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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 127 records · Page 7

Hamiltonian reduction of Vlasov–Maxwell to a dark slow manifold

In this work, we show that non-relativistic scaling of the collisionless Vlasov–Maxwell system implies the existence of a formal invariant slow manifold in the infinite-dimensional Vlasov–Maxwell phase space. Vlasov–Maxwell dynamics restricted to the slow manifold recovers the Vlasov–Poisson and Vlasov–Darwin models as low-order approximations, and provides higher-order corrections to the Vlasov–Darwin model more generally. The slow manifold may be interpreted to all orders in perturbation theory as a collection of formal Vlasov–Maxwell solutions that do not excite light waves, and are therefore ‘dark’. We provide a heuristic lower bound for the time interval over which Vlasov–Maxwell solutions initialized optimally near the slow manifold remain dark. We also show how the dynamics on the slow manifold naturally inherits a Hamiltonian structure from the underlying system. After expressing this structure in a simple form, we use it to identify a manifestly Hamiltonian correction to the Vlasov–Darwin model. The derivation of higher-order terms is reduced to computing the corrections of the system Hamiltonian restricted to the slow manifold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Compensating for pneumatic distortion in pressure sensing devices

A technique of compensating for pneumatic distortion in pressure sensing devices was developed and verified. This compensation allows conventional pressure sensing technology to obtain improved unsteady pressure measurements. Pressure distortion caused by frictional attenuation and pneumatic resonance within the sensing system makes obtaining unsteady pressure measurements by conventional sensors difficult. Most distortion occurs within the pneumatic tubing which transmits pressure impulses from the aircraft's surface to the measurement transducer. To avoid pneumatic distortion, experiment designers mount the pressure sensor at the surface of the aircraft, (called in-situ mounting). In-situ transducers cannot always fit in the available space and sometimes pneumatic tubing must be run from the aircraft's surface to the pressure transducer. A technique to measure unsteady pressure data using conventional pressure sensing technology was developed. A pneumatic distortion model is reduced to a low-order, state-variable model retaining most of the dynamic characteristics of the full model. The reduced-order model is coupled with results from minimum variance estimation theory to develop an algorithm to compensate for the effects of pneumatic distortion. Both postflight and real-time algorithms are developed and evaluated using simulated and flight data.

Whitmore, Stephen A.↗

Towards an Optimal Noise Versus Resolution Trade-Off in Wind Scatterometry

A scatterometer is a radar that measures the normalized radar cross section sigma(sup 0) of the Earth's surface. Over the ocean this signal is related to the wind via the geophysical model function (GMF). The objective of wind scatterometry is to estimate the wind vector field from sigma(sup 0) measurements; however, there are many subtleties that complicate this problem-making it difficult to obtain a unique wind field estimate. Conventionally, wind estimation is split into two stages: a wind retrieval stage in which several ambiguous solutions are obtained, and an ambiguity removal stage in which ambiguities are chosen to produce an appropriate wind vector field estimate. The most common approach to wind field estimation is to grid the scatterometer swath into wind vector cells and estimate wind vector ambiguities independently for each cell. Then, field wise structure is imposed on the solution by an ambiguity selection routine. Although this approach is simple and practical, it neglects field wise structure in the retrieval step and does not account for the spatial correlation imposed by the sampling. This makes it difficult to develop a theoretically appropriate noise versus resolution trade-off using pointwise retrieval. Fieldwise structure may be imposed in the retrieval step using a model-based approach. However, this approach is generally only practical if a low order wind field model is applied, which may discard more information than is desired. Furthermore, model-based approaches do not account for the structure imposed by the sampling. A more general fieldwise approach is to estimate all the wind vectors for all the WVCs simultaneously from all the measurements. This approach can account for structure of the wind field as well as structure imposed by the sampling in the wind retrieval step. Williams and Long in 2010 developed a fieldwise retrieval method based on maximum a posteriori estimation (MAP). This MAP approach can be extended to perform a noise versus resolution trade-off, and deal with ambiguity selection. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed.

soil moisture↗

Reduced-Order Parameterized Short-Circuit Current Model of Inverter-Interfaced Distributed Generators

The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Finally, experimental and simulation tests are presented to validate the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Here, deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

42 ENGINEERING↗

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗

Reduction of Model Complexity by Active Control

The mathematical models of many dynamic systems of interest in the aerospace industry are inherently complex and of high order. Rather than grapple with the full-complexity models for such systems, the control designers often elect to derive low-order, reduced-complexity models for which a control system can be designed. The subject of model-order reduction has received a significant amount of attention in control engineering literature. This paper describes and demonstrates a novel active-control methodology that can be used to design a control system that automatically forces the original system to behave like a chosen reduced-complexity model by treating the effects of the original complexity-related terms as disturbances acting on the reduced-complexity system. The method is demonstrated by several worked examples involving both linear and nonlinear systems, supported by simulation results.

Bukley, Angelia P.↗

Minimal modeling of the extratropical general circulation

The ability of low-order, two-layer models to reproduce basic features of the mid-latitude general circulation is investigated. Changes in model behavior with increased spectral resolution are examined in detail. Qualitatively correct time-mean heat and momentum balances are achieved in a beta-plane channel model which includes the first and third meridional modes. This minimal resolution also reproduces qualitatively realistic surface and upper-level winds and mean meridional circulations. Higher meridional resolution does not result in substantial changes in the latitudinal structure of the circulation. A qualitatively correct kinetic energy spectrum is produced when the resolution is high enough to include several linearly stable modes. A model with three zonal waves and the first three meridional modes has a reasonable energy spectrum and energy conversion cycle, while also satisfying heat and momentum budget requirements. This truncation reproduces the basic mechanisms and zonal circulation features that are obtained at higher resolution. The model performance improves gradually with higher resolution and is smoothly dependent on changes in external parameters.

O'Brien, Enda↗

Hubble, bubble, toil and trouble: A reduced-order model based saga of the Hubble Space Telescope

Low-order control of the Hubble Space Telescope (HST) to improve pointing performance in the presence of flexible solar array disturbances is demonstarted in this paper. Our controller is a disturbance accommodating controller (DAC) based on a reduced-order model (ROM) controller with a disturbance estimator and a residual mode filter (RMF). We achieve the performance goals with a very low-order multichannel controller as compared to the various Solar Array Gain Augmentation (SAGA) schemes used at present.

Balas, Mark↗

Empirical wind model for the middle and lower atmosphere. Part 1: Local time average

The HWM90 thermospheric wind model was revised in the lower thermosphere and extended into the mesosphere and lower atmosphere to provide a single analytic model for calculating zonal and meridional wind profiles representative of the climatological average for various geophysical conditions. Gradient winds from CIRA-86 plus rocket soundings, incoherent scatter radar, MF radar, and meteor radar provide the data base and are supplemented by previous data driven model summaries. Low-order spherical harmonics and Fourier series are used to describe the major variations throughout the atmosphere including latitude, annual, semiannual, and longitude (stationary wave 1). The model represents a smoothed compromise between the data sources. Although agreement between various data sources is generally good, some systematic differences are noted, particularly near the mesopause. Root mean square differences between data and model are on the order of 15 m/s in the mesosphere and 10 m/s in the stratosphere for zonal wind, and 10 m/s and 4 m/s, respectively, for meridional wind.

Hedin, A. E.↗

Time scale analysis of a digital flight control system

In this paper, consideration is given to the fifth order discrete model of an aircraft (longitudinal) control system which possesses three slow (velocity, pitch angle and altitude) and two fast (angle of attack and pitch angular velocity) modes and exhibits a two-time scale property. Using the recent results of the time scale analysis of discrete control systems, the high-order discrete model is decoupled into low-order slow and fast subsystems. The results of the decoupled system are found to be in excellent agreement with those of the original system.

Naidu, D. S.↗

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗

An adaptive human response mechanism controlling the V/STOL aircraft. Appendix 3: The adaptive control model of a pilot in V/STOL aircraft control loops

Importance of the role of human operator in control systems has led to the particular area of manual control theory. Human describing functions were developed to model human behavior for manual control studies to take advantage of the successful and safe human operations. A single variable approach is presented that can be extended for multi-variable tasks where a low order human response model is used together with its rules, to adapt the model on-line, being capable of responding to the changes in the controlled element dynamics. Basic control theory concepts are used to combine the model, constrained with the physical observations, particularly, for the case of aircraft control. Pilot experience is represented as the initial model parameters. An adaptive root-locus method is presented as the adaptation law of the model where the closed loop bandwidth of the system is to be preserved in a stable manner with the adjustments of the pilot handling qualities which relate the latter to the closed loop bandwidth and damping of the closed loop pilot aircraft combination. A Kalman filter parameter estimator is presented as the controlled element identifier of the adaptive model where any discrepancies of the open loop dynamics from the presented one, are sensed to be compensated.

Kucuk, Senol↗

Formulation of a minimum variance deconvolution technique for compensation of pneumatic distortion in pressure sensing devices

Increasingly, aircraft system designs require that aerodynamic parameters derived from pneumatic measurements be employed as control-system feedbacks. Such high frequency pressure measurements' accuracy is compromised by pressure distortion due to frictional attenuation and pneumatic resonance within the sensing system. A pneumatic distortion model is here formulated and reduced to a low-order state-variable model which retains most of the full model's dynamic characteristics. This reduced-order model is coupled with standard results from minimum variance estimation theory to develop an algorithm to compensate for pneumatic-distortion effects.

Whitmore, Stephen A.↗

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Aires, Filipe↗

Aircraft Fault Detection Using Real-Time Frequency Response Estimation

A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

Grauer, Jared A.↗

Demonstration of Broadband Contrast at 1.2 Lambda/D for the EXCEDE Phase-Induced Amplitude Apodization Coronagraph

The proposed coronagraph instrument on the EXCEDE (EXoplanetary Circumstellar Environments and Disk Explorer) mission study uses a Phase-Induced Amplitude Apodization (PIAA) coronagraph architecture to enable high-contrast imaging of circumstellar debris disks and giant planets at angular separations as close in as the habitable zone of nearby host stars. We report on the experimental results obtained in the vacuum chamber at the Lockheed Martin Advanced Technology Center in 10 percent broadband light centered about 650 nanometers, with a median contrast of 1 x 10 (sup -5) between 1.2 and 2.0 lambda /D simultaneously with 3 x 10 (sup -7) contrast between 2 and 11 =D between 2 and 11 lambda/D for a single-sided dark hole using a deformable mirror (DM) upstream of the PIAA coronagraph. The results are stable and repeatable as demonstrated by three measurements runs with DM settings set from scratch and maintained on the best 90 percent out of the 1000 collected frames. We compare the reduced experimental data with simulation results from modeling observed experimental limits; performance is consistent with uncorrected low-order modes not estimated by the Low Order Wavefront Sensor (LOWFS). Modeled sensitivity to bandwidth and residual tip/tilt modes is well-matched to the experiment.

Wavefront control↗

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗