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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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The evaluation of the dynamic behavior of a rotating system is possible by means of modal parameters (Eigenvalues and Eigenvectors). A mixed analytical and experimetal approach is used to identify the modal parameters of a specially designed test rig. The modal identification is done both for nonrotating as well as rotating systems. These modal parameters are used to validate a developed Finite Element Model.
Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.
Environmental contamination by micro- and nanoplastics (MNPs) is well documented with potential for their increased accumulation globally. Growing public concern over environmental, ecological, and human exposure to MNPs has led to exponential increase in publications, news articles, and reports. Significant knowledge gap exists in standardized analytical methods for the identification and quantification of MNPs from real world environmental samples. Here, in this study, we report comprehensive datasets utilizing thermogravimetric analyzer (TGA) coupled to a Fourier transformed infrared spectrometer (FTIR) and a gas chromatography/mass spectrometer (GC/MS) with corresponding Raman spectral data for the most common polymers documented to be present in the environment (35 plastics of 12 polymer types), to serve as a base line reference for the identification and quantitation of MNPs. Various parameters for TGA-FTIR-GC/MS data acquisition were optimized. Commercial consumer plastic product compositions were identified using this analytical database. Case studies to showcase the utility of the method for polymer mixtures analysis is included. This dataset would serve towards the development of a collaborative, global, comprehensive, and curated public database for the identification of various MNPs and mixtures.
Molecular spectroscopic parameters are compiled for a number of infrared-active molecules occurring naturally in the terrestrial atmosphere. The following molecules are included in this compilation: water vapor; carbon dioxide; ozone; nitrous oxide; carbon monoxide; methane; and oxygen. The spectral region covered extends from less than 1 micron to the far infrared, and data are presented on more than 100,000 spectral lines. The parameters included in the compilation for each line are: frequency, intensity, half-width, energy of the lower state of the transition, vibrational and rotational identifications of the upper and lower energy states, an isotopic identification, and a molecular identification. Using this data compilation, band model parameters are presented for water vapor, carbon dioxide, and ozone averaged over 20 wavenumber intervals. Using these parameters in a random model formulation, transmittance spectra are provided and compared with both degraded monochromatic calculations and laboratory data.
The purpose of the GEODYN Orbit Determination and Parameter Estimation, the SOLVE and ERODYN Programs is to recover geodetic and geophysical parameters from satellite and other data in a state-of-the-art manner. Continued solutions for gravity field, pole positions, Earth rotation, GM, and baselines were made as part of the Crustal Dynamics Project. Some tidal parameters were recovered as well. The eight digit station identification number was incorporated in the software and new techniques for constraining monthly station parameters to each other are being developed. This is allowing the analysts even more flexibility in the shaping of solutions from monthly sets of normal equations and right-hand sides.
Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.
This paper presents recent developments in statistical image processing to demonstrate feasibility of layer-by-layer nondestructive inspection for selective laser melting (SLM) of metallic parts. A matrix of 10 mm × 10 mm stainless 316 squares was deposited with the first row consisting of just the sintered surface, Row 2 a single layer, Row 3 with 2 layers and so on. These layers were scanned to emulate the layer-by-layer collection of top surface geometry using a laser sensor. The resultant data were utilized to generate ISO 25718 roughness parameters and subsequently to perform track identification. This calculation consisted of two salient steps: (1) computing the gradients of surface roughness, which vanish at the peaks and pits, and (2) extracting the scanning direction by taking Hough transform of the geometry. This algorithm was repeated for all layers, and the alignment of the roughness with the rotating scan was observed to be persistent for all layers. Correlation between surface properties obtained along various scan directions and defect probability is explored.
Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof-of-concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere-only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present-day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid-Holocene (MH) and pre-industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non-stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.
Weakly Interacting Massive Particles (WIMPs) are a leading candidate for dark matter and are expected to produce nuclear recoil (NR) events within liquid xenon time-projection chambers. Here, we present a measurement of liquid xenon scintillation characteristics in the LUX dark matter detector and develop a pulse shaped based discrimination parameter to be used for particle identification. To accurately measure the scintillation characteristics, we develop a template-fitting method to reconstruct the detection time of photons. Analyzing calibration data collected during the 2013-16 LUX WIMP search, we measure a singlet-to-triplet scintillation ratio for electron recoils (ER) that is consistent with existing literature, and we make a first-ever measurement of the NR singlet-to-triplet ratio at recoil energies below 74 keV. A prompt fraction discrimination parameter exploits the difference of the photon time spectra for NR and ER events and is optimized to have the least number of ER events that occur in the 50% NR acceptance region. When this discriminator is used in conjunction with charge-to-light discrimination on the calibration data, the signal-to-noise ratio in the NR dark matter acceptance region increases by up to a factor of two.
Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.
Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.
Most methods of system identification of large flexible structures by far are based on the lumped parameter approach. Because of the considerable computational burden due to the large number of unknown parameters, distributed parameter approach, which greatly decreases the number of unknowns, has being investigated. In this paper a distributed parameter model for the estimation of modal characteristics of NASA Mini-Mast truss has been formulated. Both Bernoulli-Euler beam and Timoshenko beam equations are used to characterize the lateral bending vibrations of the truss. The measurement of the lateral displacement at the tip of the truss is provided to the maximum likelihood estimator. Closed-form solutions of the partial differential equations and closed-form expressions of the sensitivity functions are derived so that the estimation algorithm is highly efficient. The resulting estimates from test data by using Timoshenko beam model are found to be comparable to those derived from finite element analysis.
The purpose of parameter estimation, a subset of system identification, is to estimate the coefficients (such as stability and control derivatives) of the aircraft differential equations of motion from sampled measured dynamic responses. In the past, the primary reason for estimating stability and control derivatives from flight tests was to make comparisons with wind tunnel estimates. As aircraft became more complex, and as flight envelopes were expanded to include flight regimes that were not well understood, new requirements for the derivative estimates evolved. For many years, the flight determined derivatives were used in simulations to aid in flight planning and in pilot training. The simulations were particularly important in research flight test programs in which an envelope expansion into new flight regimes was required. Parameter estimation techniques for estimating stability and control derivatives from flight data became more sophisticated to support the flight test programs. As knowledge of these new flight regimes increased, more complex aircraft were flown. Much of this increased complexity was in sophisticated flight control systems. The design and refinement of the control system required higher fidelity simulations than were previously required.
This paper presents a study of the effect of the uncertainty of identified parameters on the damage detection of structure. The identified parameters, which are obtained by applying system identification algorithms to analyze the time-domain response data, have uncertainty due to noise, environmental change, etc. The damage of some structural elements may have less effect on the identified parameters than noise or environmental change, so some damage situations cannot be distinguished under these circumstances. An algorithm is presented to determine whether the considered damage can be distinguished. A twenty-bay truss structure is used to demonstrate and verify the study.
Most of the present-day literature concerned with identification theory and techniques is directed toward lumped parameter systems, and many comprehensive surveys of the field are available. Relatively little has appeared in the literature concerning distributed identification, and even more noticeable is the scarcity of papers dealing with systems described by the one-dimensional wave equation. Perdeauville and Goodson were perhaps the first researchers with a workable but time consuming method for the identification of coefficients of the wave equation. Fairman and Shen, also considering the wave equation, used the technique of finite differencing to approximate spatial derivatives, and Poisson filter chains to approximate temporal derivatives.
The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.
The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission systm from the fuselage. A mathematical model relating applied rotor loads and inertial loads of the rotor/transmission system to the load cell response is required to allow the load cells to be used to estimate rotor loads from flight data. Such a model is derived analytically by applying a force and moment balance to the isolated rotor/transmission system. The model is tested by comparing its estimated values of applied rotor loads with measured values obtained from a ground based shake test. Discrepancies in the comparison are used to isolate sources of unmodeled external loads. Once the structure of the mathematical model has been validated by comparison with experimental data, the parameters must be identified. Since the parameters may vary with flight condition it is desirable to identify the parameters directly from the flight data. A Maximum Likelihood identification algorithm is derived for this purpose and tested using a computer simulation of load cell data. The identification is found to converge within 10 samples. The rapid convergence facilitates tracking of time varying parameters of the load cell model in flight.
This paper describes a flight test demonstration of a system for identification of the stability and handling qualities parameters of a helicopter-slung load configuration simultaneously with flight testing, and the results obtained.Tests were conducted with a UH-60A Black Hawk at speeds from hover to 80 kts. The principal test load was an instrumented 8 x 6 x 6 ft cargo container. The identification used frequency domain analysis in the frequency range to 2 Hz, and focussed on the longitudinal and lateral control axes since these are the axes most affected by the load pendulum modes in the frequency range of interest for handling qualities. Results were computed for stability margins, handling qualities parameters and load pendulum stability. The computations took an average of 4 minutes before clearing the aircraft to the next test point. Important reductions in handling qualities were computed in some cases, depending, on control axis and load-slung combination. A database, including load dynamics measurements, was accumulated for subsequent simulation development and validation.