Inverse Design Method Research Articles

An Improved Parallel Inverse Design Method of EMU Wheel Profile from Wheel Flange Wear Viewpoint

An improved parallel inverse design method is proposed for wheel profile optimization. The dominant merit of this method is the ability to automatically search the target performance curve and obtain the optimized profile without artificial experience. With the help of vehicle system dynamic theory, an EMU model has been established in Simpack, and the dynamic performance is calculated with two profiles, i.e., optimization profile and original profile. The contact and mechanical characters are analyzed by Hertz’s theory, Kalker global algorithm, and CONTACT program. It is found that the rolling radius difference (RRD) with the optimization profile is higher than the original one, especially when the lateral displacement is greater than 3 mm. The creep force density with the optimization profile is significant with a wheelset displacement of 6∼9 mm. Compared with the original one, the distribution of contact points with the optimization profile is more uniform, and the contact position is more biased towards the root of the wheel flange. It means the optimization profile can provide higher RRD value and creep force with large lateral displacement, which is beneficial for reducing wheel flange wear. The dynamic simulation indicates that the optimization profile can help reduce the wheel flange force and wheel flange wear in a sharp curve. Meanwhile, the dynamic behaviors and wheel tread wear on a tangent track or a large curved track are also favorable with the optimization profile.

Intelligent algorithms: new avenues for designing nanophotonic devices [Invited

The research on nanophotonic devices has made great progress during the past decades. It is the unremitting pursuit of researchers that realize various device functions to meet practical applications. However, most of the traditional methods rely on human experience and physical inspiration for structural design and parameter optimization, which usually require a lot of resources, and the performance of the designed device is limited. Intelligent algorithms, which are composed of rich optimized algorithms, show a vigorous development trend in the field of nanophotonic devices in recent years. The design of nanophotonic devices by intelligent algorithms can break the restrictions of traditional methods and predict novel configurations, which is universal and efficient for different materials, different structures, different modes, different wavelengths, etc. In this review, intelligent algorithms for designing nanophotonic devices are introduced from their concepts to their applications, including deep learning methods, the gradient-based inverse design method, swarm intelligence algorithms, individual inspired algorithms, and some other algorithms. The design principle based on intelligent algorithms and the design of typical new nanophotonic devices are reviewed. Intelligent algorithms can play an important role in designing complex functions and improving the performances of nanophotonic devices, which provide new avenues for the realization of photonic chips.

Inverse Design of On-Chip Thermally Tunable Varifocal Metalens Based on Silicon Metalines

High-contrast transmitarray (HCTA) metasurfaces, named as metalines in this article, a category of lithographically defined on-chip diffractive elements with minimized scattering loss, enables parallel on-chip optical signal processing. While high-performance devices with different functionalities are reported, most of them are static, and their optical functions are fixed after fabrication. However, dynamically tunable metasurfaces are of essential need in many modern optical systems. Here, we employ an inverse design method, combining FDTD parameter sweep with Fourier-optics simulations, to design thermally-controlled varifocal metalenses. Thermal tunability is accomplished through the thermo-optical effect of silicon. Maximum focal length tunability of 12% and 24% are demonstrated for $400~\mu \text{m}$ and $200~\mu \text{m}$ aperture varifocal metalens doublets at the wavelength of $1.55~\mu \text{m}$ , respectively. The focusing efficiency is more than 74% for the whole temperature range. The $400~\mu \text{m}$ and $200~\mu \text{m}$ aperture metalenses are able to concentrate the input beam to spot sizes less than one wavelength and two wavelengths, respectively. This paves the way for dynamically controlled silicon photonics computational chips.

Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle

Boron-dipyrromethene (BODIPY) is one promising class of sensitizers for dye-sensitized solar cells (DSSCs) due to unique merits of high absorption coefficient and versatile structural modification capability. However, such derivatives usually suffer from limited power conversion efficiencies (PCEs) because of narrow light absorption band and low electron injection. To aid the discovery of BODIPY sensitizers, we employ an inverse design method to design efficient sensitizers by integrating data mining and first-principle techniques. We establish robust data-mining models using genetic algorithm and multiple linear regression, where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse designs. Based on the features’ understanding, we design candidates NH1-6 and predict their PCEs, demonstrating remarkable enhancements (58% maximum) compared to previous works. Furthermore, their optoelectronic properties including maximum absorption wavelengths, oscillator strengths, bandgaps, transferred charges, charge transferred distances, TiO2 conduction band shifts, short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements (93 nm, 122.41%, 23.70%, 36.36%, 471.17%, 63.64%, 28.55%, 107.86% maximum), which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’ shortcomings. The as-designed BODIPY sensitizers can be promising candidates for DSSCs, and such method could help accelerate the discovery of other energy materials.

Computational analysis of NACA 0010 at moderate to high Reynolds number using 2D panel method

Wing structures as found in aircrafts and wind turbine blades are developed using airfoils. Computational methods are often used to predict the aerodynamic characteristics of such airfoils, typically the pressure, lift and drag force coefficients. In the present work, surface pressure coefficient distribution of NACA 0010 is evaluated using the 2D panel and Jukouwski methods for incompressible lifting flows for three Reynolds numbers, Re–3 x105, 5 x105, 1 x 106. The analysis was conducted for various AOA (angle of attack), between –20 to 100 for the airfoil with tripped and untripped conditions. The non–dimensional pressure coefficient along chord direction of airfoil is illustrated for upper and lower surfaces between –20 to 100 angle of attack. The coefficient of lift and drag as well as glide ratio are evaluated for all three Reynolds numbers. The present results from the 2D panel method are validated using the results from Hess and Smith, inverse design methods implemented on conformal mapped symmetric Jukouwski airfoil of 10% thickness to chord at 40 angle of attack.

An inverse mean-line design method for optimizing radial outflow two-phase turbines in geothermal systems

Radial outflow two-phase turbine (ROTPT) is an impulse two-phase turbine used for total flow systems in applications like geothermal fields to utilize the two-phase geofluid energy effectively. This paper presented a one-dimensional nonequilibrium inverse mean-line design method of ROTPT. With prescribed pressure and blade angle distributions, the averaged geometry and flow parameters in the rotating impeller channels were derived along the flow direction, the pressure distribution was formulated on the pressure side (PS) and the suction side (SS), and performance parameters were deduced in the implementation of the presented algorithm, including torque, output power, efficiency, etc. By using the design method, a ROTPT for a geothermal system was constructed. The flow field of the ROTPT was simulated in CFX using the thermal phase change model, which was validated by experimental results. By evaluating the averaged distribution and examining the three-dimensional flow, it was suggested that the presented design was consistent with the averaged flow in ROTPT. Meanwhile, there were three-dimensional effects in the rotating channel causing the deviation between the design and CFD results. This paper provides a nonequilibrium solver for designing ROTPTs but also can bolster the development of two-phase flow with the phase change in curved rotating channels.

Inverse design method of microscatterer array for realizing scattering field intensity shaping

It is a novel and interesting idea to inversely design the scattering structure with the desired scattering field intensity distribution in a given target area as the known information. The inverse design method proposed in this paper does not need to be optimized, and the spatial distribution and dielectric constant distribution of the micro-scatterer array can be quickly analytically calculated according to the desired scattering field intensity in the target area. First, based on the spatial Fourier transform and angular spectrum transformation, the plane wave sources required in all directions are inversely obtained from the electric field intensity distribution required in the target area. Then, based on the theory of induced source, a method of irradiating the array of all-dielectric micro-scatterers with incident electromagnetic field to generate the required plane wave source is proposed. The scattering fields generated by these micro-scatterers will be superimposed on the target area to achieve the desired scattering field strength intensity. Finally, according to the proposed inverse design theory model, a specific three-dimensional (3D) design is carried out. In the 3D example, we study the scattering field intensity distribution of the point-focused shape of the square surface target area, and show an all-dielectric micro-sphere distribution design. Its spatial distribution and permittivity distribution are both obtained through the rapid analytical calculation of the desired scattered field intensity shape in the target area. Finally, based on the principle of linear superposition, we quickly and easily generate the complex shapes of “I”, “T”, and “X” in the target area. The satisfactory results of full-wave simulation show that the proposed inverse design method is effective and feasible.

Design of Low-Speed Slotted, Natural-Laminar-Flow Airfoil Reproducing Transonic Behaviors

Airfoils tested at low speeds generally produce surface pressure distributions that differ substantially from those measured at transonic conditions, which in turn results in very different boundary-layer characteristics even for the same Reynolds number. A methodology is presented for mapping transonic pressure gradients to an equivalent low-speed airfoil. It is applied to the design of a low-speed, slotted, natural-laminar-flow (SNLF) airfoil intended to emulate the pressure gradients of a SNLF airfoil designed for a transonic commercial transport with a Reynolds number of 13.2 million. The on-design cruise operating condition of the original airfoil was chosen as the reference condition; however, the design lift coefficient of the new airfoil was lowered to account for both compressibility effects and suitability for low-speed wind-tunnel testing. The overall design process mirrors the development of the transonic airfoil by beginning with a single-element airfoil and then modifying it to include an aft element. An inverse design method based on conformal mapping was developed and employed to determine the necessary geometry of the low-speed single-element airfoil. Analysis of the new SNLF airfoil confirms that the design objectives were met, and the airfoil is found to be well behaved down to a Reynolds number of 1 million.

Bounded and inverse optimal formation stabilization of second-order agents

This paper formulates and solves a new problem of both bounded and inverse optimal formation stabilization control for a group of second-order dynamic mobile agents with collision avoidance and limited sensing range. The control design is based on new Lyapunov functions, new non-zero convergence and dominating lemmas, new pairwise collision avoidance functions, and forwarding and inverse optimal control design methods. The proposed formation stabilization control design guarantees no collision between any agents, “almost global” asymptotic stability of desired equilibrium points and instability of undesired equilibrium points, an infinite gain margin, bounded controls by a pre-specified constant, and minimization of a cost function that penalizes both stabilization errors and the control inputs without having to solve a Hamilton–Jacobi–Bellman or Hamilton–Jacobi–Isaacs equation.

Ultra-compact primary colors filter based on plasmonic digital metamaterials

A novel primary colors filter (RGB filter) is designed with ultra-compact size by using the inverse design method. The core size of filter goes to 0.5μm×0.55μm, and then the chip integration being high enhanced. The transmission characteristics of RGB filtering are simulated by FDTD algorithm. A high performance and high compact coupling region can be obtained by using inverse design method to search a full-parameter space. In addition, we investigate the relationship between the filtering performance and the digital unit size, and prove the good performance tolerance with different digital unit size. The average peak transmission efficiencies are over 70% and the average channel crosstalk are about −16 dB at 450 nm, 550 nm and 650 nm, for each channel, respectively. High performance is achieved for this RGB filter with a footprint of only 0.5μm×0.55μm, and is suitable for optical device integration.

Aeroelastic analysis and structural parametric design of composite rotor blade

Based on FEM theory, a method of dynamic analysis for hingeless rotors considering anisotropic composite materials is established. A parametric modeling method of composite blade with typical profile and high simulation degree for design is proposed. Through the finite element method, the profile characteristics of rotor blade can be obtained efficiently and accurately, and the synchronization of parametric design and finite element analysis of structural characteristics can be realized. Then a 23-degrees of freedom non-linear beam element is used to simulate the extended one-dimensional beam, thereby a non-linear differential equation describing the elastic motion of the rotor is established. To obtain the cross-sectional target characteristics of the blades, an inverse design method is proposed for cross-section components based on combinatorial optimization algorithm. The calculation and validation work show that the proposed model can effectively analyze the aeroelastic characteristics of general composite rotors. Further, the influence of cross-sectional parameters on the aeroelastic stability and hub loads of hingeless rotor is analyzed and some remarkable conclusions are obtained.

Multi-Objective Optimization of a High Specific Speed Centrifugal Volute Pump Using Three-Dimensional Inverse Design Coupled With Computational Fluid Dynamics Simulations

AbstractThis paper presents three different multiobjective optimization strategies for a high specific speed centrifugal volute pump design. The objectives of the optimization consist of maximizing the efficiency and minimizing the cavitation while maintaining the Euler head. The first two optimization strategies use a three-dimensional (3D) inverse design method to parametrize the blade geometry. Both meridional shape and 3D blade geometry are changed during the optimization. In the first approach, design of experiment (DOE) method is used and the pump efficiency is obtained from computational fluid dynamics (CFD) simulations, while cavitation is evaluated by using minimum pressure on blade surface predicted by 3D inverse design method. The design matrix is then used to create a surrogate model where optimization is run to find the best tradeoff between cavitation and efficiency. This optimized geometry is manufactured and tested and is found to be 3.9% more efficient than the baseline with reduced cavitation at high flow. In the second approach, only the 3D inverse design method output is used to compute the efficiency and cavitation parameters and this leads to considerable reduction to the computational time. The resulting optimized geometry is found to be similar to the computationally more expensive solution based on 3D CFD results. In order to compare the inverse design based optimization to the conventional optimization, an equivalent optimization is carried out by parametrizing the blade angle and meridional shape.

Classifying Crystals of Rounded Tetrahedra and Determining Their Order Parameters Using Dimensionality Reduction.

Using simulations we study the phase behavior of a family of hard spherotetrahedra, a shape that interpolates between tetrahedra and spheres. We identify 13 close-packed structures, some with packings that are significantly denser than previously reported. Twelve of these are crystals with unit cells of N = 2 or N = 4 particles, but in the shape regime of slightly rounded tetrahedra we find that the densest structure is a quasicrystal approximant with a unit cell of N = 82 particles. All 13 structures are also stable below close packing, together with an additional 14th plastic crystal phase at the sphere side of the phase diagram, and upon sufficient dilution to packing fractions below 50–60% all structures melt. Interestingly, however, upon compressing the fluid phase, self-assembly takes place spontaneously only at the tetrahedron and the sphere side of the family but not in an intermediate regime of tetrahedra with rounded edges. We describe the local environment of each particle by a set of l-fold bond orientational order parameters q̅l, which we use in an extensive principal component analysis. We find that the total packing fraction as well as several particular linear combinations of q̅l rather than individual q̅l’s are optimally distinctive, specifically the differences q̅4 – q̅6 for separating tetragonal from hexagonal structures and q̅4–q̅8 for distinguishing tetragonal structures. We argue that these characteristic combinations are also useful as reliable order parameters in nucleation studies, enhanced sampling techniques, or inverse-design methods involving odd-shaped particles in general.

A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics

Local-gradient-based optimization approaches lack nonlocal exploration ability required for escaping from local minima in non-convex landscapes. A directional Gaussian smoothing (DGS) approach was proposed in our recent work and used to define a truly nonlocal gradient, referred to as the DGS gradient, in order to enable nonlocal exploration in high-dimensional black-box optimization. Promising results show that replacing the traditional local gradient with the nonlocal DGS gradient can significantly improve the performance of gradient-based methods in optimizing highly multi-modal loss functions. However, the current DGS method is designed for unbounded and unconstrained optimization problems, making it inapplicable to real-world engineering design optimization problems where the tuning parameters are often bounded and the loss function is usually constrained by physical processes. In this work, we propose to extend the DGS approach to the constrained inverse design framework in order to find a better design. The proposed framework has its advantages in portability and flexibility to naturally incorporate the parameterization, physics simulation, and objective formulation together to build up an effective inverse design workflow. A series of adaptive strategies for smoothing radius and learning rate updating are developed to improve the computational efficiency and robustness. To enable a clear binarized design, a dynamic growth mechanism is imposed on the projection strength in parameterization. The methodology is demonstrated by an example of wavelength demultiplexer. Our method shows superior performance compared to the state-of-the-art approaches. By incorporating volume constraints, the optimized design achieves an equivalently high performance but significantly reduces the amount of material usage.

Inverse design of acoustic metamaterials based on machine learning using a Gauss–Bayesian model

Acoustic metamaterials (AMs) have attracted a substantial amount of attention in recent decades where the parameter design plays an important role. However, conventional design methods generally rely on analytical physical models and require a very large number of evaluations of acoustic performance. Here, we propose and experimentally demonstrate an inverse-design method for AMs based on machine learning using a Gauss–Bayesian model. As a result of the cycle of training and prediction and the use of adaptive acquisition functions, this method allows the parameters of AMs to be efficiently designed for specific functionalities without the need for physical models. Considering the significance of low-frequency ventilated sound absorption, we present a design for a typical acoustic metamaterial absorber with multiple structural parameters that facilitate high sound absorption at low frequencies. In the design process, the parameters were adaptively adjusted to improve the sound absorption performance at low frequencies using only 37 evaluations, and this high absorption performance was verified by the agreement of numerical and experimental results. Because of its low cost, high flexibility, and independence from physical models, this method paves the way for tremendous opportunities in the design of various AMs for particular desired functionalities.

Application of an inverse-design method for designing new branched thiophene oligomers for bulk-heterojunction solar cells

Using our recently developed theoretical inverse-design method (PooMa) a new series of branched oligothiophene molecules for photovoltaic donors are designed. PooMa uses a genetic algorithm to screen a huge pool of compounds combined with a fast electronic-structure method (Density-Functional Tight-Binding, DFTB) with reasonable accuracy. Here, we apply this inverse-design method to identify a set of 20 branched oligothiophene systems with promisingly high efficiencies by using a Quantitative Structure Property Relation (QSPR) model based on five electronic descriptors that describe the performance of organic solar cells. We consider a pool of oligomers that are modified by attaching to each of 7 different sites one out of 22 functional groups, i.e., a pool of 227 ≈ 2.5×109 molecules. Subsequently, density-functional-theory (DFT) and Time-Dependent-DFT (TD-DFT) calculations in the gas phase with a 6-31G(d,p) basis set have been carried through to give further information on the suggested oligomers. Bulk-heterojunction photovoltaic cells were designed with the suggested oligothiophenes as donors and Phenyl-C61-butyric acid methyl (PCBM) derivatives as acceptors. Conversion efficiencies of the designed photovoltaic cells were examined with the Scharber diagram model.

Inverse design of a single-step-etched ultracompact silicon polarization rotator.

We propose and experimentally demonstrate a novel ultracompact silicon polarization rotator based on equivalent asymmetric waveguide cross section in only single-step etching procedure for densely integrated on-chip mode-division multiplexing system. In the conventional mode hybridization scheme, the asymmetric waveguide cross section is employed to excite the hybridized modes to realize high performance polarization rotator with compact footprint and high polarization extinction ratio. However, the fabrication complexity severely restricts the potential application of asymmetric waveguide cross section. We use inverse-designed photonic-crystal-like subwavelength structure to realize an equivalent asymmetric waveguide cross section, which can be fabricated in only single-step etching process. Besides, a theory-assisted inverse design method based on a manually-set initial pattern is employed to optimize the device to improve design efficiency and device perform. The fabricated device exhibited high performance with a compact footprint of only 1.2 × 7.2 µm2, high extinction ratio (> 19 dB) and low insertion loss (< 0.7 dB) from 1530 to 1590 nm.

Investigation of flow pattern and hydraulic performance of a centrifugal pump impeller through the PIV method

The objective of this study is to analyse the quantitative relationship between the local Euler head distribution and the internal flow in a centrifugal impeller and provide guidance for the design specifications of inverse design methods. The local Euler head at the inlet is mainly affected by the clockwise vortex on the blade suction side. At the outlet, it is affected by the relative motion of clockwise and counterclockwise vortexes. The effect of the two types of vortexes on the local Euler head makes the pump head almost constant under deep part-load conditions, proving that the impeller has an effective diameter. The mean local Euler head distribution along the impeller passage is closely interrelated with the flow pattern. The position in which the mean local Euler head increases fastest coincides with the radial position of the clockwise vortex centre. The position of its maximum value is consistent with the radial position of the end of the clockwise vortex. The position of its maximum value moves towards the inlet as the flow rate decreases, confirming the impeller has an extreme impeller diameter. The inflection points of the mean local Euler head ratio curve correspond to the appearance of vortex structures. • The distribution rules of local Euler head are investigated by PIV experiments. • The local Euler head distribution is related to the internal flow patterns. • The local Euler head distribution determines the hydraulic performance of pump. • The mean local Euler head ratio curve can reflect the position of vortex.

Multiplexing the aperture of a metasurface: inverse design via deep-learning-forward genetic algorithm

The modern design of advanced functional materials or surfaces has increasingly undergone miniaturization and integration, so as to implement multiple functions using the same aperture. Metasurfaces, as an emerging kind of artificial functional surface, have provided unprecedented freedom in manipulating electomagnetic (EM) waves upon two-dimensional surfaces. It is desirable that the aperture of metasurfaces can be multiplexed with a number of functions for EM waves. Based on previous research, an artificial neural network-forward design can achieve an inverse design. In this paper, we propose an inverse design method of multiplexing metasurface apertures. A deep learning network (DLN) is trained to serve as a forward model for a genetic algorithm (GA), that is, a deep-leaning-forward genetic algorithm (DLF-GA). With the DLF-GA model, the phase of meta-atoms can be predicted for orthogonally linearly polarized waves simultaneously. The DLN was trained by a data set consisting of 70 000 samples with an accuracy of 95% on the test data. To demonstrate the competency of this method, we demonstrate the design of a multiplexed metasurface that can achieve focusing and diffuse scattering for polarization. The metasurface, which consists of 24 × 24 meta-atoms, can be generated monolithically with the input phase profile. A prototype was fabricated and measured. The predicted, simulated, and measured results are well consistent and underscore the validity of this inverse design method. This method provides an efficient and accurate method for the fast design of multiplexed metasurfaces and will find applications in microwave engineering such as in satellite communications.

Comprehensive Improvement of Mixed-Flow Pump Impeller Based on Multi-Objective Optimization

The spanwise distribution of impeller exit circulation (SDIEC) has a significant effect on the impeller performance, therefore, there is a need for its consideration in the optimization design of mixed-flow pumps. In this study, a combination optimization system, including a 3D inverse design method (IDM), computational fluid dynamics (CFD), Latin hypercube sampling (LHS) method, response surface model (RSM), and non-dominated sorting genetic algorithm (NSGA-Ⅱ) was used to improve the performance of the mixed-flow pump after considering the effect of SDIEC on the performance of the impeller. The CFD results confirm the accuracy and credibility of the optimization results because of the good agreement the CFD results established with the experimental measurements. Compared with the original impeller, the pump efficiency of the preferred impeller at 0.8Qdes, 1.0Qdes, and 1.2Qdes improved by 0.63%, 3.39%, and 3.77% respectively. The low-pressure region on the blade surface reduced by 96.92% while the pump head difference was less than 1.84% at the design point. In addition, a comparison of the flow field of the preferred impeller and the original impeller revealed the effect of SDIEC on mixed-flow pump performance improvement and flow mechanism.