Imperfect Components Research Articles

Effects of the Geometric Uncertainties of Bladed Disks Through the Analytical Derivatives of the Finite Element Matrices

Abstract Manufacturing processes and wear are known to create imperfections in mechanical components that may result in a significant deviation of the modal properties from their predicted values. This is especially true for periodic structures such as bladed disks, where even small variations in the blade geometry can cause mistuning. The effect of parameter variations on the model is commonly assessed through sensitivity analysis within the framework of uncertainty propagation or structural optimization. Different formulations are available when dealing with the variation of a single scalar parameter. The problem becomes more complicated when the source of the variation is the whole component geometry. To deal with this case, it is necessary to define a representation able to describe the shift from the nominal geometry through a finite set of parameters and compute the derivatives of the system matrices with respect to those parameters. This paper proposes to compute analytically the derivatives of the system matrices with respect to the geometric variations. The derivatives are calculated at the finite element (FE) level and are then assembled into global derivative matrices. Using directional derivatives, the sensitivities of the system matrices are computed with standard FE numerical schemes based on the nominal geometry.

Development of piezo-actuated x-ray deformable mirror for vertical focusing of synchrotron radiation at Indus-2

Piezo-actuated x-ray deformable mirrors (PXDMs) can effectively control the surface profile with sub-nanometre accuracy for adaptive focusing and correct the wavefront distortion generated due to imperfections in optical components or other extraneous effects like heat load, minor misalignments of synchrotron radiation beamline. Generating and controlling the desired shape of PXDMs with sub-nanometre accuracy is very challenging due to the presence of a large number of actuators and constraints related to nonlinearity of piezoactuator, boundary conditions, fabrication related limitations and the requirement for ex-situ as well as in-situ characterization of PXDM. To accomplish this, a PXDM of zerodur with multiple electrodes on a piezoactuator has been developed for vertical focusing of synchrotron x-rays for the first time in India. The PXDM has been fabricated and characterized by in-house developed technologies. The iterative piezo response function-based optimization technique is used to optimize the electric fields of individual piezoactuators in order to achieve the desired parabolic shape of the PXDM. The ex-situ characterization showed that the PXDM can achieve the target parabolic profile with 42 nm root-mean-square error. Furthermore, in-situ characterization at a beamline, BL-12, of Indus-2 synchrotron radiation source using PXDM has confirmed the focusing of collimated x-ray beam in the vertical direction from FWHM 235 μm to FWHM 83 μm.

Robust Deflectometry

Concentrating Solar Power (CSP) mirrors must have high slope accuracy in order to achieve high solar concentration. Deflectometry is an established method for measuring high-resolution, high-accuracy maps of mirror slope. In this paper we describe improvements to Sandia’s deflectometry system, SOFAST, which enable it to be accurately calibrated even in difficult conditions of poor access to system components, imperfect components, and varying ambient light. These extensions improve the robustness of the system and expand the range of problems it can solve.

Study on the applicability of a modified strain approach to predict the fatigue life of HFMI-treated transverse stiffeners under variable amplitude loading

AbstractThis paper analyses the applicability of a modified strain approach to predict the fatigue life of HFMI-treated transverse stiffeners under variable amplitude loading (VAL) with random load sequences of a p(1/3) and linear shaped spectrum. Local stresses are determined using linear-elastic finite element analyses. The measured weld geometry and component imperfections are considered. From the hardness of the HFMI-treated zone and the base material, the elastic–plastic material behaviour and Coffin-Manson parameters to describe the damage parameter Woehler curve are estimated. Based on a hysteresis counting method (HCM), the damage for each closed hysteresis is calculated. The applied notch strain approach includes the impact of residual stresses and the influence of surface roughness. Thus far, the application of similar approaches has only been validated for welded components with comparatively low residual stresses and HFMI-treated welds subjected to constant amplitude loading. To validate the accuracy of the approach for HFMI-treated welds under variable amplitude loading, the approximated fatigue life is compared to the number of cycles derived from experimental investigations. In this study, it is shown in conjunction with experimental results that it is essential to consider the strength of the base material near the weld when assessing the service life. This area can be more critical than the HFMI-treated weld toe.

2D calculation of parasitic fields in misaligned multipole electron-optical systems

The effects of geometrical imperfections in electron-optical components are usually evaluated in 3D simulations. These calculations inherently take a long time, require a large amount of memory, and do not directly produce the necessary axial field functions. We present a 2D perturbation method to calculate parasitic fields in misaligned multipole systems. Our method is based on finding an equivalent potential perturbation, similarly to Sturrock’s method, but does not rely on the potential being differentiable. The method is directly applicable to both electrostatic and non-saturated magnetic problems. It does not require any 3D data and it is fully compatible with existing finite element method codes such as EOD. The proposed method produces axial field functions with an accuracy of units to a few tens of percents, depending on the number of unperturbed multipole field components used and the geometry. The results can then be used, for instance, to determine the parasitic imaging aberrations of the misaligned optical system using standard methods, in order to evaluate the effect of mechanical design tolerances.

Microvibration simulation of reaction wheel ball bearings

Microvibrations on board satellites are a well-known problem affecting their pointing accuracy by inducing oscillations to the line-of-sight. Fast rotating devices, such as reaction wheel assemblies (RWAs) are a major cause of these onboard mechanical disturbances. Imbalances in the flywheel, as well as imperfections in the ball-bearing components yield low-amplitude vibrations that are transmitted to the satellite structure, adversely affecting the performance of sensitive onboard instruments. This research focuses on the RWA’s ball bearing microvibration frequency and amplitude characterization. This is achieved by introducing specific geometry imperfections on the ball bearing rotating elements. In addition, variables such as the ball bearing preload, the number of balls and their dimensions are altered to understand how the bearing microvibration changes in order to minimize it. Finally, available physical microvibration test data is used to compare and cross-correlate the microvibration waterfall plots obtained from the transient FEA of the parametrized bearing models introduced herein. This work provides a better understanding of the relation between the main structural ball bearing parameters and the microvibration disturbances emitted by the RWA to minimize its mechanical noise.

Enhancement and underlying fatigue mechanisms of laser powder bed fusion additive-manufactured 316L stainless steel

In this study, the enhancement of additively manufactured (AM) 316L, by annealing, to the fully reversed tension-compression fatigue performance, in terms of fatigue life and fatigue damage, were investigated under two conditions: as-built (AB) and heat-treated (HT) at 900 °C. The underlying fatigue mechanisms were comprehensively characterised through intensive microstructural observations of cyclic-strained microstructures and fracture surfaces using laser confocal scanning microscopy (LCSM) and secondary electron imaging using scanning electron microscopy (SEM). The experimental results showed that the fatigue resistance of HT 316L was significantly enhanced by 100% as the fatigue limit was increased from 75 to 150 MPa for AB and HT 316L, respectively. The fatigue cracking mechanism in AB 316L is mainly related to two imperfections of the AM-induced microstructural components: residual stresses, which cause highly localised deformation, and dendritic cellular structures, which possess a weak link in their grain boundaries against crack propagation. Upon heat treatment at 900 °C, the residual stresses and dendritic structure were effectively reduced. Consequently, the fatigue life of AM 316L was significantly enhanced by promoting the formation of high-angle boundaries. More precisely, the cyclic deformation processes in fatigued HT 316L involve persistent slip bands and strain hardening.

On the Receive Path Calibration of Magnetic Particle Imaging Systems

Magnetic nanoparticles are a valuable tool in many biomedical applications and can be used for diagnostic and therapeutic purposes. In magnetic particle imaging (MPI) and magnetic particle spectroscopy (MPS), the particles are subjected to a dynamic magnetic field and the particle magnetization response is simultaneously measured using one or multiple receive coils. Separating the particle signal from the feed-through signal is commonly done by advanced passive filtering, which distorts the particle signal. To correct this distortion, the transfer function of the receive chain needs to be known. While in principle, the transfer function can be simulated, due to imperfections in the electronic components, it is often more accurate to determine the transfer function in a calibration procedure. Although this system calibration, utilizing a calibration-coil setup, has been done by several research groups in the past, a general description of the underlying calibration model and methodology is still missing. In this article, we provide a general multi-channel calibration procedure for inductive receive paths in MPI and a blueprint to investigate model and method uncertainties. We generalized the calibration procedure to also cover nonorthogonal and nonhomogeneous receive coils. Finally, we showcase the calibration procedure and uncertainty analysis on our custom MPS system and use the MPI transfer functions of misaligned receive coils to decouple their superimposed receive signals from the receive path. The findings enable the comparison of MPI signals from different devices and can be used to normalize measurements and system functions in devices with exchangeable receive coils.

Single-shot quantum error correction with the three-dimensional subsystem toric code

Fault-tolerant protocols and quantum error correction (QEC) are essential to building reliable quantum computers from imperfect components that are vulnerable to errors. Optimizing the resource and time overheads needed to implement QEC is one of the most pressing challenges. Here, we introduce a new topological quantum error-correcting code, the three-dimensional subsystem toric code (3D STC). The 3D STC can be realized with geometrically-local parity checks of weight at most three on the cubic lattice with open boundary conditions. We prove that one round of parity-check measurements suffices to perform reliable QEC with the 3D STC even in the presence of measurement errors. We also propose an efficient single-shot QEC decoding strategy for the 3D STC and numerically estimate the resulting storage threshold against independent bit-flip, phase-flip and measurement errors to be pSTC ≈ 1.045%. Such a high threshold together with local parity-check measurements make the 3D STC particularly appealing for realizing fault-tolerant quantum computing.

Parametric reduced-order modeling enhancement for a geometrically imperfect component via hyper-reduction

In this paper, an improved nonlinear reduced-order modeling technique capable of describing the parameterized shape defect is presented. In the proposed framework, a set of defect-shapes are pre-determined based on a nominal configuration. Then, the reduced-order representation is created in a polynomial form comprising a set of reduced-tensor coefficients of defect and physical displacement fields. However, constructing reduced tensors using a large number of discretized elements usually requires enormous amounts of computational resources. Therefore, to reduce the computational expense, a quadratic-manifold-based energy-conserving sampling and weighting approach was employed to obtain the reduced tensors concerning only a few optimally selected elements. This approach can be used to conduct both time-transient and frequency response analyses on rotating mechanical components. It was found that the proposed approach can accurately estimate the broad defect-parametric variation. In particular, its computational efficiency demonstrated a significant improvement over that of existing approaches.

Stability of Self-Configuring Large Multiport Interferometers

Realistic multiport interferometers (beamsplitter meshes) are sensitive to component imperfections, and this sensitivity increases with size. Self-configuration techniques can be employed to correct these imperfections, but not all techniques are equal. This paper highlights the importance of algorithmic stability in self-configuration. Naive approaches based on sequentially setting matrix elements are unstable and perform poorly for large meshes, while techniques based on power ratios perform well in all cases, even in the presence of large errors. Based on this insight, we propose a self-configuration scheme for triangular meshes that requires only external detectors and works without prior knowledge of the component imperfections. This scheme extends to the rectangular mesh by adding a single array of detectors along the diagonal.

Sparsity-based method for ring artifact elimination in computed tomography.

Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.

Implementation and Feedback Control Tuning of an Analog Izhikevich Neuron Circuit

Analog circuits have proven to be a reliable medium for neuromorphic architectures in silicon, capable of emulating parts of the brain’s computational processes. Information in the brain is shared between neurons in the form of spikes, where a neuron’s soma emits a voltage signal after integrating multiple synaptic input currents. To preserve the quality of this information in a rate-based protocol, it is important that a postsynaptic neuron is able to adapt its firing rate to some desired or expected rate, especially in a noisy environment. This paper presents an analog Izhikevich neuron circuit and a proof-of-concept tuning algorithm that adjusts the neuron’s spike rate using proportional feedback control. The neuron circuit is implemented using discrete surface-mount components on a custom printed circuit board, and its output spike pattern is modified by adjusting one of the neuron’s four voltage parameters. Adjusting the voltage parameters gives a neuromorphic system designer the ability to calibrate these silicon neurons in the presence of noise or component imperfections, ensuring a baseline configuration where all neurons exhibit the same, expected spike response given the same input.

Further Results on Detection and Channel Estimation for Hardware Impaired Signals

Hardware impairment is inevitable in many wireless systems. It is particularly severe in low-cost applications due to the imperfect components used. In this paper, the channel estimation and non-coherent detection problems of hardware impaired signals are studied for a single-carrier, single-antenna and single-hop system. Specifically, three different cases are investigated: signals with additive distortion only, signals with in-phase and quadrature imbalance only, and signals with both impairments. The maximum likelihood and Gaussian approximation methods are used to derive the new non-coherent detectors for amplitude modulated signals, while the maximum likelihood and moment-based methods are employed to design the new channel estimators for all signals. Numerical results show that the new non-coherent detectors outperform the existing non-coherent detectors in the presence of hardware impairment. The performance gain can be as high as 8 dB. They also show that the new channel estimators have much higher accuracy than the existing estimator. In some conditions, the accuracy of the new estimator is about 100 times that of the existing estimator.

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison has been carried out using an experimental set-up with transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multi-layer perceptron. Their performance is compared with the one delivered by the previously proposed NN equalizer models: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. We present the derivation of the computational complexity associated with each NN type -- in terms of real multiplications per symbol so that these results can be applied to a large number of communication systems. We demonstrated that in the specific considered experimental system the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the highest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). We examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to lower levels, the three-layer perceptron provides the best performance. Our complexity analysis for different NNs is generic and can be applied in a wide range of physical and engineering systems.

Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration.

In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.

Analysis of steel tank shell deformation and its impact on further utilisation

Recent research in thin-walled structures and steel structures has demonstrated that, in addition to internal forces and membrane stresses of shells, initial deformation and imperfection components should also be considered when calculating design cases. This paper presents a numerical analysis of a steel water tank with an initial deformation of the shell, which is used in an industrial plant for technological purposes. The steel water tank is a typical type of thin-walled structure for which initial deformation may be important. In the numerical example, a round symmetrical steel tank 2.70 m high is analysed under two states: with an undeformed shell and a deformed shell. The complicated interaction between the initial deformation and the vertical and horizontal stresses is analysed in detail. While the initial deformation of the shell may sometimes reduce stresses, in most cases, it substantially increases the overall stresses of the analysed steel water tank shell, up to 820% (for stresses in the horizontal direction) and up to 3554% (for stresses in the vertical direction). This means that initial large deformation and imperfections effects may play an important role not only in the design of industrial structures like silos and tanks but also for very specific slim, thin-walled structural elements.

Artificial Intelligence Accelerators Based on Graphene Optoelectronic Devices

Optical and optoelectronic approaches of performing matrix–vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the device and system performance thanks to their extraordinary properties, but the nonuniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large‐scale hardware deployment. Here, a new optoelectronic architecture is presented, consisting of spatial light modulators and tunable responsivity photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, high‐power‐efficiency electro‐optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow energy consumption. Moreover, a methodology of performing accurate calculations with imperfect components is developed, laying the foundation for scalable systems. Finally, a few representative ML algorithms are demonstrated, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of the platform.

Encoder Calibration Method for High Precision Servo Systems With a Sinusoidal Encoder

The sinusoidal encoder is used as an absolute position (velocity) transducer to implement the position (velocity) of actuators in many industrial applications. However, the performance of the position (velocity) can be degraded due to nonideal factors such as differential amplitudes of the encoder signals, offset, and phase shift. To analyze this problem, we formulate the relationship between the coefficients of ideal signals and the nonideal signals characteristic, which is an ellipse formed by plotting the encoder signals versus each other. In this article, we propose a nonideal signal calibration for the imperfect components of existing sinusoidal signals to improve position (velocity) regulation performance. The fictitious cosine function is used to detect the phase shift between the sine and cosine signals, and it can be designed by using the frequency of sinusoidal signals. The proposed method is validated by simulation and experimental implementation, showing noticeable improvement in estimating nonideal signal components, Lissajous error curve, phase distribution, and the position (velocity) error signal. Experimental results show that the proposed method is able to obtain the components of the nonideal signals and the accurate position (velocity) with a small error in real time, while other calibration methods are difficult to apply to fast motion.

An economic production model with imperfect quality components and probabilistic lead times

PurposeThis study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.Design/methodology/approachThis production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.FindingsThe formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.Practical implicationsThe modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.Originality/valueThis modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.