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UPCOMING DISSERTATION DEFENSE – JEFFREY TWIGG

Author: Jeffrey Twigg

Date: Thursday, April 7th, 2022 at 3:30pm

Location: Glenn L. Martin Hall, Room EGR-2164

Committee Members:

Professor Nikhil Chopra, Chair/Advisor
Professor Miao Yu
Professor Yancy Diaz-Mercado
Professor Shapour Azarm
Professor Dinesh Manocha, Dean’s Representative
Dr. Brian Sadler

Title: On the Control of Robotic Parasitic Antenna Arrays

Abstract:

    Wireless networking is challenging in safety, security, and rescue contexts where network infrastructure may be damaged or compromised. Radio communication between ground robots at the lower end of the Very High Frequency (low-VHF) band is generally more reliable in complex indoor and urban environments when compared to communication systems such as Wi-Fi and cellular which operate at Ultra High Frequencies (UHF) and higher frequencies. Exciting antenna design research in the last 5 to 10 years has approached what is theoretically possible to create compact, moderately high bandwidth antennas at low-VHF. At the beginning of this dissertation research, we discovered that we could distribute these low-VHF antennas across closely positioned ground robots to create a robotic parasitic antenna array. When these robots are optimally positioned, they create a directional signal through the mutual coupling of their antennas. Consequently, these low-VHF arrays have the potential to extend the communication range of a reliable signal in urban and indoor environments with a proportionally small amount of robotic motion. 

In this dissertation, we research the control of robotic platforms constituting these arrays from two perspectives. First, we research how robots control their positions to optimize or maintain the gain of a single robotic parasitic array to improve the quality of a communication link. Then, we investigate where these robots should collect to form an array in a network of robotic parasitic arrays to increase a metric of overall network connectivity.

       To improve individual network communication links, we consider a two-element parasitic array formed by a static antenna and a ground robot and propose a technique by which this array can optimize its gain in a direction of interest. First, we propose and test an optimization approach for actuating spacing between the two antennas and passive antenna length to increase gain. Next, we propose and test an approach for using robotic motion to rotate the antenna array. In these experiments, we show that the robotic parasitic antenna array can provide a gain of 2 dB which is close to twice the effective transmission power in line-of-sight and non-line-of-sight conditions.

    From a network perspective, we research where robots should form arrays to maintain a metric of overall connectivity. However, existing control formulations for maintaining connectivity are not general enough to support this new capability. We first propose a generalized model that we integrate into a Fiedler value maximization approach for maintaining communication. Afterward, we develop approaches for allocating a finite number of robots for forming these robotic parasitic arrays while ensuring that our metric for overall network connectivity between robotic parasitic array forming robots remains lower bounded.

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UPCOMING DISSERTATION DEFENSE – PARTH DESAI

Author: Parth Rakesh Desai

Date: Friday, April 1, 2022 at 9:30 AM

Location: Glenn L. Martin Hall, Room EGR-0159

Committee Members:

Professor Siddhartha Das, Chair
Dr. Keir C. Neuman
Professor Peter Chung
Professor Paul Paukstelis
Professor Don DeVoe
Professor Jason Kahn, Dean’s Representative

Title of Dissertation: EFFECT OF MISMATCHED BASE PAIRS ON DNA PLECTONEMES 

Abstract: Base pair mismatches in DNA occur during replication and can result in mutations and certain types of cancer.  The exact mechanism by which mismatch repair proteins recognize mismatches is still not well understood. Structures of mismatch recognition proteins bound to a mismatch indicate that the process involves introducing a sharp bend in the DNA and flipping out the mismatched base. Under external torsional stress, an elastic rod with a defect would buckle at the defect, provided the defect reduces the local bending stiffness. In vivo, if the same energetic scenario prevails, it could localize (or pin) the mismatch at the plectoneme end loop (plectoneme refers to a structure formed by the DNA when it buckles and its helical axis wraps or writhes around itself in the presence of a critical torsional stress) and make the mismatched base pair more accessible to the mismatch repair protein. In genomic DNA, however, the entropic cost associated with plectoneme localization could make pinning unfavorable. Magnetic-tweezers-based studies of DNA supercoiling, performed at high salt concentrations, have shown that in DNA harboring a single mismatch, the plectoneme will always localize at the mismatch. Theoretical studies have predicted that under physiological salt concentrations, plectoneme localization becomes probabilistic. However, both experimental and theoretical approaches are currently limited to positively supercoiled DNA. In the current dissertation, we aim to study plectoneme localization, in physiologically relevant conditions, using state-of-the-art molecular dynamics (MD) simulations and single molecule magnetics tweezers-based experiments.

In order to simulate plectoneme localization we first develop a framework using the widely available sequence and salt dependent OxDNA2 model. We verify that the OxDNA2 model can quantitively reproduce a reduction in bending rigidity due to the presence of the mismatch(es), similar to all-atom MD simulations. We then verify that the current framework can reproduce the experimentally observed plectoneme pinning (at the location of the mismatches). Next, we simulate plectoneme pinning under physiologically relevant conditions. We find that the plectoneme pinning (at the location of the mismatches) becomes probabilistic and this probability of plectoneme pinning increases with an increase in the number of mismatches. We also simulate a longer 1010 base pair long DNA to study the influence of entropic effects on plectoneme pinning.

Next, we extend the simulation framework to simulate a negatively supercoiled, i.e., under-wound, DNA molecule.  In vivo, DNA is maintained in a negatively supercoiled state. Negative supercoiling can result in local melting at the mismatched base pairs: this local melting would further reduce the local bending rigidity at the mismatched base pairs and could enhance plectoneme pinning. We find that negative supercoiling significantly enhances plectoneme pinning in comparison with equivalent levels of positive supercoiling. We also find that the mismatched base pairs are locally melted and the plectoneme end loop is bent significantly more as compared to the positive supercoiling case. Additionally, we simulate the 1010 base pair long DNA under two different negative super-helical densities, i.e., two different degrees of unwinding. We find that the super helical density does not affect the plectoneme pinning probabilities. We also conduct simulations of DNA under different stretching forces (0.3 pN, 0.4 pN and 0.6 pN). Negatively supercoiled DNA under relatively high stretching force (~0.6 pN) absorbs tortional stress by locally melting instead of supercoiling. Simulations of DNA under different forces allow us to study the effect of mismatches on the competition between supercoiling and local melting in a negatively supercoiled DNA. We find that higher stretching forces, up to a maximum set by the onset of melting, increase plectoneme pinning at the location of mismatch.

Finally, we propose and develop a single molecule assay to validate the simulations results presented in the previous chapters. Previous single-molecule magnetic tweezers measurements of mismatch DNA buckling and pinning were limited to the high force (~2 pN) – high salt (>0.5 M NaCl) regime. We propose to overcome this limitation by attaching a small gold nano-bead via a di-thiol group close to the mismatched base pairs, which permits direct observation of transient DNA buckling at the mismatch.  We fabricate a DNA substrate that can be used to directly observe plectoneme pinning at the mismatch. We perform single-molecule magnetic tweezers measurements to verify that the presence of the di-thiol group does not result in anomalous pinning in an intact DNA molecule.


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UPCOMING DISSERTATION DEFENSE – AUSTIN LEWIS

Defense Date: Friday April 1, 2022 at 1:00pm

Location: Martin Hall 088-2162

Title: Dynamic Bayesian Network Updating Approaches for Enabling Causal Prognostics and Health Management of Complex Engineering Systems

Committee Members:

Associate Professor Katrina Groth, Chair

Assistant Professor Michelle Bensi

Professor Jeffrey Herrmann

Professor Mohammed Modarres

Professor Gregory Baecher, Dean’s Representative


Abstract:

Complex engineering systems (CESes), such as nuclear power plants or manufacturing plants, are critical to a wide range of industries and utilities; as such, it is important to be able to monitor their system health and make informed decisions on maintenance and risk management practices. However, currently available system-level monitoring approaches either ignore complex dependencies in their probabilistic risk assessments (PRA) or are prognostics and health management (PHM) techniques intended for simpler systems. The gap in CES health management needs to be closed through the development of techniques and models built from a systematic integration of PHM and PRA (SIPPRA) approach that considers a system’s causal factors and operational context when generating health assessments.

The following dissertation describes a concentrated study that addresses one of the challenges facing SIPPRA: how to appropriately discretize a CES’s operational timeline derived from multiple data streams to create discrete time-series data for use as model inputs over meaningful time periods. This research studies how different time scales and discretization approaches impact the performance of dynamic Bayesian Networks (DBNs), models that are increasingly used for causal-based inferences and system-level assessments, specifically built for SIPPRA health management. The impact of this research offers new insight into how to construct such DBNs to better support system-level health management for CESes. 

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UPCOMING DISSERTATION DEFENSE – MD. TURASH HAQUE PIAL

Author: Md. Turash Haque Pial

Date: Thursday, March 31, 2022 at 3:30 pm

Location: Martin Hall, Room EGR-2162

Committee Members:

Professor Siddhartha Das, Chair

Professor Peter W. Chung

Professor Amir Riaz

Professor Pratyush Tiwary

Professor Yifei Mo, Dean’s Representative

Title of Dissertation: ATOMISTIC EXPLORATION OF DENSELY-GRAFTED POLYELECTROLYTE BRUSHES: EFFECT OF APPLIED ELECTRIC FIELD AND MULTIVALENT SCREENING COUNTERIONS

Abstract: Polyelectrolyte (PE) or charged polymers are ubiquitous under biological and synthetic conditions, ranging from DNA to advanced technologies. PE chains can be grafted on a surface and they extend into solution to form a “brush”-like configuration if the grafting density is high. PE brushes respond to external stimuli by changing their conformation and chemical details, which make them very attractive for numerous applications. Multivalent counterions (neutralizing PE charges) and external electric field are known to significantly affect the brush behavior. Obtaining fundamental insights into PE brush’s response to ions and electric filed is of utmost importance for both industrial and academic research. In this dissertation, we use atomistic tools to improve our understanding of the PE brushes grafted on a single surface and two inner walls of a nanochannel under these two stimuli.

We start by developing an all-atom molecular dynamics simulation framework to test the behavior of the PE brushes (grafted on a single surface) in the presence of externally applied electric fields. It is discovered that the charge density of PE monomers can have significant influence on their response; a smaller monomer charge density helps the brush to tilts along the electric field, while the PE brush with higher monomer charge density bends and shrinks. We found that counterion condensation to PE chains has a substantial impact in controlling these responses.

In the subsequent study we discuss the effect of counterion size and valence in dictating counterion mediated bridging interaction of two or more negative monomers. By examining the solvation behavior, we identify that bridging interactions are not a sole function of the counterion valence. Rather, it depends on the counterion condensation on the PE chain, as well as the size of the counterion solvation shell. We also test the dynamic properties of the counterions and associated bridges.

Later, we proceeded to simulate PE brush-grafted nanochannels to explore equilibrium and flow behavior in presence of nanoconfinement. We identify the onset of overscreening: there are a greater number of coions than counterions in the bulk liquid outside the brush layer. This specific ion distribution ensures that the overall electroosmotic flow is along the direction of the coions. Furthermore, for a large electric field, some of the counterions leave the PE brush layer into the bulk, resulting in disappearance of overscreening. If the number of counterions is greater than coions, electroosmotic flow reverses its direction and follows the motion of counterions. Finally, we discover that counterion-monomer interactions control the ion distribution. As a result, a diverse range of electroosmotic flow is found for counterions with different valence and size.

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UPCOMING DISSERTATION DEFENSE – DAVID JOHNSON

Author: David Johnson

Date: Friday, March 18, 2022 at 3:30 pm

Location: Martin Hall, Room EGR-0159

List of Committee Members:
Assistant Professor Monifa Vaughn-Cooke, Advisor and Chair
Professor Mohammad Modarres
Professor Jeffrey Herrmann
Assistant Professor Allison Reilly
Professor Gregory Baecher, Dean’s Representative

Title of Paper: ROOT CAUSE ANALYSIS OF ADVERSE EVENTS USING A HUMAN RELIABILITY ANALYSIS APPROACH

Abstract:

Large scale analysis of adverse event data is challenging due to the unstructured nature of event reporting and narrative textual data in adverse event repositories. This issue is further complicated for human error adverse events, which are routinely treated as a root cause instead of as initiating events in a causal chain. Human error events are commonly misunderstood and underreported, which hinders the analysis of trends and the identification of risk mitigation strategies across industries. Currently, the prevailing means of human error investigation is the analysis of accident and incident data which are not designed around a framework of human cognition or psychomotor function. Existing approaches lack a theoretical foundation with sufficient cognitive granularity to identify root causes of human error. This research provides a cognitive task decomposition to standardize the investigation, reporting, and analysis of human error adverse event data in narrative textual form.


The proposed method includes a qualitative structure to answer six questions (when, who, what, where, how, why) that are critical to comprehensively understand the events surrounding human error. This process is accomplished in five main stages: 1) Develop guidelines for a cognitively-driven adverse event investigation; 2) Perform a baseline cognitive task analysis (when) to document relevant stakeholders (who), products or processes (what), and environments (where) based on a taxonomy of cognitive and psychomotor function; 3) Identify deviations for the baseline task analysis in the form of unsafe acts (how) using a human error classification; 4) and Develop a root cause mapping to identify the performance shaping factors (PSFs) (why) for each unsafe act.


The outcome of the proposed method will advance the fields of risk analysis and regulatory science by providing a standardized and repeatable process to input and analyze human error in adverse event databases. The method provides a foundation for more effective human error trending and accident analysis at a greater level of cognitive granularity. Application of this method to adverse event risk mitigations can inform prospective strategies such as resource allocation and system design, with the ultimate long-term goal of reducing the human contribution to risk.

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UPCOMING DISSERTATION DEFENSE – PAUL LARA

Author: Paul Lara

Thesis Title: EMBEDDED HIGH FREQUENCY SIGNAL EFFECTS ON FAILURE MECHANISMS AND MODELS

Date/Time: February 18th, 2022 | 2-4pm

Location: EGR 2154

Examining Committee:

Professor Hugh Bruck, Chair/Advisor
Professor Abhijit Dasgupta
Professor Teng Li
Professor Patrick F. McCluskey
Professor Ankem Sreeramamurthy, Dean’s Representative

Abstract: Embedded high frequency signal effects can have a deleterious effect on the fatigue resistance of structures. For example, ship structures can be subject to many operational loads (wind, pressure, temperature, etc.), one of which is the structural effects from the surrounding sea environment. Typically, the wave environment applies an ordinary wave component, which drives the primary bending stress of the vessel, along with a more stochastically driven element that manifest itself as wave impacts. To account for these effects, designers have relied on simplified assumptions, such as safety factors and/or margins of safety.  Existing academic research centered on capturing a simplified sinusoidal response associated with the primary loading event and the embedded high frequency response, but has not addressed logarithmic decay, signal frequency, or frequency of occurrence. All these factors have associated uncertainty and cause impact on fatigue life and failure mechanisms exhibited by structures. This research effort focuses on a more fundamental understanding of the effects of embedded high frequency loading on fatigue crack propagation in Aluminum 5xxx material. Carried out by accounting for the signal’s characteristics, and through an experimental evaluation assess its impact on the local failure mechanism and life cycle models.  In particular, the use of Digital Image Correlation to quantify the effects of the embedded high frequency on the plastic zone that develops ahead of the fatigue crack, and the subsequent changes in crack growth. This enabled the following four primary contributions: (1) development of a unique test configuration protocol and process to investigate HF pulse effects on fatigue crack growth in small scale specimens, (2) measured a 35% decrease in COD due to crack closure from the residual stresses associated with a larger plastic zone caused by HF loading, (3) development of a unique model that couples crack kinking and retardation behavior, and (4) elucidation on the effects of sequencing of HF pulses on crack kinking and retardation. The findings of this research can be used in future investigative efforts to develop analytical models that address secondary material effects, such as welds, provide underpinnings for high fidelity numerical modelling, and to reduce the dependency of designers on the use of safety factors and enable them to account more rigorously for failure mechanisms in digital twins.


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UPCOMING THESIS DEFENSE – JI BAE

Author: Ji Bae

Thesis Title: ENERGY CONSUMPTION REDUCTION OF COMMERCIAL BUILDINGS THROUGH THE IMPLEMENTATION OF VIRTUAL AND EXPERIMENTAL ENERGY AUDIT ANALYSIS

Date/Time: February 18th, 2022 | 4-5pm

Location: EGR 2164

Examining Committee:

Professor Michael Ohadi, Chair/Advisor
Professor Amir Shooshtari
Professor Bao Yang
Professor Jungho Kim

Abstract: According to the U.S. Energy Information Administration (EIA), about 38 quads of the total U.S. energy consumption was consumed by residential and commercial buildings in 2017, which is about 39% of the total 2017 annual U.S. energy consumption (EIA, 2018). Additionally, the building sector is responsible for about 75% of the total U.S. electricity consumption as well as for about 70% of the projected growth in the U.S. electricity demand through 2040. It is clear that the potential for energy savings and greenhouse gas emissions reduction in existing buildings today remain largely untapped and that there is still much left to explore in respect to determining the best protocols for reducing building energy consumption on a national and even a global scale. The present work investigates the effectiveness of coupling an initial virtual energy audit screening with the conventional, hands-on, energy audit processes to more quickly and less costly implement the energy savings potential for high energy consumption buildings. The virtual screening tool takes advantage of a customized cloud-based energy efficiency management software and the readily available energy consumption data of the building to identify and prioritize the buildings that have the highest energy savings potential and should be given priority for performing onsite walkthroughs and detailed energy audits and the subsequent implementation of the identified energy conservation measures (ECMs). By applying the proposed procedure to a group of buildings, the results of this study demonstrated that a combination of the software-based screening tools and a detailed experimental/onsite energy audit as necessary can effectively take advantage of the potential energy consumption and carbon footprint reduction in existing buildings today and that the low-cost/no-cost energy conservation measures alone can oftentimes result in significant savings as documented in this thesis. However, selection of the appropriate software was deemed critically important, as certain software limitations were observed to hinder the obtainment of the desired energy savings opportunities.

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Dissertation Defense – Yonatan Saadon

Author: Yonatan Saadon


Date/Time:
 October 22 | 2-4pm


Location: 
EGR 2164


Examining Committee 

Professor Patrick McCluskey, Chair
Professor Hugh Bruck
Professor Mark Fuge
Professor Peter Sandborn
Professor Mohamad Al-Sheikhly (Dean rep)


Abstract: Accurate prediction of the remaining useful life (RUL) of a degrading component is crucial to prognostics and health management for electronic systems, to monitor conditions and avoid reaching failure while minimizing downtime. However, the shortage of sufficiently large run-to-failure datasets is a serious bottleneck impeding the performance of data-driven approaches, and in particular, those involving neural network architectures. Here, this work shows a new data-driven prognostic method to predict the RUL using an ensemble of quantile-based Long Short-Term Memory (LSTM) neural networks, which represents the RUL prediction task to a set of simpler, binary classification problems that are amenable for prediction with LSTMs, even with limited data. This methodology was tested on two run-to-failure datasets, power MOSFETs and filtration system, and showed promising results on both datasets it demonstrates that this approach obtains improved RUL estimation accuracy for both the power MOSFETs and the filtration system, especially with a small training dataset that is characterized by a wide range of the RUL

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In-Person Thesis/Dissertation Defense Procedures

The Graduate School has stipulated that starting on August 30, 2021, all thesis and dissertation defenses must occur fully in person unless an exemption has been granted by the Graduate School. The defending student and committee must be physically present in the examination room during the entire defense and during the committee’s private deliberations following the examination. If either the student or any committee member wishes to request remote participation, they will need to first obtain a waiver from the Graduate School (using the link provided later in this document). 

For students planning to complete their degree program this semester, we are sending some additional guidance for the defense. The steps to follow are below.

1. Please email the Graduate Office (megrad@umd.edu) two weeks prior to your defense with the information below. If a student or committee member is requesting a waiver for remote participation, the graduate office should be notified at least three weeks prior to the defense: 

  • List of committee members
  • Abstract
  • Day/Time
  • Room/Location 
    • Notify the Graduate Office if you need help reserving a room

2. All thesis and dissertation committee members, including members external to UMD, will be able to sign the Report of the Examining Committee electronically in Adobe Sign. A request to initiate the electronic Report of Examining Committee (REC) form must be made at least 10 business days before the scheduled defense. The committee chair will submit a request for the electronic REC here. In order to complete the request:

  • The committee chair (or designee) must have the student’s information, including name and UID, as well as all committee member names and email addresses.
  • The email address provided for each committee member will serve as authentication when accessing the electronic REC. Special members who do not have a UMD login will no longer have any issues signing the form electronically.

3. The Graduate Office will still be sending out the Middle States Assessment Form and Electronic Publication Form by email to the committee chair prior to the defense.

4. It is very important to note that it will be extremely difficult to find and obtain approval for an emergency, last-minute replacement faculty member for any committee.  It is recommended that you send multiple reminders to all committee members starting at least three days prior to the defense date and ask them to re-confirm their attendance.

5. In light of the ongoing COVID-19 pandemic, the Graduate School will consider exemptions to the remote defense policy for the Fall 2021 semester. These exemptions will include unusual circumstances such as:

To request a remote participation of a committee member during the Fall 2021 semester, you can complete this form starting on August 1: go.umd.edu/gs-remote-def. Please allow ten business days for a resolution that is not an emergency, and note that a remote defense cannot occur without prior approval. 

Remote Defense Request / Procedures 

Ahead of the defense, we encourage you to review the policy on remote participation in a thesis defense or a dissertation defense.  When you make a request, you will be asked to acknowledge these policies.

In light of the ongoing COVID-19 pandemic, the Graduate School will consider exemptions to the in-person defense policy on an individual basis. Remote participation by the student or committee chair, or Dean’s Representative will be permitted in exceptional and compelling circumstances such as:

To request the remote participation of one or all participants, please complete this form go.umd.edu/remotedrequest or visit our website for more details.

  • Please allow ten business days for a resolution that is not an emergency, and note that remote participation cannot occur without prior approval from Graduate School. 
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Dissertation Defense – Hanlong Wan

Title: NEXT GENERATION HEAT PUMP SYSTEM EVALUATION METHODOLOGIES

Author: Hanlong Wan

Date/Time: 9/2/21 | 2:00PM-4:00PM

Location/Room: EGR4164B Martin Hall (CEEE’s conference room).

Advisory Committee:
Prof. Reinhard K. Radermacher, Chair
Prof. Peter Sunderland, Dean’s Representative
Research Prof. Yunho Hwang
Prof. Nikhil Chopra
Prof. Jelena Srebric
Prof. Bao Yang

Abstract:
Energy consumption of Heat Pump (HP) systems plays a significant role in the world residential building energy sector. The conventional HP system evaluation method focused on the energy efficiency during a given time scale (e.g., hourly, seasonally, or annually). Nevertheless, these evaluation methods or test metrics are unable to properly reflect the thermodynamic characteristics of the system (e.g., the start-up process). In addition, previous researchers typically conducted HP field tests within one year period. Only limited studies conducted the system performance over multiple years. Furthermore, the climate is changing faster than previously predicted beyond the irreversible and catastrophic tipping point. HP systems are the main contributor to global warming but also can be a part of the solution. A holistic evaluation of the HP system’s global warming impact during its life cycle needs to account for the direct refrigerant greenhouse gas (GHG) emissions, indirect fossil fuel GHG emissions and embodied equipment emissions. This dissertation leverages machine learning, deep learning, data digging, and Life Cycle Analysis (LCA) approaches to develop next generation HP system evaluation methodologies with three thrusts: 1) field test data analysis, 2) data-driven modeling, and 3) Enhanced Life Cycle Climate Performance (En-LCCP) analysis. This study found that first, time-average performance metrics can save time in extensive data calculation, while quasi-steady-state performance metrics can elucidate some details of the studied system. Second, deep-learning-based algorithms have higher accuracy than conventional modeling approaches and can be used to analyze the system’s dynamic performance. However, the complicated structure of the networks, numerous parameters needing to be optimized, and longer training time are the main drawbacks of these methods. Third, this dissertation improved current environmental impact evaluation method considering ambient conditions variation, local grid source structure, and next-generation low-GWP refrigerants, which led the results closer to reality and provided alternative methods for limited-data cases. Future work could be studying the uncertainty within the deep learning networks and a general process for modeling settings. People may develop a multi-objective optimization model for HP system design considering both the LCCP and cost.