Model Code Research Articles

COSMONAUT: A COmpact spectrometer for measurements of neutrons at the ASDEX upgrade tokamak.

A COmpact Spectrometer for Measurements Of Neutrons at the ASDEX Upgrade Tokamak (COSMONAUT) has been developed for spectroscopy measurements of the 2.45MeV neutron emission from deuterium plasmas at the ASDEX Upgrade. The instrument is based on a CLYC-7 inorganic scintillator, whereby the detection of fusion neutrons occurs via their interaction with 35Cl nuclei in the detector crystal, leading to a peak in the detector response function and providing excellent neutron/gamma-ray discrimination capabilities. The diagnostics is installed along a radial line of sight and makes use of a digital system to record time resolved data for the whole duration of the discharge. Measurements in ASDEX Upgrade plasmas with neutral beam injection have been carried out and are successfully interpreted using state-of-the-art modeling codes. Next step applications of the diagnostics are in experiments aimed at generating energetic particles by ion cyclotron resonance heating schemes. In these scenarios, COSMONAUT will provide unique information on the acceleration of deuterons beyond the beam injection energy and on their confinement, for comparison with modeling.

Learning a Contact Potential Field for Modeling the Hand-Object Interaction.

Estimating and synthesizing the hand's manipulation of objects is central to understanding human behaviour. To accurately model the interaction between the hand and object (referred to as the "hand-object"), we must not only focus on the pose of the hand and object, but also consider the contact between them. This contact provides valuable information for generating semantically and physically plausible grasps. In this paper, we propose an explicit contact representation called Contact Potential Field (CPF). In CPF, we model the contact between a pair of hand-object vertices as a spring-mass system. This system encodes the distance of the pair, as well as a likelihood of that contact being stable. Therefore, the system of multiple extended and compressed springs forms an elastic potential field with minimal energy at the optimal grasp position. We apply CPF to two relevant tasks, namely, hand-object pose estimation and grasping pose generation. Extensive experiments on the two challenging tasks and three commonly used datasets have demonstrated that our method can achieve state-of-the-art in several reconstruction metrics, allowing us to produce more physically plausible hand-object poses even when the ground-truth exhibits severe interpenetration or disjointedness.

Reliability of toxicokinetic modelling for PFAS exposure assessment in contaminated water in northern Italy

IntroductionLong-term contamination of tap water and groundwater by perfluoroalkyl and polyfluoroalkyl substances (PFASs) has been documented in the Veneto region of northern Italy. This study aimed to assess the exposure of individuals residing in the contaminated area and to test several toxicokinetic (TK) models of varying complexities to identify an efficient method for predicting perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) concentrations in human serum using observed data.The ultimate goal is to provide public health officials with guidance on selecting the appropriate TK model for specific contexts, a reliable and rapid tool to support human bio-monitoring (HBM) studies. MethodsTwo simpler empirical TK models and a more complex multi-compartment physiologically based toxicokinetic (PBTK) model were compared with individual and aggregate data from an HBM study. In addition, the PBPK model was modified by adjusting input parameters and introducing new terms into the equations within the original model code. These modifications aimed to optimize the results compared to the original model, with some versions incorporating adjustments to account for the influence of menstruation in women. All models were evaluated to understand their strengths and weaknesses, providing guidance on the appropriate model to use according to specific scenarios. ResultsThe results obtained from the tested models were quite similar, with significant improvements observed only in the modified models. Simpler models also provided satisfactory results in scenarios involving low PFOS serum concentrations and recent exposure cessation. In many cases, predictions demonstrated high accuracy, particularly at the aggregate level and for women. ConclusionsThese findings suggest that environmental protection agencies and health authorities may benefit from employing the tested models at the aggregate level as an initial step in HBM studies, rather than conducting more invasive and expensive screening campaigns.

Scaffolded team-based computational modeling and simulation projects for promoting representational competence and regulatory skills

BackgroundThis study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience.ResultsResults characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students’ levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills.ConclusionCharacterizing dimensions of computational model-based reasoning provides insights that showcase students’ learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation.

Supervised contrastive learning based dual-mixer model for Remaining Useful Life prediction

The problem of the Remaining Useful Life (RUL) prediction, aiming at providing an accurate estimate of the remaining time from the current predicting moment to the complete failure of the device, has gained significant attention from researchers in recent years. In this paper, to overcome the shortcomings of rigid combination for temporal and spatial features in most existing RUL prediction approaches, a spatial–temporal homogeneous feature extractor, named Dual-Mixer model, is firstly proposed. Flexible layer-wise progressive feature fusion is employed to ensure the homogeneity of spatial–temporal features and enhance the prediction accuracy. Secondly, the Feature Space Global Relationship Invariance (FSGRI) training method is introduced based on supervised contrastive learning. This method maintains the consistency of relationships between sample features with their degradation process during model training, simplifying the subsequently regression task in the output layer and improving the model’s performance in RUL prediction. Finally, the effectiveness of the proposed method is validated through comparisons with other latest research works on the C-MAPSS dataset. The Dual-Mixer model demonstrates superiority across most metrics, while the FSGRI training method shows an average improvement of 7.00% and 2.41% in RMSE and MAPE, respectively, for all baseline models. Our experiments and model code are publicly available at https://github.com/fuen1590/PhmDeepLearningProjects.

Shear strengthening of self-compacting lightweight concrete beams with steel fibers and unbonded prestressing bars

This study aims to investigate the shear behavior of self-compacting lightweight concrete (SCLC) beams strengthened with steel fibers and unbonded prestressing bars. For this, the experimental program included the use of hooked-end steel fibers with volume fractions of 0.5 %−0.75 %, and high-strength prestressing bars with varying prestressing forces and transverse reinforcement ratios. The addition of steel fibers in SCLC significantly increased the normalized shear capacity of beams, reaching up to 2.50 times higher values, whereas the substitution of normal coarse aggregates with lightweight aggregate resulted in a decrease of up to 10.1 %. The shear strengthening of SCLC beams with unbonded prestressing bars provided a substantial increase in the ultimate shear capacity up to 2.06 times higher than that of beam without reinforcement. Increasing the prestressing force of bars effectively restrained the occurrence of critical shear cracking, consequently enhancing the shear capacity. Utilizing hybrid reinforcement with fibers and prestressing bars resulted in a transition of failure mode from shear to flexural, attributed to the improved shear capacity. For the SCLC beams reinforced with steel fibers, the shear strengths were predicted by previous prediction models. When modifier for lightweight concrete was considered, the Imam’s model most precisely predicted the shear strengths with an average vu/vn ratio of 1.00 and SD of 0.07. The fib model Code, based on the modified compression field theory, provided accurate predictions for the shear capacities of SCLC beams with unbonded prestressing bars, exhibiting an average Vu/Vn ratio of 1.00 and SD of 0.09. The fiber-resisting component was reasonably considered in the model with an average Vu/Vn ratio of 1.02 and SD of 0.07.

Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.

Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.

Probabilistic curvature limit states of corroded circular RC bridge columns: Data-driven models and application to lifetime seismic fragility analyses

Reinforcement corrosion has been recognized as an influential factor in the seismic fragility, both demand and capacity models, of aging reinforced concrete (RC) bridges. For capacity models, accurate and applied prediction tools accounting for aging effects are yet to be well established. Current practices usually perform numerical analyses to obtain time-variant capacity models, which are time-consuming particularly when multi-source structural and environmental uncertainties are considered and sometimes even suffer computational non-convergence. To address these issues, this study leverages a rigorously optimized artificial neural network architecture to develop data-driven models for rapid estimates of probabilistic curvature capacity of corroded circular RC bridge columns with flexural failure modes. An extensive database of multi-level curvature limit states (i.e. slight, moderate, severe, and complete) is created through experimentally validated moment–curvature analyses. A new threshold for the moderate limit state is defined based on the strain of core concrete, rather than cover concrete, to account for the potential full erosion of the cover with drastic corrosion. The data-driven probabilistic capacity models are applied to aid the lifetime seismic fragility assessment of a typical highway bridge, where the spectral acceleration at 1.0 s (Sa-10), peak ground velocity (PGV), and Housner intensity (HI) are found consistently, for the first time, as optimal intensity measures for probabilistic demand modeling of RC columns with different extents of aging effects. For the ease of application, the database and code for the data-driven probabilistic capacity models are accessible at https://bit.ly/3uAa8EY .

The MEMENTO code for modeling of macroscopic melt motion in fusion devices

The MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks) code is a new numerical implementation of the physics model originally developed for the MEMOS-U code with the objective to self-consistently describe the generation of melt and its subsequent large scale dynamics in fusion devices and to assess the damage of metallic reactor armor under powerful normal and off-normal plasma events. The model has been validated in multiple dedicated EUROfusion experiments. MEMENTO solves the heat and phase transfer problem coupled with the incompressible Navier–Stokes equations in the shallow water approximation for the thin liquid film over the solid metal and with the current propagation equations on a domain that features a time-evolving deforming metal-plasma interface. The code utilizes non-uniform and adaptive meshing along with sub-cycling in time facilitated by the AMReX open-source framework as well as AMReX’s built-in parallelization capabilities.

ScLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main.

Short-period Variables in TESS Full-frame Image Light Curves Identified via Convolutional Neural Networks

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ∼85% of the sky throughout its 2 yr primary mission, resulting in millions of TESS 30-minute-cadence light curves to analyze in the search for transiting exoplanets. To search this vast data set, we aim to provide an approach that is computationally efficient, produces accurate predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short-period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute-cadence light curve in ∼5 ms on a single GPU, enabling large-scale archival searches. We present a collection of 14,156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of close-orbit main-sequence binaries and another of δ Scuti stars. Our neural network model and related code are additionally provided as open-source code for public use and extension.

Virtual Time III, Part 3: Throttling and Message Cancellation

This is Part 3 of a trio of papers that unify in a natural way the two historically distinct parallel discrete event synchronization paradigms, optimistic and conservative, combining the best properties of both into a single framework called Unified Virtual Time (UVT) . In this part we survey the synchronization effects that can be achieved by restricting to corner cases the relationships permitted among the control variables, GVT , CVT , TVT , and LVT , which were defined in Part 1 . We also survey various throttling policies from the literature and describe how they can be implemented in UVT by controlling the value of TVT , including policies that can take advantage of rollback in addition to LP blocking. A significant result is a new category of efficient and higher precision throttling algorithms for optimistic execution that are based on optimistic lookahead , defined in a way that is symmetric to what we now call the conservative lookahead information that is traditionally used for conservative synchronization. Finally, we present a novel algorithm allowing the choice between lazy and aggressive cancellation to be made on a message-by-message basis using either external logic expressed in the model code, or policy code internal to the simulator, or a mixture of both.

Nuclear model calculation of yield for 64Cu radionuclide produced in 65Cu(α, x)64Cu reaction at ≈ 10–40 MeV

This paper used theoretical nuclear model calculations to estimate the production yield of medically important 64Cu radionuclide produced in the interaction of α-projectile with 65Cu-target at ≈10–40 MeV. Production cross-sections were predicted using TALYS-1.9(G), EMPIRE 3.2, and ALICE/ASH reaction model codes. Generally, the TALYS-1.95(G) predicted and measured cross-sections to produce 64Cu radionuclide are in very good agreement. Further, the statistical analysis of Pearson's coefficient confirmed strong positive correlations between TALYS-1.95(G) predictions and most experimentally measured production cross-sections. The calculated thick target yields of 64Cu radionuclide showed that 65Cu(α, x)64Cu reaction resulted in a maximum yield of up to ≈ 1423 MBq/μAh at ≈ 30 MeV. Furthermore, analyzes of isotopic impurities produced during 64Cu production could only give rise to the significant yield of 66Cu impurity as much as ≈ 1988 MBq/μAh, which complicated the process of radiochemistry; however, the relatively short half-life of 66Cu isotopic impurity could only demand a waiting time of about 1.5 h to decrease its contribution to a negligible level.The yield obtained for the 64Cu radionuclide via the 65Cu(α, x)64Cu route is significant. It validates the viability of this production route and underscores its potential as a promising option for the optimized supply of 64Cu in radiopharmaceutical applications. While further investigations on quality validating parameters are necessary, the potential of the 65Cu(α, x)64Cu production route cannot be overstated.

Transmission length of multiwire tendon in existing post-tensioned concrete structures

The paper presents unique research on real-scale post-tensioned concrete crane girders dismantled from an existing hall structure. The study aimed to determine the transmission length for bonded prestressing tendons in an anchorage failure situation, a critical factor for proper assessment of the safety of many building structures, mainly those built between the 1950s and 1980s. The results of the tests indicated the feasibility of prestressing force transmission from a 12Ø5 mm Freyssinet-type tendon to the element via bond stress, with implications extendable to the other 18Ø5 mm tendon type prevalent at the time. The key factor affecting transmission efficiency is the quality of tendon duct grouting, with good quality cement injection resulting in transmission length as short as 800 mm. Furthermore, a computational model for the transmission length of prestressing force in post-tensioned concrete members has been developed similarly to the Model Code 2020 for pretensioned concrete. The level I of approximation enables quick transmission length estimation, whereas refined analysis is possible with the level II approach. The developed model and the research findings facilitate a comprehensive assessment of the post-tensioned concrete members in anchorage or tendon failure scenarios. As a result of the analyses carried out, optimal strategies for the further use of the structures can be proposed, fostering a step towards sustainable management.

Shear Design of Reinforced Concrete Elements with Steel Fibers

Experimental research shows that the addition of ductile fibers improves the tensile strength, ductility and concrete's energy absorption capacity. As a consequence of the improvement of the post-cracking behavior, the addition of dispersed ductile fibers can increase the ductility, the shear resistance and the toughness of reinforced concrete beams, changing the type of failure from brittle to ductile. Many studies have considered the possibility of using steel fibers to partially resist shear and thus decrease shear reinforcement. This concept has even been adopted by some design codes such as ACI-318 or the Fib Model Code 2010 and has great potential for application in critical points of reinforced concrete structures where it is difficult to arrange shear reinforcement, such as beam - column joints. Despite this, fiber-reinforced concrete is still rarely used in load - bearing elements. In this work, it is proposed to numerically study the behavior of fiber-reinforced concrete beams tested at shear by other researchers in order to evaluate the contribution of steel fibers to the mechanism of resistance to shear.

Simplified modified tension chord model: An alternative crack width calculation model to Eurocode 2 and fib model codes

AbstractThis paper proposes a new design code formulation for calculating crack widths for regular and more special reinforced concrete (RC) members. The more comprehensive Modified Tension Chord Model (MTCM) has been simplified (SMTCM) to facilitate an alternative method for calculating crack widths to Eurocode 2 and fib Model Codes. The model is based on mechanical principles without empirical modifications and is benchmarked against a previously published experimental crack width database. The SMTCM predicts crack widths quite as accurately as the MTCM and provides a broader range of applicability, such as for large covers and RC ties having arbitrary rebar configurations and thus a better crack width model than the current design codes for RC ties. In addition, there are no openings for ambiguous interpretations of the calculations, which can increase the risk of obtaining two different crack widths from two different designers. To further justify the SMTCM code formulation and concept, several RC ties with experimental crack width profiles were used and discussed. The results show a considerable difference between the crack width profile at the surface and at the reinforcement location, depending on the concrete cover and rebar size. These observations are interesting regarding durability design and requirements and show that the approach using a maximum design crack width at a specific surface as a decisive parameter should be further investigated, especially for large concrete covers.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network.

Ultrasound Localization Microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical trade-off between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps like pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the Gated Recurrent Unit (GRU)-based Multitasking Temporal Neural Network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. Additionally, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer substitutions to systematically evaluate the performance of various temporal neural networks, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. Additionally, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. Model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

Anchorage and lap capacity of plain surface and square twisted bars in existing R.C. Structures: A comprehensive approach

AbstractAlthough plain round or square section reinforcement is no longer used in new construction, there are many older structures still in service built with such bars. In recognition of the need for guidance on the assessment of load‐carrying capacity of structures reinforced with such bars, design codes are now introducing or re‐introducing provisions to assess the capacity of laps and anchorages of such reinforcement. The authors have contributed to the introduction of design rules in the fib Model Code 2020. This paper describes the derivation of these provisions. Starting from the form of expression proposed earlier by the authors, the data from which these proposals are derived is specified and modifications in interpretation preparatory to a statistical analysis are outlined. A rigorous multivariate statistical procedure is then employed firstly to determine a mean strength expression for the capacity of anchorages and laps which is then validated against test data. Further statistical analysis is subsequently used to determine a design expression using the approach outlined in EN 1990, taking appropriate account scatter in test data. Finally, some comparisons are presented between the assessment expressions proposed and design provisions that were in place when these types of bars were in common use. Areas where earlier design provisions may be non‐conservative are identified.

Interpretable Machine Learning Models for Prediction of UHPC Creep Behavior

The creep behavior of Ultra-High-Performance Concrete (UHPC) was investigated by machine learning (ML) and SHapley Additive exPlanations (SHAP). Important features were selected by feature importance analysis, including water-to-binder ratio, aggregate-to-cement ratio, compressive strength at loading age, elastic modulus at loading age, loading duration, steel fiber volume content, and curing temperature. Four typical ML models—Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting Machine (XGBoost), and Light Gradient Boosting Machine (LGBM)—were studied to predict the creep behavior of UHPC. Via Bayesian optimization and 5-fold cross-validation, the ML models were tuned to achieve high accuracy (R2 = 0.9847, 0.9627, 0.9898, and 0.9933 for RF, ANN, XGBoost, and LGBM, respectively). The contribution of different features to the creep behavior was ranked. Additionally, SHAP was utilized to interpret the predictions by the ML models, and four parameters stood out as the most influential for the creep coefficient: loading duration, curing temperature, compressive strength at loading age, and water-to-binder ratio. The SHAP results were consistent with theoretical understanding. Finally, the UHPC creep curves for three different cases were plotted based on the ML model developed, and the prediction by the ML model was more accurate than that by fib Model Code 2010.

Lead Fast Reactor Thermal-Hydraulic Testing Facilities in Support of the UK Advanced Modular Reactor Program

The Westinghouse lead-cooled fast reactor (LFR) is a next-generation nuclear power plant whose primary mission is to reduce front-end capital costs and generate flexible and cost-competitive electricity for global markets, while offering mission versatility and satisfying the highest safety and sustainability standards. The LFR is an economical choice for carbon-free power generation to meet the ambitious goals of reducing carbon emissions. The plant utilizes passive safety systems for high reliability and public safety. The LFR testing program aims to fill the technology gaps in the key materials, components, and systems of the LFR plant. It provides demonstrations of LFR engineering that reduce the uncertainty of the LFR design and experimental data for the verification and validation of the modeling and simulation computer codes. With the support of the LFR phenomena identification and ranking table, the phenomena important to plant safety with insufficient states of knowledge have been identified and a Westinghouse testing plan developed to address them. The United Kingdom Advanced Modular Reactor Program Phase 2 was purposed to advance the level of design maturity toward eventual regulatory approval and deployment. Westinghouse and its partners have developed eight state-of-the-art test facilities within the program. Among them, the Passive Heat Removal Facility, Versatile Lead Facility, Lead Water Interaction Facility, and the Lead Freezing Facility are thermal-hydraulic testing facilities, with the other four facilities dedicated to testing lead corrosion and material mechanical behavior in lead. The design, development, and testing of the four thermal-hydraulic facilities are presented in this paper. A variety of computer codes ranging from system-level analysis, component-level analysis, and high-resolution computational fluid dynamics analysis, are utilized to support the development of the facilities. The analyses provide operational and accidental conditions in the LFR to identify major testing conditions and inform the facility design and testing matrix. With a significant amount of experimental data being generated with these highly instrumented test facilities, the computer codes and models will be benchmarked against the experimental results to improve their validation and verification status.