Charged-particle pseudorapidity density at mid-rapidity in p-Pb collisions at root S-NN=8.16 TeV

The pseudorapidity density of charged particles, dN(ch)/d eta, in p-Pb collisions has been measured at a centre of-mass energy per nucleon-nucleon pair of root S-NN = 8.16 TeV at mid-pseudorapidity for non-single-diffractive events. The results cover 3.6 units of pseudorapidity, vertical bar eta vertical bar /2 is 4.73 +/- 0.20, where is the average number of participating nucleons, is 9.5% higher than the corresponding value for p-Pb collisions at root S-NN = 5.02 TeV. Measurements are compared with models based on different mechanisms for particle production. All models agree within uncertainties with data in the Pb-going side, while HIJING overestimates, showing a symmetric behaviour, and EPOS underestimates the p-going side of the dN(ch)/d eta distribution. Saturation-based models reproduce the distributions well for eta > -1.3. The dN(ch)/d eta is also measured for different centrality estimators, based both on the charged particle multiplicity and on the energy deposited in the Zero Degree Calorimeters. A study of the implications of the large multiplicity fluctuations due to the small number of participants for systems like p-Pb in the centrality calculation for multiplicity-based estimators is discussed, demonstrating the advantages of determining the centrality with energy deposited near beam rapidity.Peer reviewe

Energy Gap between the Poly-\u3cem\u3ep\u3c/em\u3e-phenylene Bridge and Donor Groups Controls the Hole Delocalization in Donor–Bridge–Donor Wires

Poly-p-phenylene wires are critically important as charge-transfer materials in photovoltaics. A comparative analysis of a series of poly-p-phenylene (RPPn) wires, capped with isoalkyl (iAPPn), alkoxy (ROPPn), and dialkylamino (R2NPPn) groups, shows unexpected evolution of oxidation potentials, i.e., decrease (−260 mV) for iAPPn, while increase for ROPPn (+100 mV) and R2NPPn (+350 mV) with increasing number of p-phenylenes. Moreover, redox/optical properties and DFT calculations of R2NPPn/R2NPPn+• further show that the symmetric bell-shaped hole distribution distorts and shifts toward one end of the molecule with only 4 p-phenylenes in R2NPPn+•, while shifting of the hole occurs with 6 and 8 p-phenylenes in ROPPn+• and iAPPn+•, respectively. Availability of accurate experimental data on highly electron-rich dialkylamino-capped R2NPPn together with ROPPn and iAPPn allowed us to demonstrate, using our recently developed Marcus-based multistate model (MSM), that an increase of oxidation potentials in R2NPPn arises due to an interplay between the electronic coupling (Hab) and energy difference between the end-capped groups and bridging phenylenes (Δε). A comparison of the three series of RPPn with varied Δε further demonstrates that decrease/increase/no change in oxidation energies of RPPn can be predicted based on the energy gap Δε and coupling Hab, i.e., decrease if Δε \u3c Hab (i.e., iAPPn), increase if Δε \u3e Hab (i.e., R2NPPn), and minimal change if Δε ≈ Hab (i.e., ROPPn). MSM also reproduces the switching of the nature of electronic transition in higher homologues of R2NPPn+• (n ≥ 4). These findings will aid in the development of improved models for charge-transfer dynamics in donor–bridge–acceptor systems

Integrated model concept for district energy management optimisation platforms

District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project

Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy

[EN] To reduce the energy consumption in buildings is necessary to analyze individual rooms and thermal zones, studying mathematical models and applying new control techniques. In this paper, the design, simulation and experimental evaluation of a sliding mode controller for regulating internal temperature in a thermal zone is presented. We propose an experiment with small physical dimensions, consisting of a closed wooden box with heat internal sources to stimulate temperature gradients through operating and shut down cycles.This investigation was supported by national doctoral program of the Colombian Administrative Department of Science Technology and Innovation (Colciencias), and the agreement "Analysis of the properties, applications and market opportunities of G-cover Coatings" closed between the Universitat Politecnica de Valencia (Spain) and the Mexican company G-cover.Florez, F.; Fernández De Córdoba, P.; Higón Calvet, JL.; Olivar, G.; Taborda, J. (2019). Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy. Mathematics. 7(6):1-13. https://doi.org/10.3390/math7060503S11376Delgarm, N., Sajadi, B., & Delgarm, S. (2016). Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC). Energy and Buildings, 131, 42-53. doi:10.1016/j.enbuild.2016.09.003Gorni, D., Castilla, M. del M., & Visioli, A. (2016). An efficient modelling for temperature control of residential buildings. Building and Environment, 103, 86-98. doi:10.1016/j.buildenv.2016.03.016Fazenda, P., Lima, P., & Carreira, P. (2016). Context-based thermodynamic modeling of buildings spaces. Energy and Buildings, 124, 164-177. doi:10.1016/j.enbuild.2016.04.068Bacher, P., & Madsen, H. (2011). Identifying suitable models for the heat dynamics of buildings. Energy and Buildings, 43(7), 1511-1522. doi:10.1016/j.enbuild.2011.02.005Ryzhov, A., Ouerdane, H., Gryazina, E., Bischi, A., & Turitsyn, K. (2019). Model predictive control of indoor microclimate: Existing building stock comfort improvement. Energy Conversion and Management, 179, 219-228. doi:10.1016/j.enconman.2018.10.046Fiorentini, M., Wall, J., Ma, Z., Braslavsky, J. H., & Cooper, P. (2017). Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Applied Energy, 187, 465-479. doi:10.1016/j.apenergy.2016.11.041Massa Gray, F., & Schmidt, M. (2016). Thermal building modelling using Gaussian processes. Energy and Buildings, 119, 119-128. doi:10.1016/j.enbuild.2016.02.004Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P. (2016). Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy and Buildings, 111, 131-144. doi:10.1016/j.enbuild.2015.11.033Acosta, A., González, A. I., Zamarreño, J. M., & Álvarez, V. (2016). Energy savings and guaranteed thermal comfort in hotel rooms through nonlinear model predictive controllers. Energy and Buildings, 129, 59-68. doi:10.1016/j.enbuild.2016.07.061Afram, A., & Janabi-Sharifi, F. (2014). Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment, 72, 343-355. doi:10.1016/j.buildenv.2013.11.016Nagarathinam, S., Doddi, H., Vasan, A., Sarangan, V., Venkata Ramakrishna, P., & Sivasubramaniam, A. (2017). Energy efficient thermal comfort in open-plan office buildings. Energy and Buildings, 139, 476-486. doi:10.1016/j.enbuild.2017.01.043Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., & Mangharam, R. (2018). Data-driven model predictive control using random forests for building energy optimization and climate control. Applied Energy, 226, 1252-1272. doi:10.1016/j.apenergy.2018.02.126Killian, M., Mayer, B., & Kozek, M. (2016). Cooperative fuzzy model predictive control for heating and cooling of buildings. Energy and Buildings, 112, 130-140. doi:10.1016/j.enbuild.2015.12.017Brastein, O. M., Perera, D. W. U., Pfeifer, C., & Skeie, N.-O. (2018). Parameter estimation for grey-box models of building thermal behaviour. Energy and Buildings, 169, 58-68. doi:10.1016/j.enbuild.2018.03.057Lirola, J. M., Castañeda, E., Lauret, B., & Khayet, M. (2017). A review on experimental research using scale models for buildings: Application and methodologies. Energy and Buildings, 142, 72-110. doi:10.1016/j.enbuild.2017.02.060Coutinho, C. P., Baptista, A. J., & Dias Rodrigues, J. (2016). Reduced scale models based on similitude theory: A review up to 2015. Engineering Structures, 119, 81-94. doi:10.1016/j.engstruct.2016.04.016Chew, L. W., Glicksman, L. R., & Norford, L. K. (2018). Buoyant flows in street canyons: Comparison of RANS and LES at reduced and full scales. Building and Environment, 146, 77-87. doi:10.1016/j.buildenv.2018.09.026Chen, S.-Y., & Gong, S.-S. (2017). Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control. Mechanical Systems and Signal Processing, 94, 111-128. doi:10.1016/j.ymssp.2017.02.025Huang, Y., Khajepour, A., Ding, H., Bagheri, F., & Bahrami, M. (2017). An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems. Applied Energy, 188, 576-585. doi:10.1016/j.apenergy.2016.12.033Mironova, A., Mercorelli, P., & Zedler, A. (2016). Robust Control using Sliding Mode Approach for Ice-Clamping Device activated by Thermoelectric Coolers. IFAC-PapersOnLine, 49(25), 470-475. doi:10.1016/j.ifacol.2016.12.067Norton, M., Khoo, S., Kouzani, A., & Stojcevski, A. (2015). Adaptive fuzzy multi‐surface sliding control of multiple‐input and multiple‐output autonomous flight systems. IET Control Theory & Applications, 9(4), 587-597. doi:10.1049/iet-cta.2014.0209Fux, S. F., Ashouri, A., Benz, M. J., & Guzzella, L. (2014). EKF based self-adaptive thermal model for a passive house. Energy and Buildings, 68, 811-817. doi:10.1016/j.enbuild.2012.06.01