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At least 1,261 records · Page 70

Validation of the ERBE scanner scene identification methodology: Analysis with Nimbus-7 ERB data

Maximum Likelihood Estimation (MLE) procedure for scene identification currently being utilized in the ERBE scanner data processing stream is applied to the Nimbus-7 ERB Scanner data for the month of June 1979. The Earth radiation budget parameters derived using MLE method show very good agreement with the values provided using sorting into angular bins (SAB) method. Results of the fields on different spatial scales are presented. A satellite zenith angle study indicates that the MLE procedure considerably improves the scene selection over the method of bispectral thresholds applied in the Nimbus-7 ERB data processing. Agreement with SAB results improved further when the observations were cut off at 75% in satellite zenith. Sampling constraints, however require that the cut-off angle should not be lower than 70%. Quantitative details about the reliability of the scene identification are also presented.

Vermury, S.↗

Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February

Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.

54 ENVIRONMENTAL SCIENCES↗

Rotorcraft Blade Mode Damping Identification from Random Responses Using a Recursive Maximum Likelihood Algorithm

An on line technique is presented for the identification of rotor blade modal damping and frequency from rotorcraft random response test data. The identification technique is based upon a recursive maximum likelihood (RML) algorithm, which is demonstrated to have excellent convergence characteristics in the presence of random measurement noise and random excitation. The RML technique requires virtually no user interaction, provides accurate confidence bands on the parameter estimates, and can be used for continuous monitoring of modal damping during wind tunnel or flight testing. Results are presented from simulation random response data which quantify the identified parameter convergence behavior for various levels of random excitation. The data length required for acceptable parameter accuracy is shown to depend upon the amplitude of random response and the modal damping level. Random response amplitudes of 1.25 degrees to .05 degrees are investigated. The RML technique is applied to hingeless rotor test data. The inplane lag regressing mode is identified at different rotor speeds. The identification from the test data is compared with the simulation results and with other available estimates of frequency and damping.

Molusis, J. A.↗

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗

The relationship of theory and experiment in the D-region.

To monitor the descriptive parameters of the lower ionosphere and construct a model which predicts their temporal and spatial variations is a particularly difficult task. In a given situation simultaneous measurements should be conducted of ionization sources, the altitude distribution of all neutral atmospheric constituents, the identification, number density, and altitude distribution of different species of positive and negative ions and electrons. These results are compared with theoretical predictions to serve as boundary conditions and guides to modification of the theory. It has not been possible to realize this goal in lower ionosphere research because of the large number of parameters and since certain parameters, such as positive ion species distribution and nitric oxide, can only be measured with the aid of sounding rockets. To illustrate the difficulty, recent positive ion composition measurements are compared with predictions utilizing clustering of ions and neutrals to form hydrated ions.

Aikin, A. C.↗

Improvement of observer/Kalman filter identification (OKID) by residual whitening

This paper presents a time-domain method to identify a state space model of a linear system and its corresponding observer/Kalman filter from a given set of general input-output data. The identified filter has the properties that its residual is minimized in the least squares sense, orthogonal to the time-shifted versions of itself, and to the given input-output data sequence. The connection between the state space model and a particular auto-regressive moving average description of a linear system is made in terms of the Kalman filter and a deadbeat gain matrix. The procedure first identifies the Markov parameters of an observer system, from which a state space model of the system and the filter gain are computed. The developed procedure is shown to improve results obtained by an existing observer/Kalman filter identification method, which is based on an auto-regressive model without the moving average terms. Numerical and experimental results are presented to illustrate the proposed method.

Phan, Minh↗

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Identification of Best Practices for Predicting Inlet Performance Using FUN3D Part 2: Installed Inlets.

A series of studies were performed to assess the impacts of boundary condition type and placement, grid refinement, and modeling parameters such as turbulence model and flux limiter on the predicted inlet performance for installed inlet configurations using the FUN3D flow solver. Two configurations were considered for the study; a wall-mounted Boundary Layer Ingestion (BLI) inlet and the C607 propulsion model tested in the 8x6 Supersonic Wind Tunnel at the NASA Glenn Research Center. The results of the studies were to be used to recommend best practices, as well as to assess the accuracy of FUN3D for inlet predictions. The results of BLI inlet stud-ies showed a minimal impact of grid refinement on the predicted inlet performance for a constant mass flow rate through the inlet. For the C607 propulsion model, the results showed that while the FUN3D predictions at the Aerodynamic Inter-face Plane (AIP) qualitatively agree with the experimental data, FUN3D showed a tendency to overpredict the circumferential distortion metric (IDCmax) and un-derpredict both the radial distortion metric (IDRmax) and the pressure recovery at the AIP (PRAIP ), with the differences between FUN3D and the experimental data increasing with increasing grid refinement and Mach number. Additionally, the outflow boundary location studies performed for both geometries showed that the solution at the AIP was not significantly impacted by the outflow boundary location as long as it was not placed at the location of the AIP. The modeling parameter studies did not indicate a path forward for improved predictions for either inlet con-figuration. Finally, comparisons between the mass flow plug and outflow geometry versions of the C607 propulsion model illustrated favorable agreement, which indi-cates that the differences observed are not caused by the outflow boundary model for this problem. This problem poses significant challenges to Reynolds-averaged Navier-Stokes (RANS) solvers due to the presence of shocks and flow separation in the inlet.

FUN3D↗

gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. Here, an interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a -nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

97 MATHEMATICS AND COMPUTING↗

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗

Extracting the excitonic Hamiltonian of a chlorophyll dimer from broadband two-dimensional electronic spectroscopy

We apply Frenkel exciton theory to model the entire Q-band of a tightly bound chlorophyll dimer inspired by the photosynthetic reaction center of photosystem II. The potential of broadband two-dimensional electronic spectroscopy experiment spanning the Qx and Qy regions to extract the parameters of the model dimer Hamiltonian is examined through theoretical simulations of the experiment. We find that the local nature of Qx excitation enables identification of molecular properties of the delocalized Qy excitons. Specifically, we demonstrate that the cross-peak region, where excitation energy is resonant with Qy while detection is at Qx, contains specific spectral signatures that can reveal the full real-space molecular Hamiltonian, a task that is impossible by considering the Qy transitions alone. System–bath coupling and site energy disorder in realistic systems may limit the resolution of these spectral signatures due to spectral congestion.

Chemistry↗

Characterization of structural connections using free and forced response test data

The accurate prediction of system dynamic response often has been limited by deficiencies in existing capabilities to characterize connections adequately. Connections between structural components often are complex mechanically, and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic preditions. A procedure for identifying physical connection properties from free and forced response test data is developed, then verified utilizing a system having both a linear and nonlinear connection. Connection properties are computed in terms of physical parameters so that the physical characteristics of the connections can better be understood, in addition to providing improved input for the system model. The identification procedure is applicable to multi-degree of freedom systems, and does not require that the test data be measured directly at the connection locations.

Lawrence, Charles↗

Pressure vessel burst test program - Initial program paper

The current status of a pressure vessel burst test program, aimed at the study of the blast waves and fragmentation characteristics of ruptured gas-filled pressure vessels, is reported. The program includes a series of test plans, each involving multiple bursts with burst pressures ranging to 7500 psig. The discussion covers the identification of concerns and hazards, application of the data generated, and a brief review of the current methods for assessing vessel safety and burst parameters. Attention is also given to pretest activities, including completed vessel and facility/instrumentation preparation and results of completed preliminary burst tests.

Cain, Maurice R.↗

Characterization of structural connections using free and forced response test data

The accurate prediction of system dynamic response often has been limited by deficiencies in existing capabilities to characterize connections adequately. Connections between structural components often are complex mechanically, and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic predictions. A procedure for identifying physical connection properties from free and forced response test data is developed, then verified utilizing a system having both a linear and nonlinear connection. Connection properties are computed in terms of physical parameters so that the physical characteristics of the connections can better be understood, in addition to providing improved input for the system model. The identification procedure is applicable to multi-degree of freedom systems, and does not require that the test data be measured directly at the connection locations.

Lawrence, C.↗

Use of system identification techniques for improving airframe finite element models using test data

A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

Hanagud, Sathya V.↗

Time domain identification of element matrices

Accurate mathematical models are clearly an important part of the design, modification, control, and damage assessment of large space structures. A critical part of model determination of any type of structure is the use of system identification (SI), a process for using measured excitation and response data to improve the form of a mathematical model or the values of the parameters in it. The reasons for using SI are manifold, but most of them involve uncertainties in either the form of or the parameters in the mathematical model. Some of the more common uncertainties are the following: the nature of the damping, the characteristics of sloppy or sticky joints, inelastic material properties, and parameters in a reduced Degree-of-Freedom (DOF) model. This paper contains a description of the SI algorithm followed by two illustrations: one in which all parameters in a shear building model are determined using pull back and quick release data, and another in which an equivalent reduced DOF model is obtained for a two story frame.

Matzen, Vernon C.↗

Vehicle track identification in synthetic aperture radar images

Various technologies pertaining to identification of vehicle tracks in synthetic aperture radar coherent change detection image data are described herein. Coherent change detection images are analyzed in a parameter space using Radon transforms. Peaks of the Radon transforms correspond to features of interest, including vehicle tracks, which are identified and classified. New coherent change detection images in which the features of interest and their characteristics are signified are then generated using inverse Radon transforms.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗