An optimal stochastic control problem with observation cost
Linear system optimal stochastic control and observation strategies simultaneous determination with quadratic cost by dynamic programming
Cooper, C. A.↗
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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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Influence of maneuverability on helicopter combat effectiveness
A computational procedure employing a stochastic learning method in conjunction with dynamic simulation of helicopter flight and weapon system operation was used to derive helicopter maneuvering strategies. The derived strategies maximize either survival or kill probability and are in the form of a feedback control based upon threat visual or warning system cues. Maneuverability parameters implicit in the strategy development include maximum longitudinal acceleration and deceleration, maximum sustained and transient load factor turn rate at forward speed, and maximum pedal turn rate and lateral acceleration at hover. Results are presented in terms of probability of skill for all combat initial conditions for two threat categories.
Falco, M.↗
Weak-form inference for hybrid dynamical systems in ecology
Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).
54 ENVIRONMENTAL SCIENCES↗
A stochastic regulator for integrated communication and control systems. I - Formulation of control law. II - Numerical analysis and simulation
A state feedback control law for integrated communication and control systems (ICCS) is formulated by using the dynamic programming and optimality principle on a finite-time horizon. The control law is derived on the basis of a stochastic model of the plant which is augmented in state space to allow for the effects of randomly varying delays in the feedback loop. A numerical procedure for synthesizing the control parameters is then presented, and the performance of the control law is evaluated by simulating the flight dynamics model of an advanced aircraft. Finally, recommendations for future work are made.
Liou, Luen-Woei↗
A Hybrid Stochastic-Interval Operation Strategy for Multi-Energy Microgrids
Increasing deployment of distributed energy resources intensifies interactions among electric, gas, heating, and cooling systems. Inherent uncertainties ranging from internal parameters to external inputs also impose significant challenges on system operation. A coordinated scheduling method is, therefore, desired for facilitating efficient operation of multi-energy microgrids under multiple uncertainties. In this paper, a deterministic coordinated scheduling model is developed first with detailed modeling of system dynamics and device parameters to fully characterize complex interactions among multi-energy carriers. Then, a hybrid stochastic-interval method is proposed to account for heterogeneous uncertainties in decisions of operational strategies. Specifically, performance and efficiency uncertainties of distributed energy resources, and injection uncertainty of renewable sources and multienergy demands are characterized by interval-based uncertain operation regions and probability distributions, respectively. A scenario-based two-stage algorithm is developed to solve the problem so that multiple uncertainties are preserved in the entire decision-making process. Moreover, adjustable operational strategies are determined to hedge uncertainty by considering operators’ risk preferences. The validity of the proposed method is verified by comprehensive case studies on a test system.
hybrid stochastic-interval approach↗
Stochastic self-tuning hybrid algorithm for reaction-diffusion systems
Many biochemical phenomena involve reactants with vastly different concentrations, some of which are amenable to continuum-level descriptions, while the others are not. We present a hybrid self-tuning algorithm to model such systems. The method combines microscopic (Brownian) dynamics for diffusion with mesoscopic (Gillespie-type) methods for reactions and remains efficient in a wide range of regimes and scenarios with large variations of concentrations. Furthermore, its accuracy, robustness, and versatility are balanced by redefining propensities and optimizing the mesh size and time step. We use a bimolecular reaction to demonstrate the potential of our method in a broad spectrum of scenarios: from almost completely reaction-dominated systems to cases where reactions rarely occur or take place very slowly. The simulation results show that the number of particles present in the system does not degrade the performance of our method. This makes it an accurate and computationally efficient tool to model complex multireaction systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Stochastic modeling and control system designs of the NASA/MSFC Ground Facility for large space structures: The maximum entropy/optimal projection approach
In the Control Systems Division of the Systems Dynamics Laboratory of the NASA/MSFC, a Ground Facility (GF), in which the dynamics and control system concepts being considered for Large Space Structures (LSS) applications can be verified, was designed and built. One of the important aspects of the GF is to design an analytical model which will be as close to experimental data as possible so that a feasible control law can be generated. Using Hyland's Maximum Entropy/Optimal Projection Approach, a procedure was developed in which the maximum entropy principle is used for stochastic modeling and the optimal projection technique is used for a reduced-order dynamic compensator design for a high-order plant.
Hsia, Wei-Shen↗
Improving noisy free-energy measurements by adding more noise
Estimating free-energy differences using nonequilibrium work relations, such as the Jarzynski equality, is hindered by poor convergence when work fluctuations are large. For systems governed by overdamped Langevin dynamics, we propose the counterintuitive approach of adding noise in order to increase the precision of such calculations. Here, by introducing additional stochastic fluctuations to the system and rescaling its potential energy accordingly, we leave the thermodynamics of the system unchanged while increasing its relaxation rate. For a given time-dependent protocol this modification reduces the dissipated reduced work, leading to more accurate free-energy estimates. The method is designed to be used in experiment, and we illustrate its operation using computer simulations applied to two model systems. However, the regime of applicability of this strategy is likely limited, because it requires control of the system's potential energy in a way that is feasible in only a few experimental settings.
Whitelam, Stephen [Lawrence Berkeley National Labo↗
Efficient Langevin dynamics for “noisy” forces
Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Additionally, stochastic approaches provide a gentler system-size dependency at the cost of introducing “noisy” forces, which could limit the efficiency of the sampling. When the forces are deterministic, the first-order Langevin dynamics (FOLD) offers efficient sampling by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator [G. Mazzola and S. Sorella, Phys. Rev. Lett. 118, 015703 (2017)]. However, when forces are noisy, S is set equal to the force-covariance matrix, a procedure that severely limits the efficiency and the stability of the sampling. Here, we develop a new, general, optimal, and stable sampling approach for FOLD under noisy forces. We apply it for silicon nanocrystals treated with stochastic density functional theory and show efficiency improvements by an order-of-magnitude.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Investigation of air transportation technology at Princeton University, 1991-1992
The Air Transportation Research Program at Princeton University proceeded along six avenues during the past year: (1) intelligent flight control; (2) computer-aided control system design; (3) neural networks for flight control; (4) stochastic robustness of flight control systems; (5) microburst hazards to aircraft; and (6) fundamental dynamics of atmospheric flight. This research has resulted in a number of publications, including archival papers and conference papers. An annotated bibliography of publications that appeared between June 1991 and June 1992 appears at the end of this report. The research that these papers describe was supported in whole or in part by the Joint University Program, including work that was completed prior to the reporting period.
Stengel, Robert F.↗
Improving Sensorimotor Adaptation Following Long Duration Space Flight by Enhancing Vestibular Information Transfer
Crewmember adapted to the microgravity state may need to egress the vehicle within a few minutes for safety and operational reasons after gravitational transitions. The transition from one sensorimotor state to another consists of two main mechanisms: strategic and plastic-adaptive and have been demonstrated in astronauts returning after long duration space flight. Strategic modifications represent "early adaptation" - immediate and transitory changes in control that are employed to deal with short-term changes in the environment. If these modifications are prolonged then plastic-adaptive changes are evoked that modify central nervous system function, automating new behavioral responses. More importantly, this longer term adaptive recovery mechanism was significantly associated with their strategic ability to recover on the first day after return to Earth G. We are developing a method based on stochastic resonance to enhance information transfer by improving the brain's ability to detect vestibular signals (Vestibular Stochastic Resonance, VSR) especially when combined with balance training exercises such as sensorimotor adaptability (SA) training for rapid improvement in functional skill, for standing and mobility. This countermeasure to improve detection of vestibular signals is a stimulus delivery system that is wearable/portable providing low imperceptible levels of white noise based binaural bipolar electrical stimulation of the vestibular system (stochastic vestibular stimulation). To determine efficacy of vestibular stimulation on physiological and perceptual responses during otolith-canal conflicts and dynamic perturbations we have conducted a series of studies: We have shown that imperceptible binaural bipolar electrical stimulation of the vestibular system across the mastoids enhances balance performance in the mediolateral (ML) plane while standing on an unstable surface. We have followed up on the previous study showing VSR stimulation improved balance performance in both ML and anteroposterior planes while stimulating in the ML axis only. We have shown the efficacy of VSR stimulations on enhancing physiological and perceptual responses of whole-body orientation during low frequency perturbations (0.1 Hz) on the ocular motor system using a variable radius centrifuge on both physiological (using eye movements) and perceptual responses (using a joystick) to track imposed oscillations. The variable radius centrifuge provides a selective tilting sensation that is detectable only by the otolith organs providing conflicting information from the canal organs of the vestibular system (intra-vestibular conflict). These results indicate that VSR can improve performance in sensory conflict scenarios like that experienced during space flight. We have showed the efficacy of VSR stimulation to improve balance and locomotor control on subjects exposed to continuous, sinusoidal lateral motion of the support surface while walking on a treadmill while viewing perceptually matched linear optic flow. We have shown the safety of short term continuous use of up to 4 hours of VSR stimulation and its efficacy in improving balance and locomotor function in Parkinson's Disease patients. This technique for improving vestibular signal detection may thus provide additional information to improve strategic abilities. We hypothesize that VSR stimulation will act synergistically with SA training to improve adaptability by increased utilization of vestibular information and therefore serve to optimize and personalize the SA countermeasure prescription. This forms the basis of its usefulness both as a training modality and further help in significantly reducing the number of days required to recover functional performance to preflight levels after long duration space flight.
Mulavara, A. P.↗
Dynamic features of combustion
The dynamic features of combustion are discussed for four important cases: ignition, inflammation, explosion, and detonation. Ignition, the initiation of a self-sustained exothermic process, is considered in the simplest case of a closed thermodynamic system and its stochastic distribution. Inflammation, the initiation and propagation of self-sustained flames, is presented for turbulent flow. Explosion, the dynamic effects caused by the deposition of exothermic energy in a compressible medium, is illustrated by self-similar blast waves with energy deposition at the front and the adiabatic non-self-similar wave. Detonation, the most comprehensive illustration of all the dynamic effects of combustion, is discussed with a phenomenological account of the development and structure of the wave.
Oppenheim, A. K.↗
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it.
54 ENVIRONMENTAL SCIENCES↗
Stochastic modeling of the time-advanced equations for climate dynamics
Long term, climatologically significant statistical properties of the atmospheric component of the climate system are examined in an effort to correctly and efficiently incorporate this component into larger models for climate that include oceanic and cryospheric influences and deal realistically with climate replication. The magnetic of the unpredictable (noise) component of long term atmospheric behavior is also studied in order to develop the potential for viable climatological forecasting.
Laurmann, J. A.↗
Neuromorphic heat transport effects in a molecular junction
Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system—a molecular junction—subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect—heat transport hysteresis—that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Modeling Framework for Bulk Electric Grid Impacts from HEMP E1 and E3 Effects (Tasks 3.1 Final Report)
This report presents a framework to evaluate the impact of a high-altitude electromagnetic pulse (HEMP) event on a bulk electric power grid. This report limits itself to modeling the impact of EMP E1 and E3 components. The co-simulation of E1 and E3 is presented in detail, and the focus of the paper is on the framework rather than actual results. This approach is highly conservative as E1 and E3 are not maximized with the same event characteristics and may only slightly overlap. The actual results shown in this report are based on a synthetic grid with synthetic data and a limited exemplary EMP model. The framework presented can be leveraged and used to analyze the impact of other threat scenarios, both manmade and natural disasters. This report d escribes a Monte-Carlo based methodology to probabilistically quantify the transient response of the power grid to a HEMP event. The approach uses multiple fundamental steps to characterize the system response to HEMP events, focused on the E1 and E3 components of the event. 1) Obtain component failure data related to HEMP events testing of components and creating component failure models. Use the component failure model to create component failure conditional probability density function (PDF) that is a function of the HEMP induced terminal voltage. 2) Model HEMP scenarios and calculate the E1 coupled voltage profiles seen by all system components. Model the same HEMP scenarios and calculate the transformer reactive power consumption profiles due to E3. 3) Sample each component failure PDF to determine which grid components will fail, due to the E1 voltage spike, for each scenario. 4) Perform dynamic simulations that incorporate the predicted component failures from E1 and reactive power consumption at each transformer affected by E3. These simulations allow for secondary transients to affect the relays/protection remaining in service which can lead to cascading outages. 5) Identify the locations and amount of load lost for each scenario through grid dynamic simulation. This can be an indication of the immediate grid impacts from a HEMP event. In addition, perform more detailed analysis to determine critical nodes and system trends. 6) To help realize the longer-term impacts, a security constrained alternating current optimal power flow (ACOPF) is run to maximize critical load served. This report describes a modeling framework to assess the systemic grid impacts due to a HEMP event. This stochastic simulation framework generates a large amount of data for each Monte Carlo replication, including HEMP location and characteristics, relay and component failures, E3 GIC profiles, cascading dynamics including voltage and frequency over time, and final system state. This data can then be analyzed to identify trends, e.g., unique system behavior modes or critical components whose failure is more likely to cause serious systemic effects. The proposed analysis process is demonstrated on a representative system. In order to draw realistic conclusions of the impact of a HEMP event on the grid, a significant amount of work remains with respect to modeling the impact on various grid components.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Proceedings of the 3rd Annual SCOLE Workshop
Topics addressed include: modeling and controlling the Spacecraft Control Laboratory Experiment (SCOLE) configurations; slewing maneuvers; mathematical models; vibration damping; gravitational effects; structural dynamics; finite element method; distributed parameter system; on-line pulse control; stability augmentation; and stochastic processes.
Taylor, Lawrence W., Jr.↗
Parallel and vector computation for stochastic optimal control applications
A general method for parallel and vector numerical solutions of stochastic dynamic programming problems is described for optimal control of general nonlinear, continuous time, multibody dynamical systems, perturbed by Poisson as well as Gaussian random white noise. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random atmospheric fluctuations. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer, and results exhibit high processor efficiency and numerical stability. Advanced computing techniques, data structures, and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations.
Hanson, F. B.↗