Relevant bibliographies by topics / Fracture risk estimation

Academic literature on the topic 'Fracture risk estimation'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Fracture risk estimation"

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Adamopoulos, Panagiotis A., Abraham Pouliakis, Aris T. Spathis, Christine Kottaridi, and Petros Karakitsos. "Gene Polymorphisms and their Transcripts as Factors for Computerized Assessment of Fracture Risk." International Journal of Reliable and Quality E-Healthcare 4, no. 3 (July 2015): 27–46. http://dx.doi.org/10.4018/ijrqeh.2015070103.

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The aim of this study is the evaluation of osteoporosis related genes and transcription products, demographic and medication data for the estimation of fracture risk in women with osteoporosis. Such estimations are important for the creation of computerized applications similar to the Fracture Risk Assessment Tool (FRAX®). Low cost Internet based tools were used for the creation of anonymised patient record stored in the Cloud, and for an interdisciplinary team to decide on patient inclusion via voice and video conference. 100 female patients with an osteoporotic fracture, on medication or not were included in the study. A series of seven biochemical parameters using the Luminex® technique were measured and genetic characteristics using the microarray Metabone® technique were analysed. The statistical analysis indicated that these new parameters can be important for the estimation of fracture risk; thus computer based risk estimation for fractures could be based on additional molecular data.
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Papakonstantinopoulou, Konstantina, and Ioannis Sofianos. "The role of cystatin C in the risk of hip fractures in the elderly." Journal of Research and Practice on the Musculoskeletal System 4, no. 4 (December 1, 2020): 108–12. http://dx.doi.org/10.22540/jrpms-04-108.

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World’s population is aging. The elderly are at high risk for both chronic kidney disease (CKD) and hip fractures. Severe chronic kidney disease is a well-known risk factor for fractures and death especially in the elderly. Mild and moderate stages of kidney disease are often undiagnosed and/or untreated, thus their effect on fracture risk is not well established. Many ways of estimating glomerular filtration rate (GFR) have been developed but there are very few studies recommending the best and most valuable method for estimated GFR (eGFR) calculation that could correlate with fracture risk. In this mini- review we searched the literature concerning the use of cystatin C in the estimation of GFR related to the risk of hip fractures in the elderly. Our goal was to review the most important recent evidence on whether cystatin C could become a useful biomarker for the prediction of fracture risk. We concluded that there is evidence to support the use of cystatin C in hip fracture risk prediction in elder patients with chronic kidney disease.
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Mok, Chi Chiu, Sau Mei Tse, Kar Li Chan, and Ling Yin Ho. "Estimation of fracture risk by the FRAX tool in patients with systemic lupus erythematosus: a 10-year longitudinal validation study." Therapeutic Advances in Musculoskeletal Disease 14 (January 2022): 1759720X2210744. http://dx.doi.org/10.1177/1759720x221074451.

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Background: The fracture risk assessment tool has been widely used to stratify the 10-year fracture risk to guide therapy. Using the actual fracture data of a 10-year longitudinal cohort of older patients with systemic lupus erythematosus, we reported an underestimation of the tool in predicting major symptomatic osteoporotic fractures. Treatment of osteoporosis in systemic lupus erythematosus should not be based on fracture risk estimation alone. Relevant time-dependent risk factors should be taken into account for an individualized decision. Objective: To compare the observed fracture incidence in a 10-year longitudinal cohort of patients with systemic lupus erythematosus (SLE) with the fracture risk prediction from the fracture risk assessment (FRAX) tool. Methods: Adult patients (⩾40 years) with SLE who had a first DEXA scan performed in 2005–2009 were studied. The 10-year rates of major osteoporotic and hip fractures were estimated by FRAX using clinical data at DEXA with adjustment for prednisolone dosage. The actual incidence of clinical fractures at 10 years was compared with the estimated rates. Factors associated with new fractures were studied by logistic regression. Results: A total of 229 SLE patients were studied (age: 50.2 ± 6.6 years, 93% women). Glucocorticoid was used in 148 (65%) patients at baseline (mean dose: 7.3 ± 6.9 mg/day; 34% ⩾ 7.5 mg/day). Osteoporosis (bone mineral density T score ⩽ –2.5) at the hip, femoral neck, or spine was present in 61 (27%) patients. The estimated 10-year risk of major osteoporotic and hip fractures by FRAX was 3.4 ± 4.5% and 0.95 ± 2.3%, respectively. After 10 years, three patients developed hip fracture, 6 patients had limb fractures and 20 patients had symptomatic vertebral fractures (major osteoporotic fracture 12.7%, hip fracture 1.3%). The actual major osteoporotic fracture rate was significantly higher than the FRAX estimation (12.7% vs 3.4%; p < 0.001). Logistic regression revealed that osteoporosis (odds ratio (OR): 4.07 [1.51–10.9]), previous fragility fracture (OR: 3.18 [1.02–9.90]), and a parental history of fracture (OR: 4.44 [1.16–17.0]) were independently associated with new clinical fractures at 10 years. Conclusion: The FRAX tool underestimates the major clinical fracture risk at 10 years in patients with SLE.
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Fujiwara, Saeko. "Estimation of absolute risk for fracture." Nippon Ronen Igakkai Zasshi. Japanese Journal of Geriatrics 46, no. 2 (2009): 128–30. http://dx.doi.org/10.3143/geriatrics.46.128.

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Binkley, Neil, and E. Michael Lewiecki. "The evolution of fracture risk estimation." Journal of Bone and Mineral Research 25, no. 10 (September 21, 2010): 2098–100. http://dx.doi.org/10.1002/jbmr.230.

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Kudlacek, S., B. Schneider, A. Resch, P. Bernecker, P. Pietschmann, R. Willvonseder, and H. Resch. "The fracture risk in males: Estimation of a fracture threshold." Bone 16, no. 3 (March 1995): 403. http://dx.doi.org/10.1016/8756-3282(95)90464-6.

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Mitchell, Paul James, and C. Chem. "Secondary prevention and estimation of fracture risk." Best Practice & Research Clinical Rheumatology 27, no. 6 (December 2013): 789–803. http://dx.doi.org/10.1016/j.berh.2013.11.004.

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Li, Wenjun, John Kornak, Tamara B. Harris, Joyce Keyak, Caixia Li, Ying Lu, Xiaoguang Cheng, and Thomas Lang. "Bone fracture risk estimation based on image similarity." Bone 45, no. 3 (September 2009): 560–67. http://dx.doi.org/10.1016/j.bone.2009.04.250.

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Saleh, C., M. Bukhari, and S. M. Bilgrami. "AB0913 PREDICTING PATIENTS AT RISK OF FRAGILITY FRACTURE WITH NORMAL BONE MINERAL DENSITY: AN OBSERVATIONAL STUDY." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 1758.1–1758. http://dx.doi.org/10.1136/annrheumdis-2020-eular.4544.

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Background:There is an increased risk of low-trauma fracture as bone mineral density (BMD) decreases, however a large proportion of these fragility fractures occur in those without osteoporosis or osteopenia. The widely used FRAX tool uses femoral neck (FN) BMD, amongst other parameters, to predict fracture risk. In those with normal BMD, data is lacking on the weight these other parameters hold in predicting future risk. Indeed, FN BMD can be facultative in the estimation of risk when using FRAX.Objectives:To establish predictors of fragility fracture in a patient cohort referred for BMD estimation, subsequently found to have bilateral FN BMD of greater than 1.Methods:A cohort of patients in the North West of England referred between 2004 and 2014 for BMD estimation, with both left and right FN BMD of greater than 1 were identified. Patient parameters identified and analysed included age at scan, gender, BMD at left hip, body mass index (BMI), fat mass, family history of fracture, alcohol history of 3 or more units per day, smoking status, rheumatoid arthritis (RA), and steroid exposure. Patients with fragility fracture were compared with those without fracture. Chi-square test and T-test were applied to categorical and continuous data respectively. Further univariate and multivariate logistic regression models were fitted to determine parameters associated with future fracture risk.Results:619 patients with bilateral FN BMD of greater than 1 were identified and included in analysis. Mean age at scan was 54 years (SD 11.82) and 542 (87.56%) were female. 92 (14.86%) patients had a fragility fracture. Mean left FN BMD was 1.91 (SD 0.71), and mean right FN BMD was 1.92 (SD 0.68). Results of the univariate analysis are described in Table 1 below.Table 1.Logistic regression analysis of patient parameters with unadjusted and adjusted odds ratios for fragility fracturePredictorUnadjusted odds ratio (95% CI)Odds ratio adjusted for age (95% CI)Odds ratio adjusted for age and gender (95% CI)Age at scan (years)0.99 (0.98-1.01)--Gender1.37 (0.66, 2.84)1.34 (0.64, 2.80)-BMD at left hip0.34 (0.03, 4.05)0.37 (0.03, 4.37)0.50 (0.03, 7.67)BMI1.07 (1.03, 1.10)1.07 (1.03, 1.10)1.07 (1.03, 1.10)Fat mass1.00 (1.00, 1.00)1.00 (1.00, 1.01)1.00 (1.00, 1.01)Parent fractured hip0.99 (0.57, 1.70)0.97 (0.56, 1.68)0.94 (0.54, 1.64)Alcohol (3 or more units/day)1.16 (0.47, 2.86)1.16 (0.47, 2.87)1.16 (0.47, 2.89)Current smoker1.40 (0.89, 2.21)1.40 (0.89, 2.21)1.42 (0.90, 2.23)Rheumatoid arthritis0.83 (0.32, 2.19)0.85 (0.32, 2.24)0.86 (0.34, 2.27)Steroid exposure0.53 (0.30, 0.96)0.53 (0.30, 0.96)0.54 (0.30, 0.98)Conclusion:Steroid exposure and body composition parameters influence fracture risk in this group pf patients with normal BMD, further work will be done looking at the types of fractures and other parameters in this group of patients.Disclosure of Interests:Christopher Saleh: None declared, Marwan Bukhari Speakers bureau: Bristol-Myers Squib, UCB celltech, Roche/Chugai, Pfizer, Abbvie, Merck, Mennarini, Sanofi-aventis, Eli-Lilly, Janssen, Amgen and Novartis., Syed Mujtaba Bilgrami Speakers bureau: Pfizer
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Brook, S., G. Todorov, and A. N. Comninos. "65 Comparison of Frax and Qfracture in Predicting Fragility Fractures in Patients Presenting with Falls." Age and Ageing 50, Supplement_1 (March 2021): i12—i42. http://dx.doi.org/10.1093/ageing/afab030.26.

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Abstract Introduction Falls are a major risk factor for fragility fractures and patients should be appropriately assessed to reduce future fragility fracture risk. National guidelines provide recommendations on assessing fracture risk using calculators to guide therapy initiation. FRAX and QFracture are the two main calculators used, however they differ considerably in their inputs. The aim of this study was to compare the risk estimation and performance between these two frequently used calculators to help determine their appropriate utility. Methods Data from patients aged ≥70 years admitted with a fall to the Acute Medical Units at Charing Cross Hospital between 1st Dec 2018–31st March 2019 were retrospectively collected, covering all inputs required for the two risk calculators. The 10-year major osteoporotic and hip fracture risks were calculated using FRAX and QFracture and compared. The one-year major osteoporotic and hip fracture risks from QFracture were assessed against actual one-year fracture rates. Results Conclusions Risk calculators are effective tools to aid the decision of bone therapy initiation. Here we demonstrate that there is a strong correlation between the two commonly used calculators. However, in terms of absolute risk values there is a mean 8.9% difference with QFracture providing higher risks in this “fallers” group. As absolute treatment thresholds are frequently used to guide bone therapy initiation, opposing recommendations may result. Therefore, there is a need to further explore calculator performance and determine which would more accurately serve different patient groups.
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Books on the topic "Fracture risk estimation"

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Lee, Christoph I. Decision Rules for Bone Densitometry Testing. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0034.

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This chapter, found in the bone, joint, and extremity pain section of the book, provides a succinct synopsis of a key study examining the use of decision rules for bone densitometry testing to mitigate risks for fractures associated with osteoporosis. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. Researchers found that the Osteoporosis Risk Assessment Instrument and Simple Calculated Osteoporosis Risk Estimation decision rules performed the best for targeting dual-energy x-ray absorptiometry testing among high-risk patients. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Book chapters on the topic "Fracture risk estimation"

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Haidekker, Mark A., and Geoff Dougherty. "Medical Imaging in the Diagnosis of Osteoporosis and Estimation of the Individual Bone Fracture Risk." In Medical Image Processing, 193–225. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9779-1_9.

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Wang, Xiong, Yue Pan, Mingguang Li, and Jinjian Chen. "Determining and Estimating the Fracture Risk of Diaphragm Wall from Observed Lateral Deflection." In Lecture Notes in Civil Engineering, 884–92. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5217-3_89.

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"Osteoporosis—Fracture risk estimation." In Dictionary of Rheumatology, 161. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-79280-3_840.

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Conference papers on the topic "Fracture risk estimation"

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Ciusdel, Costin Florian, Anamaria Vizitiu, Florin Moldoveanu, Constantin Suciu, and Lucian Mihai Itu. "Towards deep learning based estimation of fracture risk in osteoporosis patients." In 2017 40th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2017. http://dx.doi.org/10.1109/tsp.2017.8076069.

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Ciusdel, Costin-Florian, Anamaria Vizitiu, Florin-Dumitru Moldoveanu, Constantin Suciu, and Lucian-Mihai Itu. "Towards real time machine learning based estimation of fracture risk in osteoporosis patients." In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE, 2017. http://dx.doi.org/10.1109/optim.2017.7975126.

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Kraut, Roni, Mao Ding, and Oksana Babenko. "153 Fracture risk estimation with FRAX®: is the bone mineral density test necessary?" In Preventing Overdiagnosis Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/bmjebm-2022-podabstracts.74.

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Li, Wenjun, John Kornak, Tamara Harris, Ying Lu, Xiaoguang Cheng, and Thomas Lang. "Hip fracture risk estimation based on principal component analysis of QCT atlas: a preliminary study." In SPIE Medical Imaging, edited by Xiaoping P. Hu and Anne V. Clough. SPIE, 2009. http://dx.doi.org/10.1117/12.811743.

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Prasun, Samir, and Andrew K. Wojtanowicz. "Statistical Assessment of Alternative Methods for Well Recovery Estimation in Naturally Fractured Reservoirs With Fracture Corridors." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19355.

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Abstract Reliable predictions of well recovery are crucial for designing reservoir development. In the bottom-water naturally-fractured reservoirs (NFRs), comprising a network of distributed fracture “corridors,” spacing (and apertures) of the corridors varies throughout the reservoir. This makes oil well’s recovery a probabilistic variable as it depends upon uncertain well’s location in the network. The uncertainty is two-fold; it concerns well’s location within corridor network and well’s possible intersection with the nearest corridor. In any network’s location (with closely- or sparsely–spaced corridors), wells may intercept fracture corridors (fracture well) or go in-between two corridors in a matrix block (matrix-well). A simplified way of estimating well recovery is to ignore well’s location within corridor network and consider only probability and performance of fracture well and matrix well in a statistically-equivalent reservoir with uniform spacing and aperture equal to their expected values derived from their known statistics. Another (fully probabilistic) method considers the combined probabilities of the well’s location in the network and being a fracture well or matrix well. The study evaluates discrepancy between the two methods, explains its statistical nature, and demonstrates their implementation in a corridor-type NFR described in the literature. In the study, recovery process is simulated by coupling the inner (near-well) zone’s discrete single-porosity flow model with the outer zone Dual Porosity Dual Permeability (DPDP) simulator. The matrix well’s inner zone extends from the well to the nearest corridor and for the fracture well inner zone covers the corridor and adjacent matrix blocks. In the simulations, matrix and fracture-wells are operated at maximum rate constrained by minimum downhole flowing pressure and the surface handling limit. The study is performed using statistical data from a corridor-type NFR with power-law-distributed spacing size from 19 ft to 153 ft and corridor apertures varying from 8ft to 31ft correlated with the spacing. The simplified method gives recovery values ranging from 28% to 37%, and the single value of total recovery 33% — normalized by the matrix and corridor size fractions of the total reservoir area. Alternatively, the probabilistic method gives two separate distributions of the fracture and matrix wells’ recoveries that are weighted by their probability and converted to a single distribution of total recovery using statistical concept of weighted average. The probabilistic estimation gives higher values of recovery — from 32% to 38% with the expected value of 36.6%. Moreover, there is a considerable 30% probability of having recovery greater than 36.6%. A mathematical proof provides explanation why the probabilistic method gives recovery estimate greater than that from the simplified method. Another advantage of the method is the cumulative probability plot of well recovery that, in practical applications, would let operators make reservoir development decisions based upon the risk-benefit consideration.
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Li, Wenjun, John Kornak, Caixia Li, Alain Koyama, Isra Saeed, Ying Lu, and Thomas Lang. "Hip fracture risk estimation based on bone mineral density of a biomechanically guided region of interest: a preliminary study." In Medical Imaging, edited by Xiaoping P. Hu and Anne V. Clough. SPIE, 2008. http://dx.doi.org/10.1117/12.769588.

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Enright, M. P., R. C. McClung, S. J. Hudak, and H. R. Millwater. "Application of Nonparametric Methods to Rainflow Stress Density Estimation of Gas Turbine Engine Usage." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90780.

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The risk of fracture associated with high energy rotating components in aircraft gas turbine engines can be sensitive to small changes in applied stress values which are often difficult to measure and predict. Although a parametric approach is often used to characterize random variables, it is difficult to apply to multimodal densities. Nonparametric methods provide a direct fit to the data, and can be used to estimate the multimodal densities often associated with rainflow stress data. In this paper, a comparison of parametric and nonparametric methods is presented for density estimation of rainflow stress profiles associated with military aircraft gas turbine engine usages. A nonparametric adaptive kernel density estimator algorithm is illustrated for standard parametric probability density functions and for rainflow stress pairs associated with F-16/F100 engine usages. The kernel estimates are compared to parametric estimates, including a hybrid approach based on separate treatment of maximum stress pairs. The results provide some insight regarding the strengths and weaknesses of parametric and nonparametric density estimation methods for gas turbine engines, and can be used to develop improved stress estimates for probabilistic life predictions.
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Fong, Jeffrey T., Stephen R. Gosselin, Pedro V. Marcal, James J. Filliben, N. Alan Heckert, and Robert E. Chapman. "A Risk-Uncertainty Formula Accounting for Uncertainties of Failure Probability and Consequence in a Nuclear Powerplant." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-25168.

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This paper is a continuation of a recent ASME Conference paper entitled “Design of a Python-Based Plug-in for Benchmarking Probabilistic Fracture Mechanics Computer Codes with Failure Event Data” (PVP2009-77974). In that paper, which was co-authored by Fong, deWit, Marcal, Filliben, Heckert, and Gosselin, we designed a probability-uncertainty plug-in to automate the estimation of leakage probability with uncertainty bounds due to variability in a large number of factors. The estimation algorithm was based on a two-level full or fractional factorial design of experiments such that the total number of simulations will be small as compared to a Monte-Carlo method. This feature is attractive if the simulations were based on finite element analysis with a large number of nodes and elements. In this paper, we go one step further to derive a risk-uncertainty formula by computing separately the probability-uncertainty and the consequence-uncertainty of a given failure event, and then using the classical theory of error propagation to compute the risk-uncertainty within the domain of validity of that theory. The estimation of the consequence-uncertainty is accomplished by using a public-domain software package entitled “Cost-Effectiveness Tool for Capital Asset Protection, version 4.0, 2008” (http://www.bfrl.nist.gov/oae/ or NIST Report NISTIR-7524), and is more fully described in a companion paper entitled “An Economics-based Intelligence (EI) Tool for Pressure Vessels & Piping (PVP) Failure Consequence Estimation,” (PVP2010-25226, Session MF-23.4 of this conference). A numerical example of an application of the risk-uncertainty formula using a 16-year historical database of probability and consequence of main steam and hot reheat piping systems is presented. Implication of this risk-uncertainty estimation tool to the design of a risk-informed in-service inspection program is discussed.
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Ko, Han-Ok, Jae-Boong Choi, and Young-Jin Kim. "A Robust Method to Determine Statistical Parameters for Cleavage Fracture Evaluation." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77214.

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During the last couple of decades, several design guidelines and codes relating to major nuclear components have been introduced and revised for risk-based design and evaluation. It is an important process to determine statistical parameters in practice, for instance, by using the traditional method of moments and Maximum Likelihood Method (MLM). Since appropriate estimation of the parameters is not easy due to mathematical complexity, a robust method adopting concepts of both Chi-Square test and genetic algorithm (GA) is proposed in this paper. The Chi-Square test is a useful technique to get the goodness-of-fit of distributions, which is represented in terms of error between observed frequencies and frequencies calculated by assumed probability density function (PDF) of certain statistical distribution. The GA is an efficient optimization algorithm to solve nonlinear optimum problems. Using the Chi-Square test, statistical parameters can be determined and transferred to an optimum problem, and then solved by the GA employing proper nonlinear objective function. Reliability of the proposed method is verified against fracture toughness test data sets of SA508 reactor pressure vessel material obtained from PCVN specimens at various temperatures. The large scatter of experimental data is examined in use of a distribution reported by Neville and Kennedy, Burr type III and XII distributions by Nadarajah and Kortz as well as well-known Weibull distribution. A systematic assessment is carried out by using the new method and its results are compared with corresponding ones derived from the traditional method. Pros and corns of the alternative distributions as well as technical findings from the statistical assessment are fully discussed to show applicability of them.
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Enright, M. P., and S. J. Hudak. "Probabilistic Mission Identification of Aircraft Engine Usage Using Nonparametric Density Estimation Techniques." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27176.

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Fatigue life prediction of aircraft gas turbine engine rotating components requires estimates of the applied stress values throughout the life of the component. These values may vary considerably from flight-to-flight, and are highly dependent upon the mission type. However, engine flight data recorders currently do not have the capability to identify the mission type, so an automated mission identification method would greatly improve remaining life predictions. In this paper, a method is presented for predicting the most likely mission type for a given flight history. It is based on volume integration of the joint probability densities that are common to both the flight history and a standard mission. An analytical framework is presented, including a brief description of the adaptive kernel method used to estimate the probability densities of the flight history and standard mission. The effectiveness of the method is illustrated using rainflow stress data associated with actual flight histories. The results can be used to improve fatigue and fracture risk predictions of military gas turbine engines.
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Reports on the topic "Fracture risk estimation"

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Lacerda Silva, P., G. R. Chalmers, A. M. M. Bustin, and R. M. Bustin. Gas geochemistry and the origins of H2S in the Montney Formation. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329794.

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The geology of the Montney Formation and the geochemistry of its produced fluids, including nonhydrocarbon gases such as hydrogen sulfide were investigated for both Alberta and BC play areas. Key parameters for understanding a complex petroleum system like the Montney play include changes in thickness, depth of burial, mass balance calculations, timing and magnitudes of paleotemperature exposure, as well as kerogen concentration and types to determine the distribution of hydrocarbon composition, H2S concentrations and CO2 concentrations. Results show that there is first-, second- and third- order variations in the maturation patterns that impact the hydrocarbon composition. Isomer ratio calculations for butane and propane, in combination with excess methane estimation from produced fluids, are powerful tools to highlight effects of migration in the hydrocarbon distribution. The present-day distribution of hydrocarbons is a result of fluid mixing between hydrocarbons generated in-situ with shorter-chained hydrocarbons (i.e., methane) migrated from deeper, more mature areas proximal to the deformation front, along structural elements like the Fort St. John Graben, as well as through areas of lithology with higher permeability. The BC Montney play appears to have hydrocarbon composition that reflects a larger contribution from in-situ generation, while the Montney play in Alberta has a higher proportion of its hydrocarbon volumes from migrated hydrocarbons. Hydrogen sulphide is observed to be laterally discontinuous and found in discrete zones or pockets. The locations of higher concentrations of hydrogen sulphide do not align with the sulphate-rich facies of the Charlie Lake Formation but can be seen to underlie areas of higher sulphate ion concentrations in the formation water. There is some alignment between CO2 and H2S, particularly south of Dawson Creek; however, the cross-plot of CO2 and H2S illustrates some deviation away from any correlation and there must be other processes at play (i.e., decomposition of kerogen or carbonate dissolution). The sources of sulphur in the produced H2S were investigated through isotopic analyses coupled with scanning electron microscopy, energy dispersive spectroscopy, and mineralogy by X-ray diffraction. The Montney Formation in BC can contain small discrete amounts of sulphur in the form of anhydrite as shown by XRD and SEM-EDX results. Sulphur isotopic analyses indicate that the most likely source of sulphur is from Triassic rocks, in particular, the Charlie Lake Formation, due to its close proximity, its high concentration of anhydrite (18-42%), and the evidence that dissolved sulphate ions migrated within the groundwater in fractures and transported anhydrite into the Halfway Formation and into the Montney Formation. The isotopic signature shows the sulphur isotopic ratio of the anhydrite in the Montney Formation is in the same range as the sulphur within the H2S gas and is a lighter ratio than what is found in Devonian anhydrite and H2S gas. This integrated study contributes to a better understanding of the hydrocarbon system for enhancing the efficiency of and optimizing the planning of drilling and production operations. Operators in BC should include mapping of the Charlie Lake evaporites and structural elements, three-dimensional seismic and sulphate ion concentrations in the connate water, when planning wells, in order to reduce the risk of encountering unexpected souring.
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