Inverse Approach Research Articles

On the variability of blocked forces determined using an inverse approach

Mount force data of motors, fans, and pumps is rarely available from the suppliers. However, manufacturers are beginning to ask for and even require this information from their suppliers. In-situ estimation of these forces can be accomplished using blocked force transfer path analysis. Since these forces are difficult to measure directly, forces are calculated using an inverse approach. A simple case study is considered on a commercial air handler. Blocked forces are determined using different inlet locations and path selections. The consistency of the inversely calculated forces is evaluated.

Using torsional wave elastography to evaluate spring pot parameters in skin tumor mimicking phantoms

Estimating the tissue parameters of skin tumors is crucial for diagnosis and effective therapy in dermatology and related fields. However, identifying the most sensitive biomarkers require an optimal rheological model for simulating skin behavior this remains an ongoing research endeavor. Additionally, the multi-layered structure of the skin introduces further complexity to this task. In order to surmount these challenges, an inverse problem methodology, in conjunction with signal analysis techniques, is being employed. In this study, a fractional rheological model is presented to enhance the precision of skin tissue parameter estimation from the acquired signal from torsional wave elastography technique (TWE) on skin tumor-mimicking phantoms for lab validation and the estimation of the thickness of the cancerous layer. An exhaustive analysis of the spring-pot model (SP) solved by the finite difference time domain (FDTD) is conducted. The results of experiments performed using a TWE probe designed and prototyped in the laboratory were validated against ultrafast imaging carried out by the Verasonics Research System. Twelve tissue-mimicking phantoms, which precisely simulated the characteristics of skin tissue, were prepared for our experimental setting. The experimental data from these bi-layer phantoms were measured using a TWE probe, and the parameters of the skin tissue were estimated using inverse problem-solving. The agreement between the two datasets was evaluated by comparing the experimental data obtained from the TWE technique with simulated data from the SP- FDTD model using Pearson correlation, dynamic time warping (DTW), and time-frequency representation. Our findings show that the SP-FDTD model and TWE are capable of determining the mechanical properties of both layers in a bilayer phantom, using a single signal and an inverse problem approach. The ultrafast imaging and the validation of TWE results further demonstrate the robustness and reliability of our technology for a realistic range of phantoms. This fusion of the SP-FDTD model and TWE, as well as inverse problem-solving methods has the potential to have a considerable impact on diagnoses and treatments in dermatology and related fields.

Long-Term PM2.5 Exposure and Upregulation of CLCA1 Expression in Nasal Epithelium from Youth with Asthma.

Little is known about long-term PM2.5 exposure and airway epithelial gene expression. To test for association between long-term PM2.5 exposure and nasal epithelial gene expression in youth with asthma. Transcriptome-wide association study (TWAS) of long-term PM2.5 in nasal epithelium from youth aged 6-20 years in the: 1) Epigenetic Variation and Childhood Asthma in Puerto Ricans study (EVA-PR, n=182); 2) Vitamin D Kids Asthma Study (VDKA, n=58); and 3) Stress and Treatment Response in Puerto Rican and African American Children with Asthma study (STAR, n=81). Satellite hybrid models were used to estimate PM2.5 exposure in the prior year at each participant's residence. Multivariable negative binomial regression was used for each TWAS, adjusting for age, sex, and other covariates. A meta-analysis of all TWAS results was then conducted using an inverse variance-weighted average approach. Most participants (~95%) in the meta-analysis of TWAS for PM2.5 exposure identified as Puerto Rican or Black. Long-term PM2.5 was associated with: 1) upregulated expression of CLCA1 (calcium-activated chloride channel regulator 1, false discovery rate-adjusted P [FDR-P]=0.008), SYCP2 (synaptonemal complex protein 2, FDR-P=0.01), and CYP2A6 (cytochrome p450 family 2 subfamily A member 6, FDR-P=0.02), and 2) downregulated expression of EDAR (ectodysplasin A receptor, FDR-P=0.01). In a meta-analysis, CLCA1 upregulation was associated with ≥1 positive allergen-specific IgE (FDR-P <0.001) and increased blood eosinophils (FDR-P <0.001) and total IgE (FDR-P <0.001). In a meta-analysis of TWASs in predominantly Puerto Rican and Black youth with asthma, long-term PM2.5 exposure was associated with upregulated airway epithelial CLCA1 expression, in turn linked to biomarkers of T2-high immunity.

Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

BackgroundIn gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases.ResultsThe ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier.ConclusionsThe identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.

Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.

An Inverse Modeling Approach for Retrieving High-Resolution Surface Fluxes of Greenhouse Gases from Measurements of Their Concentrations in the Atmospheric Boundary Layer

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km2 forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

One-Shot Omnidirectional Pressure Integration Through Matrix Inversion

In this work, we present a method to perform 2D and 3D omnidirectional pressure integration from velocity measurements with a single-iteration matrix inversion approach. This work builds upon our previous work, where the rotating parallel ray approach was extended to the limit of infinite rays by taking continuous projection integrals of the ray paths and recasting the problem as an iterative matrix inversion problem. This iterative matrix equation is now ``fast-forwarded'' to the ``infinity'' iteration, leading to a different matrix equation that can be solved in a single iteration, thereby presenting the same computational complexity as the Poisson equation. We observe computational speedups of $\sim10^6$ when compared to brute-force omnidirectional integration methods, enabling the treatment of grids of $\sim 10^9$ points and potentially even larger in a desktop setup at the time of publication. Further examination of the boundary conditions of our one-shot method shows that omnidirectional pressure integration implements a new type of boundary condition, which treats the boundary points as interior points to the extent that information is available.

Predicting mechanical heterogeneity in glassy polymer nanocomposites via an inverse computational approach based on atomistic molecular simulations and homogenization methods

Probing the mechanical behavior of the region formed between a nanoparticle reinforcement and a polymer matrix in a polymer nanocomposite structure, denoted as the “interphase”, is a main challenge as such regions are difficult to investigate by experimental methods. Here, we accurately characterize the heterogeneous mechanical behavior of polymer nanocomposites, focusing on polymer/nanofiller interphases via a combination of nanomechanical simulations and numerical homogenization techniques. Initially, the global mechanical performance of a glassy poly(ethylene oxide) polymer nanocomposite reinforced with silica nanoparticles is studied using detailed atomistic molecular dynamics simulations for 1.9% and 12.7% silica volume fractions. Next, the polymer/silica interphase thickness is identified by probing the polymer atom-based density distribution profile in the vicinity of the nanofiller at equilibrium. On the basis of this thickness, the interphase is subdivided to check the position-dependent change in mechanical properties. Then, using continuum mechanics and atomistic simulations, we proceed to compute the effective Young’s modulus and Poisson’s ratio of the polymer/nanoparticle interphase as function of the distance from the nanoparticle. In the last step, an inverse numerical homogenization model is proposed to predict the mechanical properties of the interphase on the basis of a comparison criteria with the data from MD. The results were found to be acceptable, raising the possibility of accurately and efficiently predicting interfacial properties in nanostructured materials.

Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time

Abstract. Reconstructing the thermo-chemical evolution of Earth's mantle and its diverse surface manifestations is a widely recognised grand challenge for the geosciences. It requires the creation of a digital twin: a digital representation of Earth's mantle across space and time that is compatible with available observational constraints on the mantle's structure, dynamics and evolution. This has led geodynamicists to explore adjoint-based approaches that reformulate mantle convection modelling as an inverse problem, in which unknown model parameters can be optimised to fit available observational data. Whilst there has been a notable increase in the use of adjoint-based methods in geodynamics, the theoretical and practical challenges of deriving, implementing and validating adjoint systems for large-scale, non-linear, time-dependent problems, such as global mantle flow, has hindered their broader use. Here, we present the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), an advanced computational modelling framework that overcomes these challenges for coupled, non-linear, time-dependent systems by integrating three main components: (i) Firedrake, an automated system for the solution of partial differential equations using the finite-element method; (ii) Dolfin-Adjoint, which automatically generates discrete adjoint models in a form compatible with Firedrake; and (iii) the Rapid Optimisation Library, ROL, an efficient large-scale optimisation toolkit; G-ADOPT enables the application of adjoint methods across geophysical continua, showcased herein for geodynamics. Through two sets of synthetic experiments, we demonstrate the application of this framework to the initial condition problem of mantle convection, in both square and annular geometries, for both isoviscous and non-linear rheologies. We confirm the validity of the gradient computations underpinning the adjoint approach, for all cases, through second-order Taylor remainder convergence tests and subsequently demonstrate excellent recovery of the unknown initial conditions. Moreover, we show that the framework achieves theoretical computational efficiency. Taken together, this confirms the suitability of G-ADOPT for reconstructing the evolution of Earth's mantle in space and time. The framework overcomes the significant theoretical and practical challenges of generating adjoint models and will allow the community to move from idealised forward models to data-driven simulations that rigorously account for observational constraints and their uncertainties using an inverse approach.

Tailoring Phonon Dispersion of a Genetically Designed Nanophononic Metasurface.

Phonon engineering at the nanoscale holds immense promise for a myriad of applications. However, the design of phononic devices continues to rely on regular shapes chosen according to long-established simple rules. Here, we demonstrate an inverse design approach to create a two-dimensional phononic metasurface exhibiting a highly anisotropic phonon dispersion along the main axes of the Brillouin zone. A partial hypersonic bandgap of approximately 3.5 GHz is present along one axis, with gap closure along the orthogonal axis. Such a level of control is achieved through genetically optimized unit cells, with shapes exceeding conventional intuition. We experimentally validated our theoretical predictions using Brillouin light scattering, confirming the effectiveness of the inverse design method. Our approach unlocks the potential for automated engineering of phononic metasurfaces with on-demand functionalities, thus leading toward innovative phononic devices beyond the limitations of traditional design paradigms.

Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate

Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency.

Synthesis and Characterization of Thiolated Nanoparticles Based on Poly (Acrylic Acid) and Algal Cell Wall Biopolymers for the Delivery of the Receptor Binding Domain from SARS-CoV-2.

The COVID-19 pandemic required great efforts to develop efficient vaccines in a short period of time. However, innovative vaccines against SARS-CoV-2 virus are needed to achieve broad immune protection against variants of concern. Polymeric-based particles can lead to innovative vaccines, serving as stable, safe and immunostimulatory antigen delivery systems. In this work, polymeric-based particles called thiolated PAA/Schizo were developed. Poly (acrylic acid) (PAA) was thiolated with cysteine ethyl ester and crosslinked with a Schizochytrium sp. cell wall fraction under an inverse emulsion approach. Particles showed a hydrodynamic diameter of 313 ± 38 nm and negative Zeta potential. FT-IR spectra indicated the presence of coconut oil in thiolated PAA/Schizo particles, which, along with the microalgae, could contribute to their biocompatibility and bioactive properties. TGA analysis suggested strong interactions between the thiolated PAA/Schizo components. In vitro assessment revealed that thiolated particles have a higher mucoadhesiveness when compared with non-thiolated particles. Cell-based assays revealed that thiolated particles are not cytotoxic and, importantly, increase TNF-α secretion in murine dendritic cells. Moreover, immunization assays revealed that thiolated PAA/Schizo particles induced a humoral response with a more balanced IgG2a/IgG1 ratio. Therefore, thiolated PAA/Schizo particles are deemed a promising delivery system whose evaluation in vaccine prototypes is guaranteed.

Reduction of the model prediction error in vibration-based SHM applications

The accuracy of an SHM scheme is partly controlled by the accuracy of the predictive models involved within the design process of the detector that associates sensor data with a corresponding health state. On the other hand, the accuracy of the predictive model, which is quantified by the error between predictions and observations, is partly controlled by its parameter magnitudes. This work provides a unified framework that allows for reducing the model prediction error by updating the model parameters in the light of experimental observations. The problem is cast in a probabilistic setting and tackled through inferential statistics. Forward and inverse uncertainty quantification approaches are employed in a sequential manner. The framework is able to yield point estimates in accordance to a Maximum Likelihood Estimator (MLE) and is also able to construct credible intervals through computational Bayesian updating. The former is treated by genetic algorithms whereas the later through Markov Chain Monte Carlo computational statistics. The framework is showcased on a vibration problem of the simplified benchmark case named as “Jim Beam”. The first ten modal parameters are considered as the discriminant features of interest . A surrogate of the finite element model of the considered specimen serves as the predictive model and the eigenfrequencies are extracted from a linear modal analysis. The parameters of inferential interest are the Young’s modulus and an artificial stiffness related parameter that is introduced so as to model the flexural rigidity of the specimen’s bolted connections. Experimental Modal Analysis and Operational Modal Analysis are the two approaches that were employed for experimentally identifying the modes and their corresponding frequencies. Results are provided from both the MLE and the Bayesian updating process and the effectiveness of each approach is discussed.

Poster 365: Impact of Graft Type and Meniscal Status on Patellofemoral Cartilage Early after ACL Reconstruction

Objectives: Anterior cruciate ligament reconstruction (ACLR) restores passive knee stability but does not reduce the rate of osteoarthritis (OA). Meniscal status, requiring repair or meniscectomy, is a known component of degenerative progression. Regarding the patellofemoral joint (PFJ) specifically, traditional imaging modalities have shown some evidence that patellar tendon autograft (BPTB) is associated with an increased risk of OA. T2 relaxation time using quantitative MRI (qMRI) is an established method to identify early cartilage changes. However, T2 relaxation in the PFJ as a function of graft type and meniscal status has not been previously evaluated. We aimed to evaluate the effect of autograft type (BPTB, hamstring [HT] and quadriceps tendon [QT]) as well as concomitant meniscal surgery (repair or meniscectomy) at the time of ACLR on T2 relaxation time in PFJ cartilage. We hypothesized that BPTB grafts, concomitant meniscal repair, and meniscectomy would result in higher (worse) T2 relaxation times at 6 months after ACLR. We also evaluated the effect of abnormal joint loading during gait on the PFJ cartilage. We hypothesized that T2 relaxation times would not differ in any group after controlling for knee joint loading during walking. Methods: Thirty-four participants aged 15-35 years were enrolled prospectively within 1 month of ACL injury. Exclusion criteria included prior knee injury or surgery, concomitant grade III tear to other knee ligaments that was symptomatic or required surgical intervention and presence of chondral damage. Sagittal qMRIs were obtained within 1 month of injury (but prior to ACLR) and 6 months after ACLR. Articular cartilage of the PFJ was manually segmented using ITK-SNAP software (Penn Image Computing and Science Laboratory, University of Pennsylvania) and was confirmed for accuracy by a board-certified, fellowship-trained musculoskeletal radiologist (Figure 1). Mean T2 relaxation times were extracted from the trochlear and patellar cartilage. Non-physiological T2 relaxation times less than 10 ms and greater than 90 ms were excluded. Gait biomechanics were captured using two in-ground force plates (Bertec Corporation, Columbus, OH) sampled at 1,080 Hz and retroreflective markers with an 8-camera motion capture system (Qualisys, Göteborg, Sweden) sampled at 120 Hz. Data was processed using Visual 3D software (C-motion, Bethesda, MD). Using an inverse dynamic approach, joint angle and force plate data were used to calculate the knee flexion moment (KFM) impulse normalized to mass and height during the stance phase. Separate one-way ANOVAs were used to evaluate the relationship between graft type and concomitant meniscus surgery with the percent change in T2 relaxation time in PFJ cartilage from baseline to 6 months after ACLR. To determine the influence of gait biomechanics on T2 relaxation time across groups, general linear models were used with the interlimb ratio (involved/uninvolved) of KFM impulse as a covariate. Results: The 34 participants had a mean age of 19.0±4.7 years, and 21 (61.8%) were female. Grafts used during ACLR included 22 BPTB, 4 HT and 8 QT. Seventeen participants (50.0%) had a concomitant meniscus repair and 3 (8.8%) had a meniscectomy. Overall, T2 relaxation time increased 8.7±7.3% in the patellar cartilage (pre-ACLR: 39.1±2.6 ms, 6 months: 42.4±3.6 ms, p&lt;0.001) and 7.1±7.3% in the trochlear cartilage (pre-ACLR: 46.3±2.6 ms, 6 months: 49.5±2.7 ms, p&lt;0.001). The change in T2 relaxation time 6 months after ACLR did not differ based on graft type in either the patellar (p=0.729) or trochlear cartilage (p=0.525) (Figure 2). Group differences remained insignificant after controlling for knee loading (KFM impulse) during gait (patellar: p=0.594; trochlear: p=0.222). Meniscal status was also not associated with a change in T2 relaxation time in either the patellar (p=0.392) or trochlear cartilage (p=0.190) (Figure 3) although there was a trend toward higher T2 relaxation times in those with a concomitant meniscus repair or meniscectomy. Group differences remained statistically insignificant after controlling for the KFM impulse during gait (patellar: p=0.400; trochlear: p=0.197). Conclusions: Our findings provide insufficient evidence that graft type is associated with worse quantitative chondral changes within the PFJ at 6 months following ACLR. While meniscal status is a known risk factor for increased contact pressure in the involved compartment and subsequent degenerative progression, our data does not demonstrate statistically significant changes to the PFJ cartilage when meniscal tears were present, likely due to the limited sample size and large variation within groups. Future work with larger cohorts is needed to confirm our findings and further investigate the role that graft type and meniscal status may play within the PFJ after ACLR. [Figure: see text][Figure: see text][Figure: see text]

Innovative Inverse-Design Approach for On-Chip Computational Spectrometers: Enhanced Performance and Reliability

Computational spectrometers utilizing disordered structures have emerged as promising solutions for meeting the imperative demand for integrated spectrometers, offering high performance and improved resilience to fabrication variations and temperature fluctuations. However, the current computational spectrometers are impractical because they rely on a brute-force random design approach for disordered structures. This leads to an uncontrollable, non-reproducible, and suboptimal spectrometer performance. In this study, we revolutionize the existing paradigm by introducing a novel inverse design approach for computational spectrometers. By harnessing the power of inverse design, which has traditionally been applied to optimize single devices with simple performance, we successfully adapted it to optimize a complex system comprising multiple correlated components with intricate spectral responses. This approach can be applied to a wide range of structures. We validated this by realizing a spectrometer utilizing a new type of disordered structure based on interferometric effects that exhibits negligible loss and high sensitivity. For a given structure, our approach yielded a remarkable 12-times improvement in the spectral resolution and a four-fold reduction in the cross-correlation between the filters. The resulting spectrometer demonstrated reliable and reproducible performance with the precise determination of structural parameters.

Internal heat source in the vadose zone: A comprehensive exploration through theoretical spectral analysis and practical application into thermal diffusivity and hydraulic flux in a Quaternary soil water layer

AbstractThis groundbreaking study introduces a scientifically robust method for assessing internal heat generation in shallow, unsaturated aquifers, highlighting the intricate interplay between internal heat, thermal diffusivity, and hydraulic flux. The research pioneers a novel approach employing time‐frequency spectral analysis to address challenges posed by conventional methods that rely solely on prescribed thermal diffusivity and hydraulic flux for evaluating internal heat over time. Rooted in a solid theoretical framework supported by meticulous temperature and heat flux observations, the method adeptly incorporates the concept of an unknown internal heat. The study systematically explores three distinct boundary conditions, showcasing versatility: one with fixed temperatures at the inlet and outlet, another with known temperature at the inlet and no heat flux at the outlet, and a third with a prescribed inlet temperature and constrained outlet heat flux. The estimation of internal heat, coupled with prescribed thermal diffusivity and hydraulic flux, harnesses in‐situ temperature and heat flux spectra through an innovative inverse stochastic spectral approach. A notable feature of this research lies in its ability to strategically handle various parameters, including prescribed thermal diffusivity, hydraulic flux, variations in target depth, significant frequency components, and boundary conditions. The variation assessment findings emphasize the diverse range when predicting internal heat generation based on prescribed thermal diffusivity and hydraulic flux. This study facilitates the determination of potential internal heat magnitudes at different depths and provides profound insights into in‐situ conditions within the vadose zone.

Geostatistical Inversion for Subsurface Characterization Using Stein Variational Gradient Descent With Autoencoder Neural Network: An Application to Geologic Carbon Sequestration

AbstractGeophysical subsurface characterization plays a key role in the success of geologic carbon sequestration (GCS). While deterministic inversion methods are commonly used due to their computational efficiency, they often fail to adequately quantify the model uncertainty, which is essential for informed decision‐making and risk mitigation in GCS projects. In this study, we propose the SVGD‐AE method, a novel geostatistical inversion approach that integrates geophysical data with prior geological knowledge to estimate subsurface properties. SVGD‐AE combines Stein Variational Gradient Descent (SVGD) for sampling high‐dimensional distributions with an autoencoder (AE) neural network for re‐parameterizing reservoir models, aiming to accurately preserve geologic characteristics of reservoir models derived from prior knowledge. Through a synthetic example of pre‐stack seismic inversion, we demonstrate that the SVGD‐AE method outperforms traditional probabilistic methods, particularly in inverse problems with complex posterior distributions. Then, we apply the SVGD‐AE method to the Illinois Basin—Decatur Project (IBDP), a large‐scale CO2 storage initiative in Decatur, Illinois, USA. The resulting petrophysical models with quantified uncertainty enhance our understanding of subsurface properties and have broad implications for the feasibility, decision making, and long‐term safety of CO2 storage at the IBDP.

RMSE POWER COEFFICIENT OF WIND TURBINES WITH INVERSE TOQUE-SPEED CONTROL

The Blade Element Momentum (BEM) method stands out among various turbine modelling techniques due to its integration of airfoil aerodynamics with momentum theory, allowing detailed force calculations on blade elements. While traditional methods like BEM and Wake Models are efficient for initial turbine design, advancements in computational power have enabled the use of Computational Fluid Dynamics (CFD) for more accurate simulations. Although these models are precise, they are computationally intensive, leading to the continued use of simpler empirical methods, such as the static power coefficient approach, in power system studies. Future research aims to validate and implement dynamic estimation models to account for wind variability and turbine control dynamics. Traditional constant torque-speed control strategies maintain a constant generator speed by using a fixed electromagnetic torque command and adjusting the pitch angle when wind speed exceeds the rated value. However, if the pitch control does not respond quickly enough, rotor acceleration can occur, leading to increased power beyond the generator’s rating. To mitigate this, the pitch control mechanism must react promptly to adjust the turbine's torque profile, ensuring convergence to the rated operating point and avoiding prolonged generator overload. To reduce the burden on the pitch-control system, an inverse torque-speed control approach is proposed, where the electromagnetic torque is dynamically adjusted based on real-time rotor speed data. This method aims to achieve rated power operation with improved responsiveness and reduced stress on the pitch-control mechanism.

Characterisation of human penile tissue properties using experimental testing combined with multi-target inverse finite element modelling

This paper presents an inverse finite element (FE) approach aimed at estimating multi-layered human penile tissues. The inverse FE approach integrates experimental force–displacement and boundary deformation data of penile tissues with a developed FE model and uses new experimental data on human penile tissue. The experimental study encompasses whole organ plate-compression tests and individual layer tensile and compression tests, providing comprehensive insights into the tissue's mechanical behaviour. The biomechanical characterisation of penile tissue is of crucial significance for understanding its mechanical behaviour under various physiological and pathological conditions. The FE model is constructed using the realistic geometry of the penile segment and appropriate constitutive models for each tissue layer to leverage the accuracy and consistency of the model. Through systematic variation of tissue parameters in the inverse FE algorithm, simulations achieve the best match with both force–displacement and deformed boundary results obtained from the whole organ plate-compression tests. Test results from individual tissue layers are also utilised to assess the estimated parameters. The proposed inverse FE approach allows for the estimation of penile tissue parameters with high precision and reliability, shedding light on the mechanical properties of this complex biological organ. This work has applications not only in urology but also for researchers in various disciplines of biomechanics. As a result, our study contributes to advancing the understanding of human penile tissue mechanics whilst the methodology could also be applied to a range of other soft biological tissues. Statement of significanceThis research uses a multi-target inverse finite element (FE) approach for estimating the material parameters of human penile tissues. By integrating experimental data and a realistic FE model, this study achieves high-precision constitutive model parameter estimation, offering key insights into penile tissue mechanics under various loading conditions. The significance of this work lies in the use of this inverse FE approach for fresh-frozen human penile tissues, to identify the mechanical properties and constitutive models for both segregated tunica albuginea and corpus cavernosum as well as intact penile tissue segments. The study's scientific impact lies in its advancement of the understanding of human urological tissue mechanics, impacting researchers and clinicians alike.

Inverse design of crystals and quasicrystals in a non-additive binary mixture of hard disks.

The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters, such as particle interactions, particle shape, or composition and packing fraction. This optimization aims to facilitate the spontaneous formation of specific target structures through self-assembly. In this study, we expand upon a recently introduced inverse design protocol for monodisperse systems to identify the required conditions and interactions for assembling crystal and quasicrystal phases within a binary mixture of two distinct species. This method utilizes an evolution algorithm to identify the optimal state point and interaction parameters, enabling the self-assembly of the desired structure. In addition, we employ a convolutional neural network (CNN) that classifies different phases based on their diffraction patterns, serving as a fitness function for the desired structure. Using our protocol, we successfully inverse design two-dimensional crystalline structures, including a hexagonal lattice and a dodecagonal quasicrystal, within a non-additive binary mixture of hard disks. Finally, we introduce a symmetry-based order parameter that leverages the encoded symmetry within the diffraction pattern. This order parameter circumvents the need for training a CNN and is used as a fitness function to inverse design an octagonal quasicrystal.