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A Methodology for Architecting Self-Sustaining Environmental Control and Life Support Systems (ECLSS) For Lunar Habitats
No full textSince the Apollo era, lunar exploration has served as a proving ground for advancing
human spaceflight, technology development, and the pursuit of sustainable off-world
operations. Sustaining human presence on the Moon, however, presents distinct logistical, environmental, and operational challenges. Unlike missions to the International
Space Station (ISS), lunar operations must rely on systems that can function autonomously for extended periods, as the high cost, delay, and risk associated with
Earth-based resupply limit mission flexibility. Extreme temperature variations, radiation exposure, and abrasive lunar dust further intensify these challenges, demanding
the development of self-sustaining habitats capable of producing and recycling essential resources locally.
This dissertation establishes a comprehensive methodology for architecting self-
sustaining Environmental Control and Life Support Systems (ECLSS) for long-duration
lunar habitats. The approach integrates subsystem-level modeling, full-system simulation, and In-Situ Resource Utilization (ISRU) analysis within a unified Python-based framework known as HELIOS. The methodology simultaneously addresses internal ECLSS loop-closure performance and external ISRU integration, providing a
quantitative foundation for evaluating scalability, mass efficiency, and sustainability
across evolving mission architectures. This research aims to bridge the gap between
high-fidelity subsystem design and mission-level sustainability assessment—creating a
replicable, data-driven framework for future lunar and planetary habitats.
The research is organized into three major phases.
First, subsystem-level models were developed for key ECLSS functions within the
HELIOS simulation framework. Each subsystem, covering both air and water strings,
was validated against ISS performance data and system behavior, and extended to
represent lunar surface conditions. This established and validated HELIOS as a
robust methodological platform for assessing architectural feasibility under variable
mission and environmental parameters. High-fidelity simulations of 24-month crewed
missions were then conducted to project consumables usage, closure rates, and system
mass evolution over time. The results of this first experiment, the loop-closure evaluation, demonstrated that the baseline ISS-derived configuration is unable to sustain
crewed operations beyond approximately 485 days due to limited oxygen recovery
capacity. Incorporating one Sabatier reactor extended mission duration beyond 730
days, confirming the feasibility of achieving a standalone self-sustaining architecture
through advanced oxygen recovery. Additional sensitivity analyses on the number of
Sabatier units revealed diminishing returns beyond two, indicating an optimal balance between closure efficiency and added system mass. An extended evaluation on
food hydration requirements further reinforced these findings. Under the baseline
configuration, achieving the required ≥ 80% hydration fraction was found to be operationally impractical without exceeding available water reserves. Integration of one
Sabatier reactor, however, provided sufficient oxygen and water recovery to maintain
hydration needs while significantly improving design freedom in mission planning. Together, these results validate the HELIOS methodology as an effective quantitative
tool for linking subsystem-level technology performance to mission-scale sustainability outcomes, establishing a repeatable process for assessing and optimizing future
self-sustaining ECLSS designs.
Second, an architectural evaluation was conducted to determine whether the self-sustaining ECLSS configuration identified in Experiment 1 could be justified in terms
of total system mass over long-duration missions. This phase expanded the HELIOS
framework to include both development and sustaining phases, enabling a holistic
lifecycle mass assessment across varying loop-closure rates. Seven configurations,
ranging from low to high closure levels, were modeled to quantify the trade between
system complexity, initial mass, and resource consumption over time. The results
demonstrated that while high-closure configurations introduced greater initial hard-
ware mass and subsystem integration complexity, they yielded a significant reduction
in cumulative consumables transported from Earth. This trade produced measurable breakeven advantages for long-duration missions, with mass parity occurring
beyond approximately 730 days of operation. Among the evaluated configurations,
the high-loop-closure design achieved the lowest total system mass demand when both development and sustaining phases were considered. These findings validated that regenerative ECLSS architectures are not only technically feasible but also mass-efficient for sustained lunar operations. An extended breakeven analysis quantified the full-duration spectrum of mass parity
across closure configurations, confirming that the long-term benefit of regenerative
systems increases with mission duration. This analysis also provided a methodology
for determining mission-specific closure targets based on duration, resupply cadence,
and available launch mass. Collectively, these results reinforced the HELIOS framework’s capability to perform end-to-end mass assessments, establishing a transparent,
data-driven approach for justifying advanced ECLSS technologies within future lunar
and deep-space mission architectures.
Third, the integration of ISRU technologies was examined to determine their capacity to reduce the environmental and logistical constraints on ECLSS loop-closure
requirements. This phase established a lunar site-selection methodology within HELIOS to couple habitat location with available local resources, illumination, and terrain accessibility. Two representative ISRU processes, carbothermal reduction (CR)
for oxygen generation and water extraction (WE) from icy regolith, were modeled
to quantify their influence on overall life-support performance using a Design-of-
Experiments (DoE) approach. The resulting ISRU–ECLSS integrated model demonstrated that incorporating even moderate in-situ production rates can substantially
alleviate internal loop-closure demands, enabling self-sustaining operation without
driving subsystems to maximum reclamation efficiency. Sensitivity analyses across
production rate, duty cycle, and regolith composition showed that ISRU contributions scale favorably with habitat growth, providing increased operational flexibility
and redundancy as infrastructure expands from a single station to a multi-module
settlement. Furthermore, the extended evaluation quantified the power impact of
integrating ISRU systems, revealing that power availability and scheduling strongly
influence achievable production rates and, consequently, overall system sustainability. Collectively, these results confirm that coupling internal ECLSS recycling with
external ISRU supply forms a balanced and resilient design strategy, reducing dependence on Earth-supplied consumables and supporting the long-term feasibility of
lunar surface habitation.
Beyond subsystem optimization, this dissertation extends the analysis into a
system-of-systems context, linking life support architectures with habitat siting, infrastructure, and logistics. A structured site selection process was developed, considering illumination availability, terrain slope, and volatile accessibility across key south
polar regions such as Shackleton, Nobile, and Connecting Ridge. Results indicated
that coupling high-closure ECLSS designs with sites offering both persistent sunlight and accessible ice deposits yields
the most mass-efficient and sustainable outcomes. This coupling underscores that internal system performance and external resource environments must be co-optimized
rather than treated as independent design problems, thereby advancing the holistic
understanding of lunar habitat architecting.
In summary, this research delivers a scalable and quantitative methodology for developing self-sustaining ECLSS architectures that integrate internal resource recovery
with external resource utilization. The HELIOS framework enables transparent, para-
metric trade studies across mission scales and provides the computational foundation
for future multi-objective optimization, integrating mass, power, cost, and logistics
within a unified decision environment. The outcomes of this work advance both the
theory and practice of sustainable habitat design, demonstrating that self-sustaining
life support architectures are not only technically feasible but can also be systematically justified through mass-efficient, data-driven analysis. The developed method-
ology also provides a decision-support foundation for integrated habitat and mission
architecting, bridging detailed subsystem modeling with high-level planning to inform
technology selection, resource strategy, and infrastructure development across future
lunar and Martian programs.
Ultimately, this dissertation establishes a foundation upon which future research
can build increasingly integrated design methodologies that couple environmental
resources, mission operations, and system engineering principles. By doing so, it
supports NASA’s long-term vision of a self-sustaining human presence beyond Earth
and provides a roadmap for extending these principles to Martian and deep-space
habitats, where autonomy, resilience, and sustainability will define the next era of
exploration
Design, Modeling, and Control of Minimally Invasive Robotic Surgical Systems with Integrated Sensors
No full textManual manipulation of passive surgical tools can be challenging and may provide limited access to target locations deep within the body. For example, in brachytherapy cancer treatment, hollow needles must be delivered to a target area for radiation, such as the prostate, which is located around the urethra and must not be damaged. The accuracy of needle placement affects both the ability to achieve the pre-planned radiation dose distribution and to minimize damage to nearby healthy anatomical structures. This dissertation presents the design of a robotically steerable needle system capable of navigating along a desired curved path achieved by using a tendon-driven robotic joint. First, the assembly of micro-scale and meso-scale robotic joints is outlined, and different attachment methods for both nitinol and tungsten tendons are studied. The steerable needle system consists of a steerable stylet made from a micromachined superelastic nitinol tube to create a tendon-driven bending joint. By offsetting the placement of tendons from the neutral axis of the joint, the system can be bent in multiple directions. Finite element modeling is used to determine the parameters for the micromachined joint, and a model is derived to estimate the deflection due to the tendon-pulling force. Control is implemented using electromagnetic (EM) tracking, and the steerable needle is shown to effectively navigate along a desired curved path through a hydrogel tissue phantom. However, EM tracking may not be practical in an operating room due to interference from other devices. Therefore, this work investigates the use of intrinsic fiber Bragg grating (FBG) sensors. A planar FBG bending sensor is created and shown to be capable of measuring curvatures as large as 145 m−1 . This sensor is effectively implemented for state estimation of the bending angle in both meso-scale and micro-scale surgical robotic devices. To expand this work, a three-dimensional (3D) FBG-based shape sensor is created first using an FBG fiber in series with a superelastic nitinol spring. However, this sensor could not be miniaturized, so an FBG triplet is then created to achieve a miniaturized 3D shape sensor and characterized under different use cases by varying the rate and duration of deflection as well as the surrounding temperature. The sensor is found to provide a reliable response with some relatively small drift when deflected for long periods of time. This 3D FBG sensor is then implemented in the micro-scale COaxially Aligned STeerable (COAST) guidewire robot, which is modified to facilitate the integration of the sensor and to create a force-sensing tip at the distal end. The COAST guidewire is capable of follow-the-leader (FTL) motion for navigating tortuous vasculature, which is currently a challenge in cardiovascular interventions. Current visualization requires X-ray imaging, which should be minimized, so intrinsic shape feedback is implemented in this work to improve future control of this robot. Multiple shape reconstruction approaches to model the COAST guidewire using the FBG feedback are derived and compared. Furthermore, the most distal FBG sensing segment is isolated in the tip and correlated to external forces to provide feedback toward safe interaction with surrounding structures. The design of this guidewire is then adapted to create a steerable robot capable of FTL motion to facilitate the navigation of delicate tissues within the brain. The steerable robot delivers a hollow sheath capable of stiffening in a curved configuration to facilitate the passage of clinical devices such as a stereoelectroencephalography (SEEG) depth electrode used to diagnose epilepsy. Building towards the ultimate goal of this research to safely navigate complex anatomies and deliver minimally invasive procedures to target locations that are currently challenging to access, the work presented in this dissertation demonstrates my contributions towards the design, modeling, and control of minimally invasive robotic surgical systems with integrated sensors.Ph.D.Robotic
A Supply-Noise-Tolerant CMOS Temperature Sensor With Subthreshold Sensing And Current-Mode SAR Readout
No full textA Supply-Noise tolerant CMOS Temperature Sensor with Subthreshold sensing and Current Mode SAR readout is presented. The design uses a CMOS bias circuit to generate a temperature-independent current and a PTAT current that rises with temperature. A current-comparator-based SAR ADC compares these currents, producing a 5-bit digital code that varies linearly from -25 °C to 100 °C. The sensor occupies ~2000 µm², shows strong immunity to supply noise. The design in taped out in 28 nm CMOS
Highly Efficient On-Chip Dielectric Resonator Antenna on Silicon Carbide for Extreme Environments
No full textSilicon carbide (SiC) is a wide bandgap semiconductor with high dielectric breakdown strength and exceptional resilience to high temperatures. Design, simulation, fabrication, and measurement of a highly efficient dielectric resonator antenna (DRA) on a 4H-SiC for high-temperature operation is presented in this work. The novelty of this work lies in the implementation of edge corrugations on an on-chip antenna for the first time, significantly enhancing efficiency by suppressing surface waves. Additionally, this study presents the first demonstration of a DRA on SiC, leveraging its robustness in harsh environments for reliable operation at high temperatures. The simulation results were validated through measurements of return loss and peak gain up to 400 °C, along with radiation pattern measurements up to 300 °C, marking this study as the first high temperature measurement of an on-chip antenna. In the proposed design, a sapphire rectangular dielectric resonator is coupled to an aperture on a 4H-SiC substrate, fed using a coplanar waveguide (CPW), and impedance matched by a series stub inductor. The DRA operates in the fundamental TE111 mode for broadside radiation at 28.8–30.6 GHz. The proposed design was fabricated in-house and measured using a modified probe station with a hot plate and a 6-joint robot arm. The simulated gain and peak total efficiency of 8.52 dBi and 91.5%, respectively, agree with the measured values of 8.47 dBi and 90.5%. The antenna is demonstrated to have a stable reflection coefficient and radiation pattern up to 300 °C with less than 0.5 dBi reduction in peak gain.
The contents of this thesis are published in the IEEE Antennas & Wireless Propagation Letters in September 2025
Low Noise Double-Beam Absorption Spectroscopy
No full textThis thesis focuses on adapting an analog noise-cancellation circuit for double-beam laser absorption spectroscopy to improve accuracy in ammonia concentration measurements. By suppressing common-mode laser intensity noise, the circuit enhances absorption sensitivity without requiring complex modulation techniques. In this approach, a tunable diode laser beam is split into two paths: a signal beam that passes through an absorption cell and a reference beam that bypasses any absorbing medium. The two beams are independently detected and their corresponding photocurrents are fed into an electronic circuit designed to suppress the common-mode noise on laser intensity, thereby enhancing the diagnostic technique’s accuracy. This architecture remains cost-effective and has the potential to achieve shot-noise-limited performance.
A circuit originally designed by Philip Hobbs was adapted, simulated in Analog Devices LTspice, prototyped on a breadboard and ultimately implemented on a printed circuit board. Performance was evaluated using a water-vapor absorption setup and compared against both a standard DAS sensor and a commercial double-beam noise-cancellation module from MKS–Newport. Results show a clear improvement in NEA over standard DAS, with performance approaching that of the commercial system. Finally, extended-range InGaAs photodetectors were integrated to enable ammonia detection and the circuit was used to record room-temperature ammonia absorption line shapes and time-resolved absorption measurements of ammonia oxidation under post-reflected shock conditions in shock-tube experiments
Focused Electron Beam Induced Processing (FEBIP) In Ammonia-Based Liquid Films
No full textIn this dissertation, a focused electron-beam-mediated approach to nanoscale synthesis and modification is presented, spanning from fundamental redox studies to the extension of these studies towards unique fabrication strategies in water-ammonia environments. The document comprises three core chapters that collectively investigate how water-ammonia solvents can be harnessed for direct-write nanostructure synthesis via focused electron beam-induced processes. The overarching theme is that the radiolytic chemistry of water-ammonia solutions fundamentally reshapes how reducing and oxidizing pathways compete to form or remove solid material at the nanoscale.
Chapter 2 discusses the influence of introducing ammonia in an aqueous silver nitrate solution on the oxidation-reduction processes under electron beam irradiation. Ammonia preferentially scavenges oxidizing radiolysis products, thereby suppressing the oxidation of metallic silver. At the same time, it changes the role of hydrogen peroxide from an oxidizer of silver in pure water to a dual role as both an oxidizer of silver and a reducer of the silver–ammine complexes. This fundamental shift in chemical pathways makes the environment more reducing, enabling silver deposition at a faster rate but over an extended region around the primary beam spot, rather than being limited to the immediate site of irradiation.
Chapter 3 shows how leveraging the water-ammonia solvent and silver nitrate-based chemistry, a highly reducing environment is created at the point of focused electron beam. At the same time, the environment in the farfield stays oxidizing, avoiding unwanted deposition. This ‘electrochemical lensing’ enhances growth rate in the near-field of the beam without sacrificing lateral resolution, offering high resolution and rapid nanofabrication.
Chapter 4 applies the ammonia-based solvent approach to precursor-free electron-beam patterning of copper, revealing that adjusting ammonia concentration can lead to net oxidation (etching) or reduction (deposition). At lower ammonia concentrations, the oxidizing environment leads to etching of copper at short electron beam exposure times. Over longer beam exposures, the copper ions/ion-complexes released by this initial oxidation are reduced and deposited back into the etched locations, forming peak-in-valley structures. Higher ammonia concentrations result in a high rate of copper–ammine complexation and suppression of oxidizing species, creating a strongly reducing environment that enables copper deposition even at short beam exposures.
Collectively, this dissertation advances the fundamental understanding of electron-beam-induced radiolytic chemistry in water-ammonia solutions and applies these advances to liquid phase Focused Electron Beam Induced Processing of nanomaterials.Ph.D.Mechanical Engineerin
Acoustic Arrival-Time Estimation in High-Resolution Ocean Environments
No full textArrival times play a critical role in underwater acoustics, particularly in applications that require precise information about sound propagation through water. Accurate arrival times provide insights into the travel paths and speeds of acoustic waves, which are crucial for many relevant military and scientific applications. Underwater acoustic arrival times can be obtained experimentally in two ways: actively, by transmitting a known acoustic signal and matching it at the receiver; or passively through blind deconvolution techniques that analyze occurring sounds without knowledge of the impulse response. In active arrival estimation, a matched filter is typically employed to correlate a known source waveform with received signals, allowing for precise timing of specific arrivals. Conversely, with passive estimation, blind deconvolution relies on signal processing methods posed under strict assumptions to extract timing information. The current frameworks used to estimate these arrival structures typically combine experimentally measured acoustic signals in collaboration with numerical simulations (ideally performed in an accurate rendition of the ocean environment). To accomplish this in a satisfactory manner, it is required to accurately understand the relevant ocean environment with a high level of precision; and to utilize both an accurate signal estimation method and acoustic propagation model. These remain challenging, as commonly used blind deconvolution techniques do not account for realistic scenarios such as moving sources, and commonly used acoustic propagation models experience instability that scales with ocean precision. This doctoral project addresses these challenges by advancing acoustic modeling and signal estimation tools for complex ocean environments through three primary research objectives. First, a modification to the Ray-Based Blind Deconvolution (RBD) algorithm is presented to account for Doppler shifts, thereby enhancing the accuracy of arrival predictions in applications involving mobile sources. Second, the impact of ocean precision on acoustic accuracy is explored, specifically how mesoscale and submesoscale features in high-resolution ocean models introduce uncertainty in propagation estimates. Finally, optimizations to ray-tracing models for stabilizing acoustic Eigenray arrival predictions in dynamic are shown for range-dependent ocean environments. By comparing with the Parabolic Equation (PE) model, it is demonstrated that kernel smoothing and empirical orthogonal denoising of Sound Speed Profiles (SSPs) mitigate ray instability, enabling ray tracing with more viability and accuracy tool for these complex environments.Ph.D.Ocean Science and Engineerin
Rapid Fuselage and Cabin Sizing Method for Commercial Transport Aircraft Design
No full textPresented at 2026 AIAA Scitech ForumFuselage and cabin sizing are critical to clean-sheet aircraft design and existing aircraft retrofit studies due to their impact on aircraft weight and aerodynamics. Existing sizing methods from conceptual design textbooks and aircraft sizing tools rely heavily on historical data of legacy aircraft and therefore lack the accuracy and flexibility to support parametric fuselage and cabin sizing for modern aircraft. This paper addresses this functionality gap by proposing two new methods that better capture the main features of modern aircraft fuselage and cabin design: one that performs detailed sizing and buildup for all major cabin elements, and another that directly predicts fuselage and cabin dimensions based on existing aircraft data without detailed component sizing. Both methods are tested and validated across a large set of aircraft, demonstrating improved accuracy in predicting cabin dimensions and sizing the fuselage over preceding methods. Showing substantial improvement over current sizing practices, the proposed methods are ready for integration into existing conceptual design frameworks as the result of intentional design choice to produce same output format from the same minimal set of inputs
Neighborhood Attention: Fast and Flexible Sparse Attention
No full textAttention is at the heart of most foundational AI models, across tasks and modalities. In many of those cases, it incurs a significant amount of computation, which is quadratic in complexity, and often cited as one of its greatest limitations. As a result, many sparse approaches have been proposed to alleviate this issue, with one of the most common approaches being masked or reduced attention span. In this work, we revisit sliding window approaches, which were commonly believed to be inherently inefficient, and we propose a new framework called Neighborhood Attention (NA). Through it, we solve design flaws in the original sliding window attention works, attempt to implement the approach efficiently for modern hardware accelerators, specifically GPUs, and conduct experiments that highlight the strengths and weaknesses of these approaches. At the same time, we bridge the parameterization and properties of Convolution and Attention, by showing that NA exhibits inductive biases and receptive fields similar to that in convolutions, while still capable of capturing inter-dependencies, both short and long range, similar to attention. We then show the necessity for and challenges that arise from infrastructure, especially in the context of modern implementations such as Flash Attention, and develop even more efficient and performance-optimized implementations for NA, specifically for the most recent and popular AI hardware accelerators, the NVIDIA Hopper and Blackwell GPUs. We build models based on the NA family, highlighting its superior quality and efficiency compared to existing approaches, and also plug NA into existing foundational models,
and showing that it can accelerate those models by up to 1.6× end-to-end and without further training, and up to 2.6× end-to-end with training. We further demonstrate that our methodology can actually create sparse Attention patterns that realize the theoretical limit of their speedups. This work is open-sourced through the NATTEN project at natten.org
