Browsing by Subject "Sensitivity analysis"

Now showing 1 - 3 of 3
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    A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process using Artificial Neural Network (ANN)
    (2019-08) Marrey, Mallikharjun; El-Mounayri, Hazim; Zhang, Jing; Tovar, Andres
    Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of a few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process limiting the potential of the application. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, the data acquired from the experiments are employed to develop an intelligent parameter suggestion multi-layer feedforward (FF) backpropagation (BP) artificial neural network (ANN). This network estimates the fabrication time and suggests the parameter setting accordingly to the user/manufacturers desired characteristics of the end-product. Further, research is in progress to evaluate the framework for assemblies and complex part designs and incorporate the results in the network for achieving process repeatability and consistency.
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    REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS
    (2013-08-14) Li, Zuchuan; Li, Lin; Babbar-Sebens, Meghna; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
    Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale.
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    Techno-economic analysis of non-aqueous hybrid redox flow batteries
    (Elsevier, 2022-07-15) Li, Zhiguang; Fang, Xiaoting; Cheng, Lei; Wei, Xiaoliang; Zhang, Lu; Mechanical Engineering, School of Engineering and Technology
    Renewable energy has become indispensable to improving human life, but its growth is hampered by a lack of cost-effective energy storage systems to solve the intermittency problem. Non-aqueous hybrid redox flow batteries (NAqHRFBs), based on lithium metal anode and organic redox molecules (redoxmers), have been investigated as an attractive energy storage option because of their high cell voltages and energy densities compared to other redox flow battery candidates. However, little is known about the economic potential of NAqHRFBs, as well as the operational and materials impacts. This work establishes a techno-economic model to analyze the capital costs of NAqHRFBs with selected organic redoxmers, including 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO). Sensitivity analyses for current density, area-specific resistance, cell voltage, electrolyte composition, redoxmer price, and equivalent molecular weight indicate the key factors in controlling NAqHRFB capital cost. To make the current NAqHRFB cost-effective, the first priority is to increase the operation current density over 10 times of those used in lab-scale tests, followed by adjusting redoxmer-related characteristics to afford more cost reduction space such as decreasing the unit price by ∼20 fold. The results have shed light on potential material development and system engineering directions to make NAqHRFBs viable for renewable energy storage.