A Language for Probabilistic Modeling of Scientific Data
We describe a portable container which allows specification of the probablistic relations between several varibles.
models structures Stochastic Systems Scientific Da↗
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Results for “Stochastic systems”
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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Complexity of life via collective mind
e mind is introduced as a set of simple intelligent units (say, neurons, or interacting agents), which can communicate by exchange of information without explicit global control. Incomplete information is compensated by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute into dynamical formalism of the agents' evolution, and in particular, into the reflective chains of their nested abstract images of the selves and non-selves. The proposed model consists of the system of stochastic differential equations in the Langevin form representing the motor dynamics, and the corresponding Fokker-Planck equation representing the mental dynamics (Motor dynamics describes the motion in physical space, while mental dynamics simulates the evolution of initial errors in terms of the probability density). The main departure of this model from Newtonian and statistical physics is due to a feedback from the mental to the motor dynamics which makes the Fokker-Planck equation nonlinear. Interpretation of this model from mathematical and physical viewpoints, as well as possible interpretation from biological, psychological, and social viewpoints are discussed. The model is illustrated by the dynamics of a dialog.
collective intelligence↗
Quantifying Methane Leak Emissions by Fused Airborne Imaging Spectroscopy with in Situ Surface Mobile and Airborne Observations of a California Producing Oil Field
Methane (CH4) emission budgets remain uncertain and are projected to grow as oil and gas production from short-lived wells increases and their subsequent transport through aging gas distribution networks. Orders-of-magnitude variations in temporal, spatial, and emission scales present a key challenge to leak detection and quantification. Also, the probability distributions for large and stochastic, leaky systems such as geological reservoirs (by natural migration-seeps) and petroleum production from those reservoirs remain largely unknown, needed to address current approach limitations. The scale of many petroleum systems favors remote sensing, but the sensitivity of such systems often precludes detection of weak emissions. Consequently, an accurate evaluation requires that the relative contribution from the emission "tails" of small leaks also be quantified, which is best carried out using high-sensitivity in situ methods. Fusion of remote sensing and in situ approaches leverages complementary capabilities to address these limitations. We show results from mobile surface (AMOG) and airborne in situ (AJAX) and thermal-infrared (TIR) hyperspectral imaging spectroscopy (Mako) data applied to a producing oil field in the California Central Valley near Bakersfield. AMOG is an automobile-based mobile lab that measures 13 trace gases, aerosol size distributions and vertical profiles, 3D winds and other meteorology, and atmospheric column measurements by solar spectroscopy at highway speeds. AJAX measures 5 trace gases and 3D winds at ~140 m/s. Mako is a broad-area TIR imaging spectrometer that can discriminate multiple gases present in each pixel acquired. In situ-derived, total field emissions were 31±16 Gg/yr CH4. This was compared with Mako-derived emissions from all plumes identified across the study site. We found that super-emitters were not the dominant emissions mode and the spatial pattern of plume locations from production infrastructure was correlated to geological structures.
Leifer, Ira↗
The role and use of the stochastic linear-quadratic-Gaussian problem in control system design.
The role of the linear-quadratic stochastic control problem in engineering design is reviewed in tutorial fashion. The design approach is motivated by considering the control of a nonlinear uncertain plant about a desired input-output response. It is demonstrated how a design philosophy based on (1) deterministic perturbation control, (2) stochastic state estimation, and (3) linearized stochastic control leads to an overall closed-loop control system. The emphasis of the paper is on the philosophy of the design process, the modeling issue, and the formulation of the problem; the results are given for the sake of completeness, but no proofs are included. The systematic off-line nature of the design process is stressed throughout.
Athans, M.↗
Stochastic Fault Detection
This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.
Wang, Aiping↗
Stochastic availability analysis of operational data systems in the Deep Space Network
Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.
Issa, T. N.↗
Closed form evaluation of symmetric two-sided complex integrals
Evaluation of two-sided complex integrals is often required when analyzing linear systems to determine signal variances resulting from stochastic inputs and system noise bandwidths. Algebraic solutions of integrals in a closed matrix equation form, using coefficients of the numerator and denominator polynomials, are presented. The closed forms provide the possibility of obtaining some insight into parameter sensitivity in addition to greatly reducing the computational complexity required by the normal method of evaluation by residues.
Winkelstein, R.↗
Radiative transfer theory for inhomogeneous media with random extinction and scattering coefficients
The small-angle scattering approximation of the scalar radiative transfer equation (RTE) is examined for the case where the extinction and scattering coefficients have a component that is a deterministic function of position along the propagation path and a component that is a random function of position transverse to the propagation direction. It is found that the resulting stochastic RTE can be reduced to a system of two stochastic integrodifferential equations for the average and fluctuating components of the radiant intensity. Two transfer equations are obtained describing the average radiant intensity and the spatial correlation function of the intensity fluctuations. The average intensity equation is then solved and applied to a simple propagation scenario. An approximate solution is also derived for the equation giving the correlation function. The developed equations can be applied to problems involving short wavelength electromagnetic wave propagation through media possessing the variable characteristics of turbulence and turbidity, such as plasmas, the atmosphere, and the ocean.
Manning, Robert M.↗
Two examples of applications of Kalman filtering to integrated systems of navigation
Two applications of optimal stochastic filters to navigation systems are described. The first is an air navigation system consisting of an inertial device (INS) and a Loran, plus an altimeter. The second is an application to a system of submarine navigation consisting of an inertial device (SINS) and an Omega plus a depth sensor.
Ferraro, A.↗
Communications systems research - Propagation studies
Two stochastic approximation procedures for identifying linear systems
Source record↗
Comparison of Wind-Tunnel and Flight Unsteady Pressure Stochastic Characteristics for the Space Launch System Artemis I Flight
Over the course of more than ten years, numerous wind-tunnel tests have been conducted to acquire data for characterizing the unsteady pressure environments expected to act on the Space Launch System Block 1 crew launch vehicle during ascent. These wind-tunnel tests of highly-instrumented rigid models are the current standard for the estimation of unsteady environments. Following the successful launch of the Artemis I mission, the extensive flight data acquired can be analyzed to evaluate the accuracy of unsteady pressure environments predicted in subscale wind-tunnel testing in comparison to the flight test data. In this paper, analyses focusing on data from several Space Launch System wind-tunnel tests and the Artemis I flight test are presented, including assessments of test-to-test, tunnel-to-tunnel, and tunnel-to-flight stochastic characteristics and preflight modeling validity based on wind-tunnel testing. In general, the fluctuating pressure environments measured during the several preflight subscale wind-tunnel tests compare favorably and provide relatively accurate estimates of the environments measured during flight. Discrepancies in fluctuating magnitudes, spatial extent of regions of unsteadiness, and narrowband frequency peaks are noted in the multibody region aft of the solid rocket booster forward attachment to the core stage.
wind-tunnel↗
Comparison of Wind-Tunnel and Flight Unsteady Pressure Stochastic Characteristics for the Space Launch System Artemis I Flight
Over the course of more than ten years, numerous wind-tunnel tests have been conducted to acquire data for characterizing the unsteady pressure environments expected to act on the Space Launch System Block 1 crew launch vehicle during ascent. These wind-tunnel tests of highly-instrumented rigid models are the current standard for the estimation of unsteady environments. Following the successful launch of the Artemis I mission, the extensive flight data acquired can be analyzed to evaluate the accuracy of unsteady pressure environments predicted in subscale wind-tunnel testing in comparison to the flight test data. In this paper, analyses focusing on data from several Space Launch System wind-tunnel tests and the Artemis I flight test are presented, including assessments of test-to-test, tunnel-to-tunnel, and tunnel-to-flight stochastic characteristics and preflight modeling validity based on wind-tunnel testing. In general, the fluctuating pressure environments measured during the several preflight subscale wind-tunnel tests compare favorably and provide relatively accurate estimates of the environments measured during flight. Discrepancies in fluctuating magnitudes, spatial extent of regions of unsteadiness, and narrowband frequency peaks are noted in the multibody region aft of the solid rocket booster forward attachment to the core stage.
wind-tunnel↗
Mathematical model of a flexible space shuttle vehicle
The development of a mathematical model of the lateral motion of a flexible space shuttle vehicle during ascent is described. The model was developed to perform control system synthesis using stochastic constrained optimization techniques. The goals of the control system synthesis are to demonstrate the applicability of the techniques and to discover any problems peculiar to the flexible nature of a shuttle vehicle. The equations of motion are derived. A brief description of the generation of numerical data is given. Explicit definitions and numerical values of trajectory data and coefficients appearing in the equations of motion are included.
Harvey, C. A.↗
Stochastic gradient descent for optimization for nuclear systems
The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty. Furthermore, it is clearly demonstrated that low-compute time, high-variance estimates of the gradient lead to better performance in the optimization challenge problems tested here.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Sparse Control Synthesis for Uncertain Responsive Loads With Stochastic Stability Guarantees
In this report, recent studies have demonstrated the potential of flexible loads in providing frequency response services, predominantly due to their availability and cost-effectiveness. However, uncertainty and variability in various weather-related and end-use behavioral factors often impact the reliability of demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive in the power system dynamics. Recently developed mean square exponential stability (MSES) results for continuous-time linear stochastic systems are applied to pose the control synthesis problem which results in an LMI-based optimization problem. Additional costs and constraints are added to the LMI-based controller synthesis to ensure MSES, improve closed-loop transient performance, maximize tolerable uncertainties, and promote sparsity in the controller. Additionally, the fundamental limitations between the tolerable uncertainties and control efforts while ensuring MSES are discussed. Further, the control synthesis problem for the case of the full-state measurement is generalized to the case of partial-state measurements. The proposed control synthesis is illustrated on an IEEE 39 bus system with rigorous studies to demonstrate the role of sparsity, closed-loop transient performance, tolerable uncertainties, and control efforts while ensuring MSES and achieving frequency response.
42 ENGINEERING↗
Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant
This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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↗
Effective Lagrangian for Nambu-Goldstone modes in nonequilibrium open systems
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗