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With the increase in energy of the Large Hadron Collider to a centre-of-mass energy of 13 TeV for Run 2, events with dense environments, such as in the cores of high-energy jets, became a focus for new physics searches as well as measurements of the Standard Model. These environments are characterized by charged-particle separations of the order of the tracking detectors sensor granularity. Basic track quantities are compared between 3.2 fb$^{-1}$ of data collected by the ATLAS experiment and simulation of proton-proton collisions producing high-transverse-momentum jets at a centre-of-mass energy of 13 TeV. The impact of charged-particle separations and multiplicities on the track reconstruction performance is discussed. The efficiency in the cores of jets with transverse momenta between 200 GeV and 1600 GeV is quantified using a novel, data-driven, method. The method uses the energy loss, dE/dx, to identify pixel clusters originating from two charged particles. Of the charged particles creating these clusters, the measured fraction that fail to be reconstructed is $0.061 \pm 0.006 \textrm{(stat.)} \pm 0.014 \textrm{(syst.)}$ and $0.093 \pm 0.017 \textrm{(stat.)}\pm 0.021 \textrm{(syst.)}$ for jet transverse momenta of 200-400 GeV and 1400-1600 GeV, respectively. The European physical journal / C 77(10), 673 (2017). doi:10.1140/epjc/s10052-017-5225-7 Published by Springer, Berlin

This study employs an electricity system capacity panning model with detailed economic dispatch and unit commitment decisions/constraints to quantitatively answer two key questions: How does the enactment of the federal Inflation Reduction Act of 2022 impact the cost of electricity, greenhouse gas emissions, and investment in electricity capacity in the PJM Interconnection over the 2023-2035 period? Given new and expanded federal subsidies for clean electricity resources in the Inflation Reduction Act, what additional capacity investments and resource deployment would be required and at what cost for the PJM region to reduce greenhouse gas emissions 80-90% by 2035 while maintaining an affordable and reliable electricity supply? Executive summary: In August 2022, Congress passed and President Biden signed the Inflation Reduction Act (IRA), which enacts a comprehensive set of financial incentives (tax credits, grants, rebates, loans) that support all sources of carbon-free electricity, promote vehicle and building electrification and efficiency, and subsidize carbon capture and storage (CCS). The implementation of IRA means that the full financial weight of the federal government is now behind the clean energy transition. This will have transformative effects on the economics of decarbonization in the PJM Interconnection (and across the United States). IRA will spark a new, sustained period of growth in PJM electricity consumption, which could rise ~19% from 2021 to 2030. The law also subsidizes the cost of deploying new renewable energy capacity and maintaining the region’s existing nuclear fleet. As a result, this study finds that clean electricity could supply 60% [58-66% across sensitivities] of PJM demand in 2030, up from 48% [43-61%] without enactment of IRA. However, realizing this potential will require a dramatic acceleration in the pace of wind and solar interconnection and transmission expansion in the PJM Interconnection. The growth of lower-cost, carbon-free electricity under IRA will significantly reduce CO2 emissions from PJM power generation, which could fall 37% [3-66%] from 2019/2021 levels. In contrast, PJM emissions would increase 12% [0-15%] from 2021 levels without IRA. However, PJM emissions may rebound after 2032 when a production tax credit for existing nuclear reactors established by IRA is set to expire. Unless equivalent policy support is extended beyond 2032, our modeling finds 12 GW [0-33 GW] of the PJM nuclear fleet is likely to retire by 2035, with new natural gas capacity and generation increasing to fill the resulting gap and meet growing demand, reversing some of the emissions progress achieved through 2030. In addition to driving down greenhouse gas emissions, IRA also lowers the cost of electricity supply in the PJM region. We find the average cost of bulk electricity supply for PJM load serving entities (LSEs), including transmission expansion and state policy requirements, will be about $42/MWh [~$40-45/MWh] in 2030, about 5-10% lower than without IRA, and well below costs paid in 2019 ($50.2/MWh) and 2021 (~$61/MWh). The primary sources of cost savings are reduced wholesale energy prices, lower costs to meet state clean energy policy goals (due to federal subsidies), and growing demand (which spreads fixed costs over more MWh). While IRA puts the PJM region on a path to lower-cost electricity and lower greenhouse gas emissions, the new federal policy is not sufficient to drive deep decarbonization of the PJM interconnection on its own. Fortunately, by subsidizing the cost of all new carbon-free electricity resources, IRA also makes it cheaper and easier for PJM states to reduce emissions further while preserving affordability. Part 2 of this study presents a cost-optimized blueprint of the additional capacity investments and resource deployment required for the PJM region to deeply decarbonize over the 2023-2035 period. Specifically, we apply two stylized policy constraints and model the evolution of the PJM capacity mix and operations to meet those constraints: A clean electricity standard (CES) requiring increased shares of carbon-free electricity generation in the region (55% clean share by 2025, 70% by 2030, 85% by 2035), and; A CO2 emissions cap and trading scheme (cap & trade) requiring decreasing region-wide emissions (58% below 2005 emissions by 2025, 80% by 2030, 95% by 2035) This study finds that, due to passage of IRA, the PJM region could cut CO2 emissions from power generation by 80-90% by 2035 while keeping average bulk electricity supply costs for LSE’s comparable to or lower than levels experienced in recent years (2019 & 2021). However, deep decarbonization in the PJM region will require much more rapid expansion of low-carbon electricity resources and supportive transmission expansion above and beyond the rates of deployment made economical by IRA. By 2035, the region will also likely deploy more advanced ‘clean firm’ resources like gas power plants with carbon capture and storage (CCS) or long-duration electricity storage technologies (LDS), to replace coal- and gas-fired power capacity. We also identify and map several affordable resource portfolios and spatial patterns for clean electricity resource siting across the PJM region, demonstrating that the region has some flexibility to address local priorities and concerns.

Pour la durabilité et les économies d'énergie, le problème de l'optimisation du contrôle des systèmes de chauffage, de ventilation et de climatisation (CVC) a suscité de grandes attentions, mais l'analyse des signatures des environnements thermiques et des systèmes CVC et l'évaluation des politiques d'optimisation ont rencontré une inefficacité et des problèmes gênants en raison du manque d'ensemble de données publiques. Dans cet article, nous présentons l'ensemble de données sur l'énergie et l'environnement des stations de métro (SEED), qui a été collecté à partir d'une ligne de stations de métro de Pékin, fournissant des données de résolution minute concernant la dynamique de l'environnement (température, humidité, CO2, etc.), les états de fonctionnement et les consommations d'énergie des systèmes CVC (ventilateurs, réfrigérateurs, pompes), et les données de résolution horaire des flux de passagers. Nous décrivons les déploiements de capteurs et les systèmes CVC pour la collecte de données et le contrôle de l'environnement, et présentons également une enquête initiale pour la désagrégation énergétique du système CVC, les signatures de la charge thermique, de l'alimentation de refroidissement et du flux de passagers à l'aide de l'ensemble de données. Para la sostenibilidad y el ahorro de energía, el problema de optimizar el control de los sistemas de calefacción, ventilación y aire acondicionado (HVAC) ha atraído grandes atenciones, pero analizar las firmas de los entornos térmicos y los sistemas de HVAC y la evaluación de las políticas de optimización ha encontrado ineficiencia y problemas inconvenientes debido a la falta de un conjunto de datos públicos. En este documento, presentamos el Conjunto de datos de energía y medio ambiente de la estación de metro (SEED), que se recopiló de una línea de estaciones de metro de Beijing, proporcionando datos de resolución de minutos con respecto a la dinámica del entorno (temperatura, humedad, CO2, etc.) estados de trabajo y consumos de energía de los sistemas HVAC (ventiladores, refrigeradores, bombas), y datos de resolución horaria de los flujos de pasajeros. Describimos los despliegues de sensores y los sistemas HVAC para la recopilación de datos y para el control del entorno, y también presentamos la investigación inicial para la desagregación energética del sistema HVAC, las firmas de la carga térmica, el suministro de refrigeración y el flujo de pasajeros utilizando el conjunto de datos. For sustainability and energy saving, the problem to optimize the control of heating, ventilating, and air-conditioning (HVAC) systems has attracted great attentions, but analyzing the signatures of thermal environments and HVAC systems and the evaluation of the optimization policies has encountered inefficiency and inconvenient problems due to the lack of public dataset. In this paper, we present the Subway station Energy and Environment Dataset (SEED), which was collected from a line of Beijing subway stations, providing minute-resolution data regarding the environment dynamics (temperature, humidity, CO2, etc.) working states and energy consumptions of the HVAC systems (ventilators, refrigerators, pumps), and hour-resolution data of passenger flows. We describe the sensor deployments and the HVAC systems for data collection and for environment control, and also present initial investigation for the energy disaggregation of HVAC system, the signatures of the thermal load, cooling supply, and the passenger flow using the dataset. من أجل الاستدامة وتوفير الطاقة، جذبت مشكلة تحسين التحكم في أنظمة التدفئة والتهوية وتكييف الهواء (HVAC) اهتمامًا كبيرًا، ولكن تحليل توقيعات البيئات الحرارية وأنظمة التدفئة والتهوية وتكييف الهواء وتقييم سياسات التحسين واجهت مشاكل غير فعالة وغير مريحة بسبب نقص مجموعة البيانات العامة. في هذه الورقة، نقدم مجموعة بيانات الطاقة والبيئة (SEED) لمحطة مترو الأنفاق، والتي تم جمعها من خط من محطات مترو الأنفاق في بكين، والتي توفر بيانات دقيقة الدقة فيما يتعلق بديناميكيات البيئة (درجة الحرارة والرطوبة وثاني أكسيد الكربون، وما إلى ذلك) وحالات العمل واستهلاك الطاقة لأنظمة التدفئة والتهوية وتكييف الهواء (أجهزة التهوية والثلاجات والمضخات)، وبيانات دقة الساعة لتدفقات الركاب. نحن نصف عمليات نشر المستشعرات وأنظمة التدفئة والتهوية وتكييف الهواء لجمع البيانات والتحكم في البيئة، ونقدم أيضًا تحقيقًا أوليًا لتصنيف الطاقة لنظام التدفئة والتهوية وتكييف الهواء، وتواقيع الحمل الحراري، وإمدادات التبريد، وتدفق الركاب باستخدام مجموعة البيانات.

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The Pratt & Whitney TALON X low emissions combustor: Revolutionary results with evolutionary technology. In 45th AIAA Aerospace Scienc es Meeting and Exhibit (p. 386).", "Park, J., Nguyen, T. H., Joung, D., Huh, K. Y., & Lee, M. C. (2013). Prediction of NO x and CO emissions from an industrial lean - premixed gas turbine combustor using a chemical reactor network model. Energy & Fuels, 27(3), 1 643 - 1651.", "Sahu, A. B., & Ravikrishna, R. V. (2014). A detailed numerical study of NOx kinetics in low calorific value H2/CO syngas flames. International Journal of Hydrogen Energy, 39(30), 17358 - 17370.", "Samuelsen S. Rich burn, quick - mix, lean burn (RQL) com bustor[J]. The Gas Turbine Handbook, US Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory, DOE/NETL2006 - 1230, 2006: 227 - 233.", "Straub, D. L., Casleton, K. H., Lewis, R. E., Sidwell, T. G., Maloney, D. J., & Richards, G. A. (2005). Assessment of rich - burn, quick - mix, lean - burn trapped vortex combustor for stationary gas turbines (No. NETL - TPR - 0629). National Energy Technology Laboratory - In - house Research.", "Zhang, Y., Mathieu, O., Petersen, E. L., Bourque, G., & Curran, H. J. ( 2017). Assessing the predictions of a NOx kinetic mechanism on recent hydrogen and syngas experimental data. Combustion and Flame, 182, 122 - 141."]} This paper presents a kinetics study on NOx emissions of syngas gas turbine with RQL(rich-burn, quick-mix, lean-burn)combustion. The RQL combustor was simulated by a chemical reactor network (CRN) model using CHEMKIN-PRO program. The kinetic mechanism used in the simulation was developed by Zhang et al.(2017),dedicated to syngas fuel. NOx emissions of RQL combustion were systematically studied under representative gas turbine operation conditions, and results show that RQL combustion significantly reduces NOx emissions. Key parameters of RQL combustor, including airflow split and residence time split between rich and lean burn zones, were varied to investigate their effects on NOx emissions. Analyses show that airflow split is the key factor determining NOx formation. Influences of mechanisms on NOx prediction in the RQL combustor were also investigated. The GRI-Mech 3.0mechanismwas chosen for comparison. The syngas mechanism developed by Zhang et al. predicts lower overall NOx emissions when the combustor outlet temperature is 1750K, and predicts higher overall NOx emissions when the outlet temperature is 1908K. In the rich-burn zone of the RQL combustor, the syngas mechanism predicts lowerNOx production at 1750K, and almost the same NOx production at 1908Kcompared with GRI-Mech 3.0. While in the lean-burn zone of the combustor, the syngas mechanism predicts higherNOx formation atboth1750K and 1908K.Sensitivity analyses were conducted to find major reactions that influenced the NOx prediction in each mechanisms. Results show that the dominating pathways of NO formation are not same in each mechanism. ROPs (rates of production) of these pathways were calculated to further explain the differences in predictions of each mechanism.