Search

A new timetable must be calculated in real-time when train operations are perturbed. Although energy consumption is becoming a central issue both from the environmental and economic perspective, it is usually neglected in the timetable recalculation. In this paper, we formalize the real-time Energy Consumption Minimization Problem (rtECMP). It finds in real-time the driving regime combination for each train that minimizes energy consumption, respecting given routing and precedences between trains. In the possible driving regime combinations, train routes are split in subsections for which one of the regimes resulting from the Pontryagin’s Maximum Principle is to be chosen. We model the trade-off between minimizing energy consumption and total delay by considering as objective function their weighted sum. We propose an algorithm to solve the rtECMP, based on the solution of a mixed-integer linear programming model. We test this algorithm on the Pierrefitte-Gonesse control area, which is a critical area in France with dense mixed traffic. The results show that the problem is tractable and an optimal solution of the model tackled can often be found in real-time for most instances.

We propose herein the design of a heat exchanger based on clay materials in order to reduce the energy consumed due to data center cooling. Our Thermal Energy Storage (TES) system is to be placed upstream of the CRAC (Computer Room Air Conditioner) unit. A direct contact heat exchanger is thereby built as a vertical packed bed, composed of earthen spheres whose imposed heating/cooling cycles comply with ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) standards. A raw earthen material has been chosen owing to its hygrothermal characteristics, which are recognized as relevant for indoor thermal regulation, in addition to a low embodied energy required for manufacturing. Results show that the pore Reynolds number and sphere diameter exert significant effects on the energy buffering capacity. A simple relationship is thus proposed to link the mass-specific power of the energy storage system, the internal heat exchanger architecture and the pore Reynolds number for purposes of designing raw earth bricks at an elevated scale.

RESUMENEl presente trabajo analiza las condiciones de la industria de la refinación en México y los factores que enmarcan su relanzamiento en el contexto de la Reforma Energética de 2013. Para ello, se identificaran los principales retos que las empresas de refinación en el mundo deben atender para permanecer en el mercado, evaluando el caso particular de Estados Unidos y Canadá. De igual manera, se realizará un diagnóstico sobre la refinación en México, para identificar sus principales determinantes, para finalmente, llevar a cabo un análisis de sus perspectivas, considerando el rol de la iniciativa privada en el mercado abierto y de Petróleos Mexicanos (Pemex), como comodín en las áreas en donde las compañías privadas no participen.AbstractThis paper analyzes the conditions facing the oil refining industry in Mexico and the factors that shaped its overhaul in the context of the 2013 Energy Reform. To do so, the paper examines the main challenges that refining companies must tackle to stay in the market, evaluating the specific cases of the United States and Canada. Similarly, it offers a diagnosis of refining in Mexico, identifying its principal determinants in order to, finally, analyze its prospects, considering the role of private initiatives in the open market, as well as Petróleos Mexicanos (Pemex), as a placeholder in those areas where private enterprises do not participate.

High-performance computing (HPC) aims at developing models and simulationsfor applications in numerous scientific fields. Yet, the energy consumption of these HPC facilitiescurrently limits their size and performance, and consequently the size of the tackled problems.The complexity of the HPC software stacks and their various optimizations makes it difficult tofinely understand the energy consumption of scientific applications. To highlight this difficulty on aconcrete use-case, we perform an energy and power analysis of a software stack for the simulation offrequency-domain electromagnetic wave propagation. This solver stack combines a high order finiteelement discretization framework of the system of three-dimensional frequency-domain Maxwellequations with an algebraic hybrid iterative-direct sparse linear solver. This analysis is conductedon the KNL-based PRACE-PCP system. Our results illustrate the difficulty in predicting how totrade energy and runtime.; Le calcul haute performance (HPC) vise à développer des modèles et des simulationsdans de nombreux domaines scientifiques. Pourtant, la consommation d’énergie des installationsde calcul haute performance limite actuellement leur taille et leur performance et, par conséquent,la taille des problèmes abordés. La complexité des piles logicielles de calcul haute performanceet de leurs différentes optimisations rendent difficile la compréhension fine de la consommationénergétique des applications scientifiques. Pour mettre en évidence cette difficulté sur un casd’utilisation concret, nous effectuons une analyse énergétique d’une pile logicielle pour dessimulations de propagation d’ondes électromagnétiques dans le domaine fréquentiel. Ce solveurcombine une approche de discrétisation par éléments finis d’ordre élevé pour le système d’équationsde Maxwell tridimensionnelles dans le domaine fréquenciels et un solveur parallèle itératif hybrideitératif-direct pour la résolution du système creux associé. Cette analyse est effectuée sur leSystème PRACE-PCP à base de KNL. Nos résultats illustrent la difficulté à prédire un compromisen énergie et temps de calcul.

Multi-Processor System-on-Chip (MPSoC) has emerged as a promising embedded architecture to meet the increasing performance demand of embedded applications. However, due to limited energy budget, it is hard to guarantee that applications on MPSoC can be accomplished on time with a required throughput. The situation becomes even worse for applications with high reliability requirements, since extra energy will be inevitably consumed by task re-executions or duplicated tasks. Based on Dynamic Voltage and Frequency Scaling (DVFS) and task duplication techniques, this paper presents a novel energy-efficient scheduling model, which aims at minimizing the overall energy consumption of MPSoC applications under both throughput and reliability constraints. The problem is shown to be NP-complete, and several polynomial-time heuristics are proposed to tackle this problem. Comprehensive simulations on both synthetic and real application graphs show that our proposed heuristics can meet all the given constraints, while reducing the energy consumption.; Le système multiprocesseur sur puce (MPSoC) est une architecture prometteuse pour répondre à la demande de performance croissante des applications embarquées. Cependant, en raison de leur budget énergétique limité, il est difficile de garantir que les applications sur MPSoC peuvent être accomplies à temps avec un débit requis. La situation devient encore pire pour les applications présentant des exigences de fiabilité élevées, car une énergie supplémentaire sera inévitablement consommée par des ré-exécutions de tâches ou des tâches dupliquées. Basé sur le DVFS (Dynamic Voltage and Frequency Scaling) et la duplication de tâches, cet article présente un nouveau modèle d'ordonnancement, qui vise à minimiser la consommation d'énergie globale des applications MPSoC sous des contraintes de débit et de fiabilité. Le problème est montré NP-complet, et plusieurs heuristiques en temps polynomial sont proposées pour résoudre ce problème. Des simulations complètes sur des graphes d'application tant synthétiques que réels montrentque nos heuristiques peuvent répondre à toutes les contraintes données, tout en réduisant la consommation d'énergie.

EWGT 2016 - 19th EURO Working Group on Transportation Meeting, Istanbul, TURQUIE, 05-/09/2016 - 07/09/2016; When train operations are perturbed, a new working timetable needs to be computed in real-time. In the literature, several algorithms have been proposed for optimizing this computation. This optimization usually does not consider energy consumption. However, minimizing energy consumption is a central issue both from the environmental and economic perspective. In this paper, we address the real-time problem of minimizing the energy consumption. The energy consumption depends on driving regimes used by the train drivers. Hence, we focus on the decision of the appropriate driving regimes throughout each train's travel along a given infrastructure. A model and solution approach for this problem are provided. We show a proof of concept on the applicability of this solution approach on a simple test case.

EEEIC 2016 - 17th IEEE International Conference on Environment and Electrical Engineering , Florence, Italie, 06-/06/2016 - 08/06/2016; The estimation of running times is essential for the railway timetable definition. The trains travel duration must respect some constraints, which depend on the railway network. Usually, the running times minimize the trains travel duration on the railway network. They are typically calculated without considering the energy consumption. The green transportation has recently gained significant importance. In this paper, the energy consumption in the running times estimation is taken into account by defining an optimization problem to calculate the running times for the railway timetable definition. This optimization problem finds the running times which minimize the energy consumption. Precisely, an algorithm to handle the optimization problem with the integration platform for multiobjective and multi-disciplinary optimization modeFRONTIER is defined. The algorithm is tested on a case study and the results are validated with the OpenTrack Railway Simulation Tool.

Real-time energy management of hybrid electric vehicles (HEVs) is a key point for performing effective fuel economy optimization. Offline methods have been developed for energy management optimization when the drive cycle is known. Online real-time methods can provide good results but can only ensure suboptimal management. In this paper, it is assumed that information about the route is available in advance. Using this knowledge, global optimization methods can be used in real-time control to approach optimal fuel consumption while keeping the state of charge (Soc) of the batteries at a desired level. Such a method is presented in this paper. The developed strategy is implemented in a real-time experiment using the power-hardware-in-the-loop (PHIL) principle. The measured fuel consumption and the obtained battery Soc trajectory demonstrate good performance of the proposed control. Author Keywords: Energy management , fuel consumption , hybrid electric vehicle (HEV) , optimization , power hardware in the loop (PHIL) .

Adding product into the mixer exerts a strong and rapid impact during concrete mixing. Experimental data obtained from a planetary mixer in a full-scale concrete plant under laboratory conditions show that the state of mixture progress with mixing time is well described by the mixing power evolution. More specifically, a reliable method for detecting the time corresponding to the transformation of a mixture from a cohesive granular material into a granular paste (i.e. the so-called “transition time”), through use of a mixing power measurement, will be presented herein. Moreover, once this transition has been achieved, mixing power consumption will be related to mixture rheology and then to mixer geometry by means of a simplified hypothesis. This equation can also be obtained via a dimensionless analysis. Lastly, it will be shown that mixture behavior beyond the transition point is well fitted by a hyperbolic equation. The corresponding mixing power evolution can then be predicted by the level of power at this transition time. These results are suitable for application to online process monitoring.

IEEE VPPC 2015 - Vehicular Power and Propulsion Conference, MONTREAL, CANADA, 19-/10/2015 - 22/10/2015; Electric vehicles are facing a real challenge as they are expected to reduce drastically energy consumption and air pollution of transport. However their performances remain under users' expectation mainly concerning the range and its fluctuation. 'Eco-behavior' use like eco-driving could help to reduce range weakness. This paper proposes an approach based on speed profile optimization that allows the extraction of the eco-driving rules. The optimization uses an experimentally validated model associated to a dynamic programming method. The study of the more impacting parameters showed high influence of the cruising speed value whereas the acceleration rate could be considered as a second order parameter excepting for high level time constraints.

International audience; The prediction of a building's thermal behaviour within a short time horizon is necessary in many energy management applications. A numerical model can serve this purpose provided a good accuracy is obtained through a suitable calibration procedure. The paper deals with a model calibration procedure based on short-time on-site and weather measurements. It builds upon optimal control theory: an adjoint model is introduced to derive the gradient of a least squares cost function at a low computational cost. Two problems are solved. The first one is a non-linear model training problem. It consists in identifying the main influencing parameters of the system of partial differential equations that form the tendency model. The second problem is a linear identification problem that consists in identifying the unknown internal gains. This second problem can be solved in real-time in a continuous monitoring process. Both problems are solved within the same framework and same tools, illustrating the efficiency of the optimal control tools in this context. We give simulation results that show the performance of the calibration procedure under uncertainties on input parameters.