The power supply for electronic devises is demanded to be lighter and smaller in nowadays market. Therefore, the cooling problem becomes the major design challenge due to reduced heat transfer area. In this thesis, a numerical computation method is employed to numerically simulate the natural convection heat transfer field for a small power supply placed on the ground or table in atmospheric conditions. The effects of parameters are studied including internal heat sink structure, shell structure, heat rate of generation, body size and ground material. The results of the present study can provide design reference.

This study uses the case study method to investigate how IT facilitates business model innovation. The A Steel Corporation (A Company) is chosen for this study. We consider the business model and information technology (IT) factors andanalyze how A Company use IT to facilitate business model innovation. Regarding thebusiness model factor, we describe the A Company¡¦s value propositions, value configuration, and key resources for co-production; further we identify the needed abilities for each stakeholder among the value network to make the co-production business model possible. For the IT factor, we present what ITs are developed and deployed among the value configuration in order to make the business model work. Finally, we report A Company¡¦s performance after its innovation. The case company, applied for industrial technology development projects sponsored by the goverment policy as strategies to introduce their new collaborating-commerce business model.They invoked their strategies to integrate the supply chains for expansion through startup a new business model for partners to understand, communicate and improve the value chain to link to the global companies. The performances cause high impacts on collaborative effectiveness among value chain partners. This thesis, based on a case study of A Company,is to analyze and find the key factor how to use the knowledge management makes strategies success and enable to expend the enterprise worth.

Excess lubricant can be found as reservoirs on the sides of the rolling tracks when the oil flows through the Hertzian contact and the side leakage. Uniform lubricant layers adhered to both rolling surfaces can flow into the supply region by the action of surface tension. Uniform lubricant layers are separated by air so that they move with the surfaces the surface tension of the liquid-air interface and the velocity of the roller. Hence, it can be considered as the fixed flow rate conditions. Under the lubricant starvation and the fixed flow rate conditions, the meniscus in the film inlet is formed due to the action of the surface tension of the oil-air interface, where the fluid pressure in the oil layer is smaller than the ambient pressure. An empirical formula to predict the thickness of the oil layer is derived based on the theoretical analysis and the experimental results of Cann et al. [10]. Results show that this thickness increases the amount of oil in the track and the surface tension of the liquid-air interface, but it decreases with the surface velocity and the oil viscosity. Moreover, the starved, fully flooded, over-flooded regimes are established based on the theoretical analysis. Under the lubricant starvation and the fixed flow rate conditions, the central film thickness in the pressure region increases with increasing the supply flow rate, so that the location of the meniscus moves to upstream. When the supply flow rate is more than 98% flow rate of fully flooded condition, the central film thickness achieves a saturated value. Hence, when the supply flow rate is between 98% and 100% flow rate of fully flooded condition, it is called the fully flooded regime. When the supply flow rate is more than the flow rate of fully flooded condition, the central film thickness remains constant, and the excess oil accumulates in the inlet region, so that the film thickness in the inlet region increases with time. When the supply flow rate is larger than the flow rate of fully flooded condition, it is called the over-flooded region.

This dissertation examines three facets of U.S. energy use and policy. First, I examine the Gulf Coast petroleum refining industry to determine the structure of the industry. Using the duality between cost-minimization and production functions, I estimate the demand for labor to determine the underlying production function. The results indicate that refineries have become more capital intensive due to the relative price increase of labor. The industry has consolidated in response to higher labor costs and costs of environmental compliance. Next, I examine oil production in the United States. An empirical model based on the theoretical framework of Pindyck is used to estimate production. This model differs from previous research by using state level data rather than national level data. The results indicate that the production elasticity with respect to reserves and the price elasticity of supply are both inelastic in the long run. The implication of these findings is that policies designed to increase domestic production through subsidies, tax breaks, or royalty reductions will likely provide little additional oil. We simulate production under three scenarios. In the most extreme scenario, prices double between 2005 and 2030 while reserves increase by 50%. Under this scenario, oil production in 2030 is approximately the same as the 2005 level. The third essay estimates demand for fossil fuels in the U.S. and uses these estimates to forecast CO2 emissions. The results indicate that there is almost no substitution from one fossil fuel to another and that all three fossil fuels are inelastic in the long run. Additionally, all three fuels respond differently to changes in GDP. The result of the differing elasticities with respect to GDP is that the energy mix has changed over time. The implication for forecasting CO2 emissions is that models that cannot distinguish changes in the energy mix are not effective in forecasting CO2 emissions.

As technology scales into the Deep Sub-Micron (DSM) regime, circuit designs have become more and more sensitive to power supply noise. Excessive noise can significantly affect the timing performance of DSM designs and cause non-trivial additional delay. In delay test generation, test compaction and test fill techniques can produce excessive power supply noise. This will eventually result in delay test overkill. To reduce this overkill, we propose a low-cost pattern-dependent approach to analyze noise-induced delay variation for each delay test pattern applied to the design. Two noise models have been proposed to address array bond and wire bond power supply networks, and they are experimentally validated and compared. Delay model is then applied to calculate path delay under noise. This analysis approach can be integrated into static test compaction or test fill tools to control supply noise level of delay tests. We also propose an algorithm to predict transition count of a circuit, which can be applied to control switching activity during dynamic compaction. Experiments have been performed on ISCAS89 benchmark circuits. Results show that compacted delay test patterns generated by our compaction tool can meet a moderate noise or delay constraint with only a small increase in compacted test set size. Take the benchmark circuit s38417 for example: a 10% delay increase constraint only results in 1.6% increase in compacted test set size in our experiments. In addition, different test fill techniques have a significant impact on path delay. In our work, a test fill tool with supply noise analysis has been developed to compare several test fill techniques, and results show that the test fill strategy significant affect switching activity, power supply noise and delay. For instance, patterns with minimum transition fill produce less noise-induced delay than random fill. Silicon results also show that test patterns filled in different ways can cause as much as 14% delay variation on target paths. In conclusion, we must take noise into consideration when delay test patterns are generated.

Salinity is a major determinant of where and how water resources are used worldwide. Natural salt pollution severely constrains the beneficial use of large amounts of water in Texas and neighboring states. High salinity loads in several major river/reservoir systems, including the Brazos River, originate largely from salt seeps and springs in isolated areas of the upper river basins located in the Permian Basin geologic region. Research objectives were (1) to improve salinity simulation capabilities of the Water Rights Analysis Package (WRAP) modeling system, and (2) to develop a better understanding of the occurrence, transport, and impacts of salinity in the Brazos River and Lakes Possum Kingdom, Granbury, and Whitney. Water volume budgets and total dissolved solids load budgets were developed for five river reaches covering 405 miles of the upper Brazos River. Methodologies were developed for creating and applying WRAP salinity input datasets. The WRAP modeling system was expanded and applied to the entire Brazos River Basin to investigate alternative modeling premises and impacts of salinity and salinity control measures on water supply capabilities. Water and salinity budget analyses of the Brazos River system based primarily on measured stream flow, reservoir storage, and total dissolved solids data compiled by the U.S. Geological Survey were performed to explore the characteristics of flow and storage volumes and salinity loads and concentrations in the river/reservoir system. WRAP salinity input datasets were developed based on results from the salinity budget study. One dataset was designed and applied specifically for testing salinity routing methods and calibrating salinity routing parameters. A second complete basin salinity dataset was developed and applied to simulate the Brazos River Basin for alternative management strategies. The results of the simulations demonstrate, for example, that previously proposed salt control impoundments can significantly reduce salinity loads and concentrations in the three reservoirs and at all locations on the Brazos River from the impoundments downstream to the Gulf of Mexico. The WRAP salinity simulation features are designed to provide flexibility in combining water quantity simulation datasets from the Texas Water Availability Modeling System or other sources, which may be very complex, with available salinity data which varies in extent and format between different river basins. The modeling capabilities demonstrated by the Brazos River Basin study can be applied in other river basins as well.

Low drop out (LDO) voltage regulators are widely used for post regulating the switching ripples generated by the switched mode power supplies (SMPS). Due to demand for portable applications, industry is pushing for complete system on chip power management solutions. Hence, the switching frequencies of the SMPS are increasing to allow higher level of integration. Therefore, the subsequent post-regulator LDO must have good power supply rejection (PSR) up to switching frequencies of SMPS. Unfortunately, the conventional LDOs have poor PSR at high frequencies. The objective of this research is to develop novel LDO regulators that can achieve good high frequency PSR performance. In this thesis, two PSR improvement methods are presented. The first method proposes a novel power supply noise-cancelling scheme to improve the PSR of an external-capacitor LDO. The proposed power supply noise-cancelling scheme is designed using adaptive power consumption, thereby not degrading the power efficiency of the LDO. The second method proposes a feed forward ripple cancellation technique to improve the PSR of capacitor-less LDO; also a dynamically powered transient improvement scheme has been proposed. The feed forward ripple cancellation is designed by reusing the load transient improvement block, thus achieving the improvement in PSR with no additional power consumption. Both the projects have been designed in TSMC 0.18 μm technology. The first method achieves a PSR of 66 dB up to 1 MHz where as the second method achieves a 55 dB PSR up to 1 MHz.

Low Dropout Regulators (LDOs) are extensively used in portable applications like mobile phones, PDAs and notebooks. These portable applications demand high power efficiency and low output voltage ripple. In addition to these, the radio circuits in these applications demand high power supply rejection (PSR). The output voltage of a conventional DC/DC converter (generally switched mode) has considerable ripple which feeds as input to these LDOs. And the challenge is to suppress these ripples for wide range of frequencies (for radio units) to provide clean supply. Enhanced buffer based compensation is proposed for the fully on-chip CMOS LDO which stabilizes the loop for different load conditions as well as improve the power supply rejection (PSR) until frequencies closer to open loop‟s unity-gain frequency. The stability and PSR are totally valid even for load capacitor varying from 0 to 100 pF. The proposed capacitor-less LDO is fabricated in On-Semi 0.5 μm fully CMOS process. Experimental results confirm a PSR of -30 dB till 420 KHz for the maximum load current of 50mA. The load transients of the chip shows transient glitches less than 90 mV independent of output capacitance.

Test power is an important issue in deep submicron semiconductor testing. Too much power supply noise and too much power dissipation can result in excessive temperature rise, both leading to overkill during delay test. Scan-based test has been widely adopted as one of the most commonly used VLSI testing method. The test power during scan testing comprises shift power and capture power. The power consumed in the shift cycle dominates the total power dissipation. It is crucial for IC manufacturing companies to achieve near constant power consumption for a given timing window in order to keep the chip under test (CUT) at a near constant temperature, to make it easy to characterize the circuit behavior and prevent delay test over kill. To achieve constant test power, first, we built a fast and accurate power model, which can estimate the shift power without logic simulation of the circuit. We also proposed an efficient and low power X-bit Filling process, which could potentially reduce both the shift power and capture power. Then, we introduced an efficient test pattern reordering algorithm, which achieves near constant power between groups of patterns. The number of patterns in a group is determined by the thermal constant of the chip. Experimental results show that our proposed power model has very good correlation. Our proposed X-Fill process achieved both minimum shift power and capture power. The algorithm supports multiple scan chains and can achieve constant power within different regions of the chip. The greedy test pattern reordering algorithm can reduce the power variation from 29-126 percent to 8-10 percent or even lower if we reduce the power variance threshold. Excessive noise can significantly affect the timing performance of Deep Sub-Micron (DSM) designs and cause non-trivial additional delay. In delay test generation, test compaction and test fill techniques can produce excessive power supply noise. This can result in delay test overkill. Prior approaches to power supply noise aware delay test compaction are too costly due to many logic simulations, and are limited to static compaction. We proposed a realistic low cost delay test compaction flow that guardbands the delay using a sequence of estimation metrics to keep the circuit under test supply noise more like functional mode. This flow has been implemented in both static compaction and dynamic compaction. We analyzed the relationship between delay and voltage drop, and the relationship between effective weighted switching activity (WSA) and voltage drop. Based on these correlations, we introduce the low cost delay test pattern compaction framework considering power supply noise. Experimental results on ISCAS89 circuits show that our low cost framework is up to ten times faster than the prior high cost framework. Simulation results also verify that the low cost model can correctly guardband every path‟s extra noise-induced delay. We discussed the rules to set different constraints in the levelized framework. The veto process used in the compaction can be also applied to other constraints, such as power and temperature.

Supply chain network design (SCND) identifies the production and distribution resources essential to maximizing a network’s profit. Once implemented, a SCND impacts a network’s performance for the long-term. This dissertation extends the SCND literature both in terms of model scope and solution approach. The SCND problem can be more realistically modeled to improve design decisions by including: the location, capacity, and technology attributes of a resource; the effect of the economies of scale on the cost structure; multiple products and multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand; and the gradually unfolding uncertainty. The resulting multistage stochastic mixed-integer program (MSMIP) has no known general purpose solution methodology. Two decomposition approaches—end-of-horizon (EoH) decomposition and nodal decomposition—are applied. The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the last level of the scenario-tree. Imposing these optimal configurations before optimizing the design decisions of the remaining nodes produces a smaller and thus easier to solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise, this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional computational experiments for a two-stage mixed-integer stochastic program (SMIP) are included. This dissertation also presents the first application of nodal decomposition in both SCND and MSMIP. The developed column generation heuristic optimizes the nodal sub-problems using an iterative procedure that provides a restricted master problem’s columns. The heuristic’s computational efficiency rests on solving the sub-problems independently and on its novel handling of the master problem. Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically, it provides the first application of Leontief substitution flow problems in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled MSMIPs. Computational results demonstrate superior performance of the heuristic approach and also show how this heuristic still applies when the SCND problem is modeled as a SMIP where the restricted master problem is a shortest-path problem.