Composite materials have been more and more used in aircraft primary structures such as wing and fuselage. The aim of this thesis is to identify an effective way to optimize composite wing structure, especially the stiffened skin panels for minimum weight subject to stiffness and strength constraints. Many design variables (geometrical dimensions, ply angle proportion and stacking sequence) are involved in the optimum design of a composite stiffened panel. Moreover, in order to meet practical design, manufacturability and maintainability requirements should be taken into account as well, which makes the optimum design problem more complicated. In this thesis, the research work consists of three steps: Firstly, attention is paid to metallic stiffened panels. Based on the study of Emero’s optimum design method and buckling analysis, a VB program IPO, which employs closed form equations to obtain buckling load, is developed to facilitate the optimization process. The IPO extends the application of Emero’s method to an optimum solution based on user defined panel dimensional range to satisfy practical design constraints. Secondly, the optimum design of a composite stiffened panel is studied. Based on the research of laminate layup effects on buckling load and case study of bucking analysis methods, a practical laminate database (PLDB) concept is presented, upon which the optimum design procedure is established. By employing the PLDB, laminate equivalent modulus and closed form equations, a VB program CPO is developed to achieve the optimum design of a composite stiffened panel. A multi-level and step-length-adjustable optimization strategy is applied in CPO, which makes the optimization process efficient and effective. Lastly, a composite outer wing box, which is related to the author’s GDP work, is optimized by CPO. Both theoretical and practical optimum solutions are obtained and the results are validated by FE analysis.

The aim of this study was to determine the combined effects of high-heeled shoes and load carriage on gait and posture adaptation. Furthermore, the adaptation of gait and posture to the combined two conditions was examined by a comparison of the measured parameters between experienced and novice groups. 30 participants underwent a quantitative measurement of temporospatial, kinematic, and kinetic parameters of hip, knee, and ankle on both loaded and unloaded limbs using 3D motion analysis. Double support time and stride length increased during high-heeled gait and the magnitude of alteration was greater with a load. Increased plantarflexion was main cause of raised heel. Ankle plantarflexor moment increased with high-heeled but decreased with load carriage. As a result, plantarflexor moment diminished, in addition knee extensor moment exaggerated further. Hip extensor moment increased with heel height but not with load weight, however, hip angle was affected only by the load.

We study a new method of generating random $d$-regular graphs by repeatedly applying an operation called pegging. The pegging algorithm, which applies the pegging operation in each step, is a method of generating large random regular graphs beginning with small ones. We prove that the limiting joint distribution of the numbers of short cycles in the resulting graph is independent Poisson. We use the coupling method to bound the total variation distance between the joint distribution of short cycle counts and its limit and thereby show that $O(\epsilon^{-1})$ is an upper bound of the $\eps$-mixing time. The coupling involves two different, though quite similar, Markov chains that are not time-homogeneous. We also show that the $\epsilon$-mixing time is not $o(\epsilon^{-1})$. This demonstrates that the upper bound is essentially tight. We study also the connectivity of random $d$-regular graphs generated by the pegging algorithm. We show that these graphs are asymptotically almost surely $d$-connected for any even constant $d\ge 4$. The problem of orientation of random hypergraphs is motivated by the classical load balancing problem. Let $h>w>0$ be two fixed integers. Let $\orH$ be a hypergraph whose hyperedges are uniformly of size $h$. To {\em $w$-orient} a hyperedge, we assign exactly $w$ of its vertices positive signs with respect to this hyperedge, and the rest negative. A $(w,k)$-orientation of $\orH$ consists of a $w$-orientation of all hyperedges of $\orH$, such that each vertex receives at most $k$ positive signs from its incident hyperedges. When $k$ is large enough, we determine the threshold of the existence of a $(w,k)$-orientation of a random hypergraph. The $(w,k)$-orientation of hypergraphs is strongly related to a general version of the off-line load balancing problem. The other topic we discuss is computing the probability of induced subgraphs in a random regular graph. Let $0<s<n$ and $H$ be a graph on $s$ vertices. For any $S\subset [n]$ with $|S|=s$, we compute the probability that the subgraph of $\mathcal{G}_{n,d}$ induced by $S$ is $H$. The result holds for any $d=o(n^{1/3})$ and is further extended to $\mathcal{G}_{n,{\bf d}}$, the probability space of random graphs with given degree sequence $\bf d$. This result provides a basic tool for studying properties, for instance the existence or the counts, of certain types of induced subgraphs.

This master thesis is Atlas Copco Craelius’ first step in incorporating numericalmethods in load case analysis during the development process. Atlas Copco needs toconstantly evolve and refine their methods in the design process to remain as thenumber one manufacturer of mining and construction equipment. Poor knowledge ofloads results either in structural failures or in oversized structures, both very costlyfor Atlas Copco.The main goal of this thesis is to examine the potential in MBS software by using it toidentify extreme load cases in one of Atlas Copco Craelius’ surface drill rigs, Mustang5. The MBS-software ADAMS View is used to build a model of the Mustang 5 drill rigand evaluate simulation results. The rig model is subject to motions which representreal case scenarios. The feed positioning possibilities are examined thoroughly since it was expected that the positioning has impact on the load levels. 25 different feedpositioning are simulated.The main conclusion is that the load levels are highly dependent on the feedpositioning. For example, the load levels in the boom raising cylinder can increaseseven times, if the rig is operated with the most unfavorable positioning compared tothe recommended. It could also be concluded that not only one positioning isextreme in terms of loads; it depends on which part of the boom system is beingobserved. Thus, several positioning cases have to be taken into consideration in orderto optimize the design of parts in the boom system.

Increasing consumption and transportation gears the use of transport utilities which cause environmental effects over the globe. Environmental performance of three different types of pallets such as Re-load, plastic and corrugated fibreboard pallets are evaluated in this project. LCA tool is used to assess and compare their environmental performance in all phase of their life cycle but more focusing on end of life phase.   This study gives more emphasis to waste treatment options such as incineration, landfilling and recycling. Three different end of life scenarios have been used in this study such as 100% incineration, 100% landfilling and 100% recycling.   This study includes results of all the phases of all three types of pallets which are analysed in this report. More detailed results could be seen in excel sheets. Results of impact analysis tells that landfilling contributes to 14793 Kg CO2 of global warming potential in case of corrugated pallets. Incineration contributes to 12148.6 Kg CO2 of global warming potential. Recycling contributes to 7136 Kg CO2 of global warming. Re-load pallets show the major contribution of global warming is from landfilling approx 813.2 Kg CO2 of global warming potential. Recycling and incineration contribute to 438 Kg CO2 and 726.7 Kg CO2 of global warming potential respectively. In plastic case incineration contributes the most to global warming approximate 1183.8 Kg CO2 of global warming potential. Landfilling and recycling contribute almost the same approximate 932.6 Kg CO2 of global warming potential and 924.5 Kg CO2 of global warming potential respectively. Acidification impact show corrugated pallets cause high emissions when they are treat with landfilling and give negative values of incineration. In Eutriphication impact corrugated pallets are considered better in a sence they are inbetween 150 and 100 kg of PO-4. Re-load pallets give the least values when they are applied to different end of life scenarios.   According to the results recycling could be replace other waste treatment options because of less impact through out the end of life. Secondly, Reload pallets represent a environmental friendly product which can be improved more after this study. Lack of LCI data is the major problem in this study because it is not easily accessible and it is very time consuming part of this study. Results might be different if more data is available.   This study can be helpful for further study, for instance more replaceable scenarios will show different results for all three types of pallets. Moreover, it helps to compare more pallet types which are already in the market or propose to come in the market.

We study a new method of generating random $d$-regular graphs by repeatedly applying an operation called pegging. The pegging algorithm, which applies the pegging operation in each step, is a method of generating large random regular graphs beginning with small ones. We prove that the limiting joint distribution of the numbers of short cycles in the resulting graph is independent Poisson. We use the coupling method to bound the total variation distance between the joint distribution of short cycle counts and its limit and thereby show that $O(\epsilon^{-1})$ is an upper bound of the $\eps$-mixing time. The coupling involves two different, though quite similar, Markov chains that are not time-homogeneous. We also show that the $\epsilon$-mixing time is not $o(\epsilon^{-1})$. This demonstrates that the upper bound is essentially tight. We study also the connectivity of random $d$-regular graphs generated by the pegging algorithm. We show that these graphs are asymptotically almost surely $d$-connected for any even constant $d\ge 4$. The problem of orientation of random hypergraphs is motivated by the classical load balancing problem. Let $h>w>0$ be two fixed integers. Let $\orH$ be a hypergraph whose hyperedges are uniformly of size $h$. To {\em $w$-orient} a hyperedge, we assign exactly $w$ of its vertices positive signs with respect to this hyperedge, and the rest negative. A $(w,k)$-orientation of $\orH$ consists of a $w$-orientation of all hyperedges of $\orH$, such that each vertex receives at most $k$ positive signs from its incident hyperedges. When $k$ is large enough, we determine the threshold of the existence of a $(w,k)$-orientation of a random hypergraph. The $(w,k)$-orientation of hypergraphs is strongly related to a general version of the off-line load balancing problem. The other topic we discuss is computing the probability of induced subgraphs in a random regular graph. Let $0<s<n$ and $H$ be a graph on $s$ vertices. For any $S\subset [n]$ with $|S|=s$, we compute the probability that the subgraph of $\mathcal{G}_{n,d}$ induced by $S$ is $H$. The result holds for any $d=o(n^{1/3})$ and is further extended to $\mathcal{G}_{n,{\bf d}}$, the probability space of random graphs with given degree sequence $\bf d$. This result provides a basic tool for studying properties, for instance the existence or the counts, of certain types of induced subgraphs.

The primary function of a power system is to supply its customers with electrical energy as economically as possible and with acceptable reliability and quality. Generating capacity adequacy evaluation is the oldest and most extensively studied aspect of power system reliability assessment. A wide range of methods have been developed to perform this evaluation. Two computer programs were developed based on the analytical and simulation techniques and used as tools in this research work. A number of basic considerations in generating capacity adequacy evaluation are investigated. Generating unit residence time distributions and peaking load units are incorporated in the analysis.<p> Two commonly encountered misconceptions regarding the basic system reliability indices are examined by applying the two programs to two reliability test systems. Reliability index probability distributions can be used to supplement the information provided by the expected index values. The concept of creating distributions and the additional information that can be obtained is illustrated in this thesis. <p> Generating unit residence time distributions are generally categorized as being either exponential or non-exponential in form. The exponential distribution is utilized, however, in virtually all practical system studies. The impacts on the system reliability of non-exponential unit state residence time distributions are examined in this research. <p> Peaking load units and base load units have different operating characteristics. The functions of peaking load units vary with changes in the system operating conditions. This is examined in this research. <p>The conclusions and techniques presented in this thesis should prove valuable in power system planning and operation.

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of black box approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the black box approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.<p>The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the pump only model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a pump only model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. <p>It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. <p>The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the error accumulation problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response.

Load factors are an important simplification of electrical energy use data and depend on the ratio of average demand to peak demand. Based on operating hours of a facility they serve as an important benchmarking tool for the industrial sector. The operating hours of small and medium sized manufacturing facilities are analyzed to identify the most common operating hour or shift work patterns. About 75% of manufacturing facilities fall into expected operating hour patterns with operating hours near 40, 80, 120 and 168 hours/week. Two types of load factors, electrical and production are computed for each shift classification within major industry categories in the U.S. The load factor based on monthly billing hours (ELF) increases with operating hours from about 0.4 for a nominal one shift operation, to about 0.7 for around-the-clock operation. On the other hand, the load factor based on production hours (PLF) shows an inverse trend, varying from about 1.4 for one shift operation to 0.7 for around-the-clock operation. When used as a diagnostic tool, if the PLF exceeds unity, then unnecessary energy consumption may be taking place. For plants operating at 40 hours per week, the ELF value was found to greater than the theoretical maximum, while the PLF value was greater than one, suggesting that these facilities may have significant energy usage outside production hours. The data for the PLF however, is more scattered for plants operating less than 80 hours per week, indicating that grouping PLF data based on operating hours may not be a reasonable approach to benchmarking energy use in industries. This analysis uses annual electricity consumption and demand along with operating hour data of manufacturing plants available in the U.S. Department of Energy’s Industrial Assessment Center (IAC) database. The annual values are used because more desirable monthly data are not available. Monthly data are preferred as they capture the load profile of the facility more accurately. The data there come from Industrial Assessment Centers which employ university engineering students, faculty and staff to perform energy assessments for small to medium-sized manufacturing plants. The nation-wide IAC program is sponsored by the U.S. Department of Energy.

Granular aggregates of quartz subjected to triaxial compression under constant effective pressures (Pe) undergo macroscopic failure at critical stress states that depend on the effective mean stress. Although the mode of failure and mechanical response vary systematically with mean stress at failure, prefailure loading at subcritical stress states may induce yielding, and subcritical load paths may influence behavior at failure. Here, I investigate how the failure of quartz aggregates at conditions favoring compaction depends on consolidation history and load path in the transitional and ductile deformation regimes in terms of strain localization and microfracture fabric. Three distinct non-standard triaxial compression load paths were employed; the paths involve different preconsolidation of the aggregates at subcritical isotropic stress followed by differential loading with increasing or decreasing confining pressure. Deformed aggregates were injected with epoxy and studied using optical microscopy techniques to determine microscopic damage evolution for the different load paths. Microfracture data show that preconsolidation at subcritical isotropic loads facilitates formation of campaction bands during subsequent triaxial compression in the transitional regime. The preferred orientation of intragranular cracks evolves from near random fabrics for isotropic loading to strongly preferred orientations parallel to the maximum principal compression direction for differential loading, with the strongest preferred orientation within the compaction bands. Aside from the preconsolidation, different load paths have only a minor effect on the mechanical response during macroscopic failure.