Compiling Geometric Algebra Computations into Reconfigurable Hardware Accelerators

Geometric Algebra (GA), a generalization of quaternions and complex numbers, is a very powerful framework for intuitively expressing and manipulating the complex geometric relationships common to engineering problems. However, actual processing of GA expressions is very compute intensive, and acceleration is generally required for practical use. GPUs and FPGAs offer such acceleration, while requiring only low-power per operation. In this paper, we present key components of a proof-of-concept compile flow combining symbolic and hardware optimization techniques to automatically generate hardware accelerators from the abstract GA descriptions that are suitable for high-performance embedded computing

Liquid Holdup and Pressure Drop in the Gas-Liquid Cocurrent Downflow Packed-Bed Reactor under Elevated Pressures

An Experimental Investigation of the Residence Time Distribution, Liquid Holdup, and Pressure Drop in a Gas-Liquid Downflow Packed Bed Reactor with Porous Particles Operated under Elevated Pressures is Presented. the Effects of the Two-Phase Flow Rates and Reactor Pressures on the External Liquid Holdup and Pressure Drop Are Discussed. a Mechanistic Model, Which Accounts for the Interaction between the Gas and Liquid Phases by Incorporating the Shear and Velocity Slip Factors between Phases, is Employed to Predict the External Liquid Holdup and Pressure Drop for the Experimentally Covered Flow Regime. the Involved Parameters, Such as Shear and Velocity Slip Factors and Ergun Single-Phase Flow Bed Constants, Are Calculated from the Correlations Developed Via Neural Network Regression. the Model\u27s Predictions and the Experimental Observations at Elevated Pressure Are Compared. © 2004 Elsevier Ltd. All Rights Reserved

Common Property Resources, Property Rights and Natural Disasters

Property rights are the foundation of institutions that shape economic decisions ranging from individual transactions to the performance of a country’s economy. The incentives generated by the institutions in place also translate into measures of vulnerability and recovery in the face of natural disasters. Using the example of Chilean fisheries and the tsunami that affected the country in 2010, I measure how those incentives translate into production decisions before and after the natural disaster under different property right regimes. I find significant evidence that weak property rights over the resource lead to economic inefficiencies. These results contribute to the ongoing discussion of the role of property rights in the economic performance of common property resources, and how productive sectors and countries are affected by and recover from natural disasters.

Control of complex quantum structures in droplet epitaxy

We report the controllable growth of GaAs quantum complexes in droplet molecular-beam epitaxy, and the optical properties of self-assembled AlxGa1-xAs quantum rings embedded in a superlattice. We found that Ga droplets on a GaAs substrate can retain their geometry up to a maximum temperature of 490 degrees C during post-growth annealing, with an optimal temperature of 320 degrees C for creating uniform and symmetric droplets. Through controlling only the crystallisation temperature under As-4 in the range of 450 degrees C to 580 degrees C, we can reliably control diffusion, adsorption and etching rates to produce various GaAs quantum complexes such as quantum dots, dot pairs and nanoholes. AlxGa1-xAs quantum rings are also realised within these temperatures via the adjustment of As beam equivalent pressure. We found that crystallisation using As-2 molecules in the place of As-4 creates smaller diameter quantum rings at higher density. The photoluminescence of As-2 grown AlxGa1-xAs quantum rings embedded in a superlattice shows a dominant emission from the quantum rings at elevated temperatures. This observation reveals the properties of the quantum ring carrier confinement and their potential application as efficient photon emitters

Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem

Power system controller tuning considering stochastic variations

Electrical power systems are vulnerable to external disturbances, such as short circuits, that can lead to damage on the equipments and even blackouts. In order to improve the system response to external disturbances, the generators of the power system are equipped with automatic controllers devised to maintain the generators working on a constant operating condition. The tuning of the controllers is performed assuming the system loads do not have time-dependent variations, but such assumption is not realistic as the power system loads are stochastically changing due to the switching on and o of every device (PCs, TVs, cellphones, etc.) connected to it. This work proposes two new methods for the tuning of the generator controllers which takes into account the stochastic nature of the system loads. More speci cally, this work proposes two new methods for the tuning of the governors and AVRs of the power system generators: one focused on the steady state response and the other focused on the fault response. First, the system response as a function of the controller parameters is calculated. As the power system is under the e ect of stochastic loads, the resulting system response is stochastic. Then, a stochastic objective function which measures the quality of the system response is de ned. Each tuning method uses a di erent objective function. Finally, the objective function is optimized using the metaheuristic Cuckoo Search, which is used for global optimization problems and can be used to optimize stochastic functions. The method was tested in di erent benchmark systems showing better system responses

PV-Driven Heat Pump Water Heater Final Report

As part of the effort towards zero energy buildings and high efficiency water heating systems, the Florida Solar Energy Center (FSEC) working under contract with the National Renewable Energy laboratories (NREL) has successfully completed a demonstration of a photovoltaic heat pump water heater (PV-HPWH) concept. The system integrates two 310Wp solar photovoltaics (PV) modules and grid tied micro-inverters with a commercially available 50-gallon HPWH. The project showcases innovative strategies for distributed PV thermal systems that limit grid interaction and provide increased thermal energy storage. The system utilizes a custom appliance control module (ACM) interface to vary thermostat settings (115 °F to 140 °F) depending on time of day and solar radiation levels. It is also programmable to setback the thermostat setting during nighttime standby and during morning recovery. By setting the thermostat down to 115 °F, it can disrupt compressor heating recovery normally set to 125 °F and shift the remainder of recovery to times where higher solar resources are available (i.e., after 10:30 am). Coefficient of performance thru October 2016 has averaged 5.22 utilizing only 1.15 kWh per day. The total system retail cost of $2053 fares considerably well compared to traditional solar thermal systems

Genetic Algorithm Untuk Penyelesaian Optimal Power Flow Pada Sistem Distribusi Radial Mempertimbangkan DG

Dalam perkembangan sistem tenaga listrik, sistem distribusi listrik menjadi semakin luas dan kompleks sehingga menyebabkan rugirugi yang terjadi pada sistem menjadi lebih besar. Untuk mengatasi hal tersebut, cara yang umum dilakukan adalah dengan pemberian Distributed Generation (DG) yang tepat. Pada tugas akhir ini, diusulkan penyelesaian optimisasi aliran daya pada sistem distribusi dengan lokasi dan ukuran DG secara simultan untuk memperoleh minimum rugi saluran, minimum perubahan tegangan, dan menaikkan keluaran daya aktif DG. Dan untuk menyelesaikan permasalahan sedemikian tidak konveks, Genetic Algorithm (GA) metode yang diusulkan, yang mana mendekati iterative optimisasi aliran daya. Hasil simulasi kasus sistem IEEE 33 bus didapatkan aliran daya optimal saat diinjeksi DG dengan nilai pembobotan fungsi objektif yang seimbang untuk kerugian jaring dan deviasi tegangan. Total kerugian jaring menjadi (22,173 kW) turun 89,06% dari nilai awal dan total deviasi tegangan menjadi (-0,1652). Hasil simulasi kasus sistem IEEE 69 bus didapatkan aliran daya optimal saat diinjeksi DG dengan nilai pembobotan fungsi objektif yans seimbang untuk kerugian jaring dan deviasi tegangan. Total kerugian jaring menjadi (13,188 kW) turun 94,1% dari nilai awal dan total deviasi tegangan menjadi (-0,0012). ======================================================================================================================== In the development of electric power systems, electrical distribution systems become increasingly extensive and complex, causing losses that occur on the system becomes greater. To overcome this, the common way is the provision of Distributed Generation (DG) is appropriate. In this thesis, the proposed completion of the optimization of power flow in the distribution system with location and sizing DG simultaneously to obtain the minimum channel loss, minimum voltage changes, and increase the active power output of DG. To solve such problems are not convex, Genetic Algorithm (GA) method is proposed, which approached the iterative optimization of the power flow. The simulation results of IEEE 33 bus system case obtained optimal power flow when injected DG with objective function value equal weighting for network loss and voltage deviation. Total network loss become (22,137 kW) decrease 89,06% of the initial value and the total voltage deviation becomes (-0,1652). The simulation result of IEEE 69 bus system case obtained optimal power flow when injected DG with objective function value equal weighting for network loss and voltage deviation. Total network loss become (13,188 kW) decrease 94,1% of the initil value and the total voltage deviation become (-0,0012)

Dynamics of multistage systems

The project evolved from a discussion between academic staff at Loughborough University of Technology and staff from the British Petroleum Co. Ltd. A study was required of the dynamic characteristics of a particular 44-plate crude oil distillation unit at BP Refinery (Llandarcy) Ltd. A programme of experiments was conducted on the industrial unit. Step perturbations in the overhead reflux and sidestream off takes were found to give oscillatory response curves decaying over a two-hour period. A qualitative explanation for these results is given. [Continues.