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Yang Zhang

Publications and source records attributed to Yang Zhang.

14 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Harnessing Collaborative Learning Automata to Guide Multi-objective Optimization based Inverse Analysis for Structural Damage Identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

Yang Zhang↗
NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗
NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗
NASA NTRS2022

A Reinforcement Learning Hyper-Heuristic in Multi-Objective Optimization with Application to Structural Damage Identification

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Pei Cao↗
NASA NTRS2020

Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016

In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2° grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16° S, 6° E, and 10° S, 0° E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.

Yohei Shinozuka↗
NASA NTRSDate not supplied

Slat Noise Control Using a Slat Gap Filler

The leading edge slat of a high-lift system is one of the main noise contributors on many commercial aircraft during approach. This paper continues our previous studies on the gap filler for passive noise control on the 30P30N high-lift airfoil. An improved implementation of the gap filler was applied to minimize the effects of flow leakage encountered in the previous work, which resulted in spurious noise content in the far-field acoustic spectra. To evaluate the effect of passive flow control on the acoustics generated by the unsteady flow field, anechoic wind tunnel experiments are conducted on the two-dimensional, three-element high-lift airfoil with a gap filler mounted to the slat. The slat geometry modification associated with the gap filler alters the flow field in the cove region that dominates the generation of the acoustic field. A single angle of attack (훼푘=8◦) and three flow speeds corresponding to Reynolds numbersof푅푒푐=1.2푒6,1.5푒6, and1.71푒6are selected as the test conditions. Steady surface pressure measurements are conducted to assess the effect of the treatments on the overall lift. Acoustic array measurements are used to evaluate the influence of the gap filler on the radiated noise. Delay and Sum beamforming is applied to locate the noise sources on the model and to provide the integrated spectra. The gap filler is found to eliminate the narrowband peaks in the acoustic spectra and, also, to yield a 10 dB reduction in the broadband noise in comparison with the baseline case. Time-resolved Particle Image Velocimetry results show that the flow features are significantly altered with the presence of the gap filler, which leads to a more stable slat cove shear layer and, thus, to weaker pressure and velocity fluctuations.

Yang Zhang↗
NASA NTRSDate not supplied

Investigation of the 30P30N Slat Flow Field with Passive Control Devices Using Particle Image Velocimetry

The leading-edge slat of a high-lift wing is one of the main noise contributors during approach and landing. This paper describes an experimental investigation of the velocity field associated with multiple passive noise treatments, including slat-cusp extensions, a cove filler, and a gap filler, on a two-dimensional multi-element high-lift 30P30N airfoil. Previous work documented comparisons of both surface and far field pressure fluctuations in the presence of these devices with those for a baseline case at different flow conditions. However, important information related to the velocity fields was missing from the previous measurements, hindering our ability to elucidate the changes in the flow physics associated with the noise treatments. Therefore, two-component Particle Image Velocity has been used to investigate the influence of passive noise treatments on the flow fields. All measurements are taken at an effective, free-air angle of attack of 5.5 degrees and a stowed-chord-based Reynolds number of 1.71 × 10e6. The measurements show that the slat extensions shorten the slat-cove shear layer trajectory, resulting in reduced growth of disturbances within the slat-cove shear layer. This leads to a shift of tonal peaks to higher frequencies and a reduction in the tonal amplitudes. The gap filler blocks the flow path through the gap, causing the reattachment location to shift to the main wing leading edge lower surface. Consequently, the feedback loop associated with the flow-acoustic interaction in the baseline case is eliminated, and the turbulent kinetic energy in the slat-cove shear layer is significantly reduced. However, extensive flow separation is observed on the suction side of the gap filler, which does not eliminate the noise reduction benefit due to the gap filler, but does degrade the aerodynamic performance of the high-lift configuration. Finally, the overall flow field in the presence of the cove filler is similar to that in the baseline case at the design angle of attack, but the slat-cove shear layer is eliminated leading to a suppression of the cavity tones associated with the shear layer. This change accounts for the reduction in slat noise as measured in previous work.

Aeroacoustics↗
NASA NTRSDate not supplied

Effects of Porous Gap Fillers on 30P30N Leading-Edge SlatNoise. Part I: Surface Pressure and Acoustics

The leading edge slat of a high-lift system is one of the main contributors to airframe noise during approach. In a previous experimental study, we assessed the performance of an impermeable slat gap filler as a passive flow control device to reduce the slat noise associated with a two-dimensional, three-element high-lift airfoil. The present paper, the first of two parts,represents a follow-on investigation to assess the relative efficacy of permeable gap fillers thatallow successively higher amounts of flow to pass through the gap. To evaluate the influence of this passive flow control device on the acoustics generated by the unsteady flow near the slat, experiments are conducted in an anechoic wind tunnel by mounting a gap filler to the slat element of the two-dimensional 30P30N high-lift configuration. Measurements are performed at a single geometric angle of attack (𝛼𝑘=8◦) and three different flow speeds that correspond to Reynolds numbers of𝑅𝑒𝑐=1.2𝑒6,1.5𝑒6, and1.71𝑒6, respectively. Steady surface pressure measurements are used to gauge the influence of the permeable gap filler treatments on the overall lift. The effect of each treatment on the radiated noise is analyzed via acoustic array measurements, followed by delay-and-sum beamforming to locate the slat noise sources and to provide the integrated acoustic spectra. The porous gap fillers are found to eliminate the narrowband peaks in the acoustic spectra and, also, to yield a 10 dB reduction in the broadband noise in comparison with the baseline case with no gap filler. The porous gap filler with thelowest permeability acts similar to the impermeable gap filler examined previously. However, the aerodynamics and noise reduction both degrade with increasing permeability. An accompanying abstract describes the particle image velocimetry measurements of the flowfield within the slat cove and on either side of the permeable gap f

slat noise↗
NASA NTRSDate not supplied

Effects of Porous Gap Fillers on Leading-Edge Slat Noise of 30P30N. Part II: PIV Measurements

The leading edge slat of a high-lift system is one of the main contributors to the airframe noise during approach conditions. This paper, the second of two parts, continues our previous studies on the slat gap filler as a passive noise control device on the two-dimensional, 30P30N multielement airfoil. Whereas the earlier study was focused on the effects of an impermeable gap filler that completely blocks the flow through the gap, this follow-on assessment is devoted to permeable slat gap fillers that allow limited amounts of flow to pass through the gap. Part I of this two-part abstract described the aerodynamic and acoustic effects of the permeable gap fillers, as inferred from both the measurements of static and unsteady surface pressures and the microphone array data for the radiated noise. To understand the physical mechanism responsible for the noise reduction documented in Part I, as well as for the accompanying aerodynamic penalty due to the porous gap fillers, Particle Image Velocimetry (PIV) is used in this paper to measure the flow details in the slat-cove region of the 30P30N model. A single angle of attack (𝛼 = 5.5◦) and a chord based Reynolds number of 1.71𝑒6 are selected as the test conditions. The PIV results show that the slat flow features are significantly altered with the presence of the porous gap filler, resulting in a more stable slat-cove shear layer and, thus, reduced velocity fluctuations with a successive decrease in the permeability. The porous gap filler with the lowest permeability acts similar to the solid gap filler. However, flow separation is observed on the upper side of the porous interface, which leads to an aerodynamic performance penalty via a reduction in lift on the main wing.

slat noise↗