Relevant bibliographies by topics / Peak prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Peak prediction'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Peak prediction"

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Gerber, Brandon S., James L. Tangler, Earl P. N. Duque, and J. David Kocurek. "Peak and Post-Peak Power Aerodynamics from Phase VI NASA Ames Wind Turbine Data." Journal of Solar Energy Engineering 127, no. 2 (2005): 192–99. http://dx.doi.org/10.1115/1.1862260.

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Constant speed/pitch rotor operation lacks adequate theory for predicting peak and post-peak power. The objective of this study was to identify and quantify how measured blade element performance characteristics from the Phase VI NASA Ames 24m×36m80ft×120ft wind tunnel test of a two-bladed, tapered, twisted rotor relate to the prediction of peak and post-peak rotor power. The performance prediction code, NREL’s Lifting Surface Prescribed Wake code (LSWT), was used to study the flow physics along the blade. Airfoil lift and drag coefficients along the blade were derived using the predicted angl
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Keith, David, and Juan Moreno-Cruz. "Pitfalls of coal peak prediction." Nature 469, no. 7331 (2011): 472. http://dx.doi.org/10.1038/469472b.

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Tangka, George Morris William, and Lidya Chitra Laoh. "Deep Learning for Peak Load Duration Curve Forecasting." CogITo Smart Journal 10, no. 1 (2024): 603–12. http://dx.doi.org/10.31154/cogito.v10i1.694.603-612.

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As the energy landscape changes towards renewable energy sources and smart grid technologies, accurate prediction of peak load duration curve (PLDC) becomes crucial to ensure power system stability. The background to this research is the urgent need for more effective prediction methods to manage increasingly complex energy loads. This research presents a leading-edge approach to PLDC prediction, leveraging Deep Learning, a subsection of artificial intelligence. Focusing on data from the Taiwan State Electric Company, this study uses a Long Short-Term Memory (LSTM) network to capture complex l
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Soroka, Juliana, Larry Grenkow, Héctor Cárcamo, Scott Meers, Shelley Barkley, and John Gavloski. "An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies." Canadian Entomologist 152, no. 1 (2019): 110–29. http://dx.doi.org/10.4039/tce.2019.71.

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AbstractThis study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction
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Li, Haitao, Guo Yu, Yizhu Fang, Yanru Chen, Chenyu Wang, and Dongming Zhang. "Studies on natural gas reserves multi-cycle growth law in Sichuan Basin based on multi-peak identification and peak parameter prediction." Journal of Petroleum Exploration and Production Technology 11, no. 8 (2021): 3239–53. http://dx.doi.org/10.1007/s13202-021-01212-3.

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AbstractResearch on predicting the growth trend of natural gas reserves will help provide theoretical guidance for natural gas exploration in Sichuan Basin. The growth trend of natural gas reserves in Sichuan Basin is multi-cycle and complex. The multi-cyclic peak is screened by the original multi-cyclic peak judgment standard. Metabolically modified GM(1,3) gray prediction method is used to predict the multi-cycle model parameters. The multi-cycle Hubbert model and Gauss model are used to predict the growth trend of natural gas reserves. The research results show that: (1) The number of cycle
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Zhao, Mengchen, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz, and Ayan Sadhu. "Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning." Energies 17, no. 7 (2024): 1672. http://dx.doi.org/10.3390/en17071672.

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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the imple
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Zhang, Yang. "Peak Traffic Prediction Using Nonparametric Approaches." Advanced Materials Research 378-379 (October 2011): 196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.378-379.196.

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How to accurately predict peak traffic is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two non-parametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals
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Duncan, Michael J., Joanne Hankey, Mark Lyons, Rob S. James, and Alan M. Nevill. "Peak Power Prediction in Junior Basketballers." Journal of Strength and Conditioning Research 27, no. 3 (2013): 597–603. http://dx.doi.org/10.1519/jsc.0b013e31825d97ac.

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Ramesh, S., Bhaskar Natarajan, and Gopika Bhagat. "Peak load prediction using weather variables." Energy 13, no. 8 (1988): 671–79. http://dx.doi.org/10.1016/0360-5442(88)90097-7.

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Kim, Seunghawk, Gwangseob Kim, and Kyeong-Eun Lee. "Rainfall peak prediction using deep learning." Journal of the Korean Data And Information Science Society 34, no. 4 (2023): 607–17. http://dx.doi.org/10.7465/jkdi.2023.34.4.607.

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Dissertations / Theses on the topic "Peak prediction"

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Al-Rahamneh, Harran Qoblan Mefleh. "Perceived exertion relationships and prediction of peak oxygen uptake in able-bodied and paraplegic individuals." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/3005.

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Rating of Perceived Exertion (RPE) relates to how ‘hard’ or ‘easy’ an exercise feels. The Borg 6-20 RPE scale is the most widely used scale to estimate the overall, peripheral and central perception of effort. To date, there are a limited number of studies on the use and efficacy of perceived exertion in persons with spinal cord injury and/or disease. The findings from these studies are also equivocal. Therefore, the aims of this thesis were to assess: i) the relationship between the RPE and physical and physiological markers of exercise intensity during arm cranking exercise in able-bodied an
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Kiuchi, Ryota. "New Ground Motion Prediction Equations for Saudi Arabia and their Application to Probabilistic Seismic Hazard Analysis." Kyoto University, 2020. http://hdl.handle.net/2433/253095.

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Thornton, Craig Matthew. "Effects of Land Development on Peak Runoff Rate and its Prediction for Brigalow Catchments in Central Queensland, Australia." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/365709.

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The estimation of runoff volume and peak runoff rate has been the focus of significant hydrological research worldwide. The results of these studies, usually in the form of empirical relationships or models, are intrinsically linked to the environment in which the study was conducted. This often limits the applicability and accuracy of the method of runoff estimation at alternative and ungauged locations. Within the brigalow belt of central Queensland, Australia, a scarcity of stream gauging stations to measure runoff volume and peak runoff rate has impeded research on the surface water hydrol
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Birch, Wiliiam John. "The prediction of peak particle velocity vibration levels in underground structures that arise as the result of surface blasting." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659028.

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The author of this thesis has been involved in research into the environmental impact of blasting for over 34 years, initially as a founder member and then more recently as the director of the Blasting and Environmental Research Group [BERG]. BERG was originally established at the Department of Mining and Mineral Engineering at the University of Leeds and has a long history of research into the environmental impacts of blasting from quarries and opencast mines. This thesis is concerned with the prediction of peak particle velocity vibration levels in underground structures that arise as a resu
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Wang, Zijian. "DM EMI Noise Analysis for Single Channel and Interleaved Boost PFC in Critical Conduction Mode." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/32719.

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The critical conduction mode (CRM) power factor correction converters (PFC) are widely used in industry for low power offline switching mode power supplies. For the CRM PFC, the main advantage is to reduce turn-on loss of the main switch. However, the large inductor current ripple in CRM PFC creates huge DM EMI noise, which requires a big EMI filter. The switching frequency of the CRM PFC is variable in half line cycle which makes the EMI characteristics of the CRM PFC are not clear and have not been carefully investigated. The worst case of the EMI noise, which is the baseline to design the E
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Akeil, Salah. "Comparative Study On Ground Vibrations Prediction By Statistical And Neural Networks Approaches At Tuncbilek Coal Mine, Panel Byh." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605058/index.pdf.

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In this thesis, ground vibrations induced by bench blasting from the Tun&ccedil<br>bilek Coal Mine, Panel BYH, were measured to find out the site-specific attenuation and to assess the structural damage risk. A statistical approach is applied to the collected data, and from the data analysis an attenuation relationship is established to be used in predicting the peak particle velocity as well as to calculate the maximum allowable charge per delay. The values of frequencies are also analyzed to investigate the damage potential to the structures of Tun&ccedil<br>bilek Township. A new approach to
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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.

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Chen, Yuyao. "Contribution of machine learning to the prediction of building energy consumption." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0119.

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La prédiction de la consommation énergétique des bâtiments est aujourd’hui un point-clé de la transition énergétique, qui vise à atténuer les effets du réchauffement climatique. La quantité de données disponible étant de plus en plus importante, les modèles prédictifs dits « data-driven » voient leur performances croître constamment. Parmi ces modèles, ceux issus du Machine Learning sont probablement les plus efficaces. Au sein de cette thèse, nous proposons de réaliser un état de l’art complet des contributions du Machine Learning à la la prédiction de la consommation énergétique des bâtiment
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Hiesböcková, Tereza. "Předpovídání povodňových průtoků v měrných profilech Borovnice - Dalečín." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2012. http://www.nusl.cz/ntk/nusl-225458.

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Aim of a work is construction of forecasting models for prediction of flood flows of measuring profile Borovnice – Dalečín on the river Svratka. As a tool for issuing predictions will be used classic hydrological forecasting models, and models based on artificial intelligence methods. Predictive model will be consisting from summer flood flows for the years 1997-2007. In the end of the work will chosen a better method for issuing forecasts
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Preisler, Frederik. "Predicting peak flows for urbanising catchments." Thesis, Queensland University of Technology, 1992.

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Books on the topic "Peak prediction"

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Campeau, Gail Annette. Prediction of shotcrete damage through the analysis of peak particle velocity. Laurentian University, School of Engineering, 1999.

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S, Rohatgi Upendra, and U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Systems Research., eds. Bias in peak clad temperature predictions due to uncertainties in modeling of ECC bypass and dissolved non-condensable gas phenomena. Division of Systems Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1990.

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Holmgren, David. Future Scenarios: How Communities Can Adapt to Peak Oil and Climate Change. Chelsea Green Publishing, 2012.

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Prediction of peak VO ́values from 9-minute run distances in young males, 9-14 years. 1985.

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Prediction of peak VO2ș values from 9-minute run distances in young males, 9-14 years. 1985.

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Lynch, Michael C. The “Peak Oil” Scare and the Coming Oil Flood. ABC-CLIO, LLC, 2016. http://dx.doi.org/10.5040/9798400605017.

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Is the earth’s oil supply starting to run out, or is there far more oil than some experts believe? This book points out flaws in the research used to warn of an oil shortfall and predicts that large new reserves of oil are soon to be tapped. In the last decade, oil experts, geologists, and policy makers alike have warned that a peak in oil production around the world was about to be reached and that global economic distress would result when this occurred. But it didn’t happen. The "Peak Oil" Scare and the Coming Oil Flood refutes the recent claims that world oil production is nearing a peak a
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Comparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1987.

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Comparison of a prediction of maximal oxygen consumption by the YMCA Submaximal Bicycle Ergometer Test to a measurement of peak oxygen consumption. 1985.

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Peak oxygen deficit as a predictor of sprint and middle-distance track performance. 1992.

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Orlov, Dmitry. Reinventing Collapse: The Soviet Experience and American Prospects. New Society Publishers, Limited, 2011.

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Book chapters on the topic "Peak prediction"

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Wu, Wenjie, Heping Jin, Gan Wang, et al. "Research on Wind Power Peak Prediction Method." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1068-3_66.

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Zhang, Peng, Junbo Mu, and Jie Luo. "Analysis of Carbon Emission Impact Factors and Trend Prediction Based on LMDI and ARIMA Models: A Case Study of Zhejiang Province." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8401-1_13.

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AbstractThe present study proposed a method to examine the carbon emissions of various departments in Zhejiang Province from 2003 to 2020 using the IPCC sectoral method. The use of the LMDI model analyzed the factors that influence carbon emission change in Zhejiang Province. The ARIMA prediction model and grey prediction model are utilized to forecast carbon emissions of Zhejiang Province in the future. The proposed measures for carbon emission reduction in Zhejiang Province are given, and some reference basis is provided for similar provinces to carry out low-carbon transformation. The resul
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Scherbart, Alexandra, Wiebke Timm, Sebastian Böcker, and Tim W. Nattkemper. "Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting." In Advances in Neuro-Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_63.

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Xue, Weixian, and Liangmin Wang. "Prediction of Carbon Peak in Shaanxi Province and Its Cities." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_97.

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Goodwin, Morten, and Anis Yazidi. "A Pattern Recognition Approach for Peak Prediction of Electrical Consumption." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_26.

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Li, Yajing, Jieren Cheng, Yuqing Kou, Dongwan Xia, and Victor S. Sheng. "Prediction of Passenger Flow During Peak Hours Based on Deep Learning." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7161-9_17.

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Kanwar, Neeraj, Divay Bargoti, and Vinay Kumar Jadoun. "Power Transformer Summer Peak Load Prediction Using SCADA and Supervised Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1476-7_21.

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Sun, Yanli, Di Zhang, and Qiang Liu. "Prediction of peak carbon emission in Liaoning Province based on energy consumption." In Advances in Urban Engineering and Management Science Volume 2. CRC Press, 2022. http://dx.doi.org/10.1201/9781003345329-57.

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Mahmud, Khizir, Weilun Peng, Sayidul Morsalin, and Jayashri Ravishankar. "A Day-Ahead Power Demand Prediction for Distribution-Side Peak Load Management." In Proceedings of International Joint Conference on Computational Intelligence. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7564-4_27.

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Bertipaglia, Alberto, Mohsen Alirezaei, Riender Happee, and Barys Shyrokau. "A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_89.

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AbstractThis paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the pre
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Conference papers on the topic "Peak prediction"

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Kruger, Rodrigo, Abdullah Mueen, and Vinicius M. A. Souza. "Peak Prediction in Time Series: Comparing Approaches for Energy High-Load Prediction." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651140.

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Jara, Sebastían, Rodrigo Salas, Ricardo Ñanculef, Israel Valverde, Sergio Uribe, and Julio Sotelo. "Prediction of Peak-to-Peak Pressure Gradient in Patients with Aortic Coarctation Using Physics-Informed Neural Networks." In 2024 L Latin American Computer Conference (CLEI). IEEE, 2024. http://dx.doi.org/10.1109/clei64178.2024.10700502.

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Zhou, Peishan, Belinda Schwerin, and Stephen So. "U-Net Based Fetal R-peak Prediction From Abdominal ECG Signals." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671460.

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Hu, Lingli. "End-to-End Inventory Replenishment Model with Bi-LSTM Peak Prediction for Demand." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695396.

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Chen, Yikun, Lifen Huang, Ziyi Cai, Lu Qiu, Haiming Chen, and Sijing Lai. "Carbon peak and new energy vehicle market prediction system based on multimodal machine learning." In Second International Conference on Intelligent Transportation and Smart Cities (ICITSC 2025), edited by Yaxian Li, Vitaliy Mezhuyev, and Zongzhi Li. SPIE, 2025. https://doi.org/10.1117/12.3073625.

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Miao, Yuning. "Multi-Peak Vehicle Trajectory Prediction and Quantitative Safety Evaluation Based on Dynamic Graph Interaction Modeling." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085790.

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Muppawar, Harsh, and Utkarsha Pacharney. "AI-Driven Prediction Model for Forecasting the Spread and Peak of COVID-19 using Epidemiological Data." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940372.

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Singh, Rayman Preet, Peter Xiang Gao, and Daniel J. Lizotte. "On hourly home peak load prediction." In 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012. http://dx.doi.org/10.1109/smartgridcomm.2012.6485977.

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Khafaf, Nameer Al, and Ayman H. El-Hag. "Prediction of leakage current peak value." In 2018 11th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2018. http://dx.doi.org/10.1109/isma.2018.8330118.

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Weiss, M. A., A. Masarie, and R. Beard. "Peak Deviation from Prediction in Atomic Clocks." In 2007 IEEE International Frequency Control Symposium Joint with the 21st European Frequency and Time Forum. IEEE, 2007. http://dx.doi.org/10.1109/freq.2007.4319231.

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Reports on the topic "Peak prediction"

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Ronstadt, Jackie A. Post-Wildfire Peak Discharge Prediction Methods in Northern New Mexico. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1414163.

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Brown, Dr Brendan, and Dr Rick Llewellyn. Improving Agricultural Policy and Programming through Data-Driven Adoption Prediction. Asian Productivity Organization, 2024. http://dx.doi.org/10.61145/cwao6767.

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This report evaluates the Adoption and Diffusion Outcome Prediction Tool (ADOPT) as a decision support system for agricultural policy across APO member economies. Through validation studies in Bangladesh, India, and Lao PDR, the research demonstrates ADOPT's strength in predicting peak adoption rates while identifying its tendency to overestimate adoption speed in developing contexts. The study introduces the 'Four Ps' framework and concludes that ADOPT, combined with structured analysis methods, provides valuable support for data-driven agricultural policy decisions in Asian contexts.
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Si, Hongjun, Saburoh Midorikawa, and Tadahiro Kishida. Development of NGA-Sub Ground-Motion Model of 5%-Damped Pseudo-Spectral Acceleration Based on Database for Subduction Earthquakes in Japan. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2020. http://dx.doi.org/10.55461/lien3652.

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Presented within is an empirical ground-motion model (GMM) for subduction-zone earthquakesin Japan. The model is based on the extensive and comprehensive subduction database of Japanese earthquakes by the Pacific Engineering Research Center (PEER). It considers RotD50 horizontal components of peak ground acceleration (PGA), peak ground velocity (PGV), and 5%-damped elastic pseudo-absolute acceleration response spectral ordinates (PSA) at the selected periods ranging from 0.01 to 10 sec. The model includes terms and predictor variables considering tectonic setting (i.e., interplate and intrasla
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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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DiFolco, Donna, Julie Maier, Donna DiFolco, and Julie Maier. Snowshoe hare population trends at mineral and non-mineral sites in the central Brooks Range, Alaska: Final report on the snowshoe hare ecology project, 1997?2023. National Park Service, 2024. http://dx.doi.org/10.36967/2306544.

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This report presents data we have compiled while monitoring localized snowshoe hare populations in the central Brooks Range near Wiseman, Alaska, using track and pellet counts. In addition, we documented snowshoe hare geophagy?the practice of consuming soil?via trail cameras, thus confirming the use of mineral licks by snowshoe hares in this area. Evidence of geophagy by snowshoe hares (Lepus americanus) was observed during track count surveys in Gates of the Arctic National Park and Preserve west of Wiseman, Alaska, in 1997?2001 when the hare population reached an exceptionally high peak. Lon
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Alves, Jose-Henrique, Roberto Padilla-Hernandez, Deanna Spindler, et al. Development of a wave model component in the first coupled Global Ensemble Forecast System at NOAA. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49784.

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We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System at NOAA, part of the U.S. National Weather Service operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12, was a critical step in NOAA’s transition to the broader community based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact
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Wells, Beric, Scott Cooley, and Joseph Meacham. Prediction of Peak Hydrogen Concentrations for Deep Sludge Retrieval in Tanks AN-101 and AN-106 from Historical Data of Spontaneous Gas Release Events. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1148634.

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Linker, Taylor, and Timothy Jacobs. PR-457-18204-R01 Variable Fuel Effects on Legacy Compressor Engines Phase IV - Predictive NOx Modeling. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011584.

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Abstract:
The ultimate goal of this work is to improve the current control methods for large bore, lean burn natural gas engines in order to combat performance and emissions issues during variable fuel composition events. This will be achieved in the long term by simulating the effects of variable fuel composition on a large bore, natural gas engine and developing engine control strategies which work to mitigate adverse effects. The work of Phase IV adds onto previous work by enabling the prediction of NOxemissions in the validated, full-scale engine simulation of a Cooper-Bessemer GMWH-10C developed in
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Tiku, Sanjay, Arnav Rana, and Binoy John. PR214-223810-R01 Improvement in Dent Assessment and Management Tools. Pipeline Research Council International, Inc. (PRCI), 2024. http://dx.doi.org/10.55274/r0000090.

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This project builds on and supplements existing mechanical damage (MD) assessment and management tools, developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), American Petroleum Institute (API), other research organizations and individual pipeline operators, many of which are included in API Recommended Practice (RP) 1183 (1). Since the assembly of API RP 1183, PRCI has continued its mechanical damage strategic research priority in the development of a greater understanding of th
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Senz, Anja, and Belinda Uebler. Public Predictions about China’s Carbon Emissions Peak: Dynamics and Impacts. Beyond the Horizon ISSG, 2024. http://dx.doi.org/10.31175/eh4s.2014.13.

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