Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics
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The XV-15 Tilt-Rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier transformed) with cross spectral and transfer function methods. Modal frequencies and damping were determined by performing curve fits to transfer function magnitude and phase data and to cross spectral magnitude data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with earlier predictions.
The XV-15 Tilt-Rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier transformed) with cross spectral and transfer function methods. Modal frequencies and damping were determined by performing curve fits to transfer function magnitude and phase data and to cross spectral magnitude data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with earlier predictions.
The XV-15 Tilt-Rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier transformed) with cross spectral and transfer function methods. Modal frequencies and damping were determined by performing curve fits to transfer function magnitude and phase data and to cross spectral magnitude data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with earlier predictions.
The XV-15 tilt-rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier-transformed). Modal frequencies and damping were determined by performing curve fits to frequency-response magnitude and phase data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with predictions by two different programs, CAMRAD and ASAP.
The XV-15 Tilt-Rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier transformed) with cross spectral and transfer function methods. Modal frequencies and damping were determined by performing curve fits to transfer function magnitude and phase data and to cross spectral magnitude data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with earlier predictions.
Report discusses use of extremal dual control to prevent overtesting. In extremal dual control, electronic test-control subsystem automatically reduces excitation supplied to shaker subsystem to keep force applied by shaker below preset force limit and to keep acceleration of shaker below preset acceleration limit.
The XV-15 tilt-rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier transformed). All spectral data were computed using chirp z-transforms. Modal frequencies and damping were determined by fitting curves to frequency-response magnitude and phase data. The results given in this report are for the XV-15 with its original metal rotor blades. Also, frequency and damping values are compared with theoretical predictions made using two different programs, CAMRAD and ASAP. The frequency-domain data-analysis method proved to be very reliable and adequate for tracking aeroelastic modes during flight-envelope expansion. This approach required less flight-test time and yielded mode estimations that were more repeatable, compared with the exponential-decay method previously used.
The XV-15 Tilt-Rotor wing has six major aeroelastic modes that are close in frequency. To precisely excite individual modes during flight test, dual flaperon exciters with automatic frequency-sweep controls were installed. The resulting structural data were analyzed in the frequency domain (Fourier-transformed). Modal frequencies and damping were determined by performing curve fits to frequency-response magnitude and phase data. Results are given for the XV-15 with its original metal rotor blades. Frequency and damping values are also compared with new predictions by two different programs, CAMRAD and ASAP.
Three different types of maneuvers were designed to separately quantify pitch rate and angle of attack rate contributions to the nondimensional aerodynamic pitching moment coefficient. These maneuvers combined pilot inputs and automatic multisine excitations, and were own with the subscale T-2 and Bat-4 airplanes using the NASA AirSTAR flight test facility. Stability and control derivatives, in particular C(sub mq) and C(sub m alpha(.)) were accurately estimated from the flight test data. These maneuvers can be performed with many types of aircraft, and the results can be used to increase simulation prediction fidelity and facilitate more accurate comparisons with wind tunnel experiments or numerical investigations.
Composites have been extensively used in aero structure and become the predominate components in the new airframes. Thus, rapid and effective inspection for composite structures is highly desired in aerospace engineering in order to shorten the certificate cycle for new structures or provide safety guarantee for existing ones. In this paper, a laser based remote Lamb wave inspection system is presented and implemented on composite plates for simulated damage detection. The system employs pulsed laser (PL) and scanning laser Doppler vibrometer (SLDV) for noncontact and remote Lamb wave actuation and wavefield sensing. A composite plate with simulated defect (surface bonded quartz rod) is inspected with the PL-SLDV laser system. Wave scattering are observed in the SLDV acquired wavefield and the damage is further evaluated with wavefield imaging and frequency wavenumber analysis. Potential application towards automatic PL Lamb wave excitation is also explored through employing an industry robotic arm towards rapid inspection.
Douglas Aircraft Company experience in flight flutter testing is reviewed briefly, with comments on state-of-the-art excitation and instrumentation techniques used up to the present time. The limitations of previous techniques are discussed with emphasis on the problem of: (1) establishing a flutter margin of safety for predicted marginal flutter modes; (2) resolving instances of flutter not predicted by theoretical calculations in advance; and (3) delaying the airplane demonstration by time consumed in acquisition and reduction of flutter data. Current Douglas philosophy in flight flutter testing is presented and a description given of steady-state vane excitation system development, automatic data handling system, and the potential application of automatic computing methods for increasing flutter data yield.
Multiconfiguration pair-density functional theory (MC-PDFT) is a computationally efficient method that computes the energies of electronic states in a state specific or state average framework via an on-top functional. However, MC-PDFT does not include state interaction among these states since the final energies do not come from the diagonalization of an effective model-space Hamiltonian. Recently, multistate extensions such as linearized PDFT (L-PDFT) have been developed to accurately model the potentials near conical intersections and avoided crossings. However, there has not been any systematic study evaluating their performance for predicting vertical excitations at the equilibrium geometry of a molecule, when the excited states are generally well separated. In this paper, we report the performance of L-PDFT on the extensive QUESTDB data set of vertical excitations using a database of automatically selected active spaces. We show that L-PDFT performs well on all these excitations and successfully reproduces the performance of MC-PDFT. These results further demonstrate the potential of L-PDFT, as its scaling is constant with the number of states included in the state-average manifold, whereas MC-PDFT scales linearly in this regard.
Hydropower units often rely on legacy excitation systems with fixed automatic voltage regulator (AVR) settings, but parameter drift, changing operating conditions, and evolving grid-service demands can degrade terminal-voltage regulation over time. This problem should be addressed because poor voltage regulation affects reactive-power support, system stability, and reliable plant operation, and replacing installed AVR and limiter logic is costly and disruptive in practice. To address this issue, this paper proposes a retrofit-friendly enhancement for hydropower generator excitation systems that combines adaptive tuning of the AVR middle lead–lag branch with a small proportional-integral correction for removing steady bias and slow drift. The adaptive laws are normalized, projection-bounded, and equipped with leakage, while adaptation is enabled only in the middle branch. Thus, the intent of the underexcitation and overexcitation limiters, as well as legacy current and rate limits, remains unchanged. Stability is analyzed under incremental performance assumptions, yielding boundedness and asymptotic tracking in the absence of saturation. The method is evaluated using real operational data from a utility-scale hydropower unit at Rocky Reach Dam, Unit C-8. Compared with the fixed AVR baseline, the proposed method reduces terminal-voltage mean squared error by about 70%, removes long periods of steady-state bias, and improves transient tracking while coexisting with limiter actions. These results show that substantial voltage-control improvement can be achieved on existing hydropower units with minimal integration effort and without replacing the installed AVR structure.
Aims. We present an improved database of temperature-dependent rate coefficients for rotational state-to-state transitions in H 2 O + H 2 O collisions. The database includes 231 transitions between the lower para-states of H 2 O and 210 transitions between its lower ortho-states (up toj= 7) and can be employed in cometary and planetary applications up to the temperature of 1000 K. Methods. We developed and applied a new general method that allows the generation of rate coefficients for excitation and quenching processes that automatically satisfy the principle of microscopic reversibility and also helps to cover the range of low collision energies by interpolation of cross sections between the process threshold and the computed data points. Results. We find that in the range of intermediate temperatures, 150
Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA
The capability for large angle slewing maneuvers with very demanding pointing accuracy and tracking speed is increasingly required for space-based systems. This is particularly the case for space-based directed energy beam pointing. A method is thus proposed in this paper for commanding general pointing and tracking maneuvers with automatic correction for slew-excited structural deformations. All existing rigid body multiaxial slewing algorithms and flexible body vibration damping algorithms can then be used simultaneously, without design iterations. In particular, an example is given of a retargeting maneuver with specified line-of-sight settling time and no torque saturation.
The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.