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At least 109 records · Page 6

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NASA NTRS2012

Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller

This paper explores application of adaptive control architecture to a light, high-aspect ratio, flexible aircraft configuration that exhibits strong rigid body/flexible mode coupling. Specifically, an L(sub 1) adaptive output feedback controller is developed for a semi-span wind tunnel model capable of motion. The wind tunnel mount allows the semi-span model to translate vertically and pitch at the wing root, resulting in better simulation of an aircraft s rigid body motion. The control objective is to design a pitch control with altitude hold while suppressing body freedom flutter. The controller is an output feedback nominal controller (LQG) augmented by an L(sub 1) adaptive loop. A modification to the L(sub 1) output feedback is proposed to make it more suitable for flexible structures. The new control law relaxes the required bounds on the unmatched uncertainty and allows dependence on the state as well as time, i.e. a more general unmatched nonlinearity. The paper presents controller development and simulated performance responses. Simulation is conducted by using full state flexible wing models derived from test data at 10 different dynamic pressure conditions. An L(sub 1) adaptive output feedback controller is designed for a single test point and is then applied to all the test cases. The simulation results show that the L(sub 1) augmented controller can stabilize and meet the performance requirements for all 10 test conditions ranging from 30 psf to 130 psf dynamic pressure.

Che, Jiaxing↗

NASA NTRS2013

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1∕8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

extreme climate index↗

NASA NTRS2018

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Adaptive Contro↗

NASA NTRS2017

Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/ upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

land surface models↗

NASA NTRS2018

Output Measurement Equations for Flexible Aircraft Flight Dynamics

A summary of output measurement equations for onboard sensors used in flight testing of flexible aircraft is presented. These equations include the effects of structural flexibility and are considerably more complex than the standard equations for rigid-body aircraft. The output equations discussed include accelerations from linear accelerometers, strains, angular rates, angular accelerations, Euler angles, true airspeed, and air flow angles. The output equations are derived in full form and then simplified in some cases. Linearized output equations, suitable for state-space or transfer function models, are also developed. Example flight test data from the X-56A subscale aeroelastic demonstrator is discussed, for reference.

Grauer, Jared A.↗

NASA NTRS2018

Dynamic Modeling using Output-Error Parameter Estimation based on Frequency Responses Estimated with Multisine Inputs

A method is developed for estimating model parameters, such as nondimensional stability and control derivatives, by fitting transfer function or state-space models to empirical frequency response data using the output-error approach. The frequency response data were computed using Fourier transforms of measured input and output data. The control surfaces were excited with periodic multisine inputs which facilitated time-ecient estimation of multiple-input multiple-output frequency responses. The method was applied to lateral data from a nonlinear flight dynamics simulation of the F-16 aircraft, and to longitudinal data from multiple repeated flight test maneuvers of the NASA T-2 subscale aircraft. Results using simulation data showed the frequency response method compared well to other standard methods for parameter estimation. In addition to including all the available inputs, outputs, and harmonic frequencies in the estimation, relatively small subsets of the measured data could also be used to focus on identifying specific parts of the model. Results from flight test data showed that parameter estimates and uncertainties determined from repeated maneuvers were accurate and in statistical agreement with each other.

Grauer, Jared A.↗

DOE OSTI2019

Multi-Threaded Output in CMS using ROOT

CMS has worked aggressively to make use of multi-core architectures, routinely running 4- to 8-core production jobs in 2017. The primary impediment to efficiently scaling beyond 8 cores has been our ROOT-based output module, which has been necessarily single threaded. In this paper we explore the changes made to the CMS framework and our ROOT output module to overcome the previous scaling limits, using two new ROOT features: the \texttt{TBufferMerger} asynchronous file merger, and Implicit Multi-Threading. We examine the architecture of the new parallel output module, the specific accommodations and modifications that were made to ensure compatibility with the CMS framework scheduler, and the performance characteristics of the new output module.

Riley, Daniel↗

DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗

DOE OSTI2022

Fully differential rail-to-rail output amplifier with inverter-based input pair

A fully differential rail-to-rail-output amplifier includes a differential input inverter pair, folded cascode pair, class AB control pair, and class AB output rail-to-rail pair. A drain associated with the folded cascode pair is operatively coupled to the class AB control pair, and the drain associated with the folded cascode pair is unconnected to the current source associated with the class AB control pair. A method of providing fully differential rail-to-rail-output amplification includes coupling a folded cascode pair operatively to a differential input inverter pair, coupling a drain associated with the folded cascode pair operatively to a class AB control pair, and coupling a class AB output rail-to-rail pair operatively to the class AB control pair.

Dabrowski, Mieczyslaw M.↗

DOE OSTI2019

Distributed output-feedback model predictive control for multi-agent consensus

We introduce a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min–max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is demonstrated to illustrate the results.

42 ENGINEERING↗

DOE OSTI2019

Estimation of full-field dynamic strains from digital video measurements of output-only beam structures by video motion processing and modal superposition

Strain is an necessary quantity to characterize local structural behaviors and directly correlates with structural damage initiation and development that is within local regions. Strain measurement at high spatial resolution (density) locations is thus required to characterize local structural behaviors and detect potential local damage. Traditional contact-type strain gauges are mostly discrete point-wise sensors that can only be placed in a limited number of positions. Distributed optical fiber sensing techniques can measure strains at spatially dense measurement points, but their instrumentation is a time- and labor-intensive process associated with the issue of the fragility of fibers. Noncontact optical measurement techniques, such as a family of interferometry techniques using laser beams (e.g., laser Doppler vibrometers), can provide vibration measurement at high density spatial points without the need to install sensors on the structure. Yet, these measurement devices are active sensing methods that are relatively expensive and vulnerable to ambient motion. Photogrammetry is an alternative noncontact optical measurement method using (passive) white-light imaging of digital video cameras that are relatively low-cost, agile, and provides simultaneous measurements at high spatial density locations where every pixel becomes a measurement point. Among others, digital image correlation can achieve full-field deformation measurements and subsequently estimate the full-field strains. However, it is computationally extensive. This research develops a new efficient approach to estimate the full-field (as many measurement points as the pixel number of the video frame on the structure) dynamic strains at high-spatial (pixel)-resolution/density location points from the digital video measurement of output-only vibrating structures. The developed approach is based on phase-based video motion estimation and modal superposition of structural dynamic response. Moreover, the method is augmented by a high-fidelity finite element model, which is updated with the full-field experimental modal parameters “blindly” identified from the video measurement of the output-only structure. Laboratory experiments are conducted to validate the method on a bench-scale cantilever beam structure. Results demonstrate that the full-field dynamic strain estimated by the developed approach from the video measurement of the output-only vibrating beam match very well those directly measured by the strain gauges (at discrete measurement points). Some factors associated with the effectiveness of the method are experimentally studied and discussed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Output enhanced quantum correlations using a coherence-controlled atomic reservoir

We show that the output steady-state quantum correlations are prominently enhanced via dissipative atomic reservoir by employing a two-level atomic system driven by a strong laser field inside a two-mode cavity. It is found that the output two-mode quantum entanglement is obviously enhanced in comparison to that of the intracavity fields without adiabatic elimination atomic variables. On the other hand, the output asymmetrical one-way Einstein–Podolsky–Rosen steering could be obtained with unbalanced coupling strength. Physically, the enhanced quantum correlations can be essentially attributed to the coherence-controlled effective two-mode squeezed thermal state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Tensile, flexural, and light output measurements of selected organic scintillators for evaluation of their potential as structural materials

In order to design structures, such as unmanned vehicle structures, out of plastic scintillator, e.g., for radionuclide searches, suitable materials must either be identified or developed. In searches utilizing unmanned vehicles, the absence of an additional detector attached to the vehicle body as a payload could enable the vehicle to travel faster, carry a longer lived battery, or carry other auxiliary equipment which may be useful for search and/or response. To this end, four mechanical characteristics of selected organic scintillators manufactured by Eljen technologies, Sandia Livermore, and Lawrence Livermore National Lab have been measured. Specifically, tensile and flexural tests have been performed to ASTM specifications on organic scintillators with polyvinyl toluene (PVT), polystyrene (PS), or crosslinked versions of these bases. In addition to these mechanical tests, light output testing was performed in order to quantify whether crosslinking or adding organometallic complexes affects light output in the particular scintillator compositions we measured. We found that the tested plastic scintillators have strengths that are comparable to common structural plastics. We also show that chemically modifying the polymer base can show improvements in the mechanical properties without being overly detrimental to the scintillator light output.

42 ENGINEERING↗

DOE OSTI2019

Non-linearity effects on the light-output calibration of light charged particles in CsI(Tl) scintillator crystals

The light output produced by light ions ( Z ≤ 4) in Csl(Tl) crystals is studied over a wide range of detected energies (E ≤ 300 MeV). Energy-light calibration data sets are obtained with the 10 cm crystals in the recently upgraded High-Resolution Array (HiRA10). We use proton recoil data from 40,48 Ca + CH 2 at 28 MeV/u, 56.6 MeV/u, 39 MeV/u and 139.8 MeV/u and data from a dedicated experiment with direct low-energy beams. We also use the punch through points of p, d, and t particles from 40,48 Ca + 56,64 Ni, 112,124 Sn collisions reactions at 139.8 MeV/u. Non-linearities, arising in particular from Tl doping and light collection effects in the CsI crystals, are found to significantly affect the light output and therefore the calibration of the detector response for light charged particles, especially the hydrogen isotopes. A new empirical parametrization of the hydrogen light output, $L(E,Z=1,A)$, is proposed to account for the observed effects. Results are found to be consistent for all 48 CsI(Tl) crystals in a cluster of 12 HiRA10 telescopes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. Further, the accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

Energy & Fuels↗

DOE OSTI2025

Skipper-CCD Output Sensitivity Improvement with a Common Source Cascode Amplifier

A Skipper-CCD output has been traditionally readout in a source follower configuration, also known as common drain, mainly because in this configuration a good compromise between sensitivity, noise and speed is achieved. In this work a different approach is presented: the output transistor is connected in a common source configuration and an external cascode device is added in series, obtaining a voltage gain higher than unity without increasing significantly the parasitic capacitance due to Miller effect and thus increasing the obtained voltage per electron of charge. The proposed output stage improves the sensitivity 3.4 times when compared with the traditionally used stage while keeping almost the same equivalent noise charge.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Output Current Estimation and Control in Primary Side LCC Secondary Side Series Compensated Wireless Power Transfer System without Secondary Side Sensors

The estimation (and control) of the output variables is often challenging for wireless power transfer (WPT) systems. This paper presents a secondary side sensor-less closed loop estimation technique to estimate the output current in a primary side LCC and secondary side series compensated WPT system. Using information of primary side variables, the proposed estimation can estimate the output current over a wide range of load current variations. Results are presented to verify the efficacy of the proposed estimation.

Mukherjee, Subho [ORNL] (ORCID:0009000672297925)↗

DOE OSTI2020

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

electricity distribution market↗