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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.

Mechanics↗
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Toroidal modeling of runaway electron loss due to 3D fields in ITER

Mitigation of runaway electrons (REs) by three-dimensional (3D) magnetic field perturbations is numerically investigated for the ITER 15 MA baseline D–T scenario, utilizing the MARS-F code (Liu et al Phys. Plasmas 7 3681) with a drift orbit test particle tracing module. Considered are two types of 3D fields: the n = 3 (n is the toroidal mode number) resonant magnetic perturbation (RMP) utilized for the purpose of controlling the edge localized modes in ITER, and perturbations generated by the n = 1 magneto-hydrodynamic (MHD) instabilities in a post-disruption plasma. The RMP field, applied to a pre-disruption plasma, is found to be moderately effective in mitigating the RE seeds in ITER when vacuum field model is assumed. Up to ~40% loss fraction is possible at 90 kA-turn coil current. The mitigation efficiency is however substantially reduced, down to less than 5%, when the plasma response is taken into account. This is due to strong screening of the resonant magnetic field components by the plasma response resulting in much less field line stochasticity. On the other hand, the MARS-F modeling, based on the DINA-simulated post-disruption equilibria, shows that the n = 1 resistive kink instabilities develop in these plasmas, as the edge safety factor qa evolves and drops below integer numbers. RE mitigation by these MHD instabilities is sensitive to the eigenmode structure. The best mitigation is achieved as q a drops below 3, when a global kink instability occurs that encompasses both internal and external components. This global instability is found to be capable of mitigating over 80% MeV-level passing RE orbits at a field perturbation |δB|/B 0 that is comparable to that observed in DIII-D experiments, and full mitigation if the perturbation amplitude is doubled. The 'wetted' area on the ITER limiting surface, due to MHD instability induced RE loss, generally increases with the perturbation amplitude (together with increasing loss fraction). At the highest perturbation level assumed in this study, the wetted area reaches ~60% of the total limiting surface area. Lastly, the lost RE orbits mainly strike the outer divertor region of the limiting surface, with some fraction also hitting a wide area along the inboard side of the surface.

3D fields↗
DOE OSTI2021

NRAP-open-IAM: A flexible open-source integrated-assessment-model for geologic carbon storage risk assessment and management

Large-scale implementation of geologic carbon storage (GCS) to help reduce atmospheric greenhouse gas emissions requires stakeholder confidence that injected CO2 will remain contained and that potential subsurface environmental risks are acceptably small and manageable. The U.S. Department of Energy’s National Risk Assessment Partnership (NRAP) has developed an open-source integrated assessment model (NRAP-Open-IAM) to help address questions about a potential GCS site’s ability to effectively contain injected CO 2 and protect groundwater and other overlying environmentally sensitive receptors. NRAP-Open-IAM allows a user to: (1) incorporate relevant site geologic and injection scenario data; (2) characterize important site features and events;(3) couple fast prediction models of various system components of the engineered geologic system; and (4) execute stochastic, dynamic simulation of whole GCS system performance, leakage risk assessment, and uncertainty quantification. NRAP-Open-IAM is available on GitLab (https://gitlab.com/NRAP/OpenIAM), and is accompanied by multiple application examples and detailed user and developer guides.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2020

On the character of turbulent-like flows in self-consistent models of core-collapse supernovae

Neutrino-driven convection plays a crucial role in the development of core-collapse supernova (CCSN) explosions. However, the complex mechanism that triggers the shock revival and the subsequent explosion has remained inscrutable for many decades. Multidimensional simulations suggest that the growth of fluid instabilities, especially development of convection, will determine the morphology of the explosion. We have performed 3D simulations using spherical-polar coordinates covering a reduced angular extent (90° computational domain), and with angular resolutions of 2°, 1°, 1/2°, and 1/4°, to study the development of turbulent-like flows in core-collapse supernova explosions on a time scale of order 100 ms. We have employed the multi-physics Chimera code that includes detailed nuclear physics and spectral neutrino transport. Coarse resolution models do not develop an inertial range, presumably due to the bottleneck effect, such that the energy is prevented from cascading down to small scales and tends to accumulate at large scales. High-resolution models instead, start to recover the k −5/3 scaling of Kolmogorov's theory. Stochasticity and few simulation samples limit our ability to predict the development of explosions. Over the simulated time period, our models show no clear trend in improving (or diminishing) conditions for explosion as the angular resolution is increased. However, we find that disordered flow provides an effective pressure, as characterized by the Reynolds stress, behind the shock. This contribution, ~40%–50% of the thermal pressure, aids shock revival and thus the development of the explosion. Finally, we show that the kinetic energy power spectrum of reduced angular extent and full 4π models are consistent, thus indicating that a 90° computational domain is an adequate configuration to study the character of turbulence in CCSNe.

Casanova Bustamante, Jordi↗
DOE OSTI2021

Comparison of Attila Transport Code with MCNP in the Analysis of Small Modular Reactor Shielding

Of great importance in the design of a nuclear power plant is the evaluation of shielding from the ionizing radiation produced in the core of the nuclear reactor. This requires simulation software to predict the dose rates in the areas of interest. These problems are referred to as deep penetration problems, as they involve the reduction of a radiation field by two or three orders of magnitude or more. This makes accurate analyses difficult for Monte Carlo based solutions that rely on enough particles being sampled in an area of interest. Comparing the simulation of the same modeled system with more than one computer application, especially one that uses a different algorithmic approach, is a way to gain confidence in the results. In this research, two models were created, one in Monte Carlo N Particle (MCNP) which uses a stochastic algorithmic approach, and the other in Attila, which uses a deterministic algorithmic approach to test the validity of the model and to estimate the dose rates in the area of interest. Successful comparison of the two models is a way we can validate the model used for that estimation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

Soft de Sitter Effective Theory

Calculating the quantum evolution of a de Sitter universe on superhorizon scales is notoriously difficult. To address this challenge, we introduce the Soft de Sitter Effective Theory (SdSET). This framework holds for superhorizon modes whose comoving momentum is far below the UV scale, which is set by the inverse comoving horizon. The SdSET is formulated using the same approach that yields the Heavy Quark Effective Theory. The degrees of freedom that capture the long wavelength dynamics are identified with the growing and decaying solutions to the equations of motion. The operator expansion is organized using a power counting scheme, and loops can be regulated while respecting the low energy symmetries. For massive quantum fields in a fixed de Sitter background, power counting implies that all interactions beyond the horizon are irrelevant. Alternatively, if the fields are very light, the leading interactions are at most marginal, and resumming the associated logarithms using (dynamical) renormalization group techniques yields the evolution equation for canonical stochastic inflation. The SdSET is also applicable to models where gravity is dynamical, including inflation. In this case, diffeomorphism invariance ensures that all interactions are irrelevant, trivially implying the all-orders conservation of adiabatic density fluctuations and gravitational waves. We briefly touch on the application to slow-roll eternal inflation by identifying novel relevant operators. This work serves to demystify many aspects of perturbation theory outside the horizon, and has a variety of applications to problems of cosmological interest.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Scalable probabilistic estimates of electric vehicle charging given observed driver behavior

To prepare for rapid growth in global electric vehicle adoption, grid and policy planners depend on detailed forecasts of future charging demand. In this paper we propose a novel holistic, scalable, probabilistic framework to produce large-scale estimates of electric vehicle charging load for long-term planning that capture real drivers’ charging patterns. Our framework captures the uncertainty and stochasticity in charging demand by taking a graphical modeling approach. It has three core elements: driver groups, charging segment choices, and charging session time and energy requirements. The framework uses hierarchical clustering to group drivers by their charging histories, capturing their heterogeneous behaviors and preferences across different segments or types of charging. The framework uses probabilistic mixture models for each driver group’s sessions to identify the unique charging behaviors observed within each segment. We illustrate its application with a large data set from California, profiling the charging patterns and unique driver clusters it identifies. Using the model knobs representing drivers’ battery capacities, behavior, and segment access we present scenarios for California’s charging demand in 2030 with 8 million passenger electric vehicles. Peak charging demand ranged from 3.3 to 8.7 GW across scenarios. Furthermore, each was calculated in under 45 s on a laptop computer.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Variational deep learning of equilibrium transition path ensembles

Here, we present a time-dependent variational method to learn the mechanisms of equilibrium reactive processes and efficiently evaluate their rates within a transition path ensemble. This approach builds off of the variational path sampling methodology by approximating the time-dependent commitment probability within a neural network ansatz. The reaction mechanisms inferred through this approach are elucidated by a novel decomposition of the rate in terms of the components of a stochastic path action conditioned on a transition. This decomposition affords an ability to resolve the typical contribution of each reactive mode and their couplings to the rare event. The associated rate evaluation is variational and systematically improvable through the development of a cumulant expansion. We demonstrate this method in both over- and under-damped stochastic equations of motion, in low-dimensional model systems, and in the isomerization of a solvated alanine dipeptide. In all examples, we find that we can obtain quantitatively accurate estimates of the rates of the reactive events with minimal trajectory statistics and gain unique insights into transitions through the analysis of their commitment probability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Static and shock compression studies of eutectic high-entropy alloy AlCoCrFeNi 2.1 to ultrahigh pressures

The high-entropy alloy with composition AlCoCrFeNi 2.1 , additively manufactured with the laser powder-bed fusion technique, has a far-from-equilibrium BCC/FCC eutectic nanolamellar structure. We studied the high-pressure response of this alloy under both static compression and high-strain rate shock compression. The response to static compression using a diamond anvil cell was studied at pressures up to 302 GPa with synchrotron x-ray diffraction at the advanced photon source. The high-pressure FCC-only phase of the EHEA previously observed by Pope et al. [AIP Adv. 13, 035124 (2023)] is found to be stable up to the highest pressure achieved in this study with a volume compression of V/V 0 = 0.587 at ambient temperature. The shock experiments were performed by using GEKKO XII lasers at the Institute of Laser Engineering, Osaka University. The principal Hugoniot equation-of-state of the EHEA was measured up to a pressure of 515 GPa and a compression of V/V 0 = 0.613. Additionally, the thermal equation of state of the EHEA was measured up to 6.2 GPa and 1623 K using a large-volume Paris–Edinburgh cell to obtain the temperature dependence of bulk modulus and thermal expansion coefficients. The melting temperature for EHEA AlCoCrFeNi 2.1 at a pressure of 5.6 GPa was measured to be 1648 ± 25 K. These results can be used to refine stochastic (or special) quasi-random structure (SQS) models for high-pressure high-temperature behavior of high-entropy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Full Counting Statistics of Charge in Chaotic Many-Body Quantum Systems

We investigate the full counting statistics of charge transport in U(1)-symmetric random unitary circuits. We consider an initial mixed state prepared with a chemical potential imbalance between the left and right halves of the system and study the fluctuations of the charge transferred across the central bond in typical circuits. Using an effective replica statistical mechanics model and a mapping onto an emergent classical stochastic process valid at large on-site Hilbert space dimension, we show that charge transfer fluctuations approach those of the symmetric exclusion process at long times, with subleading t –1/2 quantum corrections. Here, we discuss our results in the context of fluctuating hydrodynamics and macroscopic fluctuation theory of classical nonequilibrium systems and check our predictions against direct matrix-product state calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Evaluation of Two-Dimensional to One-Dimensional Site Response for Idaho National Laboratory

We perform two-dimensional (2D) site response analyses accounting for spatial variability of soil properties and subsurface geometry of the Eastern Snake River Plane (ESRP), and quantify their effects on ground surface motion relative to one-dimensional (1D) site response analyses at the Idaho National Laboratory (INL). We first present the development of random field idealizations of the repeated basalt lava flows, heterogeneously inter-layered with sediments, from seismic velocity data collected over four decades in the ESRP. Using realizations of the stochastic fields mapped on 2D deterministic finite element models, we perform 2D viscoelastic and equivalent-linear wave propagation simulations, and quantify the mean and variance of site response aggravation factors, defined as the response spectral ratio of 2D to 1D analyses on the ground surface. Results are shown to be insensitive to the constitutive material behavior considered here, for strains induced by rock outcrop peak ground acceleration (PGA) as high as 0.7g: viscoelastic and equivalent-linear analyses predict peak mean 2D/1D aggravation factor 1.05 at period T=0.075 sec (i.e. the 2D response spectrum is 5% higher than the corresponding 1D at that period, on average), which corresponds to the wavelength of the horizontal correlation length of the random field (50m). For periods longer than the fundamental period of the site (here, T 1 =0.3125 sec), the propagating wavelengths are too long to be affected by the 1D site response and the aggravation factor becomes equal to 1. The standard deviation of the natural logarithms of the 2D/1D aggravation factors is ~0.15 for periods shorter than the fundamental period of the site, and decays thereafter at a steady rate.

58 GEOSCIENCES↗
DOE OSTI2021

Attack-Resilient Weighted $\ell_{1}$ Observer with Prior Pruning

Security related questions for Cyber Physical Systems (CPS) have attracted much research attention in searching for novel methods for attack-resilient control and/or estimation. Specifically, false data injection attacks (FDIAs) have been shown to be capable of bypassing bad data detection (BDD), while arbitrarily compromising the integrity of state estimators and robust controller even with very sparse measurements corruption. Moreover, based on the inherent sparsity of pragmatic attack signals, ℓ1 -minimization scheme has been used extensively to improve the design of attack-resilient estimators. For this, the theoretical maximum for the percentage of compromised nodes that can be accommodated has been shown to be 50%. In order to guarantee correct state recoveries for larger percentage of attacked nodes, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through some data-driven machine learning process. Existing results have shown strong positive correlation between the tolerated attack percentages and the precision of the prior information. In this paper, we present a pruning method to improve the precision of the prior information, given corresponding stochastic uncertainty characteristics of the underlying machine learning model. Then a weighted ℓ1 -minimization is proposed based on the pruned prior. The theoretical and simulation results show that the pruning method significantly improves the observer performance for much larger attack percentages, even when moderately accurate machine learning model used.

Resilient observer, Cyber-physical systems, prunin↗