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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 271 records · Page 15

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NASA NTRS1986

Optical processing and Space Station automation

Optical information processing research aimed at Space Station automation applications is reviewed. The emphasis of the NASA Ames Research Center program is on intelligent optical pattern recognition and optical control processing. Attention is given to the primary functions of an overall scene understanding system: distortion-invariant optical feature generation, dimensionality reduction, object classification, and contextual information processing. A method of using synthetic discriminant functions to facilitate learning in a high-speed optical correlator is described. A discussion is presented of candidate analog and digital architectures for the optical implementation of state-estimation algorithms needed for the control of high-dimension dynamic systems. The multivariate system chosen for the optical control technology demonstration - a segmented, adaptive mirror and interferometrically based wavefront sensor - is also described.

Ennis, D. J.↗

NASA NTRS1987

Flight test of a digital controller used in a helicopter autoland system

This paper describes the flight test evaluation of an advanced digital helicopter flight control system. The controller was designed using an optimal control design procedure for a fully coupled lateral and longitudinal vehicle model. Explicit integrals of the guidance error were included to produce a type 1 characteristic. Gain scheduling was used to account for changes in the vehicle dynamics. The digital controller was exercised by combining it with state estimators, a trajectory generator, and a closed-loop guidance algorithm to form a helicopter autoland system. A CH-47 tandem rotor helicopter was equipped with sensors, on-board digital flight computers, and electrohydraulic actuators. The system was exercised by automatically flying straight-in descending decelerating trajectories typcial of VFR manual landing approaches. A description of the test-ground facilities, the flight hardware and software, and the velocity and position tracking performance is included.

Downing, David R.↗

NASA NTRS1991

On state estimation for an orbiting single tether system

The effects of instrumentation accuracy and configuration on estimation error are studied for the small expendable-tether deployment system (SEDS) using a continuous-discrete extended Kalman filter (CDEKF) state estimator. A twelfth order model that incorporates the rigid body modes of the tether as well as the satellite attitude dynamics is developed. Simulation results using the model and the estimator indicate that the originally planned instrumentation package could not estimate the state vector adequately. Recommendations are made and results presented that reduce the estimation error by adding instruments and increasing selected measurement accuracies.

Greene, Michael E.↗

NASA NTRS1992

Recursive Filtering And Smoothing In Robot Dynamics

Techniques developed originally for electronic systems also useful for multibody mechanical systems. Report summarizes methods developed to solve nonlinear forward-dynamics problem for robot of multiple-link arms connected by joints. Primary objective to show equivalence between recursive methods of dynamical analysis and some filtering and smoothing techniques from state-estimation theory.

Rodriguez, Guillermo↗

NASA NTRSDate not supplied

Generalized Predictive Control for Active Stability Augmentation and Vibration Reduction on an Aeroelastic Tiltrotor Model

Tiltrotor aircraft are defining the state-of-the-art in vertical lift technology as they have the potential to greatly expand rotary-wing operational boundaries. However, they are often limited in forward flight speed due to complex coupled rotor and wing dynamic instabilities. The U.S. Army and NASA have been developing a new wind tunnel model, the TiltRotor Aeroelastic Stability Testbed(TRAST), to test proprotors in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT) to investigate aeroelastic stability in cruise. The test is intended to provide high-quality research data for analytical tool development and validation. In addition, the TRAST model will support, develop, and mature new technologies for the design of advanced proprotor aircraft. Stability augmentation and vibration reduction during testing is planned with the use of an active control methodology known as Generalized Predictive Control(GPC). GPC is an autoregressive control law that experimentally acquires a system identification to derive the input-output relation of controls and corresponding sensors. This type of control law is especially useful for complex dynamic interactions that are difficult to explicitly model such as proprotor pylon instability, often referred to as whirl flutter. GPC has been successfully employed on other tiltrotor vehicles to suppress whirl flutter instabilities and vibrations. To aid in the characterization of the wind-tunnel model and in tool development, an analytical representation of the wind-tunnel model was developed using the rotorcraft comprehensive analysis system (RCAS) that simulates structural dynamics and aerodynamics. RCAS was used to derive state-space estimates of the physical plant at various flight conditions to test control law effectiveness. This paper will present an overview of the test article development, a description of RCAS, an explanation of the GPC methodology, and results of GPC being applied to state-space plant estimates of the TRAST model. In these simulations, GPC was effective at stabilizing the aircraft beyond the whirl-flutter boundary while simultaneously reducing vibrations across the flight regime. Additionally, a modern advancement to GPC, termed advanced GPC (AGPC), is introduced that enables a self-adapting system identification. Preliminary results show that AGPC is successful at self-correction as the plant changes from what was used for system identification.

tiltrotor↗

DOE OSTI2018

Characterizing oxygen dynamics in multicellular tumor spheroids

In 2018, an estimated 1.7 million people in the United States will be diagnosed with cancer, and over one third of those people will die due to a lack of effective treatments and lack of a cure. To combat this issue, Lawrence Livermore National Laboratory (LLNL) is developing an ex vivo 3D tumor spheroid model that can be used to more efficiently and effectively conduct cancer research. Ultimately, the goal is for this model to become patient-derived as it would be a huge advancement in personalized medicine. To begin validating the biological relevance of these tumor spheroid models as in vivo surrogates, LLNL will use flexible microelectrode arrays (MEAs) to characterize their oxygen dynamics. LLNL MEAs were inserted into the tumor spheroids and amperometric oxygen detection was conducted using a 3-electrode system with 10 μm diameter platinum (Pt) working electrodes (WE), an Iridium Oxide (IrOx)-plated reference electrode (RE), and a platinum counter electrode (CE). Preliminary results suggest that real-time amperometric oxygen detection can feasibly be achieved in tumor spheroids using LLNL’s MEAs, however, the 10 μm diameter Pt WEs may lack both the sensitivity (-0.034 nA/% DO) and the limit of detection (LOD) (0.272 nA or 7.45% DO) needed to detect oxygen levels found in tumor spheroids in the 3.96-15.79% DO range.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Condensation Risk of Mechanically Attached Roof Systems in Cold Climate Zones

A white roof, cool roof, is constructed to decrease thermal loads from solar radiation, therefore saving energy by decreasing the cooling demands. Unfortunately, cool roofs with mechanically attached membrane, have shown to have a higher risk of intermediate condensation in the materials below the membrane in certain climates (Ennis & Kehrer, 2011) and in comparisons with similar construction with a darker exterior surface (Bludau, Zirkelbach, & Kuenzel, 2009). As a consequence, questions have been raised regarding the sustainability and reliability of using cool roof membranes in Northern U.S. climate zones. A white roof surface reflects more of the incident solar radiation in comparisons with a dark surface, which makes a distinguished difference on the surface temperature of the roof. However, flat roofs with either a light or dark surface and if facing a clear sky, are constantly losing energy to the sky due to the exchange of infrared radiation. This phenomenon exists both during the night and the day. During the day, if the sun shines on the roof surface, the exchange of infrared radiation typically becomes insignificant. During nights and in cold climates, the temperature difference between the roof surface and the sky can deviate up to 20 C (Hagentoft, 2001) which could result in a very cold surface temperature compared to the ambient temperature. Further, a colder surface temperature of the roof increases the energy loss and the risk of condensation in the building materials below the membrane. In conclusion, both light and dark coated roof membranes are cooled by the infrared radiation exchange during the night, though a darker membrane is more heated by the solar radiation during the day, thus decreasing the risk of condensation. The phenomenon of night time cooling from the sky and the lack of solar gains during the day is not likely the exclusive problem concerning the risk of condensation in cool roofs with mechanically attached membranes. Roof systems with thermoplastic membranes are prone to be more effected by interior air intrusion into the roof construction; both due to the wind induced pressure differences and due to the flexibility and elasticity of the membrane (Molleti, Baskaran, Kalinger, & Beaulieu, 2011). Depending on the air permeability of the material underneath the membrane, wind forces increase the risk of fluttering (also referred as billowing) of the thermoplastic membrane. Expectably, the wind induced pressure differences creates a convective air flow into the construction i.e. Page 2 air intrusion. If the conditions are right, moisture from the exchanging air may condensate on surfaces with a temperature below dew-point. The definite path of convective airflows through the building envelope is usually very difficult to determine and therefore simplified models (K nzel, Zirkelbach, & Scfafaczek, 2011) help to estimate an additional moisture loads as a result of the air intrusion. The wind uplifting pressure in combination with wind gusts are important factors for a fluttering roof. Unfortunately, the effect from a fluctuating wind is difficult to estimate as this is a highly dynamic phenomenon and existing standards (ASTM, 2011a) only take into account a steady state approach i.e. there is no guidance or regulations on how to estimate the air intrusion rate. Obviously, a more detailed knowledge on the hygrothermal performance of mechanically attached cool roof system is requested; in consideration to varying surface colors, roof air tightness, climate zones and indoor moisture supply.

Pallin, Simon B↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Robust Real-Time Modeling of Distribution Systems with Data-Driven Grid-Wise Observability (Final Technical Report)

The overall objective of this project is to leverage existing and emerging sensor measurements to develop data-driven observability enhancement algorithms as well as robust state estimation and parameter identification techniques to enable real-time grid-wise monitoring and modeling of loads and distributed energy resources (DERs). The project resulted in a holistic framework with the following key components: 1) data-driven and machine learning-based grid-edge visibility enhancement, 2) robust branch-current-based state estimation (BCSE), and 3) robust real-time steady-state and dynamic-state modeling of loads and DERs. The research outcomes have provided utility companies better network visibility, higher-fidelity load/DER models, and more accurate assessment of DERs’ impacts, thus, facilitating the integration of renewable energy sources. The synergistic collaborative project among Iowa State University (ISU), Argonne National Laboratory (ANL), Electric Power Research Center (EPRC), SIEMENS Industry, Alliant Energy, Cedar Falls Utilities (CFU), and Maquoketa Valley Electric Cooperative (MVEC) to leverage the team’s extensive expertise and experience in power distribution systems, state estimation, online modeling and identification, and DER integrations. The proposed frameworks have been verified with industry adopted software and attempted integrated with existing tool wherever possible

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Noncanonical Photodynamics of the Orange/Green Cyanobacteriochrome Power Sensor NpF2164g7 from the PtxD Phototaxis Regulator of Nostoc punctiforme

Forward and reverse primary (<10 ns) and secondary (>10 ns) photodynamics of cyanobacteriochrome (CBCR) NpF2164g7 were characterized by global analysis of ultrafast broadband transient absorption measurements. NpF2164g7 is the most C-terminal bilin-binding GAF domain in the Nostoc punctiforme phototaxis sensor PtxD (locus Npun_F2164). Although a member of the canonical red/green CBCR subfamily phylogenetically, NpF2164g7 exhibits an orange-absorbing 15Z P o dark-adapted state instead of the typical red-absorbing 15Z P r dark-adapted state characteristic of this subfamily. The green-absorbing 15E P g photoproduct of NpF2164g7 is unstable, allowing this CBCR domain to function as a power sensor. Photoexcitation of the 15Z P o state triggers inhomogeneous excited-state dynamics with three spectrally and temporally distinguishable pathways to generate the light-adapted 15E P g state in high yield (estimated at 25–30%). Although observed in other CBCR domains, the inhomogeneity in NpF2164g7 extends far into secondary relaxation dynamics (10 ns –1 ms) through to formation of 15E P g . In the reverse direction, the primary dynamics after photoexcitation of 15E P g are qualitatively similar to those of other red/green CBCRs, but secondary dynamics involve a “pre-equilibrium” step before regenerating 15Z P o . As a result, the anomalous photodynamics of NpF2164g7 may reflect an evolutionary adaptation of CBCR sensors that function as broadband light intensity sensors.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1976

Method of discrete modeling and its application to estimation of TF30 engine variables

A method of discrete modeling is presented that effectively isolates steady state model accuracy from dynamic model accuracy. The steady state model may be generated from the engine design equations with any desired degree of accuracy. The dynamic model is generated by applying a step disturbance of a manipulated variable to an open loop engine simulation. The sampled response of the variable is combined with the steady state model's response to form a set of weighting factors. These weighting factors are then used to weight past values of the manipulated variable, thus forming the dynamic model. The method is used to estimate various TF30-P-3 engine variables. A dynamic trim function is developed to compensate for the dynamic nonlinearities of the variables as well as for inaccuracies in dynamic definition. The trim function is shown to be realted to the square root of the sum of the squares of the weighting factors obtained at various engine operating conditions. Finally, the estimation of variables without dynamic modeling is discussed.

Arpasi, D. J.↗

NASA NTRSDate not supplied

Discriminative Methodology using the Unscented Transform in Special Euclidean Group SE(3) for Autonomous GN&C

Kalman filtering techniques usually compare a known dynamic model with some process noise to an updated, noisy measurement. Both sources of noise are generally assumed to be Gaussian and are filtered to obtain a more accurate estimate of the state of the system of interest. However, these methods assume that a relatively accurate dynamical model already exists. By including discriminative methodology to interpret differences in the predictive model and the measured states over time, an incorrect model can be corrected overtime, allowing a spacecraft to learn about its environment and adapt to changing conditions. In this paper, a discriminative unscented Kalman filter (DUKF) is introduced on the special Euclidean group SE(3) formulation framework. A spacecraft is initialized near a small body with no knowledge of its parameters, and using several consecutive conditional models the environment is studied and the filter is updated. The case study of a spacecraft in a slightly elliptical, inclined, relatively tight orbit around the irregular moon Phobos is simulated and discussed. The accuracy of this approach is quantifying the differences between the true state, the UKF with a poor model, and the DUKF.

GN&C↗

NASA NTRS2016

Fast Kalman Filtering for Relative Spacecraft Position and Attitude Estimation for the Raven ISS Hosted Payload

The Raven ISS Hosted Payload will feature several pose measurement sensors on a pan/tilt gimbal which will be used to autonomously track resupply vehicles as they approach and depart the International Space Station. This paper discusses the derivation of a Relative Navigation Filter (RNF) to fuse measurements from the different pose measurement sensors to produce relative position and attitude estimates. The RNF relies on relative translation and orientation kinematics and careful pose sensor modeling to eliminate dependence on orbital position information and associated orbital dynamics models. The filter state is augmented with sensor biases to provide a mechanism for the filter to estimate and mitigate the offset between the measurements from different pose sensors.

estimation↗

NASA NTRS2016

Fast Kalman Filtering for Relative Spacecraft Position and Attitude Estimation for the Raven ISS Hosted Payload

estimation↗

NASA NTRS1991

A game theoretic approach to a finite-time disturbance attenuation problem

A disturbance attenuation problem over a finite-time interval is considered by a game theoretic approach where the control, restricted to a function of the measurement history, plays against adversaries composed of the process and measurement disturbances, and the initial state. A zero-sum game, formulated as a quadratic cost criterion subject to linear time-varying dynamics and measurements, is solved by a calculus of variation technique. By first maximizing the quadratic cost criterion with respect to the process disturbance and initial state, a full information game between the control and the measurement residual subject to the estimator dynamics results. The resulting solution produces an n-dimensional compensator which expresses the controller as a linear combination of the measurement history. A disturbance attenuation problem is solved based on the results of the game problem. For time-invariant systems it is shown that under certain conditions the time-varying controller becomes time-invariant on the infinite-time interval. The resulting controller satisfies an H(infinity) norm bound.

Rhee, Ihnseok↗

DOE OSTI2025

Time-Varying Output Delay Compensation-A Model-Free Approach and its Application on Cooperative On-Ramp Merging

This paper presents a model-free approach to compensate for time-varying output delay in networked control systems. The proposed architecture combines a model-free observer and the Smith predictor. The model-free observer estimates the current state while handling modeling errors and uncertainties of the system. The Smith predictor moves the effect of time delay outside the control closed-loop using the estimated delayed output and the actual output of the plant. The proposed method is applied to a cooperative on-ramp merging problem. First, an ultra-local model predictive control is implemented to provide a computationally efficient online speed planner agnostic to the vehicle dynamics. After that, a model-free observer is designed to estimate the current state. Finally, the proposed architecture is tested against a time-varying output delay with an upper bound of 200 milliseconds. The results demonstrate the effectiveness of the proposed method with improved tracking of intervehicle distance.

Waleed khan, Muhammad [The University of Texas at ↗

DOE OSTI2022

Master State Distributed Estimator (masde)

The MSE technology provides a distributed state estimation scheme for power utilities by making it a real time dynamic system, versus the static snapshot method used today. This will allow utilities to verify power grid readings and identify false data on the communication network. The prevailing method of estimation takes all the data from the system into a single load flow equation at the utilities command center. This becomes a large algorithm that is very time consuming to solve, providing the engineers only snap shots of the system. The MFD distributes an algorithm to devises that are already installed at various locations on the power grid. This allows for much faster computer times because the algorithms contain magnitudes less data. When each distributed estimator completes its calculation, it sends the results back to the master device to check against all other distributed estimators.

Reen, DylanW.↗

NASA NTRS1990

Application of the operator spline technique to nonlinear estimation and control of moving elastic systems

A bilinear model of the vibrational dynamics of a deformable maneuvering body is described. Estimates of the deformation state are generated through a low dimensional operator spline interpolator of bilinear systems combined with a feedback linearized based observer. Upper bounds on error estimates are also generated through the operator spline, and potential application to shaping control purposes is highlighted.

Karray, Fakhreddine↗