High-fidelity Model Research Articles

A scalable and modular reservoir implementation for large-scale integrated hydrologic simulations

Abstract. Recent advancements in integrated hydrologic modeling have enabled increasingly high-fidelity models of the complete terrestrial hydrologic cycle. These advances are critical for our ability to understand and predict watershed dynamics, especially in a changing climate. However, many of the most physically rigorous models have been designed to focus on natural processes and do not incorporate the effect of human-built structures such as dams. By not accounting for these impacts, our models are limited both in their accuracy and in the scope of the topics they are able to investigate. Here, we present the first implementation of dams and reservoirs in ParFlow, an integrated hydrologic model. Through a series of idealized and real-world test cases, we demonstrate that our implementation (1) functions as intended, (2) maintains important qualities such as mass conservation, (3) works in a real domain, and (4) is computationally efficient and can be scaled to large domains with thousands of reservoirs. Our results have the potential to improve the accuracy of current ParFlow models and enable us to ask new questions regarding conjunctive management of ground and surface water in systems with reservoirs.

Promise and Peril: Stellar Contamination and Strict Limits on the Atmosphere Composition of TRAPPIST-1 c from JWST NIRISS Transmission Spectra

Abstract Attempts to probe the atmospheres of rocky planets around M dwarfs present both promise and peril. While their favorable planet-to-star radius ratios enable searches for even thin secondary atmospheres, their high activity levels and high-energy outputs threaten atmosphere survival. Here we present the 0.6–2.85 μm transmission spectrum of the 1.1 R ⊕, ∼ 340 K rocky planet TRAPPIST-1 c obtained over two JWST NIRISS/SOSS transit observations. Each of the two spectra displays 100–500 ppm signatures of stellar contamination. Despite being separated by 367 days, the retrieved spot and facula properties are consistent between the two visits, resulting in nearly identical transmission spectra. Jointly retrieving for stellar contamination and a planetary atmosphere reveals that our spectrum can rule out hydrogen-dominated, ≲300× solar metallicity atmospheres with effective surface pressures down to 10 mbar at the 3σ level. For high mean molecular weight atmospheres, where O2 or N2 is the background gas, our spectrum disfavors partial pressures of more than ∼10 mbar for H2O, CO, NH3, and CH4 at the 2σ level. Similarly, under the assumption of a 100% H2O, NH3, CO, or CH4 atmosphere, our spectrum disfavors thick, >1-bar atmospheres at the 2σ level. These nondetections of spectral features are in line with predictions that even heavier, CO2-rich atmospheres would be efficiently lost on TRAPPIST-1 c given the cumulative high-energy irradiation experienced by the planet. Our results further stress the importance of robustly accounting for stellar contamination when analyzing JWST observations of exo-Earths around M dwarfs, as well as the need for high-fidelity stellar models to search for the potential signals of thin secondary atmospheres.

Multifidelity uncertainty quantification for ice sheet simulations

Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations. UQ methods typically employ Monte Carlo simulation to estimate statistics of interest, which requires hundreds (or more) of ice sheet simulations. Cheaper low-fidelity models are readily available (e.g., approximated physics, coarser meshes), but replacing the high-fidelity model with a lower fidelity surrogate introduces bias, which means that UQ results generated with a low-fidelity model cannot be rigorously trusted. Multifidelity UQ retains the high-fidelity model but expands the estimator to shift computations to low-fidelity models, while still guaranteeing an unbiased estimate. Through this exploitation of multiple models, multifidelity estimators guarantee a target accuracy at reduced computational cost. This paper presents a comprehensive multifidelity UQ framework for ice sheet simulations. We present three multifidelity UQ approaches—Multifidelity Monte Carlo, Multilevel Monte Carlo, and the Best Linear Unbiased Estimator—that enable tractable UQ for continental-scale ice sheet simulations. We demonstrate the techniques on a model of the Greenland ice sheet to estimate the 2015-2050 ice mass loss, verify their estimates through comparison with Monte Carlo simulations, and give a comparative performance analysis. For a target accuracy equivalent to 1 mm sea level rise contribution at 95 % confidence, the multifidelity estimators achieve computational speedups of two orders of magnitude.

Efficient Bayesian Physics Informed Neural Networks for Surface Tomography via Ensemble Kalman Inversion

Surface travel-time tomography is a widely-used method to characterize the structure of the underground. However, conventional seismic tomography techniques often require a high-fidelity numerical forward model, which leads to significant computational burden and time consumption. Additionally, the inverse problem in travel-time tomography is prone to ill-posedness and lacks uncertainty quantification for the inferred results.

EMI-EMC Qualification of the NASA SWOT Mission Using High Fidelity Modeling

EMI-EMC Qualification of the NASA SWOT Mission Using High Fidelity Modeling

Enhanced numerical models for two-component fluid flow in multiscale porous structures

Multi-component fluid flow simulations in multiscale porous structures, many parts of which are likely to be under-resolved at practical resolution, often require a high-fidelity numerical model to account for the contribution of under-resolved structures to the fluid flow. In previous studies, a numerical model was proposed for viscous and capillary forces from under-resolved regions. It successfully showed comparable results in absolute permeability, capillary pressure, and relative permeability when compared to an equivalent fully resolved case up to ten times higher resolution. In this study, we show further extensions of the model to handle various types of structures and to capture detailed fluid behavior. First, we introduce the controllability of surface tension in the pseudo-potential lattice Boltzmann model while keeping the interface thickness and the spurious current at the same level. This helps to resolve more detailed interface dynamics in under-resolved regions. Second, we develop a numerical model to capture the residual fluid component in the under-resolved structure. Since it is difficult to capture such cell-sized or less-than-cell-sized fluid components with diffusive interface models, we try to consider them separately using local constitutive relations, such as the absolute and relative permeability and capillary pressure curves. Third, we introduce a tensorial resistivity model to handle under-resolved heterogeneous structures, such as a fiber bundle. After calculating the principal axis for resistivity using the Hessian and the gradient of the local porosity field, the tensorial resistivity is rotated in the proper direction. Through a series of benchmark test cases, including cases using practical rock geometries, these enhancements are validated and show significant accuracy improvements with respect to the transient interface dynamics, capture of local irreducible fluid components, and directionality effects of under-resolved structures.

Analyzing the inelastic response of wind-excited steel tall buildings through a reduced-order model incorporating strength and stiffness degradation along with P-Delta effects

When wind-excited tall buildings undergo vibrations beyond their linear elastic range, it becomes imperative to account for both strength and stiffness degradation and P-Delta effects. This study investigates the influence of the degradation and P-Delta effects on the inelastic response of wind-excited tall buildings through a reduced-order building model, wherein the alongwind and crosswind building responses are presumed to be contributed by the fundamental modes. The backbone curves of the hysteretic relationships between the generalized restoring forces and displacements are developed through monotonic static modal pushover analysis utilizing a high-fidelity finite element building model with consideration of P-Delta effect. A cyclic modal pushover analysis is performed to ascertain the degradation of generalized building stiffness and strength in both translation directions, stemming from the deterioration of steel material in stiffness and strength. Subsequently, a biaxial hysteretic force model is employed to depict the hysteretic relationships between generalized forces and displacements, factoring in degradation and P-Delta effects. The inelastic response of a 60-story steel building subjected to both alongwind and crosswind load excitations is quantified through response history analysis to assess the accuracy of the reduced-order building model and to evaluate the influence of degradation of material strength and pre-yield stiffness and P-Delta effects on various responses.

Evaluation of the Cardiotoxicity of Echinochrome A using Human Induced Pluripotent Stem Cell-derived Cardiac Organoids

Echinochrome A (EchA), a marine-derived natural product, has shown promise in treating cardiovascular and inflammatory diseases due to its antioxidant and anti-inflammatory properties. However, its cardiac safety remains underexplored. In this study, we utilized human induced pluripotent stem cell-derived cardiac organoids (hCOs) to validate their ability to model the cardiac safety profile of EchA in a human-relevant system. While EchA’s therapeutic effects have been reported, prior studies have not evaluated its cardiotoxicity or arrhythmogenic potential in a high-fidelity 3D human cardiac model. The hCOs, characterized by expression of key cardiac markers (cTnT) and functional ion channels (Cav1.2, Nav1.5, hERG), exhibited structural and electrophysiological properties reflective of human cardiac physiology. Using multi-electrode array (MEA) analysis, we assessed the effects of EchA at concentrations ranging from 0.1 to 30µM on electrophysiological parameters, including beat period, field potential amplitude, field potential duration, and spike slope. EchA treatment induced no significant changes in these parameters, confirming its non-toxic electrophysiological profile. Cellular viability and lactate dehydrogenase (LDH) assays revealed no cytotoxic effects of EchA across tested concentrations. Contractility assays further demonstrated that EchA did not affect contraction velocity, relaxation velocity, or time to 50% maximal contraction and relaxation. This study fills a critical gap and highlights the translational relevance of hCOs for cardiotoxicity assessment, demonstrating EchA's cardiac safety and supporting its potential therapeutic and environmental applications.

High-fidelity architecture modeling and compressive strength prediction of 3D woven composite material

High-fidelity architecture modeling and compressive strength prediction of 3D woven composite material

Revealing formation mechanism of end of process depression in laser powder bed fusion by multi-physics meso-scale simulation

ABSTRACT Unintended end-of-process depression (EOPD) commonly occurs in laser powder bed fusion (LPBF), leading to poor surface quality and lower fatigue strength, especially for many implants. In this study, a high-fidelity multi-physics meso-scale simulation model is developed to uncover the forming mechanism of this defect. A defect-process map of the EOPD phenomenon is obtained using this simulation model. It is found that the EOPD formation mechanisms are different under distinct regions of process parameters. At low scanning speeds in keyhole mode, the long-lasting recoil pressure and the large temperature gradient easily induce EOPD. While at high scanning speeds in keyhole mode, the shallow molten pool morphology and the large solidification rate allow the keyhole to evolve into an EOPD quickly. Nevertheless, in the conduction mode, the Marangoni effects along with a faster solidification rate induce EOPD. Finally, a ‘step’ variable power strategy is proposed to optimise the EOPD defects for the case with high volumetric energy density at low scanning speeds. This work provides a profound understanding and valuable insights into the quality control of LPBF fabrication.

Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models.

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming.

A collaborative system of systems simulation of urban air mobility

Abstract The implementation of urban air mobility represents a complex challenge in aviation due to the high degree of innovation required across various domains to realize it. From the use of advanced aircraft powered by novel technologies, the management of the airspace to enable high density operations, to the operation of vertiports serving as a start and end point of the flights, urban air mobility paradigm necessitates significant innovation in many aspects of civil aviation as we know it today. To understand and assess the many facets of this new paradigm, a collaborative simulation is developed to holistically evaluate the system of systems through the modeling of the stakeholders and their interactions as per the envisioned concept of operations. To this end, models of vertiport air-side operations, unmanned/manned airspace management, demand estimation and passenger mode choice, vehicle operator cost and revenues, vehicle design, and fleet management are brought together into a system of systems simulation of urban air mobility. Through collaboration, higher fidelity models of each domain can be integrated into a single environment achieving fidelity levels not easily achievable otherwise. Furthermore, the integration enables the capture of cross-domain effects and allows domain-specific studies to be evaluated at a holistic level. This work demonstrates the collaborative simulation and the process of building it through the integration of several geographically distributed tools into an agent-based simulation without the need for sharing code.

A complete workflow from embalmed specimens to life-like 3D virtual models for veterinary anatomy teaching.

Understanding normal structural and functional anatomy is critical for health professionals across various fields such as medicine, veterinary, and dental courses. The landscape of anatomical education has evolved tremendously due to several challenges and advancements in blended learning approaches, which have led to the adoption of the use of high-fidelity 3D digital models in anatomical education. Cost-effective methods such as photogrammetry, which creates digital 3D models from aligning 2D photographs, provide a viable alternative to expensive imaging techniques (i.e. computed tomography and magnetic resonance imaging) whilst maintaining photorealism and serving multiple purposes, including surgical planning and research. This study outlines a comprehensive workflow for producing realistic 3D digital models from embalmed veterinary specimens. The process begins with the preservation of specimens using the modified-WhitWell (WhitWell-Liverpool) embalming protocol, which ensures optimal tissue rigidity and improved colour enhancement, facilitating easier manipulation and better photogrammetry outcomes. Once embalmed, specimens are photographed to create digital 3D models using photogrammetry. Briefly, all images are processed to generate a sparse point cloud, which is then rendered into a 3D mesh. The mesh undergoes decimation and smoothing to reduce computational load, and a texture is applied to create a lifelike model. Additional colour enhancements and adjustments are made using digital tools to restore the natural appearance of the specimens. The 3D models are stored on a cloud repository and integrated into the University of Liverpool's Virtual Learning Environment, providing continuous, remote access to high-quality anatomical resources. The switch to embalmed specimens during the COVID-19 pandemic allowed for longer-term use and detailed dissections, enhancing the quality of digital models. Fresh specimens, though naturally coloured, are less stable for photogrammetry, making embalmed specimens preferable for accurate 3D modelling. Our method ensures embalmed specimens are rigid enough for precise modelling while allowing texture adjustments to enhance digital representation. This approach has improved logistical efficiency, educational delivery, and specimen quality. Innovative embalming techniques and advanced photogrammetry have the power to revolutionise anatomical education with the creation of a vast digital library accessible online to students at any time. This approach paves the way for integrating digital 3D models into immersive environments and assessing their impact on learning outcomes.

Lab-grown, 3D Extracellular Matrix Particles Improve Cardiac Function and Morphology in Myocardial Ischemia.

The promise of injection of extracellular matrix (ECM) from animal hearts as a treatment of myocardial ischemia has been limited by immune reactions and harsh ECM-damaging extraction procedures. We developed a novel method to produce lab-grown human 3D acellular ECM particles from human mesenchymal stem cells (MSCs) to mitigate product variability, immunogenicity, and preserve ECM architecture. We hypothesized that intramyocardial injection (I/M) of this novel ECM (dia ~200 microns) would improve cardiac function in a post-myocardial infarction (MI) murine model. WT mice aged 8-10 weeks underwent ligation of the LAD artery and I/M injection of 10 uL ECM or normal saline (n=10/group). Compared to control, ECM-treated hearts showed significant reduction in infarct size (p=0.04), increased capillary density in ischemic myocardium (p=0.01), and increased fractional shortening (FS) (p>0.05) on POD 14, 21, and 28 by echocardiography. There were no significant differences in immunogenic response as determined by TNF⍺, IL6, CD86, or CD163 levels (p>0.05 for all) in the hearts. Biodistribution of fluorophore-conjugated ECM demonstrated localized epifluorescence in the heart after I/M injection, without significant peripheral end organ epifluorescence. Proteomic analysis of ischemic and perfused myocardium from control and ECM-treated hearts using LC-MS/MS (n=3/group) detected significant changes in proteins involved in cardiomyocyte contractility and fatty acid metabolism. These findings suggest that 3D ECM particles induce recovery of ischemic myocardium, by upregulating protein networks involved in cellular contractility and metabolism. Taken together, 3D ECM particles represent a promising therapy for MI, and warrant confirmatory studies in a high-fidelity large animal model.

Laboratory-Based Micro-X-ray Computed Tomography of Energy Materials at Idaho National Laboratory

AbstractThe Idaho National Laboratory (INL) has implemented laboratory-based micro-X-ray computed tomography in a laboratory equipped for the examination of highly radioactive samples. This capability provides nondestructive three-dimensional volumetric information on samples to inform subsequent traditional destructive examinations as well as real-world inputs for high-fidelity scientific modeling. Samples can be imaged with spatial resolutions ranging from several hundred nm/voxel up to ~ 100 µm/voxel. The best usable spatial resolution achieved to date is 384 nm/voxel with this instrument, while the highest radiological dose rate of a sample imaged is ~ 60 R/h β/γ on contact. Advanced data analysis, including custom tomographic reconstruction and segmentation methods, have also been developed to support this capability. In addition to traditional digital X-ray radiography and tomography, this instrument is also able to visualize in situ tensile and compression testing as well as perform diffraction contrast tomography. This work describes the X-ray computed tomography post-irradiation examination capabilities at INL, as well as detailing a variety of applications this instrument has examined.

An Integrated Framework for Real-Time Sea-State Estimation of Stationary Marine Units Using Wave Buoy Analogy

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units.

Machine learning-based constitutive modelling for material non-linearity: A review

Machine learning (ML) models are widely used across numerous scientific and engineering disciplines due to their exceptional performance, flexibility, prediction quality, and ability to handle highly complex problems if appropriate data are available. One example of such areas which has attracted a lot of attentions in the last couple of years is the integration of data-driven approaches in material modeling. There has been several successful researches in implementing ML-based constitutive models instead of classical phenomenological models for various materials, particularly those with non-linear mechanical behaviors. This review paper aims to systematically investigate the literature on ML-based constitutive models for materials and classify these models based on their suitability for material non-linearity including Non-linear elasticity (hyperelasticity), plasticity, visco-elasticity, and visco-plasticity. Furthermore, we also reviewed and compared the ML-based approaches that have been applied for architectured materials as these groups of materials are designed to represent specific material behaviors that might not exist in classical and conventional material categories. The other goal of this review paper is to provide initial steps in understanding of various ML-based approaches for material modeling, including artificial neural networks (ANN), Gaussian processes, random forests (RF), generated adversarial networks (GANs), support vector machines (SVM), different regression models and physics-informed neural networks (PINN). This paper also outlines different data collection methods, types of data, data processing approaches, the theoretical background of the ML models, advantage and limitations of various models, and potential future research directions. This comprehensive review will provide researchers with the knowledge necessary to develop high-fidelity, robust, adaptable, flexible, and accurate data-driven constitutive models for advanced materials.

AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.

Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert-driven manual operations. Here, we propose an AI-powered computational framework for accelerated and democratized patient-specific modeling of AS hemodynamics from computed tomography (CT). First, we demonstrate that the automated meshing algorithms can generate task-ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we showthat the approach can be integrated with fluid-structure interaction and soft robotics models to accurately recapitulate a broad spectrum of clinical hemodynamic measurements of diverse AS patients. The efficiency and reliability of these algorithms make them an ideal complementary tool for personalized high-fidelity modeling of AS biomechanics, hemodynamics, and treatmentplanning.

Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry.

Digital Twins in Structural Health Assessment and Monitoring: Applications in Historical Timber Buildings

ABSTRACT This paper explores the application of Digital Twins (DTs) for supporting the structural health monitoring (SHM) of timber buildings, with a particular emphasis on historical timber structures. It reviews technologies and methods for DT creation, examining how geospatial data and Building Information Modeling (BIM) serves as foundational elements in developing digital twins. The paper discusses integrating these elements with various data and models to enhance SHM tasks, detailing actionable DT approaches for documenting, analyzing, simulating and predicting structural behavior. The study highlights that while high-fidelity geometric models are well developed, their application for advanced SHM tasks remains limited. Integration of sensor data into BIM frameworks is promising but still faces challenges such as limited automation and difficulties in handling real-time and long-term data. Additionally, the paper notes that the implementation of DTs for the simulation of the effects of hygrothermal phenomena on the long-term behavior and durability of timber structures is underexplored. Multi-dimensional BIM models and machine and deep learning techniques in data-driven, physics-based models are promising approaches for timber cultural heritage management and beyond.