Fidelity Data Research Articles

Risk factors for stroke in penetrating carotid trauma-An analysis from the PROOVIT Registry.

Penetrating carotid injuries are associated with an up to 20% risk of stroke. This study evaluated patients in the American Association for Surgery of Trauma PROspective Observational Vascular Injury Trial, with the aim of determining factors associated with stroke and stroke or death. Penetrating extracranial carotid injuries in the American Association for Surgery of Trauma PROspective Observational Vascular Injury Trial registry from 2012 to 2020 were queried. Isolated external carotid injuries were excluded. Patients with documented postinjury in-hospital stroke were compared with those without. Significant predictors (p < 0.1) for stroke and stroke or death on univariate analysis were included in multivariate analyses. One hundred two patients from 17 institutions were included. Mean age was 35 ± 18 years, and 80% were male. Average Glasgow Coma Scale (GCS) score on presentation was 9 ± 5, with an Injury Severity Score [ISS] of 22 ± 13. Operative management occurred in 51% of patients who were significantly more hypotensive (systolic blood pressure: 109 vs. 131 mm Hg; p = 0.015) with a lower initial pH (7.17 vs. 7.31; p = 0.001) and presented with hard signs of vascular injury (74% vs. 26%; p < 0.001). Overall stroke rate was 17% (23% operative vs. 10% nonoperative, p = 0.076). Rate of stroke or death was 27% (64% operative and 36% nonoperative). On multivariate analysis, lower GCS (p = 0.05) and completion angiography (p = 0.04) were associated with stroke. Likewise lower GCS (p = 0.015) and ISS (p = 0.04) were associated with stroke or death. Penetrating carotid trauma undergoing operative management had a stroke rate of 23%. Low GCS on arrival and need for completion angiography are independently associated with postinjury in-hospital stroke, whereas low GCS on arrival and ISS were associated with stroke or death. The ideal treatment strategy remains elusive, thus a dedicated multicenter study may help to achieve higher fidelity data on this rare but devastating injury. Prognostic and Epidemiological, Level III.

Intergenerational Tutoring: Older Adults Supporting Children’s Academic Needs via Video Conferencing

COVID-19 resulted in societal disruptions across the lifespan. School (K-12) closures were among the most challenging impacts of the virus, leaving many parents with the burden of schooling their children at home. Another major impact of the virus was the social isolation and loneliness felt by many retired, older adults, who were sheltering at home. The disruptions of COVID-19 led our inter-professional team to develop the Intergenerational Tutoring program. Intergenerational Tutoring addresses a service delivery gap in schools because tutors expand schools' capacity to implement evidence-based instruction with students in need of individual support. At the same time, research shows that meaningful volunteering supports the well-being of older adults across physical, psychosocial and cognitive dimensions of health. The aim of the Intergenerational Tutoring program is to pair older adults with kindergarten children in high needs schools to implement early literacy interventions remotely via Zoom. Our poster will describe the Intergenerational Tutoring program including tutor training and tutoring implementation. We will summarize the initial findings from our pilot study conducted in spring and summer 2021 with tutors and children. Data will include (1) themes from tutor interviews regarding the personal meaningfulness of the program and the program’s associated benefits and challenges; (2) implementation fidelity data; (3) impact of tutoring on children’s early literacy skills; and (4) parent feedback. We will summarize lessons learned and next steps for the program.

Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning.

Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower fidelity data. Then the moment matching method is applied to approximate the marginal prior of conditional DGP with a GP. The obtained effective kernels are implicit functions of the lower-fidelity data, manifesting the expressivity contributed by distribution propagation within the hierarchy. The hyperparameters are learned via optimizing the approximate marginal likelihood. Experiments with synthetic and high dimensional data show comparable performance against other multi-fidelity regression methods, variational inference, and multi-output GP. We conclude that, with the low fidelity data and the hierarchical DGP structure, the effective kernel encodes the inductive bias for true function allowing the compositional freedom.

Application of 3D Printing Support Material for Neurosurgical Simulation.

Brain dissection, an intricate neurosurgical skill, is central to life-saving procedures such as intrinsic brain tumor excision and resective epilepsy surgery. The aims of this manuscript are to outline the selection process of a suitable material for the development of a dissectible brain simulator and to present the use of support material, SUP 706, manufactured by Stratasys Ltd. as a non-waste alternative for sustainably engineering solutions for surgical education. A feasibility study was conducted through qualitative function deployment (QFD) followed by a material selection process. End-user requirements and manufacturing product characteristics were incorporated into the workflow. Three materials, silicone, TissueMatrix™ and support material each formed the primary component of the first two prototypes. Expert feedback, manufacturing cost, safety profiling, functional fidelity and post-processing time data were collected and analyzed. The unique break-away feature of moist support material was found to be more suitable than using silicone or TissueMatrix™ for demonstrating brain dissection techniques. In addition, support material displayed higher functional fidelity by mimicking surgical tissues such as pia mater, gray and white matter, and blood vessels. The cost of the support material prototype was 39% less that of TissueMatrix™ and roughly the same as the silicone model. It took twice as long to post-process the support material prototype than it did the TissueMatrix™ design. Support material lost its ideal dissection properties and began to disintegrate after 30 - 45 minutes. In conclusion 3D printer support material is a low-cost material for a dissectible brain simulator.Clinical Relevance- The use of support material as the primary material in developing a dissectible brain simulator is a promising way of advancing neurosurgical education.

Efficacy of different exercise parameters among men with Achilles tendinopathy: a randomised pilot and feasibility trial

Introduction: One potential reason for disparate outcomes of exercise for Achilles tendinopathy is poor knowledge about whether exercise parameters (i.e. different exercise doses) influence outcome. Despite a large number of exercise trials in the literature, none have specifically investigated the effect of specific exercise parameters on clinical outcomes in Achilles tendinopathy. This research aimed to assess the feasibility of conducting a full-scale powered randomised trial to investigate the efficacy of different load-intensity and time-under-tension exercise parameters for Achilles tendinopathy. Methods: Reporting was in accordance with the CONSORT checklist for pilot and feasibility trials and the trial was registered (ACTRN12618001315202). A factorial four-arm, randomised trial was conducted. Forty-eight male participants (18-70 years old) with mid-portion Achilles tendinopathy (≥ 3 months) were recruited. Participants were randomly allocated to high (6 repetition maximum) or low intensity (18 repetition maximum) exercise, performed with either high (6 seconds per cycle) or low (2 seconds per cycle) time-under-tension. Participants performed 12-weeks of standing and seated calf raise exercises three times per week in a gym setting using a Smith machine. One session per week was supervised (via telehealth). Primary feasibility outcomes (recruitment and retention rate, exercise adherence and fidelity [i.e. time-under-tension, volume, load intensity], incidence of adverse events, health care use and productivity cost) were collected weekly. Means and standard deviations were determined for parametric data, medians and interquartile range for non-parametric continuous data, and frequency counts for discrete data. Results and Discussion: Total recruitment (76%) and retention (90%) rates were high. Exercise adherence ranged from 45% to 63% and fidelity ranged from 8% to 83% across the groups. The fidelity data for the standing calf raise exercise is shown in Figure 1. Thirty-one participants reported 64 adverse events over the 3 months. Twenty-one participants (70%) reported mild events. Participants reported reduced presenteeism more than absenteeism. All groups displayed improvement in self-reported outcome measures during the study (Table 4). At 12 weeks, the mean improvement in VISA-A scores was between 26 to 40 points across the four groups. In a trial assessing the effect of exercise parameters, adequate adherence and fidelity is important to ensure the groups vary as intended. Home rather than gym-based exercise may improve exercise adherence in future trials. We also recommend future trialists compare total time-under-tension per set (we assessed time under tension in each repetition which may have been too stringent) Limiting recruitment to men reduces generalisability. Another limitation related to including only men is that our feasibility and pilot data (including the sample size estimate) may not extrapolate to women and this needs to be considered carefully in planning a full-scale powered trial. Second, although the outcome assessor was blinded to treatment allocation, participants and physiotherapists providing care via telehealth were not, so there is the risk of performance and detection bias. This was mitigated partly by expressing uncertainty to participants regarding potential comparative efficacy between groups. A potential limitation is that we recruited a majority of participants via social media and it is not certain whether they represent the range of individuals presenting for Achilles tendinopathy management in primary care. However, the age and severity profile (VISA-A scores) is similar to other studies also undertaken in Australia among individuals with Achilles tendinopathy [e.g. 1]. Conclusion: The proposed trial design and interventions demonstrated acceptable recruitment and retention rates and safety profile. However, exercise fidelity and adherence to the gym-based intervention was not acceptable. Strategies to improve intervention adherence and fidelity should be considered in future trials. Acknowledgements: This research was funded by a grant from the International Mechanical Diagnosis and Therapy Research Foundation (IMDTRF). REFERENCES: Yelland, M, et al. Prolotherapy injections and eccentric loading exercises for painful Achilles tendinosis: a randomised trial. Br J Sports Med, 45, 5, 421-428. 2011.

Navigating the international dissemination of an evidence-based intervention: Scaling with fidelity and cultural-specificity

Attachment and Biobehavioral Catch-up (ABC), an evidence-based parenting program, monitors intervention fidelity and provides fidelity-focused supervision to providers, or “parent coaches,“ across one year of training. Recently, ABC dissemination has grown internationally, providing the opportunity for the ABC developer team to consider how to support intervention fidelity in an international context. The current manuscript describes early international dissemination efforts with local supervisors in Russia, Germany, and Norway. As part of ABC implementation, supervisors were trained in the in-the-moment commenting and coding system, a microanalytic behavioral coding system that measures fidelity to ABC. Local supervisors were also trained to provide fidelity-focused supervision. The present study found that supervisors were able to achieve coding reliability after engaging in an intensive week-long training, with reliability scores similar to supervisors who had gone through coding training in the United States. Supervisors were then able to deliver fidelity-focused supervision in the parent coaches’ native language, without the need to include an ABC staff member during the supervision meetings or provide translation of their behavioral coding. At the end of their training year, on average, parent coaches in the Russian, German, and Norwegian cohorts all met the fidelity requirements to gain certification in ABC, with fidelity data similar to those of parent coaches delivering ABC in the United States. Results from this evaluation demonstrate that an evidence-based parenting program with rigorous fidelity monitoring can be successfully transported from the United States to international contexts, with ongoing communication and collaboration between the developer site and international partners around language, culture, and fidelity-focused consultation as a key component of this dissemination.

Data-driven algorithm to classify the degree of isotropy in the bidirectional reflectance distribution function

The bidirectional reflectance distribution function (BRDF) is used to describe reflectances of materials by calculating the ratio of the reflected radiance to the incident irradiance. While it was found that the isotropic models maintained symmetry about ϕs = π, such symmetry was not maintained about the θs = θi axis, except for close to the specular peak. This led to the development of a data-driven metric for how isotropic a BRDF measurement is. Research efforts centered around developing an algorithm that could determine material anisotropy without having to fit to models. This algorithm was tested using high fidelity data (containing off-axis BRDFs), which was collected via a modified Complete Angle Scatter Instrument (CASI®) with a CCD array detector. The algorithm accurately characterized the degree of isotropy for four out of five materials and worked for cases where the BRDF is higher than 100 sr − 1. This algorithm is intended to improve BRDF characterization, and the applications of light curve analysis, scene generation, and remote sensing.

Nonlinear Unsteady Aerodynamics Reduced Order Model of Airfoils Based on Algorithm Fusion and Multifidelity Framework

A reduced order modeling method based on algorithm fusion and multifidelity framework for nonlinear unsteady aerodynamics is proposed to obtain a low-cost and high-precision unsteady aerodynamic model. This method integrates the traditional algorithm, intelligent algorithm, and multifidelity data fusion algorithm. In this method, the traditional algorithm is based on separated flow theory, the intelligent algorithm refers to the nonlinear autoregressive (NARX) method, and the multifidelity data fusion algorithm uses different fidelity data for aerodynamic modeling, which can shorten the time cost of data acquisition. In the process of modeling, firstly, a multifidelity model with NARX description provides a general intelligent algorithm framework for unsteady aerodynamics. Then, based on the separated flow theory, the correction equation from low-fidelity model to high-fidelity result is constructed, and the cuckoo algorithm based on chaos optimization is used to identify the parameters. In order to verify the effectiveness of the method, an unsteady aerodynamic model of NACA0012 airfoil is established. Three kinds of data with low, medium, and high fidelity are used for modeling. The low-fidelity and medium-fidelity data is obtained from the CFD-Euler solver and CFD-RANS solver, respectively, while the high-fidelity data comes from the experimental results. Then, the model is established, and its prediction of unsteady aerodynamic coefficients is in good agreement with the CFD results and the experimental data. After that, the model is applied to a two-dimensional aeroelastic system, and the bifurcation and limit cycle response analysis are compared with the experimental results, which further shows that the model can accurately capture the main flow characteristics in the flow range of low speed and high angle of attack. In addition, the convergence of the model is studied; the accuracy and generalization ability as well as applicability scope of the model are compared with other aerodynamic models and finally discussed.

A multi-physics informed antenna sensor model through the deep neural network regression

A passive wireless strain sensing method using antenna sensors has significantly advanced structural health monitoring systems. Since the dimensions of antenna sensors are sensitive to their strain sensing performance and operating frequency, an iterative tuning process is required to achieve a final optimized design. Although multi-physics finite element simulation enables accurate estimation of antenna performance for each turning iteration, the simulation process requires high computational resources. Therefore, antenna tuning processes are recognized as obstacles to delay the final design process. In this study, we explore the potential of multi-physics informed models as an alternative approach for analyzing antenna sensors. Through deep neural networks, as a branch of the machine-learning algorithms, we formulate multi-physics informed models with six input parameters (antenna dimensions) and two outputs (resonance frequency and strain sensitivity). Twenty-two hundred high fidelity data sets are prepared by simulating multi-physics models: 1,600, 400, and 200 data sets are applied to deep neural network regression (DNNR) training, validating, and testing, respectively. From extensive data investigation, an optimized DNNR architecture is obtained to be two layers, with 16 neurons in each layer. Its training, validating, and testing values of mean square errors are 13.01, 44.22, 37.27, respectively. Finally, the proposed multi-physics informed model predicts the resonance frequency and strain sensitivity with errors of 0.1% and 0.07%, respectively. In addition, since the average computation speed for each tuning process is 0.007 seconds, the practical usefulness of the proposed method is also proven.

Understanding the performance of a pan-African intervention to reduce postoperative mortality: a mixed-methods process evaluation of the ASOS-2 trial

BackgroundThe African Surgical OutcomeS-2 (ASOS-2) trial tested an enhanced postoperative surveillance intervention to reduce postoperative mortality in Africa. We undertook a concurrent evaluation to understand the process of intervention delivery. MethodsMixed-methods process evaluation, including field notes, interviews, and post-trial questionnaire responses. Qualitative analysis used the framework method with subsequent creation of comparative case studies, grouping hospitals by intervention fidelity. A post-trial questionnaire was developed using initial qualitative analyses. Categorical variables were summarised as count (%) and continuous variables as median (inter-quartile range [IQR]). Odds ratios (OR) were used to rank influences by impact on fidelity. ResultsThe dataset included eight in-depth case studies, and 96 questionnaire responses (response rate 67%) plus intervention fidelity data for each trial site. Overall, 57% (n=55/96) of hospitals achieved intervention delivery using an inclusive definition of fidelity. Delivery of the ASOS-2 interventions and data collection presented a significant burden to the investigators, outstripping limited resources. The influences most associated with fidelity were: surgical staff enthusiasm for the trial (OR=3.0; 95% confidence interval [CI], 1.3–7.0); nursing management support of the trial (OR=2.6; 95% CI, 1.1–6.5); performance of a dummy run (OR=2.6; 95% CI, 1.1–6.1); nursing colleagues seeing the value of the intervention(s) (OR=2.1; 95% CI, 0.9–5.7); and site investigators' belief in the effectiveness of the intervention (OR=3.2; 95% CI, 1.2–9.4). ConclusionsASOS-2 has proved that coordinated interventional research across Africa is possible, but delivering the ASOS-2 interventions was a major challenge for many investigators. Future improvement science efforts must include better planning for intervention delivery, additional support to investigators, and promotion of strong inter-professional teamwork. Clinical trial registrationClinicalTrials gov NCT03853824.

A multi-fidelity surrogate model based on moving least squares: fusing different fidelity data for engineering design

A multi-fidelity surrogate model based on moving least squares: fusing different fidelity data for engineering design

Factors Influencing Fidelity to a Calorie Posting Policy in Public Hospitals: A Mixed Methods Study.

Background: Labelling menus with nutrition information has increasingly become an important obesity policy option. While much research to-date has focused on determining its effectiveness, few studies report the extent to which menu labelling is implemented as designed. The aim of this study was to explore factors influencing fidelity to a calorie posting policy in Irish acute public hospitals.Methods: A mixed methods sequential explanatory study design was employed, with a nested case study for the qualitative component. Quantitative data on implementation fidelity at hospitals were analysed first and informed case sampling in the follow-on qualitative phase. Maximum variation sampling was used to select four hospitals with high and low levels of implementation and variation in terms of geographic location, hospital size, complexity of care provided and hospital type. Data were collected using structured observations, unstructured non-participant observations and in-depth semi-structured interviews. The Consolidated Framework for Implementation Research guided qualitative data collection and analysis. Using framework analysis, factors influencing implementation were identified. A triangulation protocol was used to integrate fidelity findings from multiple sources. Data on influencing factors and fidelity were then combined using joint displays for within and cross-case analysis.Results: Quantitative fidelity data showed seven hospitals were categorised as low implementers and 28 hospitals were high implementers of the policy. Across the four hospitals selected as cases, qualitative analysis revealed factors influencing implementation and fidelity were multiple, and operated independently and in combination. Factors were related to the internal hospital environment (e.g., leadership support, access to knowledge and information, perceived importance of calorie posting implementation), external hospital environment (e.g., national policy, monitoring), features of the calorie posting policy (e.g., availability of supporting materials), and the implementation process (e.g., engaging relevant stakeholders). Integrated analysis of fidelity indicated a pattern of partial adherence to the calorie posting policy across the four hospitals. Across all hospitals, there was a consistent pattern of low adherence to calorie posting across all menu items on sale, low adherence to calorie information displayed per standard portion or per meal, low adherence to standardised recipes/portions, and inaccurate calorie information.Conclusion: Efforts to maximise fidelity require multi-level, multi-component strategies in order to reduce or mitigate barriers and to leverage facilitators. Future research should examine the relative importance of calorie posting determinants and the association between implementation strategies and shifts in fidelity to intervention core components.

Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks

Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network’s ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.

Towards a Digital Twin Enabled Multifidelity Framework for Small Satellites

In this work, a multi-fidelity framework for the simulation ofsmall satellites is investigated. Taking into account the conceptof digital twin, our work focuses on handling a constantstream of live data. Towards this end, current multi-fidelitymodelling methods and low fidelity surrogate models for timeseries were surveyed. A multi-fidelity approach is used tocombine a low fidelity surrogate model with a high fidelitymodel. As a high fidelity model, a previously investigatedfinite element model is assumed. As a low fidelity model,auto-regressive and recurrent neural network-based modelsare investigated. Through cokriging, the low fidelity data iscorrected by the high fidelity data through a comprehensivecorrection, where the parameters are given through Gaussianprocesses in order to perform uncertainty quantification. Asan application, the thermal simulation of a small satellite, andthe use of this framework in conjunction with sparse telemetrydata is proposed. This online, statistical approach aims toprovide a tool for performing fault detection.

Implementation and Adaptation Practices of 9th Grade Teachers Implementing the Chicago Public Schools’ Sexual Health Education Curriculum

For sexual health education implementation, program fidelity, defined as whether prescribed curricular components are delivered as prescribed, is critical. Whereas fidelity data is often collected quantitatively with little to no information about what changes were made and why, this mixed methods case study evaluation aimed to understand what kinds of adaptations are being made and why teachers are making them. This was done in order to better support teachers implementing sexual health education in Chicago Public Schools. Twenty-nine class-periods were observed in four high schools to assess curriculum fidelity. Twelve teachers were interviewed about their implementation practices. Teachers made numerous adaptations, primarily aimed to respond to students’ needs and interests. Teachers report needing more guidance on how to appropriately make adaptations that maintain fidelity to core content while allowing them to adjust to their own students’ needs and interests. Implications for sexual health education program implementation are discussed.

Can Policy Promote Adoption or Outcomes of Evidence-based Prevention Programming?: a Case Illustration of Positive Behavioral Interventions and Supports.

This study examined the impact of a state policy requiring that any school with a habitual truancy rate of 8% or higher to be trained in Tier 1 school-wide Positive Behavioral Interventions and Supports (SW-PBIS). A regression discontinuity (RD) design was used to examine how the schools' mandate status related to SW-PBIS training as well as student suspensions, truancy, and achievement in 410 public middle and high schools, of which 261 were affected by the mandate. We further examined the growth trajectories (i.e., improvement) of implementation fidelity over time using growth mixture modeling (GMM). Contrary to the intent of the policy to improve student outcomes, the RD results suggestedthat the mandate did not significantlyimpact reading and math achievement, truancy rates, or SW-PBIS training in 2010-2011 through 2013-2014. Mandated schools had higher suspension rates in 2010-2011 through 2013-2014 than the non-mandated schools; however, these differences in the suspension rates appear to have persisted from years prior to the mandate. Descriptive analyses suggested that mandated schools had statistically significantly higher rates of training, and the GMM analyses on the fidelity data indicated that mandated schools were significantly more likely to be in an improving implementationgrowth trajectory over time. Taken together, results suggested that the policy showed some promise for improving SW-PBIS training and fidelity over time, but it had little to no impact on student outcomes.

Combination adherence strategy to support HIV antiretroviral therapy and pre-exposure prophylaxis adherence during pregnancy and breastfeeding: protocol for a pair of pilot randomised trials

IntroductionTo realise the expected gains from prevention of mother-to-child HIV transmission initiatives, adherence to preventative and therapeutic antiretroviral regimens is critical and interventions deployable in busy programmatic settings with a...

(Invited) Chemical Sensing in the Big Data Era: How and Where Does the Chemical World Store Its Information?

(Invited) Chemical Sensing in the Big Data Era: How and Where Does the Chemical World Store Its Information?

Keeping the Faith While Keeping It Real: Practical, Empirical Approaches to Evaluating Treatment Fidelity

Practical approaches towards fidelity measurement are critical for large-scale uptake of evidence-based practices (EBPs). This descriptive review summarizes approaches to evaluation of EBP fidelity that are empirically tested and implemented in “real-world” settings. The systematic literature search covered a 30-year period (1988–2018) and included published papers describing treatment fidelity strategies for psychosocial interventions. Characterization of articles included “scientific-empirical,” “practical-empirical,” or “practical non-empirical.” Articles characterized as “practical-empirical” were the focus of the review. Twenty-five articles met inclusion criteria for “practical empirical.” These articles were coded for fidelity data collection methods, the source of fidelity information, who arbitrates or determines fidelity, if the approach is at the practice or program level, if the approach is for a specific EBP or is generic, and if fidelity is measured at the individual practitioner versus team level. The type of empirical evidence provided and the extensiveness of fidelity domains assessed was characterized for each study. Results indicated a wide variety of fidelity measurement approaches. Most commonly, outside experts assessed treatment fidelity. Half of the approaches examined fidelity at the practice level and half at the broader program level. A similar ratio examined fidelity at the individual practitioner level compared with the team level. Most approaches were specific to particular interventions. The vast majority focus on the adherence subdomain of fidelity. To date, there is no single predominant approach to fidelity assessment in real-world settings.

Design of Condensation Heat Transfer Experiment to Evaluate Scaling Distortion in Small Modular Reactor Safety Analysis

Abstract Designing a novel scaled modular test facility as a part of an experiment for condensation heat transfer (CHT) in small modular reactors (SMRs) is the main focus of this study. This facility will provide data to evaluate models' scalability for predicting heat transfer in the passive containment cooling system (PCCS) of SMR. The nuclear industry recognizes SMRs as future candidates for clean, economic, and safe energy generation. However, licensing requires proper evaluation of the safety systems such as PCCS. The knowledge gap from the literature review showed a lack of high-resolution experimental data for scaling of PCCS and validation of computational fluid dynamics tools. In addition, the presently available test data are inconsistent due to unscaled geometric and varying physics conditions. These inconsistencies lead to inadequate test data benchmarking. To fill this research gap, this study developed three scaled (different diameters) condensing test sections with annular cooling for scale testing and analysis. This facility considered saturated steam as the working fluid with noncondensable gases like nitrogen and helium in different mass fractions. This facility also used a precooler unit for inlet steam conditioning and a postcooler unit for condensate cooling. The high fidelity sensors, instruments, and data acquisition systems are installed and calibrated. Finally, facility safety analysis and shakedown tests are performed.