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DTSTART;TZID=America/New_York:20240408T153000
DTEND;TZID=America/New_York:20240408T163000
DTSTAMP:20260403T174717
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SUMMARY:Penn Engineering 2023-24 Heilmeier Faculty Award Lecture: Arjun Raj
DESCRIPTION:“Can a cell learn?”\nEver since the genetic code was deciphered\, we have increasingly come to view cellular control through the lens of genetic determinism. In this paradigm\, a cell’s fate is already written into its DNA\, which is in turn shaped by Darwinian evolution over the course of many generations. At the same time\, an essential part of our experience as human beings is our ability to learn: our past shapes our present in a multitude of ways\, all within a single lifetime. Is it possible that cells can adapt to their environment by learning\, thereby overcoming their genetic destiny? We explore this possibility by tracing the life history of individual cells. In the context of drug resistance in cancer\, we show that there is a special subset of cells that can store memories of past events. These memories allow cells to rewire themselves at the molecular level to adapt to challenges that evolution may have never encountered. We posit that cellular learning may be occurring across many biological systems\, affording new opportunities for the engineering of cellular behavior.
URL:https://seasevents.nmsdev7.com/event/penn-engineering-2023-24-george-h-heilmeier-faculty-award-lecture-arjun-raj/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Distinguished Lecture
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T120000
DTEND;TZID=America/New_York:20240410T133000
DTSTAMP:20260403T174717
CREATED:20240401T152812Z
LAST-MODIFIED:20240401T152812Z
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SUMMARY:ASSET Seminar: "What Should We “Trust” in Trustworthy Machine Learning?" (Aaron Roth\, University of Pennsylvania)
DESCRIPTION:ABSTRACT: \n\n\n“Trustworthy Machine Learning” has become a buzz-word in recent years. But what exactly are the semantics of the promise that we are supposed to trust? In this talk we will make a proposal\, through the lens of downstream decision makers using machine learning predictions of payoff relevant states: Predictions are “Trustworthy” if it is in the interests of the downstream decision makers to act as if the predictions are correct\, as opposed to gaming the system in some way. We will find that this is a fruitful idea. For many kinds of downstream tasks\, predictions of the payoff relevant state that are statistically unbiased\, subject to a modest number of conditioning events\, suffice to give downstream decision makers strong guarantees when acting optimally as if the predictions were correct — and it is possible to efficiently produce predictions (even in adversarial environments!) that satisfy these bias properties. This methodology also gives an algorithm design principle that turns out to give new\, efficient algorithms for a variety of adversarial learning problems\, including obtaining subsequence regret in online combinatorial optimization problems and extensive form games\, and for obtaining sequential prediction sets for multiclass classification problems that have strong\, conditional coverage guarantees — directly from a black box prediction technology\, avoiding the need to choose a “score function” as in conformal prediction. \n  \nThis is joint work with Georgy Noarov\, Ramya Ramalingam\, and Stephan Xie \n\n\n\nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/96814843409
URL:https://seasevents.nmsdev7.com/event/asset-seminar-what-should-we-trust-in-trustworthy-machine-learning-aaron-roth-university-of-pennsylvania/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T130000
DTEND;TZID=America/New_York:20240410T140000
DTSTAMP:20260403T174717
CREATED:20240328T183658Z
LAST-MODIFIED:20240328T183658Z
UID:10007920-1712754000-1712757600@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Implementation and Performance of Wall Models for Large Eddy Simulation of Non-equilibrium Turbulent Boundary Layers"
DESCRIPTION:Accurate prediction of high-Reynolds-number wall-bounded turbulent flows is essential for the understanding and flow control of many engineering applications such as aircraft\, turbomachinery\, and marine vehicles. Additionally\, most practical flows exhibit nonequilibrium effects such as pressure gradient\, flow separation\, and mean three-dimensionality. However\, the direct numerical simulation (DNS) of high-Reynolds-number wall-bounded turbulent flows is not feasible owing to the prohibitive computational cost of resolving small-scale eddies near the wall. Wall-modeled large-eddy simulation (WMLES) presents an affordable predictive alternative to the DNS via the approximate modeling of flow physics near the wall (through a wall model) while resolving the outer (larger) scales directly on the computational grid. In this work\, we focus on two aspects of wall models\, (i) development and implementation of new/existing wall models\, and (ii) application and comparison of different wall models in various nonequilibrium turbulent boundary layers. In the first part\, we develop a novel spectral formulation for the ODE equilibrium wall model\, showing its superior efficiency to the traditional approach. Furthermore\, we extend the integral nonequilibrium wall model to an unstructured-grid LES solver. In the second part\, we explore three wall models with varying degrees of computational complexity and physical fidelity\, to assess their performance in two controlled but reasonably realistic nonequilibrium flows over a flat plate. The first flow features a turbulent boundary layer undergoing a series of complex pressure gradient effects\, while the second exhibits turbulent flow separation induced by suction and blowing. While in the latter case\, the more complex model clearly produces a superior prediction of the wall shear stress\, the same is not necessarily true in the former case\, highlighting that there still exists the need to adapt the existing wall models to different flow physics by modifying their underlying formulation or assumptions. Finally\, a physic-based decomposition of skin friction\, that shows separable contributions from various physical processes in the flow\, is employed to explain the differing mechanisms of success/failure of wall models in different flows.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-implementation-and-performance-of-wall-models-for-large-eddy-simulation-of-non-equilibrium-turbulent-boundary-layers/
LOCATION:Room B13\, Chemistry Building\, 231 S. 34th Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T133000
DTEND;TZID=America/New_York:20240410T133000
DTSTAMP:20260403T174717
CREATED:20240408T141733Z
LAST-MODIFIED:20240408T141733Z
UID:10007936-1712755800-1712755800@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Scalable and Risk-Aware Verification of Learning Enabled Autonomous Systems"
DESCRIPTION:As autonomous systems become more prevalent\, ensuring their safety will become more and more important. However\, deriving guarantees for these systems is becoming increasingly difficult due to the use of black box\, learning enabled components and the growing range of operating domains in which they are deployed. The complexity of the learning-enabled components greatly increases the computational complexity of the verification problem. Additionally\, the safety predictions from verifying these systems must be conservative. This thesis explores two high-level methods for verifying autonomous systems: probabilistic model checking and statistical model checking. Probabilistic model checking methods exhaustively analyze a model of the system to reason about its properties. These methods generally suffer from scalability issues\, but if the abstraction is built correctly then the results will be provably conservative. On the other hand\, statistical model checking methods draw traces from the system to reason about its properties. These methods don’t suffer the scalability drawback of probabilistic model checking\, but their guarantees are weaker and may not even be conservative. This thesis introduces methods for improving the scalability of verifying autonomous systems with probabilistic model checking methods and incorporating notions of conservatism into statistical model checking. \nOn the probabilistic model checking side\, this thesis first explores using engineering intuitions about systems to reduce probabilistic model checking complexity while preserving conservatism. Next\, standard conservative probabilistic model checking techniques are used to synthesize runtime monitors that are conservative and lightweight. Finally\, this thesis presents a run-time method for composing monitors of verification assumptions. Verification assumptions are critical for simplifying verification problems so that they become computationally feasible. \nFor statistical model checking\, this thesis first leverages a method called conformal prediction to bound the errors of trajectory predictors\, which enables safe (i.e. conservative) planning in dynamic environments. Additionally\, a method for producing less conservative conformal prediction regions in time series settings is developed. Then a method called risk verification is developed\, which uses statistical methods to bound risk metrics of a system’s performance. Risk metrics\, which capture tail events of the system’s performance\, offer a statistical equivalent of worst case analysis.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-scalable-and-risk-aware-verification-of-learning-enabled-autonomous-systems/
LOCATION:Moore 317\, 200 S 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T133000
DTEND;TZID=America/New_York:20240410T143000
DTSTAMP:20260403T174717
CREATED:20240401T152100Z
LAST-MODIFIED:20240401T152100Z
UID:10007923-1712755800-1712759400@seasevents.nmsdev7.com
SUMMARY:ESE Spring Seminar - "Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models"
DESCRIPTION:: Generative priors are effective countermeasures to combat the curse of dimensionality\, and enable efficient learning and inversion that otherwise are ill-posed\, in data science. This talk begins with the classical low-rank prior\, and introduces scaled gradient descent (ScaledGD)\, a simple iterative approach to directly recover the low-rank factors for a wide range of matrix and tensor estimation tasks. ScaledGD provably converges linearly at a constant rate independent of the condition number at near-optimal sample complexities\, while maintaining the low per-iteration cost of vanilla gradient descent\, even when the rank is overspecified and the initialization is random. Going beyond low rank\, the talk discusses diffusion models as an expressive data prior in inverse problems\, and introduces a plug-and-play posterior sampling method (Diffusion PnP) that alternatively calls two samplers\, a proximal consistency sampler solely based on the forward model\, and a denoising diffusion sampler solely based on the score functions of data prior. Performance guarantees and numerical examples will be demonstrated to illustrate the promise.
URL:https://seasevents.nmsdev7.com/event/ese-spring-seminar-solving-inverse-problems-with-generative-priors-from-low-rank-to-diffusion-models/
LOCATION:Towne 337
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T150000
DTEND;TZID=America/New_York:20240410T160000
DTSTAMP:20260403T174717
CREATED:20240327T182337Z
LAST-MODIFIED:20240327T182337Z
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SUMMARY:Spring 2024 GRASP SFI: Michel Hidalgo\, Ekumen\, "Doing robotics in digital labs: Or how simulations fuel robotics development"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. This week’s speaker will be virtual. \nSeminar attendees are also invited to a group discussion with Michel Hidalgo on Thursday\, April 11th from 3:00 PM to 4:00 PM on this link. \nABSTRACT\nHow do you do robotics without robots? Ekumen has been profitably providing software consulting services to robotics companies for over a decade\, 10000 miles from relevant technological hubs. While multi-causal\, a non-negligible factor in the company’s success were the advancements in multi-body dynamics simulation. From prototyping through validation and testing\, the robotics industry relies on these simulations to speed up and scale its development workflows. As a byproduct\, this tendency has alleviated the need for locality with hardware assets. I will discuss present-day technology in multi-body dynamics simulation\, some of our past experience with it in real-world applications\, and what we have learned from practice along the way.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-sfi-michel-hidalgo/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T153000
DTEND;TZID=America/New_York:20240410T163000
DTSTAMP:20260403T174717
CREATED:20240116T182536Z
LAST-MODIFIED:20240116T182536Z
UID:10007812-1712763000-1712766600@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Role of Water in Underwater Adhesion" (Ali Dhinojwala\, University of Akron)
DESCRIPTION:Abstract\nRoughness and wetness can disrupt interfacial bonding and reduce adhesion\, and this phenomenon is of relevance for many biological and engineering applications. I will discuss how roughness affects both dry and wet adhesion as well as provide an overview of our current theoretical understanding in this area. My specific interest is in underwater adhesion\, focusing on overcoming the challenges for achieving adhesion in confined water\, which reduces molecular contact (particularly when in contact with rough surfaces). The trapping of confined water is a function of roughness\, surface chemistry\, and kinetics\, making this a difficult problem to explain using theoretical models. Interestingly\, nature has developed a wide range of strategies that enable organisms to stick to rough and wet surfaces. For example\, geckos and insects use fibrillar structures to create molecular contact and to improve water drainage\, spiders use hygroscopic salts to reduce interfacial water next to hydrophilic surfaces\, and mussels use specific chemical groups (catechol) to bind to polar surfaces. I will share new strategies inspired by these natural systems for improving adhesion and discuss how they are applied to biomedical and engineering applications that require adhesion to wet and rough surfaces.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-role-of-water-in-underwater-adhesion-ali-dhinojwala-university-of-akron/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240411T103000
DTEND;TZID=America/New_York:20240411T120000
DTSTAMP:20260403T174717
CREATED:20240405T135838Z
LAST-MODIFIED:20240405T135838Z
UID:10007931-1712831400-1712836800@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Thermal Architecture"
DESCRIPTION:Air conditioning accounts for nearly 20% of the total electricity used in buildings globally and cooling energy demand is predicted to significantly increase over the next decades due to urbanization\, population growth\, and global warming. Heat stress is a major environmental justice concern\, disproportionally impacting disadvantaged communities. What are the paths to reduce the massive energy consumption of the building sector and at the same time still provide people with healthy living environments? With COVID-19 and extreme heat stress events placing a strong focus on the precarious relationship between indoor spaces and human well-being\, we need a new paradigm for environmental control in buildings. \nThe Thermal Architecture Lab seeks to find sustainable and equitable cooling alternatives to replace current building practices. Working at the intersection of heat transfer\, architectural design\, and material science\, we develop novel technologies and design strategies to simultaneously reduce buildings’ energy demand and provide thermal shelter to people in a warming world. In this lecture\, a series of projects will be presented\, exploring various methods to provide both ventilation and climatic adaptation to interior spaces across different climatic zones.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-thermal-architecture/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T093000
DTEND;TZID=America/New_York:20240412T103000
DTSTAMP:20260403T174717
CREATED:20240401T170733Z
LAST-MODIFIED:20240401T170733Z
UID:10007925-1712914200-1712917800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Controlling Fracture Behavior Through Architecture"
DESCRIPTION:Many natural materials achieve excellent combinations of mechanical properties through their micro- and nano-scale structures\, which leverage a level of complexity currently unmatched in engineering design. Recent advances in digital manufacturing have enabled the introduction of these fine-scale architectures to improve the mechanical properties of materials\, but their intricacy still lags far behind that of natural materials. In particular\, the potential of these structures to create materials with enhanced fracture resistance has remained limited\, primarily due to a narrow design focus on simple\, repetitive structures optimized for idealized materials. Improving the damage-tolerance of materials is critical to the mechanical performance of structures and interfaces\, as cracks and defects often lead to failure at far-field loads that are significantly lower than the theoretical strength of the system. This thesis will demonstrate how leveraging disordered structures and considering material behavior beyond the idealized elastic-brittle regime can significantly enhance the fracture resistance of architected interfaces. Specifically\, three key aspects influencing the failure of architected interfaces are examined: the effects of plasticity\, the advantages of disordered structures\, and the impacts of stochastic material failure. Through a synthesis of mechanics frameworks\, computational modeling\, and experimental mechanics including full-field analyses using digital image correlation and photoelasticity\, it is shown that properly designed architectures lead to tunable and enhanced fracture resistance. These architectures enlarge the region of damage around the crack tip\, delocalizing stresses and increasing the resistance to crack propagation\, while also revealing novel properties such as the decoupling of toughness and strength.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-controlling-fracture-behavior-through-architecture/
LOCATION:DRL A8\, 209 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T103000
DTEND;TZID=America/New_York:20240412T114500
DTSTAMP:20260403T174717
CREATED:20231220T164100Z
LAST-MODIFIED:20231220T164100Z
UID:10007789-1712917800-1712922300@seasevents.nmsdev7.com
SUMMARY:Spring 2024 GRASP on Robotics: David Fridovich-Keil\, University of Texas at Austin\, "Information-Aware Algorithms for Smooth Dynamic Games"
DESCRIPTION:This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. \nABSTRACT\nThis talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large\, abstract systems such as ride-hailing networks to more concrete\, physically-embodied robotic settings such as collision-avoidance in traffic. We present the key theoretical underpinnings of dynamic game models for these varied situations and draw attention to the subtleties of information structure\, i.e.\, what information is implicitly made available to each agent in a game. Thus equipped\, the talk presents a state-of-the-art technique for solving several variants of these games\, as well as a set of “dual” techniques for the inverse problem of identifying players’ objectives and other structures based on observations of strategic behavior.
URL:https://seasevents.nmsdev7.com/event/spring-2024-grasp-on-robotics-david-fridovich-keil-university-of-texas-at-austin-information-aware-algorithms-for-smooth-dynamic-games/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T140000
DTEND;TZID=America/New_York:20240412T150000
DTSTAMP:20260403T174717
CREATED:20240312T191603Z
LAST-MODIFIED:20240312T191603Z
UID:10007898-1712930400-1712934000@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Modeling Lone Pair Dynamics in Materials"
DESCRIPTION:Materials properties are governed by the structure and dynamics of the bonds between their constituent atoms. In addition to covalent\, metallic\, and ionic interactions that we typically think about\, lone pair electrons can result in non-trivial directional interactions in materials. I will discuss molecular interactions involving lone pairs in materials\, focusing on results from molecular simulations of the electronic structure and dynamics of model halide perovskites and solid-state ionic conductors of interest for applications in energy science. After a brief discussion of lone pair-driven interactions in materials\, I will present recent results predicting the existence of electronic plastic crystals\, crystalline solids that exhibit dynamic rotational disorder of lone pairs. We predict that electronic plastic crystals are found in a wide range of molecular and ionic materials\, including halide perovskites\, where we anticipate that dynamic lone pair disorder plays an important role in photophysical processes. I will then present recent results from our ongoing investigations into electron pair dynamics in solid-state electrolytes for energy storage applications. We predict that rotational motion of electron pairs is coupled to translational dynamics of conducting ions\, forming electronic paddle-wheels in solid-state electrolytes. Finally\, I will discuss our ongoing efforts to reach the length and time scales necessary to model electrochemical processes in these and related systems by developing machine learning-based models that simultaneously describe electronic effects and coupling to long-range electrostatic fields. By focusing on the correct physics\, the resulting models are partially transferable and can describe electronic and nuclear response to external fields. I will then demonstrate the accuracy and transferability of this neural network approach – the self-consistent field neural network (SCFNN) – on model aqueous systems before closing with a discussion of implications of our results for the development of machine learning models.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-modeling-lone-pair-dynamics-in-materials/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T150000
DTEND;TZID=America/New_York:20240412T163000
DTSTAMP:20260403T174717
CREATED:20240328T204435Z
LAST-MODIFIED:20240328T204435Z
UID:10007921-1712934000-1712939400@seasevents.nmsdev7.com
SUMMARY:Career Steps Before Graduation Seminar
DESCRIPTION:On Friday\, April 12\,2024\, the Department of Materials Science and Engineering is hosting an information session dedicated to equipping you with insights on the essential steps to secure a career after graduation\, with staff from UPenn Career Services and ISSS (International Student and Scholar Services) lending their expertise on prevailing job trends and providing guidance on navigating the intricacies of securing employment. \nWhat sets this presentation apart is the tailored approach.  Rather than offering generic advice\, our goal is to tailor the presentation to address YOUR questions and concerns. That’s why we’re asking you to register (which is required) and submit any questions that you have in advance. Whether you’re curious about visa regulations\, resume tips\, using OPT\, or hiring trends\, we want to provide as much support as possible for your career goals. \nThis opportunity is extended to all SEAS students who are approaching graduation. Therefore\, we ask you to secure your spot at your earliest convenience\, ensuring that you don’t miss out on this opportunity.
URL:https://seasevents.nmsdev7.com/event/career-steps-before-graduation-seminar/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240412T150000
DTEND;TZID=America/New_York:20240412T170000
DTSTAMP:20260403T174717
CREATED:20240326T152430Z
LAST-MODIFIED:20240326T152430Z
UID:10007915-1712934000-1712941200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "A Multifaceted Approach to CO2 Emissions Reductions and Removals" (Maxwell Pisciotta)
DESCRIPTION:Abstract:\nThe scientific consensus is that climate change is not only actively occurring\, but that it is irrevocably due to human activities associated with greenhouse gas emissions. Greenhouse gas emissions have been accumulating in the atmosphere since the beginning of the industrial revolution. This thesis specifically focuses on one greenhouse gas in particular\, CO 2 . The continued CO 2 emissions from human activity can be quantified with the atmospheric concentration\, which amounts to upwards of 420 ppm today. To mitigate the harmful impacts of climate change\, these CO 2 emissions must be mitigated\, through pathways such as reducing their initial generation\, capturing them when they are unable to be avoided\, and removing them from the atmosphere when they cannot be captured at the source. This thesis investigates different technologies that fit into these broad categories\, notably\, deploying carbon capture technologies on natural gas combined cycle power plants\, decarbonizing industrial sectors\, and pairing direct air capture technologies to geothermal energy. To readily address the CO 2 emissions from natural gas combined cycle power plants\, a novel approach of using thermal energy storage was developed and evaluated to ensure its technological performance and economic viability. By integrating natural gas combined cycle power plants with carbon capture and storage (CCS) and thermal energy storage opportunities\, the economic viability of these plants improve. This was measured using the net present value of each of the configurations assessed over real-world locational marginal pricing (LMP) signals from NYISO and CAISO. Of the thermal energy storage options\, eight of the 19 thermal energy storage configurations led to an increased net present value on 11.5% – 98% of the LMP signals. Additionally\, a framework was developed and used to identify opportunities to integrate direct air capture (DAC) systems with geothermal energy resources to maximize the CO 2 abatement potential. The Geothermal-Framework can be used with various geothermal resources ranging from 86ºC – 225ºC\, using various working fluids\, and brine salinity ranging from 0-6%. When the integration of geothermal energy and DAC systems are compared to geothermal energy being used to generate low-carbon electricity\, the CO 2 abatement potential is increase by 105% to 452% when geothermal energy is integrated with DAC systems. This illustrates beneficial synergies between the two technologies\, namely being able to use geothermal energy as thermal energy rather than solely converting it to electricity. Lastly\, the Geothermal-DAC Framework was used to showcase opportunities for integrating DAC with the geothermal resources near Gerlach\, NV\, in preparation for a community meeting. The community feedback was then incorporated\, facilitating updates to the Geothermal-DAC Framework to account for community needs\, illustrating that engineering can be community-centered from the start of the project. All the approaches explored in this thesis highlight the need for a diverse portfolio of solutions to address the ongoing CO 2 emissions and abatement required to avoid the most harmful impacts of climate change. Furthermore\, the efforts of researchers\, scientists\, policymakers and frontline communities will be needed in concert to deploy a portfolio that meets the needs to address climate change and protect against further environmental injustices.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-a-multifaceted-approach-to-co2-emissions-reductions-and-removals-maxwell-pisciotta/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
END:VCALENDAR