Multidimensional Response Research Articles

Impairment of Cardiovascular Functional Capacity in Mild to Moderate Kidney Dysfunction.

Traditional diagnostic tools that assess resting cardiac function and structure fail to accurately reflect cardiovascular alterations in patients with chronic kidney disease (CKD). This study sought to determine whether multidimensional exercise response patterns related to cardiovascular functional capacity can detect abnormalities in mild-to-moderate CKD. In a cross-sectional study, we examined 3,075 participants from the Framingham Heart Study (FHS) and 451 participants from the Massachusetts General Hospital Exercise Study (MGH-ExS) who underwent cardiopulmonary exercise testing (CPET). Participants were stratified by estimated glomerular filtration rate (eGFR): eGFR ≥90; eGFR 60-89; eGFR 30-59. Our primary outcomes of interest were peak oxygen uptake (VO 2 Peak),VO 2 at anaerobic threshold (VO 2 AT), and the ratio of minute ventilation to carbon dioxide production (VE/VCO 2 ). Multiple linear regression models were fitted to evaluate the associations between eGFR group and each outcome variable adjusted for covariates. In the FHS cohort, N=1,712 (56%) had an eGFR ≥90 ml/min/1.73m 2 , N=1,271 (41%) had an eGFR 60-89 ml/min/1.73m 2 , and N=92 (3%) had an eGFR 30-59 ml/min/1.73m 2 . In the MGH-ExS cohort, N=247 (55%) had an eGFR ≥90 ml/min/1.73m 2 , N=154 (34%) had an eGFR 60-89 ml/min/1.73m 2 , and N=50 (11%) had an eGFR 30-59 ml/min/1.73m 2 . In FHS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function ( p <0.001); however this pattern was attenuated following adjustment for age. Percent-predicted VO 2 Peak at AT was higher in the lower eGFR groups ( p <0.001). In MGH-ExS, VO 2 Peak and VO 2 AT were incrementally impaired with declining kidney function in unadjusted and adjusted models ( p <0.05). VO 2 Peak was associated with eGFR ( p <0.05) in all models even after adjusting for age. On further mechanistic analysis, we directly measured cardiac output (CO) at peak exercise via right heart catheterization and found impaired CO in the lower eGFR groups ( p ≤0.007). CPET-derived indices may detect impairment in cardiovascular functional capacity and track cardiac output declines in mild to moderate CKD.

Multivariate sensitivity-adaptive polynomial chaos expansion for high-dimensional surrogate modeling and uncertainty quantification

This work develops a novel basis-adaptive method for constructing anisotropic polynomial chaos expansions of multidimensional (vector-valued, multi-output) model responses. The adaptive basis selection is based on multivariate sensitivity analysis metrics that can be estimated by post-processing the polynomial chaos expansion and results in a common anisotropic polynomial basis for the vector-valued response. This allows the application of the method to problems with up to moderately high-dimensional model inputs (in the order of tens) and up to very high-dimensional model responses (in the order of thousands). The method is applied to different engineering test cases for surrogate modeling and uncertainty quantification, including use cases related to electric machine and power grid modeling and simulation, and is found to produce highly accurate results with comparatively low data and computational demand.

Probing inhomogeneous cuprate superconductivity by terahertz Josephson echo spectroscopy

AbstractInhomogeneities crucially influence the properties of quantum materials, yet methods that can measure them remain limited and can access only a fraction of relevant observables. For example, local probes such as scanning tunnelling microscopy have documented that the electronic properties of cuprate superconductors are inhomogeneous over nanometre length scales. However, complementary techniques that can resolve higher-order correlations are needed to elucidate the nature of these inhomogeneities. Furthermore, local tunnelling probes are often effective only far below the critical temperature. Here we develop a two-dimensional terahertz spectroscopy method to measure Josephson plasmon echoes from an interlayer superconducting tunnelling resonance in a near-optimally doped cuprate. The technique allows us to study the multidimensional optical response of the interlayer Josephson coupling in the material and disentangle intrinsic lifetime broadening from extrinsic inhomogeneous broadening for interlayer superconducting tunnelling. We find that inhomogeneous broadening persists up to a substantial fraction of the critical temperature, above which this is overcome by the thermally increased lifetime broadening.

Academic stress in Health Sciences university students and its influence on academic performance

Introduction: The discomfort that Health Sciences students present due to physical or emotional factors called stressors, exert a significant pressure affecting their average grades obtained in an academic period. Objectives: Characterize academic stress, its triggering factors, the influence on the academic performance of university students of Health Sciences, and coping strategies for the management of the teaching and learning process.Methods: Bibliographic review, from 2020 to 2024. The sources consulted were LILACS Regional, Scielo regional, PLOS, PubMed Central, Google Scholar, Web of Science Latin America. We considered 34/57 scientific articles found in English, Spanish and Portuguese. Systematic reviews with less than 10 articles in their analysis, duplicity, plagiarism, and paid articles were excluded. Discussion: Academic stress significantly affected students, causing emotional, behavioral, and physiological problems due to task overload and demanding evaluations. Studies revealed common symptoms such as physical pain, sadness, anxiety, with high impact in Health Sciences areas. The COVID-19 pandemic exacerbated these issues, highlighting the need for effective coping strategies such as physical activity to improve well-being and academic performance. Conclusions: Academic stress is a multicausal and multidimensional individual adaptive response process studied by university students of Health Sciences. Academic stressors, risk factors and protective factors, will allow guiding confrontation strategies and the creation of a protocol for academic management as part of the teaching-learning process, for its mitigation.

Trade-offs and synergies pattern evolution of ecosystem structure-resilience-activity-services (SRAS) in the Belt and Road Initiative region

This study develops an analytical framework comprising “ecosystem pattern – trade-off and synergy process – multidimensional response mechanism – future risk management” to investigate the evolution of trade-off and synergy patterns of SRAS performance and its sustainability among BRI nations and 80 representative cities. The synergistic levels of SRAS in BRI countries declined, but will increase in the future, the increased trend of BRI cities will transfer to decline in the future. The interaction of multi-dimensional human urban activities could achieve a stronger impact on SRAS performance synergies of BRI countries and cities, and the PD-LU and PD-ED were the dominant interaction influence chains, respectively. This study was the first one to holistically reveal the evolution of trade-off and synergy patterns of SRAS performances, at both nation and city levels, in the BRI region. It also contributes new perspectives from the BRI region to the sustainable urban planning, terrestrial ecosystems conservation, and urban ecological governance programs.

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures

Methods for measuring career readiness of high school students: based on multidimensional item response theory and text mining

In contemporary society, career readiness holds paramount significance for individual life, exerting a direct influence on initial employment, job satisfaction, and the sense of career identity. Framed within multidimensional item response theory and text mining, this study embarks on exploring assessment methodologies for high school students’ career readiness by revising the “Career Readiness Questionnaire – Adolescent Version” and employing text mining techniques. Study One collected 1261 valid data points through cluster sampling. With the aid of Bayesian multivariate item response theory parameter estimation procedures and R language, the career readiness measurement tool was revised, yielding a concise scale that aligns with psychometric requirements. The research findings indicated that the concept of “career readiness” is more suitable for the multidimensional graded response model than for the bifactor model. The dataset’s discrimination parameters fell within the range of [1.59, 3.84], the difficulty parameters fell between [−2.91, 2.24], and the peak values of the maximum information functions fell within [0.24, 2.35]. After six items with the lowest peaks were removed (Items 4, 5, 6, 31, 32, and 33), the remaining 30 items composed the Chinese concise version “Career Readiness Questionnaire – Adolescent Version,” with discrimination parameters ranging from [1.45, 3.38], difficulty parameters between [−3.31, 1.76], and maximum information function peaks within [0.50, 2.64]. Building upon the effective participants from Study One, Study Two matched questionnaire data with textual information, resulting in 1012 valid participants. Leveraging text mining, a machine learning model was constructed to predict high school students’ career readiness based on essay texts. The results of Study 2 prove that the revised lexicon was more accurate in feature extraction. Building upon this, the machine learning model for essay text demonstrated excellent performance in predicting career readiness, with random forest outperforming the other algorithms. This study provides a novel approach for schools and parents to comprehend the state of career readiness among high school students, offering a convenient and effective tool for educational activities related to students’ career development.

Clusters of multidimensional exercise response patterns and estimated heart failure risk in the Framingham Heart Study.

New tools are needed to identify heart failure (HF) risk earlier in its course. We evaluated the association of multidimensional cardiopulmonary exercise testing (CPET) phenotypes with subclinical risk markers and predicted long-term HF risk in a large community-based cohort. We studied 2532 Framingham Heart Study participants [age 53±9years, 52% women, body mass index (BMI) 28.0±5.3kg/m2, peak oxygen uptake (VO2) 21.1±5.9kg/m2 in women, 26.4±6.7kg/m2 in men] who underwent maximum effort CPET and were not taking atrioventricular nodal blocking agents. Higher peak VO2 was associated with a lower estimated HF risk score (Spearman correlation r: -0.60 in men and -0.55 in women, P<0.0001), with an observed overlap of estimated risk across peak VO2 categories. Hierarchical clustering of 26 separate CPET phenotypes (values residualized on age, sex, and BMI to provide uniformity across these variables) identified three clusters with distinct exercise physiologies: Cluster 1-impaired oxygen kinetics; Cluster 2-impaired vascular; and Cluster 3-favourable exercise response. These clusters were similar in age, sex distribution, and BMI but displayed distinct associations with relevant subclinical phenotypes [Cluster 1-higher subcutaneous and visceral fat and lower pulmonary function; Cluster 2-higher carotid-femoral pulse wave velocity (CFPWV); and Cluster 3-lower CFPWV, C-reactive protein, fat volumes, and higher lung function; all false discovery rate<5%]. Cluster membership provided incremental variance explained (adjusted R2 increment of 0.10 in women and men, P<0.0001 for both) when compared with peak VO2 alone in association with predicted HF risk. Integrated CPET response patterns identify physiologically relevant profiles with distinct associations to subclinical phenotypes that are largely independent of standard risk factor-based assessment, which may suggest alternate pathways for prevention.

Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.

Discrimination of tulip cultivars with different floral scents using sensory assessment, electronic nose, and gas chromatography–mass spectrometry

Floral scent holds significant aesthetic and biological values. However, tulips have received less attention for their floral fragrance than the vibrant petal colors and exquisite bud shapes. Here, sensory assessment, E-nose, and HS-SPME-GC-MS were carried out to determine the scent intensity and characteristics of 14 tulip cultivars. Based on their scent intensity, the cultivars were divided into four groups, among which group Ⅰ was the most aromatic (MS = 4.20), followed by group Ⅱ (3.03 ≤ MS ≤ 3.97), group Ⅲ (2.43 ≤ MS ≤ 2.77), and finally group Ⅳ (1.00 ≤ MS ≤ 1.57). Compared with the sensory assessment, E-nose also categorized the 14 tulips into four categories according to the multi-dimensional sensor responses, suggesting a relatively strong relationship between them (R2 ≈ 0.57), and consistently confirming the scientific evaluation of tulip cultivars’ scent. For HS-SPME-GC-MS analysis, 21 volatiles were putatively detected, mainly aliphatics (9, 42.86 %), terpenoids (7, 33.33 %), and benzenoids/phenylpropanoids (4, 19.05 %). Octanal, nonanal, phenylethyl alcohol, and phenylacetaldehyde (17.82 ≤ ROAV ≤ 100) were key components in T. ‘Delta Queen’, contributing to its distinctive aroma reminiscent of citrus and rosy. T. ‘Spryng Break’ cultivar contained octanal, eucalyptol, and nonanal (22.51 ≤ ROAV ≤ 100), imparting citrusy and herbal aromas. The fragrance of T. ‘Lion King’ is likely a blend of citrus, grassy, and green scents due to the presence of nonanal and hexanal (71.81 ≤ ROAV ≤ 100). In T. ‘Red Judo’, octanal, nonanal, and decanal (10.64 ≤ ROAV ≤ 100) combined to create its pure citrus aroma. These findings are expected to provide a valuable reference for tulip floral fragrance analysis, establish a theoretical foundation for its fragrant germplasm breeding, and promote the development of tulip fragrance industry.

Hurricane-Force Grief: A Mirror of Fathers' Love.

A child's death is a traumatic experience that profoundly impacts parents' psychological, physical, and social wellbeing. The literature describes gender differences in grief response associated with child loss, yet less is known about fathers' grief in comparison that of mothers due to fathers' underrepresentation in bereavement research. There is a need for scholarly exploration to advance understanding of fathers' grief. This article presents selected findings on fathers' grief responses that emerged from a larger qualitative study, which aimed to describe fathers' lived experience of bereavement after a child's death. Profound grief emerged as one essential theme of fathers' bereavement experience. Participants described the meaning of their grief, their multidimensional grief responses, their grief triggers, and their grief intensity. Findings deepen understanding of fathers' grief associated with child loss and advance the body of parental bereavement literature.

Development of the neonatal pain response variable set: a mixed methods consensus process

While over 40 neonatal pain assessment scales have been published, owing to a lack of consensus and standardized metrics, there are more than 100 assessment indicators with varying descriptors and quality differences. This study aims to reach a consensus on optimal and comprehensive variables for neonatal pain assessment, leading to the development of a multidimensional neonatal pain response variable set. This study consisted of three phases: (1) A literature review was conducted to identify influencing factors and assessment indicators of neonatal pain response. (2) Panel meetings involving neonatal healthcare professionals evaluated and screened factors and indicators to develop an initial draft of the variable set. (3) Through two rounds of Delphi study achieved consensus, and determined the neonatal pain response variable set. Through a literature review and a panel meeting, the identified factors and indicators were categorized into contextual, physiological, and behavioral variables, forming an initial draft of the variable set. Sixteen professionals participated in two rounds of the Delphi study, with response rates exceeding 70%, and authority coefficients surpassing 0.7 in both rounds. The final iteration of the variable set includes 9 contextual variables, 2 physiological variables, and 5 behavioral variables. Conclusion: Neonatal pain response variable set developed in this study is scientific, comprehensive, and multidimensional, aligning with the characteristics of neonatal pain response and clinically applicable. The inclusion of contextual variables enhances the ability to confront the complexity of clinical environments and individual differences. It can provide a practical and theoretical basis for clinical research on neonatal pain assessment.What is Known:• Neonatal pain assessment relies on scales used by healthcare professionals currently. But there is no “gold standard” for neonatal pain assessment.• While over 40 neonatal pain assessment scales have been published, owing to a lack of consensus and standardized metrics, there are more than 100 assessment indicators with varying descriptors and quality differences. Most of scales overlook the clinical environment complexity individual differences in pain responses, diminishing the accuracy and applicability.What is New:• In addition to the commonly used physiological and behavioral variables in the scales, we have incorporated contextual variables to better address the complexity of clinical environments and individual differences in pain responses.• Through an evidence-based approach, developed a neonatal pain response variable set comprising 9 contextual variables, 2 physiological variables, and 5 behavioral variables.

16‐1: A Model for the Appearance of Interocular Colorimetric Differences in Binocular XR Displays

Many extended reality (XR) devices present different views to the left and right eyes. Unwanted colorimetric differences between these views can cause perceptual artifacts that degrade binocular image quality. We present an image‐computable model designed to predict the appearance of binocular views with colorimetric differences in XR displays. The model is fitted to data from a recent perceptual study in which people provided multidimensional responses about the appearance of stimuli simulating an optical see‐through augmented reality device with interocular intensity differences. This work can be used to create preliminary assessments of binocular artifact appearance and inform XR display design.

Whole-Body Photobiomodulation Therapy Propels the Fibromyalgia Patient into the Recomposition Phase: A Reflexive Thematic Analysis.

Recent evidence has identified great promise for the novel whole-body photobiomodulation therapy (PBMT) for individuals with fibromyalgia (FM). However, currently no evidence has documented the experiences of participants. The objective of this study was to qualitatively assess treatment experience and response in a group of participants with FM undergoing a course of whole-body PBMT. An interpretive hermeneutic phenomenological study situated within the worldview of pragmatism was undertaken. A convenience sample of individuals with FM were included if they had undertaken a novel 6-week trial of PBMT. Individuals undertook semi-structured interviews exploring treatment experience and multidimensional treatment responses during Week 3 and Week 6. Sixteen trial participants (47.3 ± 10.9 years) took part in this study. The analysis produced three overarching themes that were previously identified from a baseline study (namely, 'Body Structure & Function', 'Activities & Participation', and 'Environment') with an additional five sub-themes that highlighted the intervention experience. Subsequently, four important processes were observed and identified: increased motivation; feeling proud; improved confidence; feeling like 'old self'. This ultimately culminated in the identification of a positive spiral, which we have termed 'recomposition'. We believe our study is the first in the field of chronic pain management to utilise qualitative methodology to directly assess the acceptability and efficacy of a specific medical intervention in a clinical trial, and the first study to qualitatively assess whole-body PBMT experience. The findings are compelling and warrant further work to support the introduction of this device into the National Health Service (NHS).

How Do Physical Disability Cues Influence Assumptions about Consumer Tastes? Unpacking the Disability Preference Stereotype

Abstract Across 10 experimental studies, this research identifies and provides evidence of a disability preference stereotype whereby observers infer that disabled consumers prefer utilitarian products more than nondisabled consumers and prefer hedonic products less than nondisabled consumers. We show that this stereotype occurs because of societal associations between physical disability and pity. Pity elicits a multidimensional response such that considering the interests of a disabled person increases feelings of personal discomfort, driving both an inclination to help (help-giving orientation) and a tendency to assess the perceived misfortune (misfortune appraisal) in parallel. Thus, when considering the preferences of disabled individuals, the help-giving orientation increases focus on functional (utilitarian) goods, while the misfortune appraisal decreases focus on pleasurable (hedonic) goods. Importantly, this stereotype can be mitigated through increased disability representation. Representation of empowered disabled individuals in media can dampen the help-giving orientation, reducing inferred utilitarian preferences, while representation of disabled people partaking in daily pleasures through increased accessibility can reduce misfortune perceptions, increasing inferred hedonic preferences. This work addresses the paucity of disability-related consumer research, identifies how aspects unique to consumption can limit consumers with disabilities, and highlights opportunities to minimize ableist stereotypes by expanding representation and increasing marketplace inclusion.

A Comparative Study on the Impact of Gratitude on Well-Being among Youth and Adult Populations

Gratitude's impact on the welfare of young and old populations is primarily examined in this research paper. Gratitude, one of the multi-dimensional responses towards emotional behavior, has attracted a great deal in positive psychology for its capability to produce better mental health and quality well-being. The goal of this study is to analyze and contrast the impact of gratitude on subjective well-being, life satisfaction, and psychological resilience in two different age groups. This study uses a mixed-methods design, integrating quantitative and qualitative interviews to obtain detailed data. All participants from youth and adult age groups practice gratitude interventions, reflective exercises, as well as self-report measures. Such quantitative analyses use statistical comparisons of pre- and post-intervention well-being scores to shed light on any potential age variations in the performance of gratitude practices. The subjective perception of gratitude and its impact on overall well-being is also reflected by qualitative data, which emerged from in-depth interviews. The research takes into account situational variables including life events, social ties, and cultural elements that contribute to gratitude expression among different age groups. The results add to the literature on positive psychology by highlighting possible differences in efficacy, which may be related to the age cohort. These findings have implications beyond the application of targeted well-being interventions designed for youth and adult populations concerning their individual needs, bringing a more profound understanding of the role gratitude plays in ensuring mental health satisfaction is achieved. Keywords: Gratitude, Well-being, Age groups, Positive psychology, Intervention

Multidimensional response of China’s grassland stability to drought

Drought usually affects the normal functioning of grassland ecosystems. Due to sustained global warming, the frequency and intensity of drought have significantly increased, leading to greater impacts on many aspects of grassland ecosystem functions, such as soil moisture, vegetation physiological structure and ecosystem community structure. Ecosystem stability dimension can help us better understand the changes induced by drought occurred within ecosystems. Using the Normalized Vegetation Index (NDVI), climate and biodiversity data, we quantified resistance, resilience and temporal stability of grassland ecosystems under long term drought in northern China and calculated the ecosystem stability dimension based on the correlation between the three stability indicators. The results indicated that Qinghai Tibet Plateau and Inner Mongolian Plateau showed strong resistance to drought, while Loess Plateau region presented rapid recovery after drought. The resistance, compared to resilience and temporal stability, exhibited stronger sensitivity to drought in space. Drought promoted the transformation of grassland ecosystems into multidimensional structures. The reduction of aboveground biomass caused by frequent droughts accelerated the transformation in grassland ecosystems. Our findings provide the new insights for guiding the response mechanism of drought on grassland ecosystems.

Co-occurring dominance and ideal point processes: A general IRTree framework for multidimensional item responding

Responding to rating scale items is a multidimensional process, since not only the substantive trait being measured but also additional personal characteristics can affect the respondents’ category choices. A flexible model class for analyzing such multidimensional responses are IRTree models, in which rating responses are decomposed into a sequence of sub-decisions. Different response processes can be involved in item responding both sequentially across those sub-decisions and as co-occurring processes within sub-decisions. In the previous literature, modeling co-occurring processes has been exclusively limited to dominance models, where higher trait levels are associated with higher expected scores. However, some response processes may rather follow an ideal point rationale, where the expected score depends on the proximity of a person’s trait level to the item’s location. Therefore, we propose a new multidimensional IRT model of co-occurring dominance and ideal point processes (DI-MIRT model) as a flexible framework for parameterizing IRTree sub-decisions with multiple dominance processes, multiple ideal point processes, and combinations of both. The DI-MIRT parameterization opens up new application areas for the IRTree model class and allows the specification of a wide range of theoretical assumptions regarding the cognitive processing of item responding. A simulation study shows that IRTree models with DI-MIRT parameterization provide excellent parameter recovery and accurately reflect co-occurring dominance and ideal point processes. In addition, a clear advantage over traditional IRTree models with purely sequential processes is demonstrated. Two application examples from the field of response style analysis highlight the benefits of the general IRTree framework under real-world conditions.

The multi-dimensional joint response of the electric potential time–frequency-field in the fracture process of flawed coal under dynamic load

The fracture failure of flawed coal has always been a problem in underground engineering. Therefore, it is necessary to monitor crack propagation and damage evolution during coal fracture. In this study, the split Hopkinson pressure bar (SHPB) and digital image correlation (DIC) were combined to analyze the impact failure electric potential (EP) of flawed coal samples. The multi-dimensional joint response characteristics of coal EP time–frequency-field under dynamic load were studied and the differential expression and multi-fractal characteristics of EP responses at different measuring points were analyzed. Based on the EP signals, the evolution of damage to the coal was calculated. The results show that the time series evolution of EP and stress–strain curves during coal failure is consistent. The generation of fracture zones will excite the high-value area of local EP, and the spectral distribution of EP signals in this area shows the characteristics of low frequency and high amplitude. Each channel’s EP signal and the degree of deformation at the corresponding place are perfectly matched. The multi-fractal spectrum of the EP signal close to the fracture zone has a higher Δα and a lower Δf, which demonstrates the dominance of the large-scale EP signal produced in this region by the destruction of sample. Theoretical study indicates that the main causes of the anomalous distribution of the EP field are crack tip potential, diffusion potential, etc. The results can provide theoretical basis for the evaluation of coal fracturing by EP technology.

A Multi-Fidelity Uncertainty Propagation Model for Multi-Dimensional Correlated Flow Field Responses

Given the randomness inherent in fluid dynamics problems and limitations in human cognition, Computational Fluid Dynamics (CFD) modeling and simulation are afflicted with non-negligible uncertainties, casting doubts on the credibility of CFD. Scientifically and rigorously quantifying the uncertainty of CFD is paramount for assessing its credibility and informing engineering decisions. In order to quantify the uncertainty of multidimensional flow field responses stemming from uncertain model parameters, this paper proposes a method based on Gappy Proper Orthogonal Decomposition (POD) for supplementing high-fidelity flow field data within a framework that leverages POD and surrogate models. This approach enables the generation of corresponding high-fidelity flow fields from low-fidelity ones, significantly reducing the cost of high-fidelity flow field computation in uncertainty propagation modeling. Through an analysis of the impact of uncertainty in the coefficients of the Spalart–Allmaras (SA) turbulence model on the distribution of wall friction coefficients for the NACA0012 airfoil and pressure coefficients for the M6 wing, the proposed multi-fidelity modeling approach is demonstrated to offer significant advancements in both accuracy and efficiency compared to single-fidelity methods, providing a robust and efficient prediction model for large-scale random sampling.