Inverse Scheme Research Articles

Study on diffusion and Kirkendall effect in diffusion triples for fcc Ni–Al–Ta alloys

In the present work based on two diffusion triples, the composition-dependent interdiffusivity matrices in the fcc Ni–Al–Ta alloys at 1373K and 1473K were efficiently deduced by using our newly developed two-dimensional (2D) inverse scheme. This scheme determines interdiffusivities from point and one-dimensional (1D) diffusion path to 2D composition region, yet requires much less experimental efforts, with the measured 2D composition profiles being well reproduced. Further, the interdiffusivities deduced from the inverse scheme were directly compared with those extracted by the traditional Sauer-Freise method and Whittle-Green method based on nine diffusion couples designed for verification. The interdiffusivities inferred from two distinct ways are fairly consistent, either at the binary boundaries or at the intersection points within the ternary composition range. Besides, the interdiffusion flux and the shift of the Kirkendall plane over the whole diffusion zone were simulated by applying the present 2D inverse scheme. The resulting 2D mapping presents a non-uniform but curved Kirkendall plane, which is in contrast to the flat shape generated in 1D diffusion couples and well explained by the calculated diffusion variables.

Inferring Pyramidal Neuron Morphology using EAP Data.

We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

Temperature-Dependent Thermal Parameter Identification of Ceramic Nanorod Aerogels Composite in the Ultrahigh Temperature Environment

The identification of the thermal properties of advanced materials is of great importance in engineering application. In this work, an inverse scheme is proposed to identify the temperature-dependent thermal parameters of ceramic nanorod aerogels (CNRAs) composite in the ultrahigh temperature environment up to 1873 K. The feasibility of the proposed method is successfully tested by an analytical solution for CNRAs with known material properties. Furthermore, the temperature-dependent thermal properties of CNRAs are successfully obtained using the experimental data acquired from the heat conduction experimental testing for a CNRAs plate. Lastly, the obtained thermal properties are used to simulate an arc-heated wind tunnel (AHWT) test of a receiving satellite antenna assembly using the FEM method. The simulated temperature at measured data has a very good agreement with the experimental data. This further demonstrates the strong capability of the proposed inverse method. In summary, the proposed approach provides an important approach for the identification of nonlinear and temperature-dependent thermal properties of advanced materials.

Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme.

In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three-dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well.

Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels.

Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation.

Prescribed performance-based robust inverse optimal control for spacecraft proximity operations with safety concern

This paper explores and analyzes the robust proximity control problem for spacecraft in the presence of external disturbance, model uncertainty, and safety concern. To fulfill the safety proximity requirement, a novel two-stage time-depended performance function is constructed to guarantee the transient and steady-state proximity performance. Then, by assimilating the advantages of the prescribed performance control methodology and inverse optimal control approach, a neuroadaptive prescribed performance-based inverse optimal control scheme is proposed, which ensures all system signals comply with the preassigned performance function. The developed controller shows optimality concerning convergence performance and energy consumption, whilst satisfying the well-designed safety corridor. Finally, simulation scenarios are provided to demonstrate the effectiveness of the presented control scheme.

A combined inverse prediction and experimental analysis for the frozen ground heat transfer in a structure foundation

This paper takes the representative buried structure in permafrost regions, a transmission line tower foundation, as the research object. An inverse prediction is conducted in a scaled-down experimental system mimicking actual heat conduction of the frozen ground in a tower foundation. In permafrost regions, global warming and the heat transfer through the buried structures bring significantly adverse thermal effects on the stability of the infrastructures. In modeling the thermal effects, it has been a challenge to determine the ground surface boundary condition and heat source strength from the buried structures due to the complex climate and environmental conditions. In this work, based on the improved model predictive inverse method with an adaptive strategy, an inverse scheme is successfully implemented to simultaneously identify the time-varying surface temperature and the time-space-dependent heat source representing the buried structures. In this scheme, an adaptive time-varying predictive model is established by the rolling update of the sensitivity response coefficients according to the predicted temperature field to overcome the influence of nonlinear characteristics in the heat transfer process. The inverse method is verified by simulation and experimental data. According to the experimental inversion results, the reconstructed temperature distribution efficiently predicts the thermal state evolution of the permafrost foundation under seasonal freezing and thawing. It is found that, under the experimental conditions, the intensified thawing and freezing are significantly severe, e.g., the increased area ratio of active layer thickness is as high as 28% after building a tower, and the depth of permafrost table ranges from about 14 mm to about 38 mm, which could be detrimental to the stability and safety of the tower foundation. This study will provide valuable guidance for risk assessments or optimizing the design and maintenance of the real tower foundation and similar buried structures.

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

Inverse scheme for sound source identification in a vehicle trailer

Tire/road noise is a highly relevant topic for improving the comfort and experience of drivers and residents living in high-traffic areas. With the growing numbers of electric cars, the relevance of tire noise even increases since it is the dominant sound source in the middle-speed range. We investigate acoustic sources relevant to tire/road interactions. In doing so, we apply different sound source localization algorithms to measurement data acquired inside a large measurement trailer equipped with microphone arrays. The methods for sound source identification used are well-known beamforming-based algorithms and inverse schemes based on finite element or boundary element simulations. The latter schemes require the identification of the acoustic properties of the trailer in the stationary case. In this contribution, we present the characterization process and the results of the sound source localization in this stationary case.

An analytic solution for Counter-Current spontaneous imbibition in porous media by the perturbation method

Following primary production of hydrocarbon from naturally fractured reservoirs, secondary recovery by waterflooding relies on Spontaneous Imbibition (SI) as the primary mechanism for oil expulsion to the fracture network, where it can be easily transported to neighboring production wells. Thus, a more complete understanding of this phenomenon is warranted and essential for process improvement. The imbibition process, taking advantage of inherent preferential affinity for water contact over oil of high energy solids, is the rate determining step in recovery and can proceed in either co-current or counter-current fashion, depending upon entrance and exit boundary conditions. Due to the prominence of linear flow normal to extensive planar fracture faces, the phenomena can be largely described as one-dimensional displacement. In this study, the nonlinear one-dimensional diffusivity equation, derived by combining Darcy’s law for both wetting and non-wetting phases with the mass conservation equation, is solved for counter-current spontaneous imbibition using a direct regular perturbation scheme. Saturation and phase pressure profiles in time and space were found to agree with literature solutions. In its analytic formulation, the method constitutes a simpler, more accurate, and more direct way to compute spontaneous imbibition performance than previously available. We claim generality with regard to functional forms for relative permeability and capillary pressure and applicability over the entire range of wettability, provided these effects are reflected in the parameterization of capillary pressure and saturation normalization. In particular, the present method avoids the difficulty in solving nonlinear integral equations. Instead, we reduce computations to a set of simple numerical integrations of the diffusion coefficient. Such a streamlined solution method can more easily provide a compute engine for inverse modeling or optimization schemes to recovery rock and fluid properties from displacement experiments. The solution method is benchmarked against oil–water and air–water solutions by alternative techniques and experimental data. The case of spontaneous imbibition into dry diatomite represents extreme contrast in phase mobility and a rigorous test of the perturbation method as an easier and more direct way to model 1D SI. Following method construction and benchmarking of the new streamlined countercurrent spontaneous imbibition solution method, a subsequent parametric study contributes to a greater understanding of controlling factors in fracture-matrix exchange in porous media.

Mathematical Eigenfunctions Analysis for 2nd Kind of Fredholm Integral equations

The current paper is a trial to investigate the Eigen functions appears through mathematical treatment of 2nd kind of Fredholm integral equations by making use a new developed an Inverse Iterative Numerical Scheme (IINS). To test the applicability and accuracy of the proposed IINS, two numerical examples were solved and compared with previous results. The discussion of the results gave a good coincides up to some decimal places of results with the available ones.

Influence of Interpolation Scheme on the Accuracy of Overset Method for Computing Rudder-Propeller Interaction

Abstract The overset method and associated interpolation schemes are usually thoroughly verified only on synthetic or academic test cases for which conclusions might not directly translate to real engineering problems. In the present work, an overset grid method is used to simulate a rudder-propeller flow, for which a comprehensive verification and validation study is performed. Three overset-related interpolation schemes (first order inverse distance, second order nearest cell gradient and third order least squares) are tested to quantify and qualify numerical errors on integral quantities, mass imbalance, flow features and rudder pressure distributions. The performance overhead is also measured to help make accuracy versus performance balance decisions. Rigorous solution verification is performed to estimate time and space Discretization, iterative and statistical uncertainties. Validation of the propeller-rudder flow against experimental data is also done. The results show that, while the choice of interpolation scheme has minimal impact on time-averaged integral quantities (like propeller and rudder forces), they do influence the smoothness of the time signals, with the first order scheme resulting in large intensity high-frequency temporal oscillations. Lower order interpolation methods also produce more interpolation artifacts in fringe cells, which are then convected downstream. Mass imbalance is also affected by the interpolation scheme, with higher order schemes such as the third order least squares approach resulting in an order of magnitude lower flux errors. The limitations of first order schemes do not, however, result in significant lower computational overhead, with the second order nearest cell gradient being even cheaper than the inverse distance scheme in the tested implementation. Lastly, validation shows promising results with rudder forces within 10% of the experiments.

LASSO reconstruction scheme for radioactive source distributions inside reactor building rooms with spectral information and multi-radionuclide contaminated situations

Clarification of radioactive source distributions is one of the most important steps in initial decommissioning of not only normally shutdown reactors but also damaged ones by accidents like Fukushima Daiichi Nuclear Power Plants (FDNPP). Generally, since radioactive hot spots are restricted into specific areas in normal operating conditions, the clarification scheme can be mapped onto the inverse estimation in sparse source distributions. On the other hand, the fact that radioactive hot spots are largely spread in unknown manner as seen in FDNPP motivates to construct an inversion scheme in non-sparse source conditions. Thus, a reconstruction scheme applicable to both sparse and non-sparse radioactive distributions is highly in demand. In addition, a variety of radionuclides is produced in reactors. Thus, we also need a scheme to distinguish each source distribution in mixed multi radionuclides. In this paper, we confirm that the inverse estimation scheme using Least Absolute Shrinkage and Selection Operator (LASSO) method with spectral information commonly shows excellent performance in the above all situations. The proposed LASSO scheme with the spectral information enables to reduce the number of measurement points in sparse conditions, while information proliferation by sensing the spectrum makes it possible to directly reconstruct source distribution as almost solvable problems in non-sparse ones. Moreover, the LASSO scheme allows to reconstruct the source distribution of each potential radionuclide in multi-radionuclide coexisting situations. Consequently, we confirm that the LASSO scheme to reconstruct radioactive sources is promising for the future nuclear decommissioning projects widely from normally shutdown reactors to damaged ones like FDNPP.

Adaptive Implicit Inverse Control for a Class of Butterfly-Like Hysteretic Nonlinear Systems and Its Application to Dielectric Elastomer Actuators

In this article, a butterfly-like Prandtl–Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach, which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{\infty }$</tex-math></inline-formula> norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator based motion control platform.

Inverse design of microwave waveguide devices based on deep physics-informed neural networks

Using physics-informed neural networks to solve physical inverse problems is becoming a trend. However, it is difficult to solve the scheme that only introduces physical knowledge through the loss function. Constructing a reasonable loss function to make the results converge becomes a challenge. To address the challenge of physics-informed neural network models for inverse design of electromagnetic devices, a deep physics-informed neural network is introduced by using the mode matching method. The physical equations have been integrated into the network structure when the network is constructed. This feature makes the deep physics-informed neural network have a more concise loss function and higher computational efficiency when solving physical inverse problems. In addition, the training parameters of deep physics-informed neural networks are physically meaningful compared with those of traditional physics-informed neural networks. Users can control the network by parameters more easily. Taking the scattering parameter design of a two-port waveguide for example, we present a new metal topology inverse design scheme and give a detailed explanation. In numerical experiments, we target a set of physically realizable scattering parameters and inversely design the metallic septum by using a deep physics-informed neural network. The results show that the method can not only achieve the design target but also obtain solutions with different topologies. The establishment of multiple solutions is extremely valuable in implementing the inverse design. It can allow the designer to determine the size and location of the design area more freely while achieving the performance requirements. This scheme is expected to promote the application and development of the inverse design of electromagnetic devices.

High-throughput measurements of interdiffusivities in fcc Co–Ni–Ta alloys at 1373K and 1473K

The composition-dependent interdiffusivities in the fcc Co–Ni–Ta alloys at 1373 K and 1473 K were deduced by using our newly developed numerical inverse scheme that combines two-dimensional (2D) simulations with diffusion triple experiments. This approach largely reduced the experimental efforts by analyzing only one single-phase diffusion triple at each temperature yet covering a much wider composition range than diffusion couples. The reliability of the high-throughput results was firstly verified by reproducing the experimental 2D composition profiles in each triple. In order to further validate the deduced interdiffusivities, several traditional one-dimensional (1D) diffusion couples were also devised, and the widely recognized Sauer-Freise method and Whittle-Green method were then employed to calculate the interdiffusivities for the binaries and at the intersection points within the ternary composition range, respectively. The interdiffusivities extracted from the inverse scheme agree well with those from the traditional approaches, which strongly proves the applicability of the present new scheme. Besides, the constructed main interdiffusivity planes at 1373 K and 1473 K provide an overview of the diffusion behavior in the fcc Co–Ni–Ta system and promote further kinetic studies.

Time-dependent hyper-viscoelastic parameter identification of human articular cartilage and substitute materials

Numerical simulations are a valuable tool to understand which processes during mechanical stimulations of hydrogels for cartilage replacement influence the behavior of chondrocytes and contribute to the success or failure of these materials as implants. Such simulations critically rely on the correct prediction of the material response through appropriate material models and corresponding parameters. In this study, we identify hyper-viscoelastic material parameters for numerical simulations in COMSOL Multiphysics® v. 5.6 for human articular cartilage and two replacement materials, the commercially available ChondroFillerliquid and oxidized alginate gelatin (ADA-GEL) hydrogels. We incorporate the realistic experimental boundary conditions into an inverse parameter identification scheme based on data from multiple loading modes simultaneously, including cyclic compression-tension and stress relaxation experiments. We provide individual parameter sets for the unconditioned and conditioned responses and discuss how viscoelastic effects are related to the materials’ microstructure. ADA-GEL and ChondroFillerliquid exhibit faster stress relaxation than cartilage with lower relaxation time constants, while cartilage has the largest viscoelastic stress contribution. The elastic response predominates in ADA-GEL and ChondroFillerliquid, while the viscoelastic response predominates in cartilage. These results will help to simulate mechanical stimulations, support the development of suitable materials with distinct mechanical properties in the future and provide parameters and insight into the time-dependent material behavior of human articular cartilage.

Transmission and transformation of entangled states with high fidelity in a non-Hermitian system

The study of non-Hermitian systems has attracted more and more attention, because physical properties in many real systems should be described by non-Hermitian Hamiltonians. Due to the existence of absorption and dissipation, transmission and transformation of entangled states with high fidelity are very difficult to realize in the non-Hermitian systems. How to realize transmission and transformation of entangled states with high fidelity in these dissipative systems becomes an open problem. Here we provide an inverse design scheme for topologically protected channels to solve such a problem. From our scheme, topologically protected channels can be designed according to the requirements for the overlap integrals among the initial states, target states, and eigenstates of the system. As a result, robust transmission and transformation of entangled states with high fidelity can be achieved in the non-Hermitian systems. Our proposed scheme has been demonstrated experimentally using the constructed non-Hermitian quantum walk platform. This work interconnects topology, quantum physics, and non-Hermitian systems, and opens up an avenue for quantum engineering in real systems.

IFKMS: Inverse Function-based Key Management Scheme for IoT networks

Due to the rapid growth of the Internet of Things (IoT), secure communication is becoming a significant concern. Nodes that compose such a dynamic network need to exchange sensitive and valuable data. The data must be kept safe against attacks. This protection requires the development of efficient key management protocols. However, this task is challenging because of the high resource constraints of most IoT devices in terms of storage, communication, processing, and energy capabilities. Some existing key management techniques have certain weaknesses since sensitive parameters are not protected during transmission, and cryptographic keys are stored in plain text and usually renewed at a fixed period of time. This paper proposes a new key management protocol aiming to secure communications before and after key establishment. Our scheme uses hash and one–one functions to achieve security during the key establishment process. The symmetrical character of the invertible functions is thus exploited to conceal critical data and pairwise keys stored in nodes’ memories. Moreover, the key refresh period is variable, which can be adjusted according to the number of occurred attacks in the network. BAN (Burrows Abadi Needham) logic is employed to assess the correctness of the proposed scheme. The results show that our scheme operates correctly and does not have redundancies or security flaws. Furthermore, the security and performance analysis point out that the proposed scheme is resilient against well-known attacks, and efficient in terms of storage, communication, computation overhead, and energy consumption.

Steady-state performance analysis of permanent magnet synchronous generator with capacitive assistance

This paper presents the steady-state performance analysis of an electrical connection scheme that improves the power factor and output power of a three-phase interior-rotor permanent magnet generator (IPMSG). The inverse saliency ratio ranges from 1 to 6. A three-phase capacitor is connected across the stator winding, close to the generator load, for each ratio. Leading current from the capacitor was used to improve the lagging power factor of the machine, maintain the load voltage under varying load conditions, and enhance the output power conversion ability, which the inverse saliency ratio scheme could not do alone, and without the need for external power electronics control circuitry. The numerical solution of the study was implemented in MATLAB®. The proposed IPMSG performance curves were enhanced with the generator yielding an improved output power of about 11.1% when compared with the inverse saliency ratio scheme without capacitors.