A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part II — Emission Prediction Utilizing Different Levels of Design Information

2018 ◽  
Author(s):  
Moritz Lipperheide ◽  
Thomas Bexten ◽  
Manfred Wirsum ◽  
Martin Gassner ◽  
Stefano Bernero
Keyword(s):  
Gas Turbine ◽  
A Priori ◽  
Model Development ◽  
Fine Tuning ◽  
Plant Operator ◽  
Engine Model ◽  
Physically Based ◽  
Design Type ◽  
The Impact

Reliable engine and emission models allow for an online monitoring of commercial gas turbine operation and help the plant operator and the original equipment manufacturer (OEM) to ensure emission compliance of the aging engine. However, model development and validation require fine-tuning on the particular engines, which may differ in a fleet of a single design type by production, assembly and aging status. For this purpose, Artificial Neural Networks (ANN) offer a good and fast alternative to traditional physically-based engine modeling, because the model creation and adaption is merely an automatized process in commercially available software environments. However, ANN performance depends strongly on the availability of suitable data and a-priori data processing. The present work investigates the impact of specific engine information from the OEM’s design tools on ANN performance. As an alternative to a strictly data-based benchmark approach, engine characteristics were incorporated into ANNs by a pre-processing of the raw measurements with a simplified engine model. The resulting ‘virtual’ measurements, i.e. hot gas temperatures, then served as inputs to ANN training and application during long-term gas turbine operation. When processed input parameters were used for ANNs, overall long-term NOx prediction improved by 55%, and CO prediction by 16% in terms of RMSE, yielding comparable overall RMSE values to the physically-based model.

scholarly journals Frame me if you must: PrEP framing and the impact on adherence to HIV Pre-exposure Prophylaxis

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S439-S439
Author(s):  
Eric Ellorin ◽  
Jill Blumenthal ◽  
Sonia Jain ◽  
Xiaoying Sun ◽  
Katya Corado ◽  
...  
Keyword(s):  
A Priori ◽  
Social Groups ◽  
Continuous Measure ◽  
Gay Community ◽  
Randomized Controlled ◽  
Exposure Prophylaxis ◽  
The Impact ◽  
Actual Prevalence ◽  
Tenofovir Diphosphate

Abstract Background “PrEP whore” has been used both as a pejorative by PrEP opponents in the gay community and, reactively, by PrEP advocates as a method to reclaim the label from stigmatization and “slut-shaming.” The actual prevalence and impact of such PrEP-directed stigma on adherence have been insufficiently studied. Methods CCTG 595 was a randomized controlled PrEP demonstration project in 398 HIV-uninfected MSM and transwomen. Intracellular tenofovir-diphosphate (TFV-DP) levels at weeks 12 and 48 were used as a continuous measure of adherence. At study visits, participants were asked to describe how they perceived others’ reactions to them being on PrEP. These perceptions were categorized a priori as either “positively framed,” “negatively framed,” or both. We used Wilcoxon rank-sum to determine the association between positive and negative framing and TFV-DP levels at weeks 12 and 48. Results By week 4, 29% of participants reported perceiving positive reactions from members of their social groups, 5% negative, and 6% both. Reporting decreased over 48 weeks, but positive reactions were consistently reported more than negative. At week 12, no differences in mean TFV-DP levels were observed in participants with positively-framed reactions compared with those reporting no outcome or only negatively-framed (1338 [IQR, 1036-1609] vs. 1281 [946-1489] fmol/punch, P = 0.17). Additionally, no differences were observed in those with negative reactions vs. those without (1209 [977–1427] vs. 1303 [964–1545], P = 0.58). At week 48, mean TFV-DP levels trended toward being higher among those that report any reaction, regardless if positive (1335 [909–1665] vs. 1179 [841–1455], P = 0.09) or negative (1377 [1054–1603] vs. 1192 [838–1486], P = 0.10) than those reporting no reaction. At week 48, 46% of participants reported experiencing some form of PrEP-directed judgment, 23% reported being called “PrEP whore,” and 21% avoiding disclosing PrEP use. Conclusion Over 48 weeks, nearly half of participants reported some form of judgment or stigmatization as a consequence of PrEP use. However, individuals more frequently perceived positively framed reactions to being on PrEP than negative. Importantly, long-term PrEP adherence does not appear to suffer as a result of negative PrEP framing. Disclosures All authors: No reported disclosures.

scholarly journals Modeling Building Stock Development

2021 ◽  
Vol 13 (2) ◽  
pp. 723
Author(s):  
Antti Kurvinen ◽  
Arto Saari ◽  
Juhani Heljo ◽  
Eero Nippala
Keyword(s):  
Model Development ◽  
Housing Demand ◽  
Quality Data ◽  
Building Stock ◽  
Test Arena ◽  
Climate Targets ◽  
Wide Range ◽  
Key Variables ◽  
The Impact

It is widely agreed that dynamics of building stocks are relatively poorly known even if it is recognized to be an important research topic. Better understanding of building stock dynamics and future development is crucial, e.g., for sustainable management of the built environment as various analyses require long-term projections of building stock development. Recognizing the uncertainty in relation to long-term modeling, we propose a transparent calculation-based QuantiSTOCK model for modeling building stock development. Our approach not only provides a tangible tool for understanding development when selected assumptions are valid but also, most importantly, allows for studying the sensitivity of results to alternative developments of the key variables. Therefore, this relatively simple modeling approach provides fruitful grounds for understanding the impact of different key variables, which is needed to facilitate meaningful debate on different housing, land use, and environment-related policies. The QuantiSTOCK model may be extended in numerous ways and lays the groundwork for modeling the future developments of building stocks. The presented model may be used in a wide range of analyses ranging from assessing housing demand at the regional level to providing input for defining sustainable pathways towards climate targets. Due to the availability of high-quality data, the Finnish building stock provided a great test arena for the model development.

scholarly journals A Parametric Starting Study of an Axial-Centrifugal Gas Turbine Engine Using a One-Dimensional Dynamic Engine Model and Comparisons to Experimental Results: Part 1 — Model Development and Facility Description

1998 ◽  
Author(s):  
A. Karl Owen ◽  
Anne Daugherty ◽  
Doug Garrard ◽  
Howard C. Reynolds ◽  
Richard D. Wright
Keyword(s):  
Gas Turbine ◽  
Initial Conditions ◽  
Model Development ◽  
Ground Level ◽  
Gas Turbine Engine ◽  
Calibration Data ◽  
Gas Generator ◽  
Turbine Engine ◽  
One Dimensional ◽  
Engine Model

A generic one-dimensional gas turbine engine model, developed at the Arnold Engineering Development Center, has been configured to represent the gas generator of a General Electric axial-centrifugal gas turbine engine in the six kg/sec airflow class. The model was calibrated against experimental test results for a variety of initial conditions to insure that the model accurately represented the engine over the range of test conditions of interest. These conditions included both assisted (with a starter motor) and unassisted (altitude windmill) starts. The model was then exercised to study a variety of engine configuration modifications designed to improve its starting characteristics and thus quantify potential starting improvements for the next generation of gas turbine engines. This paper discusses the model development and describes the test facilities used to obtain the calibration data. The test matrix for the ground level testing is also presented. A companion paper presents the model calibration results and the results of the trade-off study.

scholarly journals Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physically-based analytical model

2015 ◽  
Vol 12 (4) ◽  
pp. 4081-4155 ◽  
Author(s):  
A. Gallice ◽  
B. Schaefli ◽  
M. Lehning ◽  
M. P. Parlange ◽  
H. Huwald
Keyword(s):  
Model Development ◽  
Stream Temperature ◽  
Predictor Variables ◽  
Statistical Regression ◽  
Stream Network ◽  
Ungauged Catchments ◽  
Physical Point ◽  
Data Set ◽  
Physically Based ◽  
The Impact

Abstract. The development of stream temperature regression models at regional scales has regained some popularity over the past years. These models are used to predict stream temperature in ungauged catchments to assess the impact of human activities or climate change on riverine fauna over large spatial areas. A comprehensive literature review presented in this study shows that the temperature metrics predicted by the majority of models correspond to yearly aggregates, such as the popular annual maximum weekly mean temperature (MWMT). As a consequence, current models are often unable to predict the annual cycle of stream temperature, nor can the majority of them forecast the interannual variation of stream temperature. This study presents a new model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to the models developed to date, which mostly rely upon statistical regression to express stream temperature as a function of physiographic and climatic variables, this one rests upon the analytical solution to a simplified version of the energy-balance equation over an entire stream network. This physically-based approach presents some advantages: (1) the functional form linking stream temperature to the predictor variables is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) the regression coefficients can be interpreted from a physical point of view – their values can therefore be constrained to remain within plausible bounds. The evaluation of the model over a new freely available data set shows that the monthly mean stream temperature curve can be reproduced with a root mean square error of ±1.3 °C, which is similar in precision to the predictions obtained with a multi-linear regression model. We illustrate through a simple example how the physical basis of the model can be used to gain more insight into the stream temperature dynamics at regional scales.

scholarly journals Medical students’ mood adversely affected by COVID-19 pandemic: An interim analysis from the SPICE-19 prospective cohort study of 2075 medical students and interim foundation doctors

2020 ◽  
Author(s):  
Soham Bandyopadhyay ◽  
Ioannis Georgiou ◽  
Bibire Baykeens ◽  
Conor S Gillespie ◽  
Marta de Andres Crespo ◽  
...  
Keyword(s):  
Medical Students ◽  
A Priori ◽  
Workforce Planning ◽  
Short Term ◽  
The United Kingdom ◽  
Exam Preparation ◽  
Course Material ◽  
The Impact ◽  
Exam Preparation Course

Abstract Background:Currently, we can only speculate on what the effects of the COVID-19 pandemic have been on medical students and interim foundation year doctors. In order to support them appropriately both now and, in the future, it is imperative that we understand the impact it has had upon them. This study assessed the effects of the COVID-19 pandemic on medical students and interim foundation year doctors across the United Kingdom (UK), and the support that they received and sought. Methods:A prospective, observational, multicentre study was conducted. All medical students and interim foundation year doctors were eligible to participate. The data analysis was carried out as detailed a priori in the protocol. Findings:A total of 2075 individuals participated in the SPICE-19 survey from 33 medical schools. There was a significant (p < 0.0001) decrease in participants’ mood when comparing their mood before the pandemic to during the pandemic. Social distancing and more time at home/with family were the factors that negatively and positively respectively impacted the mood of the greatest number of participants. All areas of life included in the survey were found to have been significantly more negatively impacted than positively impacted (p < 0.0001). 931 participants wanted more support from their university. Participants were mainly seeking support with exam preparation, course material, and financial guidance. Discussion:Medical and foundation schools need to prepare adequate and effective support. If no action is taken, there may be a knock-on effect on workforce planning and the health of our future workforce. When medical students return to their universities, there is likely to be need for enhanced wellbeing support, adaptations in the short-term and long-term strategies for medical education, and provision of financial guidance.

A Parametric Starting Study of an Axial-Centrifugal Gas Turbine Engine Using a One-Dimensional Dynamic Engine Model and Comparisons to Experimental Results: Part II—Simulation Calibration and Trade-Off Study

1999 ◽  
Vol 121 (3) ◽  
pp. 384-393 ◽  
Author(s):  
A. K. Owen ◽  
A. Daugherty ◽  
D. Garrard ◽  
H. C. Reynolds ◽  
R. D. Wright
Keyword(s):  
Gas Turbine ◽  
Initial Conditions ◽  
Model Development ◽  
Gas Turbine Engine ◽  
Calibration Data ◽  
Gas Generator ◽  
Trade Off ◽  
Turbine Engine ◽  
One Dimensional ◽  
Engine Model

A generic one-dimensional gas turbine engine model, developed at the Arnold Engineering Development Center, has been configured to represent the gas generator of a General Electric axial-centrifugal gas turbine engine in the six-kg/sec airflow class. The model was calibrated against experimental test results for a variety of initial conditions to insure that the model accurately represented the engine over the range of test conditions of interest. These conditions included both assisted (with a starter motor) and unassisted (altitude windmill) starts. The model was then exercised to study a variety of engine configuration modifications designed to improve its starting characteristics and thus quantify potential starting improvements for the next generation of gas turbine engines. This paper presents the model calibration results and the results of the trade-off study. A companion paper discusses the model development and describes the test facilities used to obtain the calibration data.

Validation of the S5P Formaldehyde L2 product using MAX-DOAS network observations

2020 ◽  
Author(s):  
Isabelle De Smedt ◽  
Gaia Pinardi ◽  
Corinne Vigouroux ◽  
Steven Compernolle ◽  
Kai Uwe Eichman ◽  
...  
Keyword(s):  
A Priori ◽  
Retrieval Algorithm ◽  
Comparison Results ◽  
Seasonal And Diurnal Variations ◽  
Wide Range ◽  
Polluted Sites ◽  
German Aerospace ◽  
The Impact

&lt;p&gt;The Sentinel-5 Precursor (S5P) was launched on the 13th of October 2017, with on board the TROPOspheric Monitoring Instrument (TROPOMI). The formaldehyde (HCHO) L2 product is operational since the end of 2018. The prototype of the tropospheric HCHO retrieval algorithm is developed at BIRA-IASB and implemented at the German Aerospace Center (DLR) in the S5P operational processor (De Smedt et al., 2018).&lt;/p&gt;&lt;p&gt;In this work, we investigate the quality of the HCHO tropospheric column product and its validation within the MPC framework (Mission Performance Center) and the S5PVT NIDFORVAL project (S5P NItrogen Dioxide and FORmaldehyde VALidation). Within NIDFORVAL, the S5P HCHO product has been validated using the full FTIR and MAXDOAS dataset. Validation results have been assessed against reported product uncertainties taking into account the full comparison error budget, showing that the product quality reaches its requirements.&lt;/p&gt;&lt;p&gt;Here, we focus on satellite-satellite comparison based on the OMI QA4ECV HCHO product and on ground-based validation using MAX-DOAS and Pandora network observations. About 15 HCHO measuring stations are involved, providing data corresponding to a wide range of observation conditions at mid and low latitudes, and covering remote, sub-urban, and urban polluted sites. Comparison results show usually negative biases for large HCHO columns, while a positive offset is observed for the lowest columns. For the MAX-DOAS stations providing vertical profile retrievals, the impact of a priori profiles on the comparison is assessed. The dataset allows to discuss validation results as a function of emission source. Seasonal and diurnal variations are compared. Long term variation are also monitored using the OMI and MAX-DOAS QA4ECV dataset.&lt;/p&gt;

A Non-Dimensional Approach for Generalizing the Particle Impact Behavior of Gas Turbine Fouling

2019 ◽  
Author(s):  
Alessio Suman ◽  
Nicola Casari ◽  
Elettra Fabbri ◽  
Michele Pinelli ◽  
Luca di Mare ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Particle Deposition ◽  
A Priori ◽  
Interdisciplinary Approach ◽  
Experimental Tests ◽  
Impact Behavior ◽  
Particle Adhesion ◽  
Blade Surface ◽  
Knowledge Based ◽  
The Impact

Abstract The consequences of particle deposition on gas turbine blade surface are studied since the first gas turbine application. The effects generated by particle adhesion range from performance deterioration to life reduction to complete loss of power. Even if, the effects generated by fouled blade surface are well known, the mechanisms responsible for the particle adhesion are still less clear. The variability related to the nature of materials, the impact conditions and the presence of promoting substances (such as water, oil, glue agents, etc.) imply several difficulties for comparing the results and for extracting general trends and rules useful for generating up-to-date predictive models. In the present work, an attempt to realize a general comparison among several different particle deposition tests is carried out. Starting from the previous review work, which has collected experimental tests carried out over thirty years, in the present study, an original elaboration data is proposed. Over seventy adhesion tests realized with particle composition, size, velocity, and temperature similar to those characterize gas turbine fouling are collected and post-processed. After a dimensional analysis, the data are then classified using non-dimensional groups such as Reynolds, Weber, and Ohnesorge numbers. In this way, general threshold values for the transitions between erosion, deposition, and splashing are identified according to the literature data. This general tool allows the a priori identification of the driving phenomena (such as inertia, viscous/capillary forces), based on the knowledge of basic inputs (such as impact and particle characteristics). The general approach adopted in this work gives the opportunity to increase the gas turbine fouling knowledge based on an interdisciplinary approach.

scholarly journals A Parametric Starting Study of an Axial-Centrifugal Gas Turbine Engine Using a One-Dimensional Dynamic Engine Model and Comparisons to Experimental Results: Part 2 — Simulation Calibration and Trade-Off Study

1998 ◽  
Author(s):  
A. Karl Owen ◽  
Anne Daugherty ◽  
Doug Garrard ◽  
Howard C. Reynolds ◽  
Richard D. Wright
Keyword(s):  
Gas Turbine ◽  
Initial Conditions ◽  
Model Development ◽  
Gas Turbine Engine ◽  
Calibration Data ◽  
Gas Generator ◽  
Trade Off ◽  
Turbine Engine ◽  
One Dimensional ◽  
Engine Model

A generic one-dimensional gas turbine engine model, developed at the Arnold Engineering Development Center, has been configured to represent the gas generator of a General Electric axial-centrifugal gas turbine engine in the six-kg/sec airflow class. The model was calibrated against experimental test results for a variety of initial conditions to insure that the model accurately represented the engine over the range of test conditions of interest. These conditions included both assisted (with a starter motor) and unassisted (altitude windmill) starts. The model was then exercised to study a variety of engine configuration modifications designed to improve its starting characteristics and thus quantify potential starting improvements for the next generation of gas turbine engines. This paper presents the model calibration results and the results of the trade-off study. A companion paper discusses the model development and describes the test facilities used to obtain the calibration data.

scholarly journals Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Darío Ramos-López ◽  
Ana D. Maldonado
Keyword(s):  
Variable Selection ◽  
A Priori ◽  
Imbalanced Data ◽  
Selection Procedure ◽  
Fine Tuning ◽  
Imbalanced Datasets ◽  
Validation Metrics ◽  
Classification Errors ◽  
The Impact ◽  
Sensitive Variable

Multi-class classification in imbalanced datasets is a challenging problem. In these cases, common validation metrics (such as accuracy or recall) are often not suitable. In many of these problems, often real-world problems related to health, some classification errors may be tolerated, whereas others are to be avoided completely. Therefore, a cost-sensitive variable selection procedure for building a Bayesian network classifier is proposed. In it, a flexible validation metric (cost/loss function) encoding the impact of the different classification errors is employed. Thus, the model is learned to optimize the a priori specified cost function. The proposed approach was applied to forecasting an air quality index using current levels of air pollutants and climatic variables from a highly imbalanced dataset. For this problem, the method yielded better results than other standard validation metrics in the less frequent class states. The possibility of fine-tuning the objective validation function can improve the prediction quality in imbalanced data or when asymmetric misclassification costs have to be considered.

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