, individually taken, some of the deep convective enhancements can improve certain aspects of the variability, and here we show that combining their effects can lead to robust improvements in the variability. This model configuration can form the basis for future studies to examine the response of tropical and diurnal variability under various climate states and their relationships with other modes of variability.
From one vantage point, a shock is a continuous but drastic change in state variables that occurs over very small time and length scales. These scales and associated changes in state variables can be measured experimentally. From another vantage point, a shock is a mathematical singularity consisting of instantaneous changes in state variables. This more mathematical view gives rise to analytical solutions to idealized problems. And from a third vantage point, a shock is a structure in a hydrocode prediction. Its width depends on the simulation’s grid resolution and artificial viscosity. These three vantage points can be in conflict when ideas from the associated fields are combined, and yet combining them is an important goal of an integrated modeling program. This presentation explores an example of how models for real materials in the presence of real shocks react to a hydrocode’s numerical shocks of finite width. The presentation will include an introduction to plasticity for the novice, an historical view of plasticity algorithms, a demonstration of how pursuing the meaning of “shock” has resulted in hydrocode improvements, and will conclude by answering some of the questions that arise from that pursuit. After the technical part of the presentation, a few slides advertising LANL’s Computational Physics Student Summer Workshop will be shown.
We generalize the notion of quantum state designs to infinite-dimensional spaces. We first prove that, under the definition of continuous-variable (CV) state t -designs from [Blume-Kohout , ], no state designs exist for t ≥ 2 . Similarly, we prove that no CV unitary t -designs exist for t ≥ 2 . We propose an alternative definition for CV state designs, which we call rigged t -designs, and provide explicit constructions for t = 2 . As an application of rigged designs, we develop a design-based shadow-tomography protocol for CV states. Using energy-constrained versions of rigged designs, we define an average fidelity for CV quantum channels and relate this fidelity to the CV entanglement fidelity. As an additional result of independent interest, we establish a connection between torus 2-designs and complete sets of mutually unbiased bases. Published by the American Physical Society 2024
Modeling and simulation of the dynamic response of materials is important to many applications including the development of armor systems, understanding the safety of explosives, and assessing the crashworthiness of vehicles. Within such applications it is often critical to accurately compute the evolution of the temperature because it is a state variable that affects the kinetics of competing active processes within the material (e.g., dislocation motion, phase transformation, decomposition). Depending on the selection of an independent state variable, e.g. temperature or entropy, the approach for computing temperature is well understood based on the thermodynamic framework attributed to Coleman and Noll. However, different computational codes used for modeling the dynamic response of materials adopt different independent state variables. In this work, two thermodynamically consistent strategies for computing the temperature of a coupled thermodynamic state are compared and implemented into two different Lagrangian computational codes. The equivalence of these two approaches is established through the numerical solutions of several test problems. Finally, the implication of various approximations made to each of these approaches within the literature are assessed in the context of uniaxial stress conditions (split Hopkinson pressure bar experiments) and uniaxial strain conditions (plate impact experiments). Here, it is shown that the temperature rate or energy partition approaches are equivalent when implemented in their complete forms, but that several common simplifying assumptions, that are warranted in the case of uniaxial stress, lead to significant errors in the resulting Hugoniot state for plate impact.