Inverse Approach Research Articles

Inverse-designed acoustic metasurfaces for broadband sound absorption

Acoustic metasurfaces are widely used for noise attenuation due to their outstanding acoustic performance and subwavelength characteristics. The paper introduces a topology-optimized inverse design approach for broadband sound-absorbing metasurfaces, aiming to achieve efficient sound absorption performance across a wide frequency spectrum. By integrating genetic algorithms with the finite element method and driven by objectives such as the absorption frequency range and absorption coefficient, we can transcend the limitations of manual design and fully exploit the design potential of structures. Furthermore, the study investigates the sound absorption mechanism and thermal-viscous dissipation through finite element simulations and experimental validations. The research findings reveal that the proposed broadband sound-absorbing metasurfaces demonstrate excellent sound absorption capabilities across the designated frequency range, boasting an average sound absorption coefficient exceeding 85%. These findings offer fresh perspectives and methodologies for advancing the research and practical application of broadband sound-absorbing metasurfaces.

Evaluating interseismic deformation patterns in the North Tabriz Fault (Iran) using enhanced fitting of velocity field and analysis of surface deformation

Understanding the mechanisms and locations of interseismic strain accumulation along faults is essential for assessing earthquake hazards. However, the mechanical response during the transition from deep fault locking to creep behavior remains uncertain. Estimating the slip deficit within these transition zones is challenging. This study challenges the assumption of a constant depth distribution of interseismic slip rate along the fault over time and proposes variable locking depths as an alternative model. By rejecting the constant locking depth assumption, singularity issues during stress theorem resolution are resolved. To address this, we employ a methodology considering creep propagation within a fully elastic medium. This approach incorporates long-term deformation resulting from viscoelastic flow in the upper mantle and lower crust. Including viscoelastic effects improves the fit to interseismic deformation rates, yielding lower locking depths compared to fully elastic models. To conduct the investigation, the GPS velocity field is recovered using the forward problem and the boundary element method. Subsequently, a physics-based inversion approach, deep interseismic creep, is employed to determine interseismic deformation patterns on a strike-slip fault. Furthermore, this study examined the correlation between the dislocation parameters and their relationship, as well as established the probability distributions associated with each faulting parameter. This research highlights the importance of considering variable locking depths in understanding interseismic strain accumulation and the transition to creep behavior along faults. The findings contribute to improved earthquake hazard assessment and mitigation strategies by providing valuable insights into fault behavior mechanics along the North Tabriz Fault.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

Exploring the digital gray zone of online medicinal markets emerging from search

AbstractThis explorative study investigates the emergence of gray zone markets from search engines amidst the global expansion of online markets. With the analytical approach of infrastructural inversion, we examine how the search infrastructure constructs access to a gray zone market including both authorized online pharmacies and unauthorized vendors. Using Sweden and Google Search as a case, we explore the online presence of three products (vitamin D, paracetamol, and Viagra), through search engine result page analysis, web crawling, and network analysis. Infrastructural inversion unveils the typically invisible mechanisms of search engines, considering user queries, algorithmic priorities, SEO practices, and pharmacy regulations. We find gray zones only emerge in searches for erectile disfunction medicinal products and information, where unauthorized vendors successfully competed for visibility in search engine rankings. A complex web of conditions can steer consumers toward gray zone markets, complicating the access to safe and regulated medicinal products. This can expose individuals to risks associated with unverified medicinal products, but also challenges the integrity of the online health information infrastructure.

Development of an intelligent-based telemetry hexapod robotic system for surveillance of power system components

A robot is an artificial system that performs specific programmed tasks to aid day-to-day human activities. Robot finds applications in industries for large-scale production, healthcare for computer-aided surgeries, security for surveillance of electrical power system equipment, and the military for carrying out dangerous reconnaissance. Most robotic systems employed for different operations use a wheeled mode of locomotion which has limitations over rough terrains and the issue of communication setup of the system. This work developed a hexapod, a six-legged robotic system, to overcome these identified challenges. The robot was developed in tandem with a telemetry system for surveillance. Although, automating the surveillance process using robotic systems has been in the works for some time. However, this work developed a telemetry hexapod robotic system for enhanced surveillance for the security of power systems equipment and other critical infrastructures using a Raspberry Pi for fast, secure data transmission, and precise system synchronization. An inverse kinematics approach was used to determine joint configurations for better positions of each endpoint. A digital camera was integrated into the robot to relay real-time images of adversaries of power system components intelligently. In addition, the system incorporates data processing capability. In the result obtained, the developed telemetry hexapod robotic system receives instructions over a distance slightly above 300 m. It establishes effective control of the telemetry system without any hindrance of a limited range as observed in Bluetooth-controlled systems. The system's performance includes an average efficiency of 98.2 %, latency of 0.48 s at the peak distance, and an endurance of 4 h. Further analysis of the system shows that the corresponding increase in the latency with the distance is negligible. The system performed better than other related systems considered in the literature. The correct implementation of the developed telemetry hexapod robotic system further enhances mobility, stability in rough terrain, reliable communication system. Also, it brings about a notable reduction in component theft in electrical power systems.

Seismic scattering inversion for multiple parameters of overburden-stressed isotropic media

Subsurface reservoirs are compressed by overburden stress resulting from the gravity of overlying masses, and the resulting stress changes significantly affect the seismic reflection responses generated at the reservoir interfaces. Several exact reflection coefficient equations have been well established to delineate the role that in situ stress plays in altering the energy transition and amplitude of seismic reflection responses. These exact equations, however, cannot be effectively used in practice due to their intricate formulations and the difficult geophysical estimation for the third-order elastic constants (3oECs) embedded in reflection coefficients. Based on the theories of nonlinear elasticity and elastic wave inverse scattering, we derive an approximate seismic reflection coefficient equation for overburden-stressed isotropic media in terms of the P-wave modulus, shear modulus, density, and a defined stress-related parameter (SRP). The SRP is a combined quantity of elastic moduli, 3oECs, and overburden stress, which can be naturally treated as a dimensionless stress-induced anisotropy parameter. Its inclusion effectively eliminates the need for 3oECs information when using our equation to estimate the desired reservoir properties from seismic observations. By comparing our equation to the exact one, we confirm its validity within the moderate-stress range. Then, we introduce a Bayesian inversion approach incorporating the new reflection coefficient equation to estimate four model parameters. In our approach, the Cauchy and Gaussian distribution functions are used for the a priori probability and the likelihood distributions, respectively. The synthetic tests from two well-log data sets and a field example demonstrate that four parameters can be reasonably inverted using our approach with rather smooth initial models, which illustrates the feasibility of our inversion approach.

Impact of Gut Microbiota and Inflammatory Cytokines on Immune Thrombocytopenia.

Immune thrombocytopenic purpura (ITP) is an autoimmune disorder, and recent research suggests that gut microbiota and inflammatory cytokines may play a significant role in its pathogenesis. However, the specific effects of these factors on ITP and their relationships remain unclear. We conducted a two-step, two-sample Mendelian randomization study using an inverse variance-weighted approach to investigate the causal role of the gut microbiota in ITP and the mediating effect of inflammatory cytokines on their association. The results showed that among the 473 gut microbiota species, 11 were positively associated and 12 were negatively associated with the risk of ITP. Among the 91 screened inflammatory cytokines, five (CXCL10, CXCL5, IL-12RA, TRAIL, and VEGF-A) were found to have a causal relationship with ITP. Mediation analysis revealed that the gut microbiota UBA1066 promoted the occurrence of ITP through CXCL10 mediation, with a mediation effect of 0.118932 (95% CI: 0.049471-0.188393) accounting for 9.95% of the total effect. Gut microbiota Treponema promoted ITP through VEGF-A mediation, with a mediation effect of 0.045873 (95% CI: 0.01456-0.07718) accounting for 4.28% of the total effect. Gut microbiota Haloplasma promoted the occurrence of ITP via CXCL5. The mediating effect of CXCL5 was 0.038409 (95% CI = 0.00107718-0.07575082), with a mediating ratio of 16.79%. This study revealed a causal relationship between gut microbiota composition and ITP risk, highlighting three inflammatory cytokines as potential causal mediators of this relationship. These findings provide potential targets and biomarkers for the prevention and treatment of ITP with significant clinical implications.

Research on Digital Twin Method for Spaceborne Along-Track Interferometric Synthetic Aperture Radar Velocity Inversion of Ocean Surface Currents

In this paper, an end-to-end system framework is proposed for the Digital Twin study of spaceborne ATI-SAR ocean current velocity inversion. Within this framework, a fitting inversion approach is proposed to enhance the conventional spaceborne ATI-SAR ocean current velocity inversion algorithm. Consequently, the issue of possible local inversion errors stemming from the mismatch between the traditional spaceborne ATI-SAR inversion algorithm and various dual-antenna configurations is resolved to a certain extent. A simulated spaceborne ATI-SAR system, featuring a dual-antenna configuration comprising a baseline direction perpendicular to the track and a squint angle, is presented to validate the efficacy of the Digital Twin methodology. Under the specified simulation parameters, the average inversion error for the final ocean current velocity is recorded at 0.0084 m/s, showcasing a reduction of 0.0401 m/s compared with the average inversion error prior to optimization.

Research on defect image inversion method based on multiple lift-off values magnetic memory signals

Metal magnetic memory (MMM) technique can detect macroscopic defects and stress concentration zones of ferromagnets, and it can identify the approximate positions of these flaws, while a little further information about defects characteristics can be provided. To promote study in this area, defects were modeled as located magnetic dipoles whose strength and locations should be determined, and the technique of truncated generalized inverse and reduced space inversion were used to image the dipole strength. Through the simulation experiment, we found that the inversion image quality was influenced by the lift-off value. When the magnetic field data for inversion was measured with a small lift-off value, the inner dipoles contributions were easily buried and the inversion image can only shown the surface dipole well. In contrast, if the magnetic field data was measured with a greater lift-off value, it was possible to inverse the deep dipoles while the image was somewhat out of focus. To overcome this limitation, we composed the magnetic field data measured under different lift-off values together for inversion. We used the same model to test this approach and applied it on real crack defects magnetic field data. It demonstrates that the new inversion approach shows better accuracy than the single lift-off value and it was possible to infer the location and size of defect by the evaluation of preset dipoles.

Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach

Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach

Identification of ultrasound characteristics in viscoelastic architectured materials by angular measurements in double through-transmission

Recent advances in additive manufacturing of viscoelastic materials have paved the way towards the design of increasingly complex multi-material structures with heterogeneities from the micro- to the macroscale. However, the measurement of ultrasound characteristics in such viscoelastic structures is a challenging problem, due to the strong attenuation related to both the constituent materials and the architecture. We propose here a methodological framework to identify the phase velocity and attenuation in viscoelastic multi-material quasi-heterogeneous samples. The proposed approach relies on measurements achieved in double through-transmission at various incidence angles, referred as theta-scan. The forward modeling of the wave propagation in such media allows then to identify the ultrasound characteristics based on an inverse approach. Experiments were performed on multi-material samples made of a random pattern of inclusions of a soft elastomer inside a rigid glassy polymer. The effects of the volume fraction of the constituent materials on the ultrasound characteristics was evaluated. This methodology open the path towards ultrasonic measurements in highly attenuating architectures

Inverse design of acoustic metamaterial-based vibration control for offshore wind turbines

This paper presents an inverse design approach for mechanical metamaterial turbines utilising a deep learning algorithm. We first establish a theoretical model for metamaterial turbines using the spectral element method to analyse their vibration characteristics. Subsequently, we predict the vibration transmission properties of these turbines and achieve on-demand inverse design of metastructures through a fully connected deep-learning neural network model. The efficacy of the forward prediction and reverse design network models is validated using an inverse neural network. Our results demonstrate a strong agreement between the predicted and target values, affirming the feasibility of employing deep learning for on-demand meta-turbine design. This intelligent design approach holds promise for expediting and enhancing the design of metamaterials tailored for low-frequency vibration isolation in engineering structures.

Identification of radiated modes by a small-scale ducted fan

Various industrial applications involve fluid flow in ducted fans. Turbojet engines or air condition systems are just a few examples. The long-term objective is to reduce the associated noise pollution generated by rotor-stator interaction within a small-scale duct. Modal detection strategies help to understand the characteristics of sound radiation of such systems involving fluid flow. In this work, the authors analyze the modal amplitudes generated by a rotor-stator interaction in the presence of a background mean flow. The inverse approach detecting the modal amplitudes is applied using experimentally as well as numerically determined sound pressure values outside the circular duct. The results are discussed and compared against mode amplitudes estimated by an in-duct microphone array whose data is measured simultaneously with that of the far-field one. Some physical interpretations regarding varying amplitudes are discussed. The approach is finally used to assess the impact of homogeneous and heterogeneous stator vanes on the rotor-stator generated noise.

Micromechanical investigation of DLC based coating systems by means of combined characterization and simulation methods

The assessment of the elastic-plastic material behavior is imperative for benchmarking the performance of thin coating systems. Linear-elastic approaches can lead to deviating results and experimental investigations, such as fatigue tests, frequently overlook the intricacies of the failure mechanism. Therefore, the elastic-plastic material behavior of DLC based coating systems with CrN support interlayer, deposited on 100Cr6 substrates and Si, was investigated. Using an inverse approach, a nanoindentation simulation with spherical indenter geometry was iteratively optimized to experimental curves by adjusting the material parameters Young's modulus E, yield strength Y, hardening coefficient K and hardening exponent n for DLC and CrN layers. Mechanical parameters were measured and the yield strength of DLC was analyzed with a scratch procedure to implement appropriate initial values. The results showed an overall good agreement between the measured and optimized force-displacement curves with reasonable determined material parameters. The control parameter Y exhibited nearly identical outcomes for scratch measurements and the calculated value from simulations although DLC was deposited on different substrates. The employment of a spherical indenter tip mitigates the influence of the microstructure in the CrN layer through homogenization effects and a larger affected area of the plastic zone. Furthermore, enhancing the performance of the coating systems can be achieved by increasing the DLC or CrN interlayer thickness.

A place in the sun: Farmers' co-benefits from solar irrigation in Bangladesh

Solar irrigation pumps (SIPs) are gradually replacing diesel pumps in relatively water-intensive agricultural production systems and geographies to reduce carbon emissions from food systems. However, beyond its climate change mitigation potential and fulfillment of Nationally Determined Contributions commitments, the adoption of solar irrigation also has direct co-benefits for farmers. Taking the case of Bangladesh and anchored on primary data collected among solar and diesel pump users, this article analyses the role access to solar irrigation has on household and farm-level outcomes. The propensity score matching and inverse probability matching approaches identify a positive effect of SIP access on food security and profitability from dry-season paddy for adopters. Different transmission channels are explored to explain these co-benefits. Lower costs of irrigation and labor are identified as the strongest pathways. This analysis strengthens the case for investments toward the solarization of agriculture in developing countries where it delivers significant development co-benefits in addition to climate change mitigation benefits. The valuation of the farmers' co-benefits, along with global climate mitigation impacts, also highlights the potential role of such programs toward climate justice.

Data-driven inverse design of a multiband second-order phononic topological insulator

Second-order phononic topological insulators (SPTIs) have sparked vast interest in manipulating elastic waves, owing to their unique topological corner states with robustness against geometric perturbations. However, it remains a challenge to develop multiband SPTIs that yield multi-frequency corner states using prevailing forward design approaches via trial and error, and most inverse design approaches substantially rely on time-consuming numerical solvers to evaluate band structures of phononic crystals (PnCs), showing low efficiency particularly when applied to different optimization tasks. In this study, we develop and validate a new inverse design framework, to enable the multiband SPTI by integrating data-driven machine learning (ML) with genetic algorithm (GA). The relationship between shapes of scatterers and frequency bounds of multi-order bandgaps of PnCs is mapped via developing artificial neural networks (ANNs), and a multiband SPTI with multi-frequency topological corner states is cost-effectively designed using the proposed inverse optimization framework. Our results indicate that the data-driven approach can provide a high-efficiency solution for on-demand inverse designs of multiband second-order topological mechanical devices, enabling diverse application prospects including multi-frequency robust amplification and confinement of elastic waves.

A global approach to detecting and characterizing water leakage in a concrete bridge deck: Parametric study to validate an adapted full-waveform inversion method

The objective of this study is to address issues related to defects in waterproofing membranes through the use of Ground Penetration Radar to detect water leakage into the concrete layer of bridge decks. Given that processing radar signals based solely on temporal data introduces significant estimation errors, an advanced method optimized from Full-Waveform Inversion (FWI) is employed. This method accounts for several unknown factors, including boundary conditions, antenna positioning inaccuracies, and approximations inherent in 2D modeling during the inversion process. The method is adaptable and capable of reconstructing both the dielectric and geometric parameters of a multilayered structure with any number of layers and unknown parameters using just a few A-scans (up to 10, depending on the number of parameters and layers). In contrast, other methods typically require several hundred A-scans. This efficiency is achieved due to the two-dimensional nature of the layer system. Additionally, the simplicity of the structure facilitates a much faster and more straightforward inversion algorithm, hence making these features especially advantageous for practical applications. The optimized Full-Waveform Inversion approach allows for an accurate determination of unknown parameters within a multilayered medium. The high accuracy of this method is validated through a direct comparison with experiments, wherein the exact parameters are known. Such an approach enables the attainment of a low relative error using the currently available measurement device.

Deep learning coupled Bayesian inference method for measuring the elastoplastic properties of SS400 steel welds by nanoindentation experiment

Nanoindentation experiment has shown broad application prospects due to its ability to measure the mechanical properties of various materials at multiple scales. In this paper, a deep learning coupled Bayesian inverse approach is proposed for measuring the elastoplastic parameters of SS400 steel welds by nanoindentation experiment. The nanoindentation experiments were performed on the SS400 steel welds, including base metal (BM), weld zone (WZ), and heat affected zone (HAZ), and the experiment load–displacement (P-h) curves were obtained. The hyper-parameters tunable artificial neural network (ANN) was established to correlate elastoplastic parameters with indentation P-h curves. Based on Bayesian inference theory, the posterior density function for estimating the unknown material parameters was established. Transitional Markov chain Monte Carlo was used for sampling from the posterior density function, and the elastoplastic properties in different regions of SS400 steel welds were identified. The advantage of the established measuring method is that the hyper-parameters optimized ANN model can provide the very accurate forward relationship between material properties and indentation P-h curves. Besides, the inverse Bayesian framework can quantify the potential uncertainty of the identified elastoplastic parameters. The measured elastoplastic properties of the base metal of SS400 steel show good agreement with tensile experiment data, of which the maximum measuring error is less than 12%. The measured elastoplastic properties in WZ and HAZ are also proved to be effective. The uncertainty of the identified elastoplastic parameters of SS400 steel welds can be quantified by posterior marginal distribution, using Mean and Variance values. The results proved that the proposed inverse measuring method is reliable and effective.

An Accurate Deep Neural Network-Based Inversion Approach for Multichannel Microwave Interferometer Characterization of Plasma Wake Electron Density Distribution

An Accurate Deep Neural Network-Based Inversion Approach for Multichannel Microwave Interferometer Characterization of Plasma Wake Electron Density Distribution

Real-Time Aerodynamic Load Estimation for Hypersonics via Strain-Based Inverse Maps

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Precomputation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle.