Effect of atmospheric aging on volatility and reactive oxygen species of biodiesel exhaust nano-particles

In the prospect of limited energy resources and climate change, effects of alternative biofuels on primary emissions are being extensively studied. Our two recent studies have shown that biodiesel fuel composition has a significant impact on primary particulate matter emissions. It was also shown that particulate matter caused by biodiesels was substantially different from the emissions due to petroleum diesel. Emissions appeared to have higher oxidative potential with the increase in oxygen content and decrease of carbon chain length and unsaturation levels of fuel molecules. Overall, both studies concluded that chemical composition of biodiesel is more important than its physical properties in controlling exhaust particle emissions. This suggests that the atmospheric aging processes, including secondary organic aerosol formation, of emissions from different fuels will be different as well. In this study, measurements were conducted on a modern common-rail diesel engine. To get more information on realistic properties of tested biodiesel particulate matter once they are released into the atmosphere, particulate matter was exposed to atmospheric oxidants, ozone and ultra-violet light; and the change in their properties was monitored for different biodiesel blends. Upon the exposure to oxidative agents, the chemical composition of the exhaust changes. It triggers the cascade of photochemical reactions resulting in the partitioning of semi-volatile compounds between the gas and particulate phase. In most of the cases, aging lead to the increase in volatility and oxidative potential, and the increment of change was mainly dependent on the chemical composition of fuels as the leading cause for the amount and the type of semi-volatile compounds present in the exhaust.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Climate forcing on avian life history

In the 21st century, human-induced global climate change has been highlighted as one of the most serious threats to ecosystems worldwide. According to global climate scenarios, the mean temperature in Finland is expected to increase by 1.8 4.0°C by the end of the century. The regional and seasonal change in temperature has predicted to be spatially and temporally asymmetric, where the High-Arctic and Antarctic areas and winter and spring seasons have been projected to face the highest temperature increase. To understand how species respond to the ongoing climate change, we need to study how climate affects species in different phases of their life cycle. The impact of climate on breeding and migration of eight large-sized bird species was studied in this thesis, taking food availability into account. The findings show that climatic variables have considerable impact on the life-history traits of large-sized birds in northern Europe. The magnitude of climatic effects on migration and breeding was comparable with that of food supply, conventionally regarded as the main factor affecting these life-history traits. Based on the results of this thesis and the current climate scenarios, the following not mutually exclusive responses are possible in the near future. Firstly, asymmetric climate change may result in a mistiming of breeding because mild winters and early spring may lead to earlier breeding, whereas offspring are hatching into colder conditions which elevate mortality. Secondly, climate induced responses can differ between species with different breeding tactics (income vs. capital breeding), so that especially capital breeders can gain advantage on global warming as they can sustain higher energy resources. Thirdly, increasing precipitation has the potential to reduce the breeding success of many species by exposing nestlings to more severe post-hatching conditions and hampering the hunting conditions of parents. Fourthly, decreasing ice cover and earlier ice-break in the Baltic Sea will allow earlier spring migration in waterfowl. In eiders, this can potentially lead to more productive breeding. Fifthly, warming temperatures can favour parents preparing for breeding and increase nestling survival. Lastly, the climate-induced phenological changes in life history events will likely continue. Furthermore, interactions between climate and food resources can be complex and interact with each other. Eiders provide an illustrative example of this complexity, being caught in the crossfire between more benign ice conditions and lower salinity negatively affecting their prime food resource. The general conclusion is that climate is controlling not only the phenology of the species but also their reproductive output, thus affecting the entire population dynamics.

Substance flow analysis in Finland : Four case studies on N and P flows

Nitrogen (N) and phosphorus (P) are essential elements for all living organisms. However, in excess, they contribute to several environmental problems such as aquatic and terrestrial eutrophication. Globally, human action has multiplied the volume of N and P cycling since the onset of industrialization. The multiplication is a result of intensified agriculture, increased energy consumption and population growth. Industrial ecology (IE) is a discipline, in which human interaction with the ecosystems is investigated using a systems analytical approach. The main idea behind IE is that industrial systems resemble ecosystems, and, like them, industrial systems can then be described using material, energy and information flows and stocks. Industrial systems are dependent on the resources provided by the biosphere, and these two cannot be separated from each other. When studying substance flows, the aims of the research from the viewpoint of IE can be, for instance, to elucidate the ways how the cycles of a certain substance could be more closed and how the flows of a certain substance could be decreased per unit of production (= dematerialization). In Finland, N and P are studied widely in different ecosystems and environmental emissions. A holistic picture comparing different societal systems is, however, lacking. In this thesis, flows of N and P were examined in Finland using substance flow analysis (SFA) in the following four subsystems: I) forest industry and use of wood fuels, II) food production and consumption, III) energy, and IV) municipal waste. A detailed analysis at the end of the 1990s was performed. Furthermore, historical development of the N and P flows was investigated in the energy system (III) and the municipal waste system (IV). The main research sources were official statistics, literature, monitoring data, and expert knowledge. The aim was to identify and quantify the main flows of N and P in Finland in the four subsystems studied. Furthermore, the aim was to elucidate whether the nutrient systems are cyclic or linear, and to identify how these systems could be more efficient in the use and cycling of N and P. A final aim was to discuss how this type of an analysis can be used to support decision-making on environmental problems and solutions. Of the four subsystems, the food production and consumption system and the energy system created the largest N flows in Finland. For the creation of P flows, the food production and consumption system (Paper II) was clearly the largest, followed by the forest industry and use of wood fuels and the energy system. The contribution of Finland to N and P flows on a global scale is low, but when compared on a per capita basis, we are one of the largest producers of these flows, with relatively high energy and meat consumption being the main reasons. Analysis revealed the openness of all four systems. The openness is due to the high degree of internationality of the Finnish markets, the large-scale use of synthetic fertilizers and energy resources and the low recycling rate of many waste fractions. Reduction in the use of fuels and synthetic fertilizers, reorganization of the structure of energy production, reduced human intake of nutrients and technological development are crucial in diminishing the N and P flows. To enhance nutrient recycling and replace inorganic fertilizers, recycling of such wastes as wood ash and sludge could be promoted. SFA is not usually sufficiently detailed to allow specific recommendations for decision-making to be made, but it does yield useful information about the relative magnitude of the flows and may reveal unexpected losses. Sustainable development is a widely accepted target for all human action. SFA is one method that can help to analyse how effective different efforts are in leading to a more sustainable society. SFA's strength is that it allows a holistic picture of different natural and societal systems to be drawn. Furthermore, when the environmental impact of a certain flow is known, the method can be used to prioritize environmental policy efforts.

The application of islanded photovoltaic cells and battery storage on the reliability of distribution networks

Renewable energy resources, in particularly PV and battery storage are increasingly becoming part of residential and agriculture premises to manage their electricity consumption. This thesis addresses the tremendous technical, financial and planning challenges for utilities created by these increases, by offering techniques to examine the significance of various renewable resources in electricity network planning. The outcome of this research should assist utilities and customers for adequate planning that can be financially effective.

Systems Analysis of Tank Irrigation: II. Delayed Start and Water Deficit

Tank irrigation systems in the semiarid regions of India are discussed in this paper. To optimize the grain yield of rice, it is essential to start the agricultural operations in the second week of July so that favorable climatic conditions will prevail during flowering and yield formation stages. Because of low inflow during the initial few weeks of the crop season, often farmers are forced to delay planting until sufficient sowing rain and inflow have occurred or to adopt deficit irrigation during this period. The delayed start affects the grain yield, but will lead to an improved irrigation efficiency. A delayed start of agricultural operations with increased irrigation efficiency leads to the energy resources becoming critical during the peak requirement week, particularly those of female labor and animal power. This necessitates augmenting these resources during weeks of their peak use, either by reorganizing the traditional methods of cultivation or by importing from outside the system.

Sustainable bioenergy production strategies for rural India

The paper aims to assess the potential of decentralized bioenergy technologies in meeting rural energy needs and reducing carbon dioxide (CO2) emissions. Decentralized energy planning is carried out for the year 2005 and 2020. Decentralized energy planning model using goal programming technique is applied for different decentralized scales (village to a district) for obtaining the optimal mix of energy resources and technologies. Results show that it is possible to meet the energy requirements of all the services that are necessary to promote development and improve the quality of life in rural areas from village to district scale, by utilizing the locally available energy resources such as cattle dung, leaf litter and woody biomass feedstock from bioenergy plantation on wastelands. The decentralized energy planning model shows that biomass feedstock required at village to district level can even be obtained from biomass conserved by shifting to biogas for cooking. Under sustainable development scenario, the decentralized energy planning model shows that there is negligible emission of CO2, oxide of Sulphur (SOx) and oxide of nitrogen (NOx), even while meeting all the energy needs.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Challenges in Earth sciences: the 21st century

The 21st century poses many challenges for global sustainability. Among them, most importantly, the human race will encounter scarcity of raw materials and conventional energy resources. And, India may have to take the brunt of these problems as it is going to be the most populated region of the world with concomitant increase in energy demand and requirement of other resources. India will be the testing ground for introducing newer ways of green technology and innovative principles of resource management and utilization. With the vagaries of potential climate change gathering clouds in the background, Earth sciences will have a special and predominant role in guiding the society in prioritizing our resource discovery, utilization and their consumption and the upkeep of environment. On the fundamental level, Earth sciences are going through a most exciting phase of development as a born-again science. Technological breakthroughs including the satellite-based observations augur well for gaining new insights into Earth processes. A set of exciting fundamental problems that are globally identified will set the stage for an exhilarating period of new discoveries. Improvements in numerical and computer-based techniques will assist in modelling of Earth processes to unprecedented levels. India will have to take special effort in improving the existing experimentation facilities in the Earth science departments of the country, and also the general level of Earth science education to meet the global standards. This article presents an Earth science vision for the 21st century in an Indian context.

Mixed convection in non-Newtonian fluids along a horizontal plate in a porous medium

The problem of mixed convection from horizontal surfaces in a porous medium saturated with a power-law-type non-Newtonian fluid is investigated. The transformed conservation laws are solved numerically for the case of variable wall hear pur conditions. Results for the details of the velocity and temperature fields as well as the Nusselt number have been presented. The viscosity index ranged from 0.5-1.5.

Distributed Source Coding for Sensor Data Model and Estimation of Cluster Head Errors Using Bayesian and K-Near Neighborhood Classifiers in Deployment of Dense Wireless Sensor Networks

The lifetime calculation of large dense sensor networks with fixed energy resources and the remaining residual energy have shown that for a constant energy resource in a sensor network the fault rate at the cluster head is network size invariant when using the network layer with no MAC losses.Even after increasing the battery capacities in the nodes the total lifetime does not increase after a max limit of 8 times. As this is a serious limitation lots of research has been done at the MAC layer which allows to adapt to the specific connectivity, traffic and channel polling needs for sensor networks. There have been lots of MAC protocols which allow to control the channel polling of new radios which are available to sensor nodes to communicate. This further reduces the communication overhead by idling and sleep scheduling thus extending the lifetime of the monitoring application. We address the two issues which effects the distributed characteristics and performance of connected MAC nodes. (1) To determine the theoretical minimum rate based on joint coding for a correlated data source at the singlehop, (2a) to estimate cluster head errors using Bayesian rule for routing using persistence clustering when node densities are the same and stored using prior probability at the network layer, (2b) to estimate the upper bound of routing errors when using passive clustering were the node densities at the multi-hop MACS are unknown and not stored at the multi-hop nodes a priori. In this paper we evaluate many MAC based sensor network protocols and study the effects on sensor network lifetime. A renewable energy MAC routing protocol is designed when the probabilities of active nodes are not known a priori. From theoretical derivations we show that for a Bayesian rule with known class densities of omega1, omega2 with expected error P* is bounded by max error rate of P=2P* for single-hop. We study the effects of energy losses using cross-layer simulation of - large sensor network MACS setup, the error rate which effect finding sufficient node densities to have reliable multi-hop communications due to unknown node densities. The simulation results show that even though the lifetime is comparable the expected Bayesian posterior probability error bound is close or higher than Pges2P*.

Combustion and heat transfer characteristics of nanofluid fuel droplets: A short review

With the pressing need to meet an ever-increasing energy demand, the combustion systems utilizing fossil fuels have been the major contributors to carbon footprint. As the combustion of conventional energy resources continue to produce significant Green House gas (GHG) emissions, there is a strong emphasis to either upgrade or find an energy-efficient eco-friendly alternative to the traditional hydrocarbon fuels. With recent developments in nanotechnology, the ability to manufacture materials with custom tailored properties at nanoscale has led to the discovery of a new class of high energy density fuels containing reactive metallic nanoparticles (NPs). Due to the high reactive interfacial area and enhanced thermal and mass transport properties of nanomaterials, the high heat of formation of these metallic fuels can now be released rapidly, thereby saving on specific fuel consumption and hence reducing GHG emissions. In order to examine the efficacy of nanofuels in energetic formulations, it is imperative to first study their combustion characteristics at the droplet scale that form the fundamental building block for any combustion system utilizing liquid fuel spray. During combustion of such multiphase, multicomponent droplets, the phenomenon of diffusional entrapment of high volatility species leads to its explosive boiling (at the superheat limit) thereby leading to an intense internal pressure build-up. This pressure upsurge causes droplet fragmentation either in form of a microexplosion or droplet puffing followed by atomization (with formation of daughter droplets) featuring disruptive burning. Both these atomization modes represent primary mechanisms for extracting the high oxidation energies of metal NP additives by exposing them to the droplet flame (with daughter droplets acting as carriers of NPs). Atomization also serves as a natural mechanism for uniform distribution and mixing of the base fuel and enhancing burning rates (due to increase in specific surface area through formation of smaller daughter droplets). However, the efficiency of atomization depends on the thermo-physical properties of the base fuel, NP concentration and type. For instance, at dense loading NP agglomeration may lead to shell formation which would sustain the pressure upsurge and hence suppress atomization thereby reducing droplet gasification rate. Contrarily, the NPs may act as nucleation sites and aid boiling and the radiation absorption by NPs (from the flame) may lead to enhanced burning rates. Thus, nanoadditives may have opposing effects on the burning rate depending on the relative dominance of processes occurring at the droplet scale. The fundamental idea in this study is to: First, review different thermo-physical processes that occur globally at the droplet and sub-droplet scale such as surface regression, shell formation due to NP agglomeration, internal boiling, atomization/NP transport to flame zone and flame acoustic interaction that occur at the droplet scale and second, understand how their interaction changes as a function of droplet size, NP type, NP concentration and the type of base fuel. This understanding is crucial for obtaining phenomenological insights on the combustion behavior of novel nanofluid fuels that show great promise for becoming the next-generation fuels. (C) 2016 Elsevier Ltd. All rights reserved.

Complete agent based simulation of mini-grids

EuroPES 2009

Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Contribuição para o planejamento brasileiro de exploração de petróleo e gás natural através da formulação de uma política nacional de uso sustentável das reservas de petróleo e gás natural

Petróleo e gás natural são recursos naturais não renováveis que possuem grande participação na matriz energética mundial e tendência de crescimento na matriz nacional, cujo marco regulatório limita-se a definir critérios técnicos e procedimentais sem incorporar o modelo de sustentabilidade instituído pela Constituição Federal de 1988. A natureza finita dos recursos não renováveis, como o petróleo e o gás natural, exige uma visão do planejamento de sua exploração de longo prazo na definição dos objetivos e metas. Essa perspectiva de longo prazo traduz uma das preocupações do desenvolvimento sustentável: a garantia de direitos para as futuras gerações. Assim, ao procurar fornecer elementos para a tradução do modelo de desenvolvimento sustentável no arcabouço institucional e legal da indústria petrolífera vigente no Brasil, o presente trabalho busca contribuir para o aprimoramento da regulação petrolífera nacional e a qualidade de vida das gerações presentes e futuras. E, mais do que propor a elaboração de um projeto de lei, como modalidade de implantação de uma política pública, queremos contribuir para o fortalecimento das práticas e ações governamentais voltadas para a aplicação do desenvolvimento sustentável, consoante apregoa a Constituição Federal brasileira. Trata-se aqui de demonstrar, através de metodologia quali-quantitativa, a tese de que é possível incorporar o princípio constitucional de desenvolvimento sustentável na atividade de exploração e produção de petróleo e gás natural, formulando uma política pública que incorpore, no regime de propriedade do petróleo, a variável ambiental e o uso intergeracional que já haviam sido e continuam sendo aplicados a algumas fontes renováveis de energia. Inicialmente, identificamos a composição da matriz energética brasileira desde a inserção do petróleo como uma questão de Estado a partir dos anos 50 do século XX. Em seguida, analisamos a concepção legal e doutrinária para propor, então, a conceituação de um modelo de desenvolvimento energético sustentável, estruturante para a proposição de uma política nacional para a indústria petrolífera. Com base nessa conceituação, analisamos o marco regulatório e os procedimentos institucionais praticados atualmente para identificar as lacunas existentes no ordenamento a serem supridas pela política nacional proposta. A partir da análise dos contextos legal e institucional, e das políticas energética e ambiental, propomos a tradução de conceitos, objetivos, princípios e instrumentos num projeto de lei de Política Nacional de Uso Sustentável das Reservas de Petróleo e Gás Natural. Concluímos tecendo considerações gerais e específicas sobre a proposição aqui formulada com vistas ao aprimoramento do modelo nacional de gestão de recursos energéticos e ao fomento das discussões voltadas para a sustentabilidade das políticas públicas e as práticas privadas enraizadas na exploração irracional de recursos não renováveis