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We present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO2. The deep-learning models were trained using 3D datasets describing the pressure plume, CO2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

Climate change is a significant public health danger, with a disproportionate impact on low-income and communities of color that threatens to increase health inequities. Many important social determinants of health are at stake in California climate change policy-making and planning, and the distribution of these will further impact health inequities. Not only are these communities the most vulnerable to future health impacts due to the cumulative impacts of unequal environmental exposures and social stressors, they are also least likely to be represented in climate change decision-making processes. Therefore, it is imperative that public health and social equity advocates participate in climate change policy-making that protects and enhances the health and well-being of vulnerable communities. Regions have emerged as important policy-making arenas for both climate change and public health in California, because many drivers of climate change are also social determinants of health (e.g. land use, housing, and transportation planning); these play out regionally and are under regional governmental authority. However, the public health sector is not engaged adequately with climate change planning given the magnitude of risks and opportunities inherent for health. Examination of where public health and equity partners have engaged in regional climate change planning and policy-making may offer lessons for how to change the drivers of health inequities and climate change through this work.This dissertation examines why the public health sector in California is not more engaged with climate change work and regional scale planning given current threats to and opportunities for health, and whether and how public health and social equity stakeholders’ participation in climate change solutions and regional scale planning can improve health and inequities outcomes and decision-making processes. The overarching goal of this research was to inform efforts to increase public health work on climate change and regional-scale planning, strengthen partnerships between public health, social equity, and climate change stakeholders, and formulate strategies that address climate change and health equity. The first chapter of this dissertation was conducted in conjunction with a study at the Center for Climate Change and Health at the Public Health Institute, where we conducted semi-structured in-depth interviews (n=113) with public health and climate change professionals and advocates. I performed structured coding and conducted inductive-deductive thematic analysis within and across respondent groups. I found that individual-level barriers to public health engagement with climate change include perceptions that climate change is not urgent, immediate, or solvable, and insufficient understanding of public health impacts, connections, and roles. Institutional barriers include a lack of public health capacity, authority, and leadership due to risk aversion and politicization of climate change; a narrow framework for public health practice; and professional compartmentalization. Opportunities include integrating climate change into current public health practice; providing support for climate solutions with health co-benefits; and communicating, engaging and mobilizing impacted communities and public health professionals.In the second chapter, I conducted two case studies of Sustainable Communities Strategies planning to achieve greenhouse gas reduction targets through integrated regional land use and transportation planning under California Senate Bill 375 (San Francisco Bay Area and Southern California). I used in-depth interviews (n=50) with SCS planning participants, public document review, and participant observation. I analyzed interviews using thematic analysis in an iterative inductive-deductive process. In both regions, climate change planning was a major lever for increasing the language, consideration, funding, and measurement of health impacts into the SCS plans. Public health’s analytic skills and social determinants of health conceptual framework were valuable for both regional planning agencies and equity groups. Political context influenced the priority concerns, framing, and outcomes. Desire to improve public health was influential in both of these environments. In the Bay Area, a health equity frame promoted regional solutions that can improve health, equity, and climate change. In SCAG, a public health frame increased awareness, language, and future funding for active transportation. Public health was a less contested and commonly held value across diverse political jurisdictions that may be an entry point for future discussions of equity and climate change. In both regions, reform of regional governance processes was pursued to sustain institutionalization of health and equity concerns and improve regional democracy. I discuss implications and recommendations for engaging in multi-system integrated regional planning that can simultaneously improve climate change, health, and equity.In the third chapter, I analyze the same data as a case for understanding regional-scale public health, social equity, and regional planning staff efforts to slow climate change and improve social determinants of health and social equity. In both regions multi-year SCS planning processes, public health and equity stakeholder engagement was instrumental in getting health goals, targets, and indicators into plans. In the Bay Area, advocacy efforts yielded health and equity language in policies and implementation funding guidelines and changes to the basic governance structure. In SCAG, advocacy efforts yielded significant future funding for active transportation and more metrics to monitor the health and equity impacts of planning. Participants in the SCS planning process described their motivations for engaging at the regional level, the barriers to effective regional planning, the achievements of their engagement, and recommendations for improving future efforts. In the interviews, three main themes emerged related to the opportunities and challenges of working at the regional scale: (1) Building regional identity as a foundation for advancing health and equity; (2) The importance of governance structures for health and equity, and the need for regional governance reform; (3) The prospects and barriers of building regional coalitions both within public health networks and with regional equity partners. I discuss implications and recommendations for public health’s engagement with regional planning agencies, creation of coalitions, and reforming of regional governance structures to sustain better consideration of climate change, health, and equity.

Abstract To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NOx), primary fine particulate matter (PM2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM2.5 concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios.

In heavy-ion-driven inertial fusion accelerator concepts, dynamic aperture is important to the cost of the accelerator, most especially for designs which envision multibeam linacs, where extra clearance for each beam greatly enlarges the transverse scale of the machine. In many designs the low-energy end of such an accelerator uses electrostatic quadrupole focusing. The dynamic aperture of such a lattice has been investigated here for intense, space-charge-dominated ion beams using the 2-D transverse slice version of the 3-D particle-in-cell simulation code WARP. The representation of the focusing field used is a 3-D solution of the Laplace equation for the biased focusing elements, as opposed to previous calculations, which used a less-accurate multipole approximation. 80-85% radial filling of the aperture is found to be possible. Results from the simulations, as well as corroborating data from the High Current Experiment at LBNL, are presented.

In our laboratories we have previously developed a mild coal conversion process. This involves the use of a superacid system consisting of HF and BF/sub 3/ in presence of hydrogen and/or a hydrogen donor solvent. In order to understand the chemistry involved in the process of depolymerization of coal by the HF:BF/sub 3/:H/sub 2/ system we are carrying out a systematic study of a number of coal model compounds. The model compounds selected for present study have two benzene rings connected with various bridging units such as alkylidene, ether, sulfide etc. From studies so far carried out it appears that high pyridine extractibilities achieved by treating coal at temperature below 100/sup 0/C results from the cleavage of bridges such as present in bibenzyl, diphenyl methane, dibenzyl ether, dibenzyl sulfide etc. On the other hand the increased cyclohexane extractibility and distillability observed at relatively higher temperatures and hydrogen pressures reflects the hydrogenation and cleavage of the aromatic backbone in coal structure similar to what is seen in the conversion of model compounds such as biphenyl, diphenyl ether, diphenyl sulfide, anthracene, etc.

AbstractFast‐acting smart inverters that utilize preset operating conditions to determine real and reactive power injection/consumption can create voltage instabilities (over‐voltage, voltage oscillations and more) in an electrical distribution network if set‐points are not properly configured. In this work, linear distribution power flow equations and droop‐based Volt–Var and Volt–Watt control curves are used to analytically derive a stability criterion using Lyapunov analysis that includes the network operating condition. The methodology is generally applicable for control curves that can be represented as Lipschitz functions. The derived Lipschitz constants account for smart inverter hardware limitations for reactive power generation. A local policy is derived from the stability criterion that allows inverters to adapt their control curves by monitoring only local voltage, thus avoiding centralized control or information sharing with other inverters. The criterion is independent of the internal time‐delays of smart inverters. Simulation results for inverters with and without the proposed stabilization technique demonstrate how smart inverters can mitigate voltage oscillations locally and mitigate real and reactive power flow disturbances at the substation under multiple scenarios. The study concludes with illustrations of how the control policy can dampen oscillations caused by solar intermittency and cyberattacks.