Literatura académica sobre el tema "Random dynamic load profile"

This study aims to predict the Dynamic Load Coefficient (DLC) of tyre forces from truck axles. Dynamic Load Coefficient is frequently used to characterise the dynamic loads generated by axles. It is a simple measurement of the dynamic variation magnitude of the axle load, for a specific combination of road roughness and speed. Under normal operating conditions, the DLCs value is typically in the range of 0.05-0.3, and close to zero when the trucks wheels are moving over a perfectly smooth road. To achieve the objectives of this study, which is to determine the DLCs value for seven different types of axles, a simple validated quarter-truck model was excited by a random road surface profile, in order to simulate a vehicle-road interaction. Points are equally spaced along the simulated road to generate dynamic loadings over a broad range of truck speeds. Multiple trucks gross-weight conditions were used to present realistic traffic behaviour. The results showed that irregular road profiles, exciting the vehicle as it travelled, caused continually changing tyre forces. Also, dynamic loading was seen to be fundamentally influenced by the type of suspension (i.e., air and steel), loading condition, and vehicle speed. For example, the DLC value of the tyre forces of the quarter-truck fitted with a steel suspension was found to be more than twice that of the truck fitted with an air suspension. Tyre forces of the one-third laden truck were more aggressive than any other loading condition, due to the uncertain body-bounce generated by the truck, which was strongly dependent on surface irregularities. At low speed, the DLC was greatly decreased if the load was increased. Furthermore, DLC value was always lower for trucks with air suspension over steel suspension, for the same load and vehicle speed. However, air suspension efficiency was clearly better for higher axle loads.

The damage and collapse accidents of wind turbines during violent typhoons and rainstorms have increased in recent years. To determine the dynamic response characteristics of high-power wind turbines under extreme conditions, wind load and rain load are simulated. The typhoon average wind velocity and fluctuating wind velocity are simulated by the unstable wind profile and harmony superposition method. The raindrop size distribution is simulated by the M-P spectrum, and the rain load is calculated according to the momentum theorem. A finite element model is established to study the aerodynamic responses of a wind turbine under random typhoon load and typhoon-rain loads. The maximum displacements and accelerations at the tower top and the maximum von Mises stresses at the tower bottom are calculated and compared after considering various combinations of wind direction deflections and rainfall intensities. The results indicate that instantaneous wind direction deflection has a substantial impact on the dynamic responses of wind turbines, and after introducing the effect of rain, the dynamic responses increase up to 13.7% with increasing rainfall intensities. This study has significant implications for analysing collapse accidents of wind turbines and for optimising the design of wind turbines under extreme typhoon conditions.

Electronic load (e-load) is essential equipment for power converter performance test, where a designated load profile is executed. Electronic load is usually implemented with the analog controller for fast tracking of the load profile reference. In this paper, a low-power low-cost electronic load is proposed. MOSFETs (metal-oxide-semiconductor field-effect transistors) are used as the power consumption devices, which are regulated to the active region as controlled current-sink. In order to achieve fast transient response using the low-cost digital signal controller (DSC) PWM peripherals, the interleaving PWM method is proposed to achieve active current ripple mitigation. To obtain the system open-loop gain for current-sink operation, an offline digital system identification method, followed by model reduction, is proposed by applying Pseudo-Random Binary Sequence (PRBS) excitation. Pole-zero cancelation method is used in the control system design and later implemented in a DSC. The prototype is built and tested, in which meaningful testing scenarios under constant current-sink mode, pulse current sink mode, and double line-frequency current mode are verified. The experimental results indicate that the proposed e-load can sink pre-programmed current profile with well-attenuated ripple for static and dynamic load testing, and is applicable to fully digitalized power testing equipment.

We present a three-dimensional (3D) dynamic model for the bridge-road-vehicle interaction system. A slab-on-girder bridge is modeled as a grillage system subjected to multiple moving truck loads. Multi-axle semi-tractor-trailer is idealized as a 3D vehicle model with a nonlinear tire-suspension system, having eleven independent degrees of freedom. Road roughness profiles are generated from the random Gaussian process as well as limited measurements of actual road profiles. Truck wheel loads are applied at any point and then transferred to nodes as equivalent nodal forces. The Newmark-\#946; integration method is applied as a numerical algorithm for solving the bridge-road-vehicle dynamic interaction equations. The major parameters affecting the bridge dynamic response (or the dynamic load factor) include road roughness, truck weight, speed and mechanical properties of the tire-suspension system and bridge stiffness and boundary conditions. Results from other dynamic models as well as field tests are compared with those from the current 3D model. The results show that the dynamic load factor is highly dependent on road roughness, vehicle suspension, and bridge geometry.

Data concerning vertical vibration levels on an operator’s seat and a wheel suspension deflection calculated using three calculation models of a tractor vibratory system, namely chain three-mass, plain and spatial twelve-mass ones, in the mode of moving on the reference track of random profile are given. Application inadmissibility of the chain model because of inadequacy of vibration level to operational one and possibility of restricted using of the plain model are shown. The spatial model ensures the best convergence to the measured data.

Machine learning is a branch of artificial intelligence (AI) that consists of the application of various algorithms to obtain information from large data sets. These algorithms are especially useful to solve nonlinear problems that appear frequently in some engineering fields. Geotechnical engineering presents situations with complex relationships of multiple variables, making it an ideal field for the application of machine learning techniques. Thus, these techniques have already been applied with a certain degree of success to determine such things as soil parameters, admissible load, settlement, or slope stability. Moreover, dynamic penetrometers are a very common type of test in geotechnical studies, and, in many cases, they are used to design the foundation solution. In addition, its continuous nature allows us to know the variations of the terrain profile. The objective of this study was to correlate the drilling parameters of deep foundation machinery (Measurement-While-Drilling, MWD) with the number of blows of the dynamic penetrometer test. Therefore, the drilling logs could be equated with said tests, providing information that can be easily interpreted by a geotechnical engineer and that would allow the validation of the design hypotheses. Decision trees and random forest algorithms have been used for this purpose. The ability of these algorithms to replicate the complex relationships between drilling parameters and terrain characteristics has allowed obtaining a reliable reproduction of the penetrometric profile of the traversed soil.

This paper focuses on the analysis and controlling automotive vibration using semi-active air spring suspension system by implementing fuzzy and Proportional-Integral derivative (PID) controllers for light vehicles. Due to low transmissibility coefficients and their ability to varying the force generated depends on load capacities the air spring is modelled as an actuator. The dynamic behavior of semi active actuator controlled is contrasted with passive suspension under single bump, double bump and random road profile. The performance of air spring controlled suspension has been investigated. Results show that the fuzzy controller gives optimized results.

This paper summarizes the recent work by the first author’s research group related to the performance evaluation of existing bridges under vehicle dynamic effects. Based on the data from short-term monitoring of existing bridges, a framework to estimate the extreme structure responses from the live load in a mean recurrence interval is developed in the first part. The Gumbel distribution of the extreme values was derived from an extreme value theory and Monte Carlo Simulation. In the second part, a framework of fatigue damage and reliability assessment for existing bridges is presented to include the effects of the progressively deteriorated road conditions and random dynamic vehicle loads in bridge’s life cycle. The random effects of vehicle speed and type, road profiles, and stress ranges are included. Studies have shown that the vehicle-induced dynamic allowance IM value prescribed by the AASHTO LRFD code may be underestimated under poor road surface conditions (RSCs) of some existing bridges. In addition, multiple dynamic stress ranges induced by vehicles cannot be included in the maximum displacement-based dynamic allowance IM values. In the third part of this paper, the reliability indices of a selected group of prestressed concrete girder bridges are calculated by modeling the IM explicitly as a random variable for different RSCs. Nevertheless, a reliability based dynamic amplification factor on stress ranges (DAFS) for fatigue design is proposed to include the fatigue damages from multiple stress range cycles due to each vehicle passage at varied vehicle speeds under various road conditions in the bridge’s life cycle.

A novel approach to the road unevenness classification based on the power spectral density with consideration of vehicle vibration response and broad interval of road waviness (road elevation PSD slope) is presented. This approach enables transformation of two basic parameters of road profile elevation PSD (unevenness index,C, and waviness,w) into a single-number indicatorCwwhen using a correction factorKwaccounting forw. For the road classification proposal two planar vehicle models (passenger car and truck), ten responses (reflecting ride comfort, dynamic load of road and cargo, ride safety) and three different vehicle velocities have been considered. The minimum of ten estimated vibration response ranges sum for a broad waviness interval typical for real road sections (w= 1.5 to 3.5) has been used for the correction factor estimation. The introduced unevenness indicator,Cw, reflects the vehicle vibration response and seems to be a suitable alternative to the other currently used single-number indicators or as an extension of the road classification according to the ISO 8608: 1995, which is based on constant waviness value,w= 2.

Road bridge designs are based on technical standards, which, to date, consider dynamic loading as equivalent static loads. Additionally, the few engineers who perform a dynamic analysis typically do not consider the effects of bridge-vehicle interaction and also simplify the road’s irregularity profile. Moreover, often, even when a simplified dynamic analysis is carried out and shows that there will be a high dynamic amplification factor (DAF), designers prefer to solve this problem by adopting high safety factors and thereby oversizing the bridge, rather than using energy dissipation devices that would allow reducing the amplitude of vibration. In this context, the present work proposes a complete methodology to minimize the dynamic response of road bridges by optimizing multiple tuned mass dampers (MTMD), taking into account the bridge-vehicle interaction, the random profile of pavement irregularities, and the uncertainties present in the coupled system and in the excitation. For illustrative purposes, the coupled vibration problem of a regular truck traveling on a random road profile over a typical Brazilian bridge is analyzed. Three different scenarios for the MTMD are considered. The proposed optimization problem is solved by employing the Whale Optimization Algorithm (WOA). The results showed the excellent ability of the proposed methodology, reducing the bridge’s DAF to acceptable values for all analyzed cases, considering or not the uncertainties present in the system. Furthermore, the results obtained by the proposed methodology are compared with results obtained using classical tuned mass damper (TMD) design methods, showing the best performance of the proposed optimization method. Thus, the proposed method can be employed to optimize MTMD, improving bridge design.

Les systèmes de piles à combustible offrent une solution durable à la production d'énergie électrique dans le secteur des transports, même s'ils rencontrent encore des problèmes de fiabilité et de durabilité. Le recours à des systèmes multi-piles à combustible (MFC) au lieu de piles à combustible uniques est une solution prometteuse pour surmonter ces limitations en répartissant de manière optimale la demande de puissance entre les différentes piles tout en tenant compte de leur état de santé, au moyen d'une stratégie de gestion de l'énergie (EMS) efficace. Dans ce travail, différentes stratégies ont été développées pour des applications automobiles, avec l'objectif d'optimiser la durée de vie du système de piles à combustible.Le premier défi est de développer un modèle reliant la détérioration de chaque pile avec la puissance délivrée, de manière à être en mesure de prédire l'effet d'une allocation de charge sur la détérioration de chaque pile, et ainsi prendre une décision post-pronostic pertinente. Plusieurs modèles stochastiques de détérioration, allant du modèle classique de processus Gamma à des modèles plus complexes avec des effets aléatoires, sont développés et adaptés aux spécificités des piles à combustible. Sur la base de ces modèles, plusieurs stratégies de décision post-pronostic pour une MFC sont proposées et, pour chacune d'entre elles, le problème d'optimisation associé est formulé.Tout d'abord, sous un profil de charge constant, en prenant en compte dans le processus de décision à la fois la consommation totale de combustible et la détérioration attendue, une stratégie de gestion de l'énergie tenant compte de la détérioration est proposée pour un système constitué de trois piles à combustible. Le problème d'optimisation multi-objectif associé à cette stratégie est résolu à l'aide d'un algorithme évolutionnaire, ce qui permet d'obtenir les allocations de charge optimisées pour chacune des piles du système. La durée de vie moyenne obtenue dans le cadre de la stratégie proposée s'avère plus longue que celle résultant de stratégies classiques (Average Load, Daisy Chain).De plus, sous un profil de charge dynamique aléatoire, et en prenant en compte les phénomènes de détérioration dus à la fois au niveau et aux variations de la charge, une stratégie de prise de décision est proposée pour un système de deux piles à combustible. La prise de décision est réalisée à chaque événement de modification de la demande, et les allocations de charge optimales sont obtenues en minimisant la fonction objectif qui est estimée sur la base de la prévision de la détérioration future du système. Une étude de l'influence des charges dynamiques aléatoires sur les performances de la stratégie proposée montre que la durée de vie moyenne obtenue dans le cas d’une durée inconnue entre deux modifications de demande est proche de celle obtenue avec une durée d'événement connue, ce qui prouve la robustesse de la stratégie proposée. De plus, il est montré que la durée de vie moyenne du système est augmentée par rapport au cas avec une stratégie de charge moyenne, sur plusieurs modèles de détérioration stochastique différents.Enfin, une étude plus exploratoire ouvre des perspectives de recherche dans le cas où le système multi-piles est composé de trois piles, dont deux seulement fonctionnent en même temps. Pour optimiser la durée de vie des piles, tout en répondant à la demande de charge, le système de gestion de l’énergie doit également optimiser le démarrage et l'arrêt des différentes piles. En fait, l'optimisation du remplacement des piles est également nécessaire pour une tâche d'exploitation à long terme. Par conséquent, cette étude ouvre la voie à des approches de maintenance pour les systèmes multi-piles
Fuel cell systems offer a sustainable solution to electrical power generation in the transportation sector, even if they still encounter reliability and durability issues. Resorting to Multi-stack Fuel Cells systems (MFC) instead of single fuel cells is a promising solution to overcome these limitations by optimally distributing the power demand among the different stacks while taking into account their state of health, by means of an efficient Energy Management Strategy (EMS). In this work, different strategies have been developed for vehicle applications, with the objective of optimizing the fuel cell system lifetime.The first challenge is to develop a model linking the deterioration trend of each stack with the power delivered by the stack, so as to predict the effect of a load allocation on each stack deterioration, and thus make a relevant post-prognostics decision. Several stochastic deterioration models, from the classical Gamma process model to more complex models with random effects are developed and tailored to the fuel cell specificities. Based on these models, several post-prognostics decision-making strategies for an MFC are proposed and, for each of them, the associated optimization problem is formulated.First, under a constant load profile, taking into consideration both the expected whole fuel consumption and the expected deterioration in the decision-making process, a deterioration-aware energy management strategy is proposed for a three-stack fuel cell system. The multi-objective optimization problem associated to this strategy is solved using an evolutionary algorithm, giving the optimized load allocations among stacks. The average lifetime obtained under the proposed strategy is demonstrated to be larger than those resulting from the classical Average Load and Daisy Chain strategies.Furthermore, under a random dynamic load profile, taking into consideration the deterioration phenomena due to both the load magnitude and the load variations, an event-based decision-making strategy is built for a two-stack fuel cell system. The optimal load allocations are obtained by minimizing the objective function which is estimated based on the prevision of the future system deterioration. An investigation on the influence of the random dynamic loads on the proposed strategy performance shows that the average lifetime obtained with unknown event duration is close to that with known event duration, which proves the robustness of the proposed strategy. Moreover, it is shown that the average system lifetime is increased when compared to the case with an Average Load strategy, on several different stochastic deterioration models.Lastly, a more exploratory study opening research perspectives in the case where the multi-stack system is composed of three stacks, only two of which are operating at the same time. To optimize the lifetime of the stacks, while meeting the load demand, the EMS must also optimize the start and stop of the different stacks. In fact, the optimization of stack replacement is also required for a long-term operation task. Therefore, this study opens the way to maintenance approaches to multi-stack systems

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.
Master of Science

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption.
[ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años.
[CAT] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys.
Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236
TESIS

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.
Libyan government

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

AbstractFloating nuclear containment is in a harsher environment than conventional onshore nuclear containment. In view of the Marine environment under the condition of floating nuclear power plant containment structure safety, combined water dynamics and structural mechanics, considering the containment response under random movement of hull in the Marine environment, the influence of the containment structure load calculation, thus checking containment when working in pile structure safety, provide theoretical basis for the safe operation of floating nuclear power plants. In this paper, taking a floating nuclear power plant as an example, ANSYS 2021R1, Workbench, Fluent and other software of finite element analysis are used to conduct fatigue simulation of floating nuclear power plant. The time course curve of the 6-dof motion of the ship’s center of gravity is obtained, then, a remote displacement method is adopted to transfer the hull motion to the containment vessel to realize the numerical simulation of the containment vessel movement with the hull, thus to solve maximum normal stress and strain, the maximum load component of containment bearing under the action of Marine environmental load is obtained. The results show that the maximum stress and strain of the vessel increase obviously in the moving state compared with the static state of the vessel, which indicates that the random motion response of the vessel must be considered in the structural safety analysis of the floating nuclear power plant containment.

AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.

With the ever-increasing size of the systems, there is a greater need for load balancing. Various algorithms are used for balancing the overall load of the cloud and a few of them are the honeybee foraging algorithm, a biased random sampling on a random walk procedure and active clustering. Here, the authors focus on the honeybee foraging algorithm. There is a type of bees called the forager bees who continually search for food sources and upon finding the same they return to the hive and advertise their discovery by a dance called a waggle. In case of load balancing in the web servers, whenever the demand sees a spike there is a dynamic allocation of services to regulate the changing demands of the user. The servers are grouped under Virtual servers (VS), each virtual server is assigned a specific queue for itself. Each server while processing a request calculates the reward and this is analogous to the quality of the find. The dance floor in case of the bees can be analogous to the advert board here which advertises the reward to the entire colony.

A Quasi Oppositional Gray Wolf Optimization (QOGWO) algorithm has been used in this work to decipher the economic load dispatch of hydrothermal system. Dynamic economic load dispatch problem involves scheduling of committed generators to meet the load demand with minimum fuel cost and several constraints which are dynamic in nature. It is basically short-term hydrothermal scheduling (STHS) problems through cascaded reservoirs. Instead of pseudo-random numbers quasi-opposite numbers are used to initialize population in the proposed QOGWO method so that the convergence rate of GWO increases. The viability of the projected approach is verified in three standard multi-chain cascaded hydrothermal systems with four interconnected hydro systems. The load and number of thermal units differ from one system to another. Water transportation delay between interconnected reservoirs, Valve Point Loading (VPL) have been considered in different combination in three cases. The technique put forth with established superior to many recent findings for the STHS problems with increased complexities.

Load balancing is one of the major issue in cloud computing. Load balancing helps in achieving maximum resource utilization and user satisfaction. This mechanism transparently transfer load from heavily loaded process to under loaded process. In this paper we have proposed a hybrid technique for solving task assignment problem in cloud platform. PSO based heuristic has been developed to schedule random task in heterogeneous data centres. Here we have also used variants of Particle Swarm Optimization(PSO) which gives better result than PSO and other heuristics for load balancing in cloud computing environment.

Cloud computing has evolved as an innovation that facilitates tasks by dynamically distributing virtual machines. User has to pay for the resources as per the demand. This is a challenging task for cloud service providers. The problems caused in load balancing are selecting random solutions, low speed convergence and picking up the original optima. To attain the best result, a mutation-based glow worm swarm optimization (MGWSO) technique is proposed. With this method, the makespan is reduced for a single work set across multiple datacentres. The work is motivated to decrease the consumption of resources in dynamic contexts while simultaneously increasing their availability. The simulated result shows that the suggested load balancing method dramatically reduces makespan in comparison to mutation-based particle swarm optimization.

Abstract For more realistic gear dynamic behaviour predictions, detailed gear dynamic models are needed, allowing taking into consideration the most important influencing factors. System model is presented, based on the separate handling of individual tooth pairs, with their specific profile corrections, manufacturing errors etc. Further on, non-linear single tooth pair force-deflection curve is considered, resulting in load dependent eigenfrequency characteristics. Simulation results are presented for gears with randomly distributed pitch errors. Gears with normal tooth profile and with tip relief are compared, and vibration response characteristics are analysed based on Fourier analysis of simulated transmission error response curves.

Abstract Portable air screw compressors are the most reliable, effective, and well-suited for the unique needs of building roads, performing restoration work, conducting civil engineering, mining, sandblasting, oil and gas exploration etc. This compressors must be designed to withstand tough site circumstances and difficult-to-reach site locations that may have challenging topography. Compressed air at remote locations is also used to power tools on the site, such as power hammers, drills, saws, wrenches, nail guns etc. These portable compressors are exposed to higher load such as dynamic load, lifting/towing load and random vibration load during their operation such as towing/lifting and transportation from one location to another. Fatigue life, stability and performance of majority of the structures and systems depend significantly on dynamic loadings applied on them. In order to guarantee effective product performance without trolley base frame failure over product life, it is crucial to examine the structural strength of the compressor base frame under such dynamic loading scenarios. Ingersoll Rand India is equipped a portable compressor unit using cutting-edge technology to meet the requirements of the business. The Modal and random vibration analysis of the portable compressor trolley base frame has been analyzed in the current study to evaluate the compressor trolley base frame. To verify dynamically, the unit’s Natural Frequency has been computed using modal analysis. According to SAE J 2380 standard, random vibration analysis has been performed to examine the base frame’s strength and its fatigue life against random loading for 92.6 hours of testing duration. Successful validation of a given equipment implies 100000 miles of life with 90% success probability without any random vibration fatigue failure. A thorough investigation of modal and random vibrations analysis was performed using ANSYS 2020 R2. Random vibration analysis was performed by applying PSD G acceleration at support base as per SAE J 2380 standard. The strength of the trolley base frame has been evaluated through a number of parameters, such stress accumulation, natural frequencies, fatigue life, and damage factor. According to the results of the investigation, estimated system natural frequency was found away from the operating frequency of the system which ensure the system will operate without critical resonance and high stresses while in operation. Under the random vibration analysis, random fatigue life, random fatigue damage factor and equivalent stress were found to be acceptable. According to the numerical investigation, the compressor’s base frame was unlikely to fail during its service life against the dynamic loads random in nature. Physical test of the compressor unit was also carried out for various road profiles and found the system structurally strong enough & having good degree of agreement with virtual validation.

A vehicle suspension subsystem consists of springs, shock absorbers and linkages, which attach to the wheels and chassis. The interaction between vehicle suspension and road generates dynamic forces in the suspension subsystem. In fact, vehicles travel at variable speed suffering a wide spectrum of vibrations and, usually, road profiles describe the roughness of the pavement surface, which represent their elevation as a function of a distance traveled or time. Consequently, suspension subsystem undergoes complex loading history that never reaches a level sufficient to cause failure in a single application. This means that components may fail due to fatigue. At this context a Double A suspension, which has two sets of linkages called lower and upper control arms, could fail by fatigue according to MBS and FEA. Thus, this work aims to present a MBS analysis of a Double A suspension submitted to a random road profile using a multibody commercial software. The fatigue life analysis focus in the lower control arm (LCA). For this, forces components (longitudinal, vertical and lateral force) and torques, composed by eleven loading history acting on pre-defined points at the LCA, characterize the response of the component in such a situation. In fact, forces computed by multibody simulations improve the FEA by providing better assessment of how they vary throughout the random profile. For FEA, a methodology is proposed to simplify the loads acting at this element and perform the fatigue analysis by finite element method, in which transient analysis was performed. This work represents a secondary stage of a broader study concerning the proposition of a constant amplitude proportional loading road profile based on the fatigue life results obtained from the complex loading history. Thus, it is expected to obtain in future works a simple signal to reproduce in numerical simulations, especially in fatigue analysis, and in experimental tests.

Abstract The Paper presents a novel computationally efficient physics based framework for continuous assessment of cyclic and time dependent damage consumption for Siemens Aeroderivative power turbine components based on actual engine operation. The framework discussed in paper provides the capability for Siemens’ customers to move away from fixed overhaul schedule to a customized schedule which is based on a given gas turbines actual operation and inspection findings. This customized overhaul schedule enables the customers a flexibility to maximize the unit availability and minimize operating costs. Semi-empirical framework discussed in this paper, utilizes dynamic systems theory-based approach to estimate the cyclic & creep damage as a response to transient engine operation; characterized by relevant installed engine instrumentation data from the Engine Health Monitoring system. To estimate damage response through any given complex transient operating cycle, algorithm solves a set of ordinary differential equations (ODEs), that have been calibrated to the engine control and safety instrumentation parameters such as shaft speed, turbine temperatures, pressures etc. by pre-analyzed operating envelope cases. The framework can be setup for predicting accumulated cyclic and creep damage for all type of turbine components (Aerofoils, disks, casings, diffusers etc.), transient stress state complexity (in-phase, out-of-phase, uniaxial, multiaxial stress profiles) and is capable to handle unit specific ramp rates, start-up times, restart/cooldown effects specific and random changes in load, history. The framework for discussion in this paper has been demonstrated as applied to the stage-1 blade of an A-35 RT62 power turbine as an example.

Abstract The issue of ride comfort for vehicle operations has recently generated considerable interest especially in heavy vehicle systems since long-distance drivers are more likely to experience high levels of vibration. This paper introduces the general concept of vibration-related health problems, discusses ride comfort assessment criteria and methods, and then focuses on the methodology of using computer simulation to analyze ride comfort. The computer-based ride comfort model can be divided into three sub-models: vehicle model, driver/seat model, and road profile input model. Several vehicle models and driver/seat models are reviewed and detailed modeling techniques are introduced. A five-axle tractor/semi-trailer/driver combination ride comfort simulation model is developed in this paper using the software DADS. Both four-spring tandem suspension and independent air spring suspension are studied. Road profiles are assumed as static zero mean Gaussian random process. Vertical acceleration at the interface between seat and driver body is obtained from simulation results. Power spectral density and root mean square (RMS) vertical acceleration are calculated based on simulation results. RMS acceleration at ISO classified good and average roads are compared with ISO 8-hour fatigue vibration limit. It is found that RMS acceleration of this particular vehicle simulated in this paper is below the ISO 8-hour fatigue limit for both good and average roads when traveling at the speed of fifty miles per hour. This implies a good ride comfort. Axle dynamic load coefficients (DLC) are calculated for four suspension configurations that are combinations of air springs and steel springs. Results show that large DLC doesn’t necessarily indicate bad ride quality.

Rock drilling is one of the elementary processes in mining industry. As larger diameter holes are drilled by hitting with units attached to the crone adapter down in the hole, the smaller blast holes are hit with units impacting the rod neck at rock surface. Key question in the performance and energy efficiency of the blast-hole drilling process is then, how completely the impact energy can be transmitted to the crone over the relatively long wave guide. There have been discussions about the effect of wave length and shape on to the penetration dynamics of rock drills. As the drilling process is a continuous set of hits following each other at relatively high constant period and the response is a random overlapped mixture of coming waves and returning reflected waves from multiple delayed hits, a detailed analysis of penetration dynamics is rather a complex problem. To overcome this difficulty, a full-scale half-manual test-drill has been designed and built to produce single hits for a systematic production and analysis of optimally shaped stress waves. The test-drill is an air-powered pneumatic gun, whose impact energy can be adjusted by setting the initial pressure level to correspond the desired end velocity of the piston. The design parameters, by which the pulse shape can be modified, are the length and the geometric profile of the piston body. The first problem to be faced is then to determine the optimal pulse shape for maximized penetration depth and the second one is to produce such desired shape by an optimal choice of the design parameters. The rig has been modelled using finite elements for the rod system and adiabatic state equations for the compressed and expanding air volumes. By modifying the design parameters, different penetration responses can be produced. In the first step, the model has been updated by means of experimental response measurements. The second step has been to modify the geometrical profiles of the piston body by starting from piece-vice linear and parabolic cross-sectional distributions. The output of the numerical analysis is to evaluate the penetration depth pro hit for different geometrical profiles. The most promising geometry has then been selected for the fabrication of the prototype piston. An experimental hitting test then completes the analysis, whose repeatability showed to be limited due to the random variation of the rock properties in the test bed. Test results obtained by using more regular concrete specimens exhibited reduced deviations in the responses, but the weakness in the test is the different damaging mechanism during the penetration. Another option is the use of an artificial load-sensing endsupport in order to produce a known boundary condition to replace the tool-rock resistance in the model-updating phase.

The blade modeling of axial compressor mostly adopts curved and swept design, which results in strong three-dimensional unsteady effects in the compressor. Experiment is the main method to obtain the flow field and characteristics of compressors, but it needs too much time and money. Compared with experiments, throughflow calculation and Computational Fluid Dynamics (CFD) save the cost in the design stage greatly. With the increase of compressor load, compressor blades become lighter and thinner, which leads to the blades deviating from the design profile. The manufacturing deviation may influence the aerodynamic performance and stability boundary of compressors. The key issue is how to predict the influence of manufacturing deviation on the compressor aerodynamic performance in the design stage. As the manufacturing deviations are random, it will occupy a huge amount of computing resources if we add deviations to CFD models directly. To solve this problem, we propose a method based on stage data, which is expected to predict the impact of manufacturing deviations on the aerodynamic performance of the compressor quickly. The reliable loss and deviation models based on stage data can predict the spanwise flow field parameters and performance of the compressor better.

The study of efficiency and safety for wind turbine structures under variable operating conditions is increasingly important for wind turbine design. Optimum aerodynamic performance of a wind turbine demands that serviceability effects and ultimate strength loads remain under safety design limits. From the perspective of wind turbine efficiency, variations in wind speed causes bluffing effects and vortex shedding that lead to vibration intensities in the longitudinal and transversal direction that can negatively impact aerodynamic performance of the turbine. From the perspective of wind turbine safety, variations in loading may lead to transient internal loads that threaten the safety of the structure. Inertial effects and asynchronous delays on rotational-force transmission may generate similar hazards. Monitoring and controlling displacement limits and load demands at critical tower locations can improve the efficiency of wind power generation, not to mention the structural performance of the turbine from both a strength and serviceability point of view. In this study, a probabilistic monitoring approach is developed to measure the response of the combined tower/nacelle/blade system to stochastic loading, estimate peak demand, and compare that demand to building code-derived estimates of structural resistance. Risk assessment is performed for the effects of along and across-wind forces in a framework of quantitative risk analysis with the goal of developing a near real-time estimate of structural risk that may be used to monitor safety and serviceability of the structure as well as regulate the aggressiveness of the controller that commands the blade angle of attack. To accomplish this goal, a numerical simulation of the aerodynamic performance of a wind turbine (including blades, the nacelle and the tower) is analyzed to study the interaction between the structural system and incoming flow. A model based on distributed-stationary random wind load profile for the combined along-wind and across-wind responses is implemented in Matlab to simulate full aero-elastic dynamic analysis to simulate tower with nacelle, hub, rotor and tower substructures. Self-weight, rotational, and axial effects of the blades, as well as lateral resistance of substructure elements are incorporated in the finite element model, including vortex-shedding effects on the wake zone. Reliability on the numerical solution is inspected on the tower structure by comparing the numerical solution with established experimental-analytical procedures.

Objective. To assess the comparative benefits and harms across three airway management approaches (bag valve mask [BVM], supraglottic airway [SGA], and endotracheal intubation [ETI]) by emergency medical services in the prehospital setting, and how the benefits and harms differ based on patient characteristics, techniques, and devices. Data sources. We searched electronic citation databases (Ovid® MEDLINE®, CINAHL®, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and Scopus®) from 1990 to September 2020 and reference lists, and posted a Federal Register notice request for data. Review methods. Review methods followed Agency for Healthcare Research and Quality Evidence-based Practice Center Program methods guidance. Using pre-established criteria, studies were selected and dual reviewed, data were abstracted, and studies were evaluated for risk of bias. Meta-analyses using profile-likelihood random effects models were conducted when data were available from studies reporting on similar outcomes, with analyses stratified by study design, emergency type, and age. We qualitatively synthesized results when meta-analysis was not indicated. Strength of evidence (SOE) was assessed for primary outcomes (survival, neurological function, return of spontaneous circulation [ROSC], and successful advanced airway insertion [for SGA and ETI only]). Results. We included 99 studies (22 randomized controlled trials and 77 observational studies) involving 630,397 patients. Overall, we found few differences in primary outcomes when airway management approaches were compared. • For survival, there was moderate SOE for findings of no difference for BVM versus ETI in adult and mixed-age cardiac arrest patients. There was low SOE for no difference in these patients for BVM versus SGA and SGA versus ETI. There was low SOE for all three comparisons in pediatric cardiac arrest patients, and low SOE in adult trauma patients when BVM was compared with ETI. • For neurological function, there was moderate SOE for no difference for BVM compared with ETI in adults with cardiac arrest. There was low SOE for no difference in pediatric cardiac arrest for BVM versus ETI and SGA versus ETI. In adults with cardiac arrest, neurological function was better for BVM and ETI compared with SGA (both low SOE). • ROSC was applicable only in cardiac arrest. For adults, there was low SOE that ROSC was more frequent with SGA compared with ETI, and no difference for BVM versus SGA or BVM versus ETI. In pediatric patients there was low SOE of no difference for BVM versus ETI and SGA versus ETI. • For successful advanced airway insertion, low SOE supported better first-pass success with SGA in adult and pediatric cardiac arrest patients and adult patients in studies that mixed emergency types. Low SOE also supported no difference for first-pass success in adult medical patients. For overall success, there was moderate SOE of no difference for adults with cardiac arrest, medical, and mixed emergency types. • While harms were not always measured or reported, moderate SOE supported all available findings. There were no differences in harms for BVM versus SGA or ETI. When SGA was compared with ETI, there were no differences for aspiration, oral/airway trauma, and regurgitation; SGA was better for multiple insertion attempts; and ETI was better for inadequate ventilation. Conclusions. The most common findings, across emergency types and age groups, were of no differences in primary outcomes when prehospital airway management approaches were compared. As most of the included studies were observational, these findings may reflect study design and methodological limitations. Due to the dynamic nature of the prehospital environment, the results are susceptible to indication and survival biases as well as confounding; however, the current evidence does not favor more invasive airway approaches. No conclusion was supported by high SOE for any comparison and patient group. This supports the need for high-quality randomized controlled trials designed to account for the variability and dynamic nature of prehospital airway management to advance and inform clinical practice as well as emergency medical services education and policy, and to improve patient-centered outcomes.