Academic literature on the topic 'SEU rate prediction and measurement'
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Journal articles on the topic "SEU rate prediction and measurement"
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Weatherford, T. R., P. T. McDonald, A. B. Campbell, and J. B. Langworthy. "SEU rate prediction and measurement of GaAs SRAMs onboard the CRRES satellite." IEEE Transactions on Nuclear Science 40, no. 6 (1993): 1463–70. http://dx.doi.org/10.1109/23.273517.
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Akay, Mehmet Fatih, Ozge Bozkurt, Ebru Cetin, and Imdat Yarim. "Multiple linear regression-based physical fitness prediction models for Turkish secondary school students." New Trends and Issues Proceedings on Humanities and Social Sciences 5, no. 4 (September 17, 2018): 58–64. http://dx.doi.org/10.18844/prosoc.v5i4.3704.
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Physical fitness is a necessary component for daily activities. Measurement of physical activity is essential for determining physical fitness rate. This study aims to develop new prediction models for predicting the physical fitness of Turkish secondary school students by using multiple linear regression (MLR). The datasets comprise data of various number of subjects according to the target variables including the test scores of the 30m speed, 20m stage run, balance and hand-grip (right/left). The predictor variables used to develop the prediction models are gender, age, body mass index (BMI), body fat, number of curl-up and push-ups in 30 seconds. Eight physical fitness prediction models for each target have been created with the predictor variables listed above. The performance of the prediction models has been calculated by using standard error of estimate (SEE). The results show that MLR-based prediction models can be safely used to predict the physical fitness of Turkish secondary school students.Keywords: Physical fitness, multiple linear regression, machine learning, validation.
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Boudreau, Dominique, Fabiano Santos, Andre Robidoux, and Jean-Francois Boileau. "Early breast cancer response to neoadjuvant chemotherapy: Defining the optimal timing and response rate using clinical tumor measurement." Journal of Clinical Oncology 33, no. 28_suppl (October 1, 2015): 121. http://dx.doi.org/10.1200/jco.2015.33.28_suppl.121.
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121 Background: Breast cancer pathological complete response (pCR) following neoadjuvant therapy (NAT) is associated with better survival in some tumor subtypes. There is interest in identifying methods to increase early prediction of pCR during NAT. A simple and inexpensive technique such as early clinical breast examination has been shown to correlate with pCR during NAT in some studies. However, the optimal timing of the measurement and the best tumor response (TR) rate to predict pCR still need to be defined. Methods: We conducted a retrospective cohort study of patients prospectively enrolled in the following NSABP trials in one academic center (Montreal, Canada): B-40, B-41, FB-AX-003, FB-4, FB-5 and FB-6. Patients with T4 disease or disease progression were excluded. Clinical tumor measurements were recorded before each cycle of NAT. TR was measured as the percentage decrease in the largest tumor diameter. ROC curves for TR measurements at each time point were generated, comparing areas under the curve using the DeLong method. P-value ² 0.05 was considered significant. Results: We analyzed data of 155 patients recruited from Aug. 2005 to May 2011. Results are presented in Table 1. The best time point to predict pCR was after cycle 2. At this time point, a TR of 46% was the best cutoff value to predict for pCR. Among hormone receptor positive (HR+) and HER-2 positive (HER-2+) breast cancer patients, a TR of 47% after cycle 2 was significantly predictive for pCR. These findings were similar using a 50% TR cutoff after 2 cycles. Conclusions: Observing a 50% reduction in largest tumour diameter on clinical breast examination after cycle 2 of NAT is predictive for pCR in HR+ and HER-2+ breast cancer patients. We recommend using this definition of clinical response in future trials evaluating novel methods to improve early prediction of pCR during NAT. [Table: see text]
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Chapuis, Robert P. "Quantitative measurement of the scour resistance of natural solid clays." Canadian Geotechnical Journal 23, no. 2 (May 1, 1986): 132–41. http://dx.doi.org/10.1139/t86-023.
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The erosion of clayey soils is a complex phenomenon that includes various types of erosive actions. A tentative classification of erosion processes is proposed. This paper concentrates on the scour resistance of solid clays. Available prediction methods are reviewed. Generally they propose relationships between physical or mechanical parameters and the critical hydraulic shear stress, [Formula: see text]c, that defines a boundary between "no erosion" and "erosion". It became apparent that the physicochemical parameters of both the clay and the eroding water control the erosion process. However, subsequent studies on these parameters have yet to yield reliable predictions of natural clay erodibility from indirect measurements. An erosion testing program was therefore performed on three Quebec clays with the improved rotating cylinder technique. In this test, intact or remolded samples can be tested, physicochemical parameters can be controlled, and the hydraulic shear stress [Formula: see text] and the erosion rate ė can be adequately determined. A relatively complete and accurate graph of ė versus [Formula: see text], including ė values for [Formula: see text] values lower than critical can be established. Previous and present research underline the need to exert a strict control of all clay and water parameters, including that of sample preparation, in order to adequately simulate a field problem. Key words: erosion, clay, scour resistance, physicochemical properties, rotating cylinder test.
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Fomin, Petr, and Christian Kargel. "Performance Evaluation of a Time-Gated Fluorescence Spectroscopy Measurement System for the Classification and Recycling of Plastics." Applied Spectroscopy 73, no. 6 (June 2019): 610–22. http://dx.doi.org/10.1177/0003702819831278.
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Plastics have many unrivaled properties and are thus indispensable materials with an ever-increasing production. Since the plastics recycling rates are still small, the plastic waste that ends up on landfills, in the environment including oceans, and incineration plants also steadily grows. Large-scale automated sorting of waste plastics may help solve the problem. However, most state-of-the-art approaches are limited to a (very) small number of plastic types that can be simultaneously sorted and have severe difficulties with black plastics. The labeling of plastics with fluorescent tracers (“markers”) during their production has the potential to enable the fast and highly reliable classification and automated sorting of many different plastic types simultaneously. Unfortunately, the unique marker fluorescence (“optical fingerprint”) might be obscured by the autofluorescence (AF) often emitted by plastics. Recently, we have shown that time-gated fluorescence spectroscopy (TGFS) can be successfully applied to eliminate the influences from AF. In this study, we present the prototype of a TGFS measurement system for the classification and sorting of plastics and evaluate its performance in a typical industrial situation. With 150 000 investigated plastics flakes from 10 different plastic types that were labeled with binary combinations of six fluorescent markers, an overall plastics recovery rate (mean true positive rate [Formula: see text]) of 99.76%, and a plastics purity (positive predictive value [Formula: see text]) of 99.88% were achieved. Additional simulations that were carried out based upon the experiments allow a prediction of the performance as a function of the signal-to-noise (S/N) ratio such that various system parameters can be adapted to the particular situation.
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Symmans, W., F. Peintinger, C. Hatzis, H. Kuerer, V. Valero, B. Hennessy, M. Green, E. Singletary, G. Hortobagyi, and L. Pusztai. "A new measurement of residual cancer burden to predict survival after neoadjuvant chemotherapy." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 536. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.536.
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536 Background: The strength of association between tumor response and survival is critical for neoadjuvant chemotherapy trials. Pathologic complete response (pCR) reliably predicts survival benefit, but residual disease contains a range of pathologic responses that likely contain different prognostic groups, including near complete response and resistance. Methods: Pathologic slides and reports were reviewed from 432 patients in two completed neoadjuvant trials: 1) fluorouracil, doxorubicin and cyclophosphamide (FAC) in 189 patients, and 2) paclitaxel followed by FAC (T/FAC) in 243 patients. Paclitaxel was administered as twelve weekly (n=126) or four 3-weekly cycles (n=117). Residual cancer burden (RCB) was calculated as an index that combines pathologic measurements of primary tumor (size and cellularity) and nodal metastases (number and size). We compared four RCB categories, from RCB-0 (pCR) to RCB-3 (chemoresistant), and post-treatment revised AJCC Stage (0-III) for prediction of distant relapse-free survival (DRFS) in multivariate Cox regression analyses (stratified by ER status). Results: The pCR rate was greater after T/FAC than FAC (24% vs. 16%, LR p<0.05), and after weekly (vs. 3-weekly) paclitaxel in T/FAC (30% vs. 16%, LR p<0.01). In patients with residual disease, RCB measurements were significantly lower after T/FAC than FAC (t-test, p<0.0001), but were not different between paclitaxel schedules in T/FAC. RCB was a continuous predictor of DRFS after T/FAC (HR=1.86, 95%CI 1.51–2.30) or FAC (HR=1.67, CI 1.38–2.01) with median follow-up 5 and 8 years, respectively. The resistant category RCB-3 predicted relapse more strongly than AJCC Stage III and identified a larger group of high-risk patients ( Table ). Conclusions: RCB is a new continuous measure of pathologic response that is defined from routine pathologic materials, represents the distribution of residual disease, is a significant predictor of DRFS, and defines chemotherapy resistance more effectively than revised AJCC Stage. [Table: see text] [Table: see text]
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Dercle, Laurent, Susan Michelle Geyer, Andrew B. Nixon, Federico Innocenti, Qian Shi, Sawyer B. Jacobson, Lyndon Luk, et al. "Radiomic signatures to predict survival in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib +/- doxorubicin: Correlative science from CALGB 80802 (Alliance)." Journal of Clinical Oncology 39, no. 3_suppl (January 20, 2021): 343. http://dx.doi.org/10.1200/jco.2021.39.3_suppl.343.
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343 Background: Alliance/CALGB 80802, a randomized phase III trial, evaluated sorafenib plus doxorubicin vs. doxorubicin in pts with HCC and showed no improvement in median overall survival (OS) (HR[95CI] 1.05[0.83-1.31]) or PFS (HR[95CI] 0.93[0.75-1.16]). In HCC surrogacy of tumor response with OS remains controversial, in part due to varying criteria used for response evaluation (e.g., RECIST1.1 and mRECIST). We evaluated the performance of several models to predict OS using pretreatment clinical and radiomic variables. Methods: In CALBG 80802, we segmented all measurable tumor lesions on sequential CT scans. A lesion’s imaging phenotype was deciphered with 23 uncorrelated quantitative imaging features measured at baseline and week (wk) 10 (first follow-up). An OS landmark survival analysis was conducted at wk 10. Patients were randomly assigned (3:1) to training (n = 92) and validation (n = 37) sets. In a training set, 6 random forest predictive models (6 signatures) used features that best predicted OS using 3 sets of variables: radiomics only (n = 23), clinical only (n = 9), radiomics and clinical (n = 32). Two time points (baseline only or baseline + wk 10) were assessed. Each signature's output was an individualized prediction and a continuous value ranging from 0 to 1 (from most to least favorable predicted OS). The primary endpoint was to compare these models' performance to predict OS using error rate (Harrell's concordance-index) in the validation set. Results: Of the 6 training signatures evaluated, the one achieving the highest performance in the validation set was an 8-feature signature combining radiomics and clinical variables measured at two time points (baseline + wk 10) with an error rate of 35.6%. The variables [rank of importance] (table) selected by the signature included baseline clinical features (albumin[1], AFP[2], Child-Pugh[4]), baseline radiomics features (component 17[3], component 1[5], component 9[7], tumor volume[8]) and wk 10 radiomics features (delta tumor volume[6]). Variable delta tumor volume [6] used a more enhanced estimation of tumor burden at baseline and a delta tumor volumetric measurement; compared to RECIST1.1 measurement of percentage change in unidimensional measurement of a subset of target lesions. The four quartiles of the signature were significantly associated with OS (Log-Rank, P < 0.0001). Conclusions: The selected combined radiomic and clinical composite signature provided the best prediction for OS in the 80802 study patients’ population. It is a suggested way forward to go beyond single anatomic measurement techniques such as RECIST or mRECIST. [Table: see text]
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Renouf, Deane, and Rosemary Gales. "Seasonal variation in the metabolic rate of harp seals: unexpected energetic economy in the cold ocean." Canadian Journal of Zoology 72, no. 9 (September 1, 1994): 1625–32. http://dx.doi.org/10.1139/z94-216.
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The metabolic rate of nine harp seals was measured chronically over a 12-month period using indirect calorimetry. The extent to which the seals' oxygen consumption was predicted by the allometric equation relating basal metabolic rate to body mass depended upon how the former was operationally defined and on the breeding status, sex, and age of the animal. There were large seasonal changes in the oxygen consumption of adult males and reproductive females when metabolic rate was defined as the lowest hourly mean [Formula: see text] in, typically, 23 h of measurement. From April until August, the males' metabolic rate averaged as much as 83% higher than the allometric prediction from body mass, but for the rest of the year their oxygen consumption was not different from the expected value for mammals. Pregnant and pseudopregnant females showed a brief spring elevation in metabolic rate, but otherwise their oxygen consumption was well below that predicted by allometry. In one female who spontaneously aborted some 7 months after insemination, [Formula: see text] increased to the value for mammals shortly thereafter, to a level resembling that of the only female who was not pregnant and who showed no seasonal variation in oxygen consumption. The immature seals' records are highly variable, showing no clear intra-annual pattern; however, their metabolic rates were lower than expected for young mammals. All seasonal shifts were in the opposite direction to the large changes in body mass exhibited by these seals. The effect of these findings in reducing the calculated impact of harp seals on the North West Atlantic fishery is discussed.
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Jadhav, Ranjit S., and Qin Chen. "FIELD INVESTIGATION OF WAVE DISSIPATION OVER SALT MARSH VEGETATION DURING TROPICAL CYCLONE." Coastal Engineering Proceedings 1, no. 33 (October 25, 2012): 41. http://dx.doi.org/10.9753/icce.v33.waves.41.
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Wave data were measured along a 28 m transect using 3 pressure transducers over a 2-day period during a tropical storm. The tropical storm force winds produced waves up to 0.4 m high (zero-moment) that propagated over vegetation of Spartina alterniflora submerged under a surge of over 1 m above the marsh floor. Measured wave heights, energy losses between gages and spectral energy dissipation models of rigid vegetation were utilized to estimate wave height decay rates, integral and frequency-dependent bulk drag coefficients, and frequency distribution of energy dissipation induced by the vegetation. Measurements showed that incident waves attenuated exponentially over the vegetation. The exponential wave height decay rate decreased as Reynolds number increased. The swell was observed to decay at a slower rate than the wind sea regardless of the wave height. The linear spatial wave height reduction rate increased from 1.5% to 4% /m as incident wave height decreased. The bulk drag coefficient estimated from the field measurement decreased with increasing Reynolds and Keulegan-Carpenter numbers. The energy dissipation varied across the frequency scales with the largest magnitude observed near the spectral peaks, above which the dissipation gradually decreased. The wave energy dissipation did not linearly follow the incident energy, and the degree of non-linearity varied with the frequency. For a given spectrum, the frequency-distributed drag coefficient increased gradually up to the peak frequency and remained approximately at a stable value at the higher frequencies. This spectral variation was parameterized by introducing a frequency-dependent velocity attenuation parameter inside the canopy. The spectral drag coefficient is shown to predict the distribution of energy dissipation with more accuracy than the integral coefficients, which results in a more accurate prediction of the mean wave period and spectral width of a wave field with vegetation.
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Petersen, E. L., and J. H. Adams. "Comparison of SEU rate prediction techniques." IEEE Transactions on Nuclear Science 39, no. 6 (1992): 1836–39. http://dx.doi.org/10.1109/23.211374.
Full textDissertations / Theses on the topic "SEU rate prediction and measurement"
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Howes, William A. "On-Orbit FPGA SEU Mitigation and Measurement Experiments on the Cibola Flight Experiment Satellite." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2474.
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This work presents on-orbit experiments conducted to validate SEU mitigation and detection techniques on FPGA devices and to measure SEU rates in FPGAs and SDRAM. These experiments were designed for the Cibola Flight Experiment Satellite (CFESat), which is an operational technology pathfinder satellite built around 9 Xilinx Virtex FPGAs and developed at Los Alamos National Laboratory. The on-orbit validation experiments described in this work have operated for over four thousand FPGA device days and have validated a variety of SEU mitigation and detection techniques including triple modular redundancy, duplication with compare, reduced precision redundancy, and SDRAM and FPGA block memory scrubbing. Regional SEU rates and the change in CFE's SEU rate over time show the measurable, expected effects of the South Atlantic Anomaly and the cycle of solar activity on CFE's SEU rates. The results of the on-orbit experiments developed for this work demonstrate that FPGA devices can be used to provide reliable, high-performance processing to space applications when proper SEU mitigation strategies are applied to the designs implemented on the FPGAs.
Book chapters on the topic "SEU rate prediction and measurement"
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Delnoij, L. E. C., J. P. W. Janssen, K. J. H. Dirkx, and R. L. Martens. "Designing an Online Self-assessment for Informed Study Decisions: The User Perspective." In Addressing Global Challenges and Quality Education, 74–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57717-9_6.
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AbstractThis paper presents the results of a study, carried out as part of the design-based development of an online self-assessment for prospective students in higher online education. The self-assessment consists of a set of tests – predictive of completion – and is meant to improve informed decision making prior to enrolment. The rationale being that better decision making will help to address the ongoing concern of non-completion in higher online education. A prototypical design of the self-assessment was created based on an extensive literature review and correlational research, aimed at investigating validity evidence concerning the predictive value of the tests. The present study focused on investigating validity evidence regarding the content of the self-assessment (including the feedback it provides) from a user perspective. Results from a survey among prospective students (N = 66) indicated that predictive validity and content validity of the self-assessment are somewhat at odds: three out of the five tests included in the current prototype were considered relevant by prospective students. Moreover, students rated eleven additionally suggested tests – currently not included – as relevant concerning their study decision. Expectations regarding the feedback to be provided in connection with the tests include an explanation of the measurement and advice for further preparation. A comparison of the obtained scores to a reference group (i.e., other test-takers or successful students) is not expected. Implications for further development and evaluation of the self-assessment are discussed.
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Nguyen, B. M., Yafei Wang, Sehoon Oh, Hiroshi Fujimoto, and Yoichi Hori. "GPS Based Estimation of Vehicle Sideslip Angle Using Multi-Rate Kalman Filter with Prediction of Course Angle Measurement Residual." In Lecture Notes in Electrical Engineering, 597–609. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33829-8_56.
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Carstensen, Bendix. "Parametrization and prediction of rates." In Epidemiology with R, 119–58. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198841326.003.0007.
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This chapter provides an overview of parametrizing quantitative covariate effects, that is, describing how variables (covariates) influence the outcome variable, be that disease odds, rates, or some quantitative measurement. It begins by differentiating between predictions and contrasts. When reporting rate ratios between two groups or the odds ratio of a disease associated with a certain difference in exposure, one is using contrasts of the outcome variable between different values of a covariate to describe the effect. Thus, one uses ratios or differences. But when reporting the mortality rate in, say, 60-year-old males, one is making a prediction of the outcome. This requires a set of values for all covariates in a model. When describing the effects of covariates by a model, one normally uses a linear predictor. The chapter then discusses the prediction of a single rate; categorical variables; the task of modelling the effect of quantitative variables; quantitative predictors; and quantitative interactions.
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Doveton, John H. "Permeability Estimation." In Principles of Mathematical Petrophysics. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199978045.003.0008.
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Because it is a measure of flow, permeability is a vector quantity, as contrasted with conventional petrophysical log data, which are responses to static properties of the rock. In the absence of a direct measurement of permeability, predictions must be inferred from the rock framework characteristics that control the ability of fluids to move through the rock. In this chapter, we consider methods that predict absolute permeability, that is, permeability with respect to a single fluid. This is the most widely used meaning of the term and would be immediately applicable to aquifers. In engineering applications to reservoirs, a relative permeability is assigned to each fluid phase, so that relative fluid rates and volumes can be characterized explicitly. Although the fundamental physics of permeability in tubes has been understood for many years, reliable estimations are difficult to make in all but the simplest rock types. As we shall see, one approach attempts to adapt modifications to a tube model to accommodate the complexity of pore-system geometry. This model-driven methodology tends to be favored by engineers and contrasts with a data-driven geological approach that applies empirical relationships from core data from mercury porosimetry measurements. The most fundamental property used to predict permeability is that of pore volume. Both porosity and permeability are routine measurements from core analysis. If a useable relationship can be developed to predict permeability from porosity, then predictions of permeability can be made in wells that were logged with conventional measurements but not cored. The simplest quantitative methods used to predict permeability from logs have been keyed to empirical equations of the type: . . . log k = P +Q.Φ or log k = P +Q.log Φ. . . where P and Q are constants determined from core measurements and applied to log measurements of porosity (Φ) to generate predictions of permeability (k). These equations are the basis for statistical predictions of permeability in regression analysis, where porosity is the independent variable and logarithmically scaled permeability is the dependent variable. The fitted function minimizes the sum of the squared deviations of the permeability about the trend line.
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Doğan, Onur. "Data Linkage Methods for Big Data Management in Industry 4.0." In Optimizing Big Data Management and Industrial Systems With Intelligent Techniques, 108–27. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5137-9.ch005.
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In recent years, the use of various digital devices that continuously generate massive amounts of heterogeneous, structured or unstructured data has increased. In parallel to generation, data collection, storage, and analysis technologies have developed. Big data sources have a variety of data quality. Preparing and clearing data is one of the first step of mining big data. It is often important to address the full data set found in different data sources to achieve the right result. Various techniques have been used to increase the accuracy of the data comparison. Deterministic and probabilistic linkage algorithms are the two main techniques used in literature. They have different steps to reach qualified and integrated results. To easily interpret the results of the linkage algorithm, a confusion matrix can be used. Measurements such as sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate, are considered to evaluate output quality.
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Saltzman, W. Mark. "Diffusion in Biological Systems." In Drug Delivery. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195085891.003.0009.
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Drug diffusion is an essential mechanism for drug dispersion throughout biological systems. Diffusion is fundamental to the migration of agents in the body and, as we will see in Chapter 9, diffusion can be used as a reliable mechanism for drug delivery. The rate of diffusion (i.e., the diffusion coefficient) depends on the architecture of the diffusing molecule. In the previous chapter a hypothetical solute with a diffusion coefficient of 10-7 cm2/s was used to describe the kinetics of diffusional spread throughout a region. Therapeutic agents have a multitude of sizes and shapes and, hence, diffusion coefficients vary in ways that are not easily predictable. Variability in the properties of agents is not the only difficulty in predicting rates of diffusion. Biological tissues present diverse resistances to molecular diffusion. Resistance to diffusion also depends on architecture: tissue composition, structure, and homogeneity are important variables. This chapter explores the variation in diffusion coefficient for molecules of different size and structure in physiological environments. The first section reviews some of the most important methods used to measure diffusion coefficients, while subsequent sections describe experimental measurements in media of increasing complexity: water, membranes, cells, and tissues. Diffusion coefficients are usually measured by observing changes in solute concentration with time and/or position. In most situations, concentration changes are monitored in laboratory systems of simple geometry; equally simple models (such as the ones developed in Chapter 3) can then be used to determine the diffusion coefficient. However, in biological systems, diffusion almost always occurs in concert with other phenomena that also influence solute concentration, such as bulk motion of fluid or chemical reaction. Therefore, experimental conditions that isolate diffusion—by eliminating or reducing fluid flows, chemical reactions, or metabolism—are often employed. Certain agents are eliminated from a tissue so slowly that the rate of elimination is negligible compared to the rate of dispersion. These molecules can be used as “tracers” to probe mechanisms of dispersion in the tissue, provided that elimination is negligible during the period of measurement. Frequently used tracers include sucrose [1, 2], iodoantipyrene [3], inulin [1], and size-fractionated dextran [3, 4].
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Kaur, Parminder, Prabhpreet Kaur, and Gurvinder Singh. "Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 281–342. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch015.
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Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as neuro-fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using normal shrink homomorphic technique. Secondly, the features are extracted using gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM), intensity histogram (IH), and rotation invariant moments (IM). Thirdly, neuro-fuzzy using genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of-the-art methods in terms of parameters such as sensitivity, specificity, recall, f-measure, and precision rate.
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Prasad Verma, Umesh, Madhurendra Narain Sinha, Pushan Kumar Dutta, and Subhra Mullick. "Stress-Strain Relationship: Postulated Concept to Understand Genetic Mechanism Associated with a Seismic Event." In Computational Optimization Techniques and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97377.
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In this study, we propose the design methodology for monitoring the earthquake and for detecting and tracking micro-seismic changes in the earthquake prediction system. The alert device includes these sensors will be drastically different from current early warnings using the dozens of seismometers network across seismically active regions for measurement of small acceleration signals directly and, as the first, low-noise stage of the instruments measuring low-noise velocity signals. Strain develops over considerable time in the overlying stratum at right angle to the applied shearing (max) stress, obeying the internal friction of the stratum, available seismic energy and law of stress–strain relationship. Using estimated energy (seismic), stress accumulation, the addition or subtraction in the strain rate due to stress developed can be analyzed for a seismic event. This concept may lead to better understanding of stress generation; build up, transfer and final drop. Then we propose a methodology to identify type of data can be used for the spectral analysis in earthquake seismology and what type of instrument can be used for the spectral analysis in for data acquisition.
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Salem, Osman, Alexey Guerassimov, Ahmed Mehaoua, Anthony Marcus, and Borko Furht. "Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models." In E-Health and Telemedicine, 466–86. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8756-1.ch024.
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This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.
Conference papers on the topic "SEU rate prediction and measurement"
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Kim, Kyu Tae, Hyung Ju Lee, Jong Guen Lee, Bryan D. Quay, and Domenic Santavicca. "Flame Transfer Function Measurement and Instability Frequency Prediction Using a Thermoacoustic Model." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-60026.
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The dynamic response of a turbulent premixed flame to an acoustic velocity perturbation was experimentally determined in a lean-premixed, swirl-stabilized, lab-scale gas turbine combustor. Fuel was injected far upstream of a choked inlet to eliminate equivalence ratio oscillations. A siren-type modulation device was used to provide acoustic perturbations at the forcing frequency of 100 ∼ 400 Hz. To measure global heat release rate, OH*, CH*, and CO2* chemiluminescence emissions were used. The two-microphone method was utilized to estimate inlet velocity fluctuations, and it was calibrated by direct measurements using a hot wire anemometer under cold-flow conditions. Gain of the flame transfer function (FTF) shows a low pass filter behavior, and it is well-fitted by a second-order model. Phase difference increases quasi-linearly with the forcing frequency. Using the n-τ formulation, gain and phase of FTF were incorporated into an analytic thermoacoustic model in order to predict instability frequencies and corresponding modal structures. Self-excited flame response measurements were also performed to verify eigenfrequencies predicted by the thermoacoustic model. Instability frequency predicted by the thermoacoustic model is supported by experimental results. Two instability frequency bands were measured in the investigated gas turbine combustor at all operating conditions: f ∼ 220 Hz and f ∼ 350 Hz. Results show that the self-excited instability frequency of f ∼ 220 Hz results from the fact that the flames amplify flow perturbations with f = 150 ∼ 250 Hz. This frequency range was observed in the flame transfer function measurements. The other instability frequency of f ∼ 350 Hz occurs because the whole combustion system has an eigenfrequency corresponding to the 1/4-wave eigenmode of the mixing section. This was analytically and experimentally demonstrated. Results also show that the flame length, LCH*max, plays a critical role in determining self-induced instability frequency.
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Wright, Chance, Thomas Dessein, Yanping Li, and Suzanne Ward. "Evaluation of Corrosion Growth Prediction Methodologies Using Burst Pressure Comparisons From Repeated In-Line Inspections." In 2018 12th International Pipeline Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipc2018-78294.
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At the forefront of the effort to understand and mitigate pipeline corrosion is the prediction of corrosion growth rates. It is important to understand the effect of corrosion growth estimates on integrity management decisions. An overly conservative approach results in unnecessary digs, while removing conservatism increases the potential for a missed feature to grow to a threatening size. While approaches to feature depth growth have been well-established, there has been less investigation into the growth of feature lengths. A literature review was performed on the methodologies applicable to length growth, and their performance was compared to those that only account for depth growth using a sample analysis. For pipelines with multiple in-line inspection (ILI) runs, feature or signal matching can be used to estimate the change in feature size. These rates can be used directly on individual features, averaged across pipe joints, or compiled into a statistical distribution. Alternatively, only one ILI measurement can be used and an assumption made on the age of the defect. These approaches are more commonly applied to depth growth but could be used to predict length growth as well. To compare the growth methodologies, the study used historical ILI measurements of a liquid pipeline to predict feature sizes and estimated burst pressures determined at the time of the latest ILI. The number of defects correctly predicted to have an insufficient burst pressure safety factor for safe operation was compared to the number of defects that were erroneously predicted to not meet this criterion, and those that were predicted to be safe but later found to not meet the safety factor requirement. The number of erroneously flagged defects was found to vary the most between methodologies. For the assessed data set, using the joint average rate based on feature box-matching was non-conservative on average. It was also found that incorporating length growth did not significantly affect the accuracy of the burst pressure predictions.
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Greco, Matteo, Roberta Ferri, Andrea Achilli, Stefano Gandolfi, Cinzia Congiu, Gustavo Cattadori, Fosco Bianchi, et al. "Two-Phase Flow Measurement Studies for the SPES3 Integral Test Facility for IRIS Reactor Simulation." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29306.
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The measurement of two-phase flow parameters has never been an easy task in the experimental thermal-hydraulics and the need of such measurements in the SPES3 facility has led to investigation of different possibilities and evaluation methods to determine mass flows and energies. This paper deals with the theoretical prediction of the two-phase mass flow rate by the development of a mathematical model for a spool piece, consisting of a drag disk, a turbine and a void fraction detector. Data obtained by simulation of DBAs in the SPES3 facility, with the RELAP5 thermal-hydraulic code, have provided the reference conditions for defining the main thermal-hydraulic parameter ranges and selecting a set of instruments potentially suitable to measure and derive the required quantities. The governing equation and the instrumentation output are defined for each device. Three different turbine models (Aya, Rouhani and volumetric) have been studied to understand which one better adapts to two-phase flow conditions and to investigate the best instrument combination. The mathematical model has been tested versus the RELAP5 results with a reverse process where calculated variables, like void fraction, quality and slip ratio, are given as input to a specifically developed program to get back the mass flow rate. The analytical results, verified versus the DVI break transient, well agree with the RELAP5 mass flow rate. Specific tests on proper experimental loops are required to verify the analytical studies.
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Eason, Cormac, Tara Dalton, Cian O’Mathu´na, Mark Davies, and Orla Slattery. "Direct Comparison Between a Variety of Microchannels: Part 2 — Experimental Description and Flow Friction Measurement." In ASME 2004 2nd International Conference on Microchannels and Minichannels. ASMEDC, 2004. http://dx.doi.org/10.1115/icmm2004-2330.
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Part 1 of this paper (Eason et al 2004) investigates the manufacturing of a variety of microchannels, produced by wet and dry etching in silicon, as well as precision mechanical sawing in silicon and thermoset plastic. This paper describes the experimental equipment and methods used to measure the pressure flow characteristics of the manufactured channels. A custom designed test system has been built in order to test each sample using the same inlet and outlet manifolds, pressure tappings, pumping system and instrumentation. The pressure drop across each set of channels was measured using an inductive pressure transducer. The mass flow rate through the system is measured by weighing the flow from the system in a given time. The measured pressure flow behaviour was compared with theoretical values as calculated from macro scale theory. Channel dimensions used for this calculation are as measured in part 1 of this paper. Error analysis was then carried out in order to determine the overall accuracy of the experimental work and determine whether any deviation from theoretical values is of experimental significance. This step is essential in any attempt to determine whether microchannel flows are indeed different to macro scale flows in a fundamental way. The deep reactive ion etched (DRIE) channels show the most significant lack of correlation with theoretical predictions. Compensation must be introduced to deal with the difference in cross section between the perfectly rectangular channels used for the theoretical prediction and the actual cross section of the channels.
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Lall, Pradeep, Sandeep Shantaram, Mandar Kulkarni, Geeta Limaye, and Jeff Suhling. "Constitutive Behavior of SAC Leadfree Alloys at High Strain Rates." In ASME 2011 Pacific Rim Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/ipack2011-52194.
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Electronic products are subjected to high G-levels during mechanical shock and vibration. Failure-modes include solder-joint failures, pad cratering, chip-cracking, copper trace fracture, and underfill fillet failures. The second-level interconnects may be experience high-strain rates and accrue damage during repetitive exposure to mechanical shock. Industry migration to leadfree solders has resulted in proliferation of a wide variety of solder alloy compositions. Few of the popular tin-silver-copper alloys include Sn1Ag0.5Cu and Sn3Ag0.5Cu. The high strain rate properties of leadfree solder alloys are scarce. Typical material tests systems are not well suited for measurement of high strain rates typical of mechanical shock. Previously, high strain rates techniques such as the Split Hopkinson Pressure Bar (SHPB) can be used for strain rates of 1000 per sec. However, measurement of materials at strain rates of 1–100 per sec which are typical of mechanical shock is difficult to address. In this paper, a new test-technique developed by the authors has been presented for measurement of material constitutive behavior. The instrument enables attaining strain rates in the neighborhood of 1 to 100 per sec. High speed cameras operating at 300,000 fps have been used in conjunction with digital image correlation for the measurement of full-field strain during the test. Constancy of cross-head velocity has been demonstrated during the test from the unloaded state to the specimen failure. Solder alloy constitutive behavior has been measured for SAC105, and SAC305 solders. Constitutive model has been fit to the material data. Samples have been tested at various time under thermal aging at 25°C and 125°C. The constitutive model has been embedded into an explicit finite element framework for the purpose of life-prediction of leadfree interconnects. Test assemblies has been fabricated and tested under JEDEC JESD22-B111 specified condition for mechanical shock. Model predictions have been correlated with experimental data.
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Youzwishen, Oliver O., Audrey Van Aelst, Peter F. Ehlers, and Alberto Nettel. "A Statistical Model for the Prediction of SCC Formation Along a Pipeline." In 2004 International Pipeline Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ipc2004-0267.
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Near-neutral stress corrosion cracking (SCC) is an operational integrity problem experienced by pipeline transporation companies since the 1970’s. Current in-line inspection (ILI) technology allows for the detection of SCC in pipelines using ultrasonic measurement. However, these tools have size limitations (not available for small diameter pipelines) and can only accurately detect cracks above a certain threshold dimension. Predictive modeling of SCC has been used when direct detection was not feasible. To date, predictive models have focused mainly on establishing quantitative relationships between environmental factors and SCC formation or growth. In general, models used to predict SCC growth have been more successful than models used to predict the location of SCC formation. A model to predict locations of SCC formation has been developed, in conjunction with a pipeline operator, by statistically analyzing data related to locations where SCC was either found or not found during investigative digs on a particular pipeline. Data acquired at the investigative dig sites (such as soil conditions, drainage patterns and local geography) was incorporated into the analysis. In addition, data acquired for the entire length of the pipeline (such as geometry, metal loss features, close-interval cathodic protection readings and operating pressures) was combined with the dig site data in the analysis process. The combined data set was analyzed using statistical regression techniques and various multi-variable logistic regression models were created. Misclassification analysis and regression tress were used to determine the most accurate model for application to the pipeline. The model was then applied to the pipeline to determine probabilities of SCC at specified increments along its length (approximately every 20 metres). Ten locations with high SCC probabilities were selected for verification excavation. In addition, one site with a lower SCC probability was chosen for excavation. Of the ten high-probability locations, SCC was discovered at seven sites. At the lower probability site, SCC was not discovered. The combined success rate of themodel was 73%, a significant improvement over predictive models previously applied to the pipeline. Additional investigative digs are planned to further test the model and to compare its predictions to SCC detected by a recently developed ultrasonic ILI tool. By examining the occurrence of SCC using statistical methods, the ability to make an unbiased prediction of the probability of SCC along a pipeline of interest has been achieved. The pipeline operator has gained an increased ability to assess the likelihood of SCC along its pipeline, showing due diligence in mitigating the risks associated with this pipeline integrity concern.
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Chen, Hongquan, Deepthi Sen, Akhil Datta-Gupta, and Masahiro Nagao. "Model-Free Assessment of Inter-Well Connectivity in CO2 WAG Projects Using Statistical Recurrent Unit Models." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205944-ms.
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Abstract Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. The study results in a reliable and efficient data analytics framework that is well-suited for large field applications.
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Pavlin, B., G. Carabin, G. Pernigotto, A. Gasparella, and Renato Vidoni. "An Embedded Mechatronic Device for Real-Time Monitoring and Prediction of Occupants’ Thermal Comfort." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87632.
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It is well recognized in the literature that thermal sensation and comfort are dependent on both core and skin temperatures. In particular, some regions of the human skin, such as the forehead, have a higher density of thermal receptors, giving a higher sensitivity to the skin temperature. Some studies suggest that the forehead skin temperature and its rate ofchange alone could potentially be a good indicator of one’s overall thermal comfort. To validate this claim, an idea for a smart sensor which would be able to read the occupants’ forehead temperature and other environmental parameters in a proximal way is here considered. Moreover, with the aim of exploiting the system not only in lab or test facility environments but, considering the 4.0 revolution, also in the building automation context, a non-invasive solution has been searched so as the occupants are not disturbed while the measurement is performed. Therefore, in this study, a new cheap and smart mechatronic sensor device for a non-invasive measurement of the occupants’ thermal comfort is proposed. The main components consist of a central unit, i.e. microprocessor, a small infrared sensor for thermal imaging, i.e a Lepton infrared camera by FLIR, as well as other sensors for measuring distance, humidity and temperature. The setup was imagined as the sensor being placed on the top of each desk, so it is not easily obstructed by a laptop or a similar object that can be found on top of the working surface. After the conceptual hardware definition and software development, an accurate experimental calibration has been performed exploiting an ad-hoc developed set-up based on a hot plate with an emissivity factor similar to the one of the human skin and with adjustable temperature. Finally, a first design for embedding the whole smart mechatronic system in a unique box has been developed and built.
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Walsh, Ed, Roy Myose, and Mark Davies. "A Prediction Method for the Local Entropy Generation Rate in a Transitional Boundary Layer With a Free Stream Pressure Gradient." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30231.
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To design an aerodynamically efficient blade the distribution of entropy generation on the blade surface should be known. Having only knowledge of the integrated loss, makes the task of improving the efficiency of a blade extremely difficult. A method to predict the entropy generation rate in steady, two-dimensional, incompressible, adiabatic boundary layer flows is presented, which gives both the distribution and magnitude of the entropy generation rate. This prediction method is based upon five correlations which are used to determine the: 1. entropy generated in the laminar region; 2. entropy generated in the turbulent region; 3. location of transition; 4. length of transition; 5. entropy generated in the transition region. These are then used to predict the entropy generation rate on the suction surface of a turbine rotor blade at a moderate Reynolds number; comparisons are then drawn with past measurements. The aim is to develop a quick, simple and relatively accurate method for the prediction of entropy in the boundary layers of turbomachines, although the method is not confined to this application. The only information required to implement this prediction method is the boundary layer edge velocity distribution and the turbulence intensity. A benefit of this method is that it does not rely upon dissipative CFD predictions, which are both slow to use in a design process and not yet sufficiently trustworthy. The dissipation coefficient and entropy generation rate predicted for this test case compare well to experimental measurements, with the percentage difference between the integrated entropy measured and predicted being approximately 13%. However, the difference in the turbulent region is found to be as high as 30%.
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Chana, Kam S., Udai K. Singh, and Thomas Povey. "Turbine Heat Transfer and Aerodynamic Measurements and Predictions for a 1.5 Stage Configuration." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53951.
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This paper considers the surface heat transfer rate to, and aerodynamics of, a 1.5 stage turbine — a high pressure (HP) turbine stage followed by an intermediate pressure (IP) vane. Unsteady interactions arising from the relative motions of vane/blade rows are examined. The IP vane was of structural type, and was designed with a vane count of 26. Measurements of time-mean heat transfer and aerodynamics are seldom performed in 1.5 stage turbines, because of the necessary complexity of the experimental setup required for such investigations. This paper presents both steady and unsteady measurements conducted in an engine-size research turbine, operated at engine-representative non-dimensional conditions. Measurements are compared to the predictions from a three-dimensional, viscous, non-linear, time accurate flow solver. An unstructured grid was used, and, by using the Erdos direct storage technique, the code was extended to allow for non-integer vane/blade pitch ratios. A prediction was performed in which all three vane/blade rows were simultaneously modelled. The measured and predicted unsteady surface heat transfer rates for the IP vane and HP rotor blade were in good agreement, and demonstrated that the interactive effects arising from the relative motion of these components were significant.
Reports on the topic "SEU rate prediction and measurement"
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Koga, Rokutaro, Wojciech A. Kolasinski, and Michael T. Marra. Techniques of Microprocessor Testing and SEU (Single Event Upset)-Rate Prediction. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada173176.
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