Academic literature on the topic 'Model-free long-term prediction'
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Journal articles on the topic "Model-free long-term prediction"
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Szott, Wiesław, Piotr Łętkowski, Andrzej Gołąbek, and Krzysztof Miłek. "Modelling of the Long-Term Acid Gas Sequestration and Its Prediction: A Unique Case Study." Energies 13, no. 18 (September 9, 2020): 4701. http://dx.doi.org/10.3390/en13184701.
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A twenty-four-year on-going project of acid gas sequestration in a deep geological structure was subject to detailed modelling based upon a large set of geological, geophysical, and petrophysical data. The model was calibrated against available operational and monitoring data and used to determine basic characteristics of the sequestration process, such as fluid saturations and compositions, their variation in time due to fluid migrations, and the gas transition between free and aqueous phases. The simulation results were analysed with respect to various gas leakage risks. The contribution of various trapping mechanisms to the total sequestrated amount of injected gas was estimated. The observation evidence of no acid gas leakage from the structure was confirmed and explained by the simulation results of the sequestration process. The constructed and calibrated model of the structure was also used to predict the capacity of the analysed structure for increased sequestration by finding the optimum scenario of the risk-free sequestration performance.
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Waldron, Levi, Paul A. Cooper, and Tony Y. Ung. "Prediction of long-term leaching potential of preservative-treated wood by diffusion modeling." Holzforschung 59, no. 5 (September 1, 2005): 581–88. http://dx.doi.org/10.1515/hf.2005.095.
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Abstract An approach to modeling leaching and leaching impacts of preservative components from treated wood is presented based on three simple laboratory determinations: the amount of preservative component available for leaching (Le), equilibrium dissociation of preservative into free water in wood (Di) and diffusion coefficients for component leaching in different wood directions (D). In this study, the following inorganic wood preservative systems were investigated: chromated copper arsenate (CCA), the copper component of copper azole (CA) and alkaline copper quaternary (ACQ), and boron in disodium octaborate tetrahydrate (DOT). Aggressive leaching of finely ground wood showed that amounts of preservative compounds available for leaching were highest for borates, followed by copper in copper amine systems and arsenic in CCA, copper in CCA and chromium in CCA. The equilibrium dissociation or solubility of components in free water in the wood was much higher for borates and copper amine, followed by copper and arsenic in CCA and chromium in CCA. Use of the applicable diffusion coefficient (D) and Di or Le values in a diffusion model allows the prediction of total amount leached and emission or flux rate at different times of exposure for products with different dimensions and geometries. The approach was tested and generally validated through application of the model to results of laboratory water spray leaching of full-size lumber samples. The approach explains the rapid leaching of boron compounds (large diffusion coefficient and high initial dissociated concentration) compared to other preservative components and predicts that ACQ will have higher initial leaching rates compared to CCA and CA, but the latter preservatives will continue to leach copper at a measurable rate for a much longer time. The practical implications and limitations of the approach are discussed.
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Kim, Ji-Yeon, Kyunghee Park, Woong-Yang Park, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Se-Kyung Lee, Zhengyan Kan, and Yeon Hee Park. "Genomic characteristics of breast cancer to predict response of neoadjuvant chemotherapy and long-term prognosis." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 557. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.557.
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557 Background: To precisely predict neoadjuvant chemotherapy (NAC) response and long-term prognosis, we developed prediction model with clinical and genomic characteristics of breast cancer (BC). Methods: We included early and locally advanced BC that would be scheduled to receive standard NAC (four cycles of anthracycline and cyclophosphamide and four cycles of docetaxel or docetaxel plus trastuzumab for HER2+ BC) followed by curative surgery. For each patient, tumor tissue and matched blood were prospectively collected three times: at diagnosis (T1), three weeks after the first cycle of chemotherapy (T2), and curative surgery (T3). Whole exome sequencing (WES) was performed to detect somatic mutation, mutational signature and tumor mutational burden (TMB) while RNASeq with PAM50 prediction was to classify intrinsic subtype. In terms of clinical variables, clinical stage and IHC subtype at diagnosis, residual cancer burden (RCB) class and distant recurrence free survival (DRFS) were used. Logistic regression was used for predicting RCB class with clinical and genomic variables at T1. Univariate and multivariate Cox regression were performed to identify prognostic factors for DRFS. Results: In total, 210 patients were enrolled and treated with NAC as scheduled. We successfully conducted WES in 231 BC tissues (T1:117, T2:101 and T3:13) from 117 patients. In NAC response, 13 patients were in RCB class 3, 39 in class 2, 14 in class 1 and 46 in class 0. Median follow up duration was 44months and distant recurrence was observed in 13 patients. TP53 mutation (68%) was the most commonly detected genetic alteration. ARID1A, CDH1, CSMD3, LRP1B, PIK3CA, RUNX1 and TP53 were significantly mutated genes in driver gene analysis. Median TMB was 87 (range, 14-570) and signature 3 was most frequently observed. Among genetic characteristics, high TMB was significantly associated with better NAC response compared with low TMB (hazard ratio[HR] for RCB class III: 0.11, 95% confident interval[CI]: 0.01, 0.74, p = 0.05). In prediction model, combination of seven variables: intrinsic subtype, TMB, LRRK1, OPLAH, and PIK3CA hotspot mutation, ERBB2 amplification, and clinical stage had 0.83 in area under curve (AUC) and 0.75 in accuracy. High clinical stage, PTEN and PIK3CA hotspot mutation negatively affected to DRFS while high TMB had protective effect (all ps < 0.05). Prediction model made with five variables: intrinsic subtype, TMB, PTEN mutation, PIK3CA hotspot mutation and clinical stage had 0.88 in c-index (95% CI: 0.81, 0.95). Conclusions: TMB, PIK3CA hotspot mutation and clinical stage showed predictive roles on NAC response and distant recurrence of BC in NAC setting. In prediction model, intrinsic subtype, TMB, LRRK1, OPLAH, and PIK3CA hotspot mutation, ERBB2 amplification, and clinical stage affected to RCB class while intrinsic subtype, TMB, PTEN, PIK3CA hotspot mutation and clinical stage did to DRFS. Clinical trial information: NCT02591966.
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Huang, Yi-Cheng, and Yu-Hsien Chen. "Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process." Sensors 21, no. 15 (July 22, 2021): 4978. http://dx.doi.org/10.3390/s21154978.
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The complexity of the internal components of dental air turbine handpieces has been increasing over time. To make operations reliable and ensure patients’ safety, this study established long short-term memory (LSTM) prediction models with the functions of learning, storing, and transmitting memory for monitoring the health and degradation of dental air turbine handpieces. A handpiece was used to cut a glass porcelain block back and forth. An accelerometer was used to obtain vibration signals during the free running of the handpiece to identify the characteristic frequency of these vibrations in the frequency domain. This information was used to establish a health index (HI) for developing prediction models. The many-to-one and many-to-many LSTM frameworks were used for machine learning to establish prediction models for the HI and degradation trajectory. The results indicate that, in terms of HI predicted for the testing dataset, the mean square error of the many-to-one LSTM framework was lower than that that of a logistic regression model, which did not have a memory framework. Nevertheless, high accuracies were achieved with both of the two aforementioned approaches. In general, the degradation trajectory prediction model could accurately predict the degradation trend of the dental handpiece; thus, this model can be a useful tool for predicting the degradation trajectory of real dental handpieces in the future.
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Duan, Jianchun, Hua Bai, Yiting Sun, Fei Gai, Shenya Tian, and Wenfang Xu. "Construction and validation of an eight-gene risk prediction model for stage II-IIIA lung adenocarcinoma." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e20553-e20553. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e20553.
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e20553 Background: Clinical characters cannot precisely evaluate long-term survival of patients with resectable lung adenocarcinoma. Genomics studies of lung adenocarcinoma (LUAD) have advanced our understanding of LUAD's biology. Thus, genomics-based robust models predicting survival outcome for patients with operatable LUAD needs to be investigated. Here, we aimed to identify new gene signatures to construct a risk prediction model via integrating Omics data from The Cancer Genome Atlas (TCGA) to better evaluate the long-term clinical outcome of LUAD patients. Methods: A cohort of one hundred and eighty-nine stage II-IIIA lung adenocarcinoma cases receiving tumor resection were screened out and downloaded from TCGA database. Tumor samples without survival information and genes with low or no expression were removed. Genes associated with cancer and immune were further narrowed down using a Master Panel Gene Set (Amoydx). Lasso-Cox regression analysis was used to screen gene-survival outcome, and then a risk prediction model was established. LUAD cases were divided into high-risk or low-risk groups as per the scores, to assess differential expressed genes and pathways. Results: A total of 8 most survival outcome related genes (CLEC7A, PAX5, XCR1, KRT7, PLCG1, DKK1, CLEC10A, IKZF3) were identified after Lasso-Cox regression analysis and used for model construction. The overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) from the subgroups within the high- and low-risk groups were assessed and showed significant prolonged in low-risk group, the hazard ratio (HR) of OS was 2.72 (95%CI: 2.04-3.61, P = 5.91e-12) in high-risk group. Hierarchical clustering analysis, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) revealed that genes involved in immune responses were significantly suppressed in high-risk group, while as genes involved in antioxidative metabolism were activated, which gave us a hint that immune-metabolism interaction might play a vital role in determining the distal survival outcome of LUAD. Conclusions: Our risk prediction model enables precise evaluation of long-term survival for patients with LUAD. Further, it provides a novel and comprehensive understanding of biological impacts on LUAD prognosis, which offers new insights for future development of precise diagnostic and therapeutic approaches.[Table: see text]
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Ali, Ghulam, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem, and Claudia Martis. "IoT Based Smart Parking System Using Deep Long Short Memory Network." Electronics 9, no. 10 (October 15, 2020): 1696. http://dx.doi.org/10.3390/electronics9101696.
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Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.
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D'Anna, Maurizio, Deborah Idier, Bruno Castelle, Goneri Le Cozannet, Jeremy Rohmer, and Arthur Robinet. "IMPACT OF UNCERTAINTIES IN MODEL FREE PARAMETERS AND SEA LEVEL RISE ON SHORELINE CHANGES: A 20-YEAR HINDCAST AT TRUC VERT BEACH, SW FRANCE." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 10. http://dx.doi.org/10.9753/icce.v36v.management.10.
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Chronic erosion of sandy coasts is a continuous potential threat for the growing coastal communities worldwide. The prediction of shoreline evolution is therefore key issue for robust decision making worldwide, especially in the context of climate change. Shorelines respond to various complex processes interacting at several temporal and spatial scales, making shoreline reconstructions and predictions challenging and uncertain, especially on long time scales (e.g. decades or century). Despite the increasing progresses in addressing uncertainties related to the physics of Sea Level Rise, very little effort is made towards understanding and reducing the uncertainties related to wave driven coastal response. To fill this gap, we analyse the uncertainties associated with long-term (2 decades) modelling of the cross-shore transport dominated high-energy sandy coast around Truc Vert beach, SW France, which has been surveyed semi-monthly over the last 12 years.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/_NBJ2v-koMs
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Lee, Junghoon, Min Soo Choo, Sangjun Yoo, Min Chul Cho, Hwancheol Son, and Hyeon Jeong. "Intravesical Prostatic Protrusion and Prognosis of Non-Muscle Invasive Bladder Cancer: Analysis of Long-Term Data over 5 Years with Machine-Learning Algorithms." Journal of Clinical Medicine 10, no. 18 (September 20, 2021): 4263. http://dx.doi.org/10.3390/jcm10184263.
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We aim to investigate the significance of intravesical prostate protrusion (IPP) on the prognosis of non-muscle invasive bladder cancer (NMIBC) after the transurethral resection of bladder tumors (TURBT). For newly diagnosed NMIBC, we retrospectively analyzed the association between prognosis and IPP for at least a 5-year follow-up. A degree of IPP over 5 mm in a preoperative CT scan was classified as severe. The primary endpoint was recurrence-free survival, and the secondary endpoint was progression-free survival. The machine learning (ML) algorithm of a support vector machine was used for predictive model development. Of a total of 122 patients, ultimately, severe IPP was observed in 33 patients (27.0%). IPP correlated positively with age, BPH, recurrence, and prognosis. Severe IPP was significantly higher in the recurrence group and reduced in the recurrence-free survival group (p = 0.038, p = 0.032). Severe IPP independently increased the risk of intravesical recurrence by 2.6 times. The addition of IPP to the known oncological risk factors in the prediction model using the ML algorithm improved the predictability of cancer recurrence by approximately 6%, to 0.803. IPP was analyzed as a potential independent risk factor for NMIBC recurrence and progression after TURBT. This anatomical feature of the prostate could affect the recurrence of bladder tumors.
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Molot, Lewis A., and P. J. Dillon. "Nitrogen/Phosphorus Ratios and the Prediction of Chlorophyll in Phosphorus-Limited Lakes in Central Ontario." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 1 (January 1, 1991): 140–45. http://dx.doi.org/10.1139/f91-019.
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The response of mean ice-free chlorophyll a in 15 stratified, P-limited oligotrophic and mesotrophic lakes in central Ontario to changes in mean epilimnetic total phosphorus (TPepi) within a lake was highly variable between years during the period 1976–87. The linear regression coefficient of determination, R2, using all annual means was only 0.36, and within-lake regressions revealed mostly random associations between chlorophyll a and TPepi. Neverthless, by using the long-term average of annual means for each lake, a bivariate linear regression model was developed relating the long-term, average response of chlorophyll a to the long-term, average TPepi concentration in these lakes (R2 = 0.78). Annual variation could not be explained by changes in epilimnetic total nitrogen to total phosphorus ratio (TN/TP). The R2 increased slightly from 0.78 to 0.82 with TN/TP as a second independent variable using long-term averages but remained at 0.78 with 1/TPepi as a second variable. Reanalysis of published data excluding lakes which were not P limited showed that TN/TP is of little or no benefit as an independent variable. A minimum of six consecutive years of sampling was required to avoid anomalously poor fits (defined as R2 < 0.6) for this set of lakes.
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DeMaria, Mark. "A Simplified Dynamical System for Tropical Cyclone Intensity Prediction." Monthly Weather Review 137, no. 1 (January 1, 2009): 68–82. http://dx.doi.org/10.1175/2008mwr2513.1.
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Abstract A simplified dynamical system for tropical cyclone intensity prediction based on a logistic growth equation (LGE) is developed. The time tendency of the maximum sustained surface winds is proportional to the sum of two terms: a growth term and a term that limits the maximum wind to an upper bound. The maximum wind evolution over land is determined by an empirical inland wind decay formula. The LGE contains four free parameters, which are the time-dependent growth rate and maximum potential intensity (MPI), and two constants that determine how quickly the intensity relaxes toward the MPI. The MPI is estimated from an empirical formula as a function of sea surface temperature and storm translational speed. The adjoint of the LGE provides a method for finding the other three free parameters to make the predictions as close as possible to the National Hurricane Center best-track intensities. The growth rate is assumed to be a linear function of the vertical shear (S), a convective instability parameter (C) determined from an entraining plume, and their product, where both S and C use global model fields as input. This assumption reduces the parameter estimation problem to the selection of six constants. Results show that the LGE optimized for the full life cycle of individual storms can very accurately simulate the intensity variations out to as long as 15 days. For intensity prediction, single values of the six constants are found by fitting the model to more than 2400 Atlantic forecasts from 2001 to 2006. Results show that the observed intensity variations can be fit more accurately with the LGE than with the linear Statistical Hurricane Intensity Prediction Scheme (SHIPS) formulation, and with a much smaller number of constants. Results also show that LGE model solution (and some properties of real storms) can be explained by the evolution in the two-dimensional S–C phase space. Forecast and other applications of the LGE model are discussed.
Dissertations / Theses on the topic "Model-free long-term prediction"
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Freitag, Steffen, Wolfgang Graf, and Michael Kaliske. "Prognose des Langzeitverhaltens von Textilbeton-Tragwerken mit rekurrenten neuronalen Netzen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1244048026002-79164.
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Zur Prognose des Langzeitverhaltens textilbetonverstärkter Tragwerke wird ein modellfreies Vorgehen auf Basis rekurrenter neuronaler Netze vorgestellt. Das Vorgehen ermöglicht die Prognose zeitveränderlicher Strukturantworten unter Berücksichtigung der gesamten Belastungsgeschichte. Mit unscharfen Größen aus Messungen an Versuchstragwerken werden rekurrente neuronale Netze trainiert. Anschließend ist die unscharfe Prognose des Tragverhaltens möglich.
Conference papers on the topic "Model-free long-term prediction"
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Barrios, Lissett, and Vinay Pydah. "Perdido GoM Total Field ESP Integrated Full-Field Modeling for Surveillance and Forecast Prediction." In SPE Gulf Coast Section Electric Submersible Pumps Symposium. SPE, 2021. http://dx.doi.org/10.2118/204498-ms.
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Abstract The paper reports on the integrated modeling approach for the Gulf of Mexico ultra-Deepwater Perdido Electrical Submersible Pump (ESP) meant for performance surveillance and operational predictions n viscous fluid and multiphase flow regimes. The ESP system uses in Perdido field are multistage centrifugal pumps for high flow rate and high boost applications. Multiphase flow and viscous fluids introduce challenges for ESP operations such as performance degradation and system inefficiency which are imperative and to be considered in modeling work for accuracy and proper surveillance. The effect results from free gas and viscous fluid could be difficult to model and if not considered can result in wrong prediction of pump performance and power estimation over promising forecast and incorrect performance for surveillance and optimization. The objective of modelling work is aimed to correctly predict ESP performance by using proper modeling equations and correction factors that to understand the degradation on head, flow and horsepower, therefore further guide offshore surveillance activities and near and long term forecast predictions. The ESP system used in Perdido Field is 1025 series tandem P360 mixed flow type pumps with a 725 series 1600 hp motor. The modeling work revealed mathematical relationships among pump performance parameters (pump speed/frequency, brake horsepower, pump head and pump capacity) and free Gas/Viscosity correction factors required to accurately predict pump performance and forecasting. Using the correct approach for modeling high boost ESPs, an accuracy of 5% or less can be aimed with field operation for precise pump performance troubleshoot, optimization and long term forecast for economic analysis. The main technical contributions of this work are the detail modelling approach, analysis and data comparison with Field operation for predicting pump and motor performance under two important variables, high viscosity and two-phase flow inside the ESP.
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Wang, Jiamin, Sunit K. Gupta, and Oumar Barry. "Towards Data-Driven Modeling of Pathological Tremors." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22147.
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Abstract Understanding the dynamics of pathological tremors (e.g., Parkinson’s Disease, Essential Tremor) is crucial to developing effective treatments for these neurological disorders. This paper studies the data-driven modeling of periodic and quasiperiodic tremors. A general neuromusculoskeletal model is proposed to serve as the theoretical basis of this study. The Parkinsonian tremor data is first observed in terms of periodicity, frequency composition, and chaotic characteristics, which confirm tremor is a nonlinear dynamics problem. Two data-driven models are then proposed to predict the nonlinear dynamics of tremor: (1) a model-free approach via long short-term memory recurrent neural network, and (2) a model-based approach via extended dynamical mode decomposition. These models are compared to existing models and the results show that the proposed models outperform existing models for long term prediction of tremor.
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Yang, Xiaojun, Yingqi Hu, Tianhao Yu, and Zhigang Liu. "Numerical Modeling of Particle Deposition in Turbine Cascade." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90739.
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Abstract Impurities ingested in the fuel and air may deposit on the surface of turbine components during the operation of aero engines. The presence of such deposits will change the roughness of the blade surface and even seriously affect the aerodynamic performance of each turbine component and the overall safety of the engine. In this study, a new deposition prediction model was developed, which utilizes the User Defined Function (UDF) of Fluent and mesh reconstruction technique to simulate the long-term accumulation of deposition on a nozzle guide vane. Based on the critical viscosity model and the critical velocity model, considering the elasticity and viscosity of particles, some improvements to the model were proposed. The shear removal of individual particles and bulk deposits have been considered in the new deposition model. The simulation result of deposition distribution was verified by comparing with the experimental results obtained under the low temperature wax deposition. Its consistency and problems were also discussed. Subsequently, the increase of deposition thickness with time was shown. The effects of the detachment model and free flow temperature on the deposition distribution were studied. The results show that the deposition at the leading edge of turbine vanes increases fastest with time. The deposition distribution may be more accurate if the detachment model is taken into account. The amount of deposition on the vane pressure surface and stagnation line will increase with the increase of free flow temperature.
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Haque, Mohammad Shafinul. "Extrapolation of Creep Rupture Data Using Parametric Numerical Isothermal Datum (P-NID) Method for Inconel 617." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21316.
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Abstract The development of advanced power plants requires alloys to operate at elevated temperature and pressure for an extended period of time. It is critical to consider creep during the design process to avoid catastrophic failure. Creep rupture data are often not available for desired operating conditions. Accurate extrapolation of creep life is necessary. One of the earliest and most widely used life prediction model is the classic Larson-Miller Parametric (LM) model. Over time numerous time-temperature parametric (TTP) models have been proposed such as Manson-Haferd, Orr-Sherby-Dorn, Manson-Succop, Graham-Walles, Goldhoff-Sherby parametric models. Non-TTP models such as the Wilshire equation is available. The prediction models vary in mathematical form, and number of material constants but shares a common calibration approach. Each model is calibrated against data for every available isotherm. A recently proposed model calibration approach is the parametric numerical isothermal datum (P-NID) method that can be applied to an existing model for improved long-term extrapolation. The P-NID approach is different than the traditional approach as the data are transferred to a datum temperature followed by model calibration against the transferred data at the datum temperature. The calibrated model is then transferred back to the original temperatures. In this study, the P-NID method is applied to the LM model to perform extrapolation for Inconel 617 alloy. Creep rupture data for five isotherms ranging from 800 to 1000°C and stress levels from 9MPa to 170 MPa are used. A detail step by step procedure is provided for the application of the P-NID method to calibrate the LM model (LM-NID). The extrapolation performance of the classic LM and LM-NID models are compared. Normalized Mean Squared Error (NMSE) is used to analyze prediction accuracy. It is observed that the LM-NID model provides a realistic inflection free prediction compared to the LM model. A 10% data-cull from the lowest stress data is performed to assess the reliability of extrapolation. Based on the comparison a recommendation is provided.
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Carvalho de Castro, Henrique, and Bruno Henrique Groenner Barbosa. "Multi-gene Genetic Programming for Structure Selection of Polynomial NARMAX models." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1384.
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In the area of black-box identication, NARMAX models are of great interest. The main diculty faced when working with such models is the selection of the correct structure to represent the underlying system in the data. Orthogonal Least Squares (OLS) methods are widely used for this task, however, there are systems with a high degree of non-linearity and long term dependencies, which makes the use of traditional OLS methods computationally impracticable. In this sense, this paper studies the use of Multi-Gene Genetic Programing (MGGP) together with the traditional OLS method to increase the search space and turn the structure selection practicable for average performance computer. It is shown that, in real-life problem data, the algorithm can nd better models than previous works' models. The MGGP found a model for a hydraulic pumping system with a better one-step-ahead prediction error (0:058 mlc2 against 0:070 mlc2) using PEM technique and better free-run simulation error (0:997 mlc2 against 1:120 mlc2) using SEM technique. The MGGP found a model with such a degree of non-linearity and maximum input-output lags that totalizes 142505 candidate terms for traditional OLS analysis, which is impracticable for average performance computers.
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Ulveseter, Jan V., and Svein Sævik. "In-Line Vibrations of Flexible Pipes." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61325.
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A semi-empirical prediction tool for pure in-line vortex-induced vibrations is under development. The long-term goal is to be able to realistically model the dynamic behavior of free spanning pipelines exposed to arbitrary time dependent external flows at low velocities. Most VIV programs operate in frequency domain, where only steady currents and linear structural models can be simulated. In contrast, the proposed model predicts hydrodynamic forces as function of time, enabling a time integration scheme to solve the equation of motion. Non-linear time domain simulations allow for modelling of excitation from non-steady currents. In addition, non-linear effects such as soil-pipe interaction, varying tension, and response dependent material, stiffness and damping properties may be included in the analysis, when combining the hydrodynamic force model with a structural non-linear finite element model. Hydrodynamically, the proposed prediction tool consists of the general Morison equation plus two vortex shedding forcing terms. The latter two are able to synchronize with the structural motion for a given frequency band, to induce vibrations in lock-in regimes. In this paper, the proposed pure in-line VIV model is compared to the frequency domain model VIVANA and DNV Recommended Practice, simulating experiments with a model-scale flexible pipe exposed to current velocities at which cross-flow vibrations have not yet developed. A few experimental data points are included in verifying the performance of the newly developed time domain model. The effect of changing empirical coefficients in the vortex shedding forcing terms, and allowing only one of the terms to excite structural vibrations during a simulation, is numerically investigated. A goal is to obtain increased understanding of how the proposed time domain model performs when simulating VIV of a flexible pipe, which is more complex than that of an elastically mounted rigid cylinder since several natural frequencies and corresponding modes might be excited.
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Yang, Jing, Luis San Andrés, and Xueliang Lu. "Leakage and Dynamic Force Coefficients of a Pocket Damper Seal Operating Under a Wet Gas Condition: Tests vs. Predictions." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90331.
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Abstract High performance centrifugal compressors presently favor pocket damper seals (PDSs) as a choice of secondary flow control element offering a large effective damping coefficient to mitigate rotor sub synchronous whirl motions. Current and upcoming multiple-phase compression systems in subsea production facilities must demonstrate long term operation and continuous availability, free of harmful rotor instabilities. Plain annular seals and labyrinth seals are notoriously bad choices, whereas a PDS, by stopping the circulation of trapped liquid, operates stably. This paper presents experimental and computational fluid dynamics (CFD) results for the leakage and dynamic force coefficients obtained in a dedicated test facility hosting a fully partitioned PDS, four ribbed and with eight pockets per cavity. The test PDS, operating at a rotor speed 5,250 rpm (surface speed 35 m/s) and under a supply pressure/discharge pressure ratio up to 3.2, is supplied with a mixture of air and ISO VG 10 oil whose maximum liquid volume fraction (LVF) is 2.2%, equivalent to a liquid mass fraction of 84%. When supplied with just air (dry condition), the measured leakage increases nonlinearly with supply pressure. Under a wet gas condition, the recorded mass flow increases on account of the large difference in density between the liquid and the gas. CFD derived mass flow rates for both dry and wet gas conditions agree with the measured ones. The test dry gas PDS produces a direct dynamic stiffness (HR) increasing with frequency whereas the direct damping (C) and cross-coupled dynamic stiffness (hR) coefficients remain relatively constant. The CFD predicted damping agrees best with the test C albeit over predicting HR at low excitation frequencies and hR at all frequencies (< 175 Hz ∼ twice rotor speed). Under a wet gas condition with LVF = 0.4%, the test force coefficients show great variability over the excitation frequency range; in particular HR < 0, though growing with frequency due to the large liquid mass fraction. The CFD predictions, on the other hand, produce a dynamic direct stiffness HR > 0 for all frequencies. Both experimental hR and C for the wet gas PDS are larger than their counterparts for the dry gas seal. The CFD predicted C and hR, wet vs. dry, show a modest growth, yet remaining lower than the test data. The CFD derived flow field for a wet gas condition shows the seal radial partition walls (ridges) reduce the circumferential flow velocity and liquid accumulation within a pocket. Both the test data and CFD prediction show that the magnitude of the flexibility function for the PDS test system reduces when the two component mixture flows through the seal, hence revealing the additional effective damping, more pronounced for the test data rather than that from the predictions. Further work, experimental and CFD based, will continue to advance the technology of wet gas seals while bridging the gap between test data and computational physics model simulations.
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Gotoda, Hiroshi, Kenta Hayashi, Ryosuke Tsujimoto, Shohei Domen, and Shigeru Tachibana. "Dynamical Properties of Combustion Instability in a Laboratory-Scale Gas-Turbine Model Combustor." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-58170.
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We present an experimental study on nonlinear dynamics of combustion instability in a lean premixed gas-turbine model combustor with a swirl-stabilized turbulent flame. Intermittent combustion oscillations switching irregularly back and forth between a burst and pseudo-periodic oscillations exhibit the deterministic nature of chaos. This is clearly demonstrated by considering two nonlinear forecasting methods: the extended version [1] of the Sugihara & May algorithm [2] as a local predictor, and the generalized radial basis function network as a global predictor [3], [4]. The former enables us to extract the short-term predictability and long-term unpredictability nature of chaos, while the latter can produce surrogate data to test for determinism as a free running approach. Permutation entropy is estimated by a symbolic sequence approach for the surrogate data to test for determinism and is also used as an online detector to prevent lean blowout.
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Hendriko, Emmanuel Duc, and Gandjar Kiswanto. "Analytical Cut Geometry Prediction for Free Form Surface During Semi-Finish Milling." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1086.
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In five-axis milling, determination of continuously changing Cutter Workpiece Engagement (CWE) is still a challenge. Solid model and discrete model are the most common method used to predict the engagement region. However, both methods are suffering with the long computational time. This paper presents an analytical method to define CWE of toroidal cutter during semi-finishing of sculpture part. The workpiece from 2.5D rough milling is represented by a number of blocks. The length of cut at every engagement angle can be determined by calculating the outermost engagement point called upper CWE point. This point was determined by first assumed that the workpiece surface is flat. A recalculation for CWE correction is then performed for the engagement occurred in two workpiece blocks. The method called Z-boundary and X-boundary are employed to obtain the upper CWE point when the engagement occurred on toroidal side. Meanwhile Cylinder-boundary method was used when the engagement occurred on the cylinder side. The developed model was examined to ensure its accuracy. A sculptured surface part was tested by comparing the depth of cut generated by the simulation developed and the depth of cut measured by Unigraphic. The result indicates that the proposed method is very accurate. Moreover, due to the method is analytically, and hence it is efficient in term of calculation time.
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Porombka, Paul. "Modelling of Free Surface Flow Within the Two-Fluid Euler-Euler Framework." In ASME/JSME/KSME 2015 Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/ajkfluids2015-05133.
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Two-phase flows are regularly involved in the heat and mass transfer of industrial processes. To ensure the safety and efficiency of such processes, accurate predictions of the flow field and phase distribution by means of Computational Fluid Dynamics (CFD) are required. Direct Numerical Simulations (DNS) of large-scale two-phase flow problems are not feasible due to the computational costs involved. Therefore the Euler-Euler framework is often employed for large-scale simulations which involves macro-scale modelling of the turbulent shear stress and the interphase momentum transfer. As a long term objective, the research activities at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pursue the development of general models for two-phase flows which are based on first principles and include less empiricism. Part of this effort is focused on the development of an algebraic interfacial area density model (AIAD) which enables the simulation of two-phase flows with general morphologies including bubble, droplet and stratified flow regimes with the two-fluid approach. In this work a short overview of the AIAD model is given and recent developments are presented. The modelling of the interfacial drag in free surface flows is of particular interest and subject to ongoing research. Apart from empirical correlations, which are limited to certain flow regimes, different models for the local calculation of the interfacial drag have been developed. The latter approach is followed in the AIAD model and has recently been subject to modifications which are presented and validated as a part of this study. Furthermore, special attention is paid to the turbulence treatment at the phase boundary of free surface flows. A general damping of the gas-side turbulent fluctuations in the near interface region has been described previously in the literature but has not yet found its way into eddy viscosity turbulence models. In this work, a previously proposed damping source term for the k-ω turbulence model is validated. Model validation is performed by comparing the simulation results to experimental data in case of stratified, countercurrent air-water flow in a closed channel.