Gage Harris – Iowa State University

The objective of my Ph.D. research is to test the hypothesis that large-scale discrete multidisciplinary design optimization (MDO) can maximize complex, next-generation engineering systems’ performance automatically, which has not been possible with existing numerical methods. Specifically, I am looking at a new optimization framework considering low-thrust trajectory optimization with discrete fly-by options to enable more frequent and affordable missions, which aligns well with NASA’s mission to explore and extend our knowledge about the universe. MDO is a promising approach to tackle the above optimization because it can automatically use multiphysics simulations to find the best possible design, significantly reducing the design time. Existing gradient-based MDO algorithms can efficiently handle many design variables but cannot deal with discrete variables. In my PhD research, I will create a new large-scale discrete MDO framework (LSDMDO) to tackle the above challenge. LSDMDO is a novel class of optimization algorithms that efficiently synergize the gradient-based and evolutionary optimization methods to enable large-scale MDO problems with discrete variables. The LSDMDO algorithm will be rigorously derived, characterized, and evaluated in my PhD research.

Alexandra Grajales – Iowa State University

On Earth today, plants comprise the vast majority of fixed carbon in biomass and plant growth may be limited by bioavailable N. Studying the evolution of the terrestrial C cycle depends on understanding how plants fixed C and the availability of N in their environments. The relationship between C and N may reflect a combination of atmospheric isotopic signatures of CO2 and soil N availability. By cultivating plants in growth chambers with various CO2 and N2 fluctuating conditions, we can track plant adaptability and compare isotope plant tissue concentrations through time. I will periodically sample plant material throughout the experiment to track how plant growth can vary and how different concentrations of CO2 are stored within plant tissue. Also, I will track nitrogen and nutrient availability in the soil to understand the effect that strenuous conditions can have on the microbial communities that contribute to plant nutrient uptake. This research will allow for a deeper understanding of how plant tissues adapt to environmental changes in trying conditions and can be directly applied in growth chambers experiments done in space. It will open doors to studying plant adaptability and the minimal conditions needed for efficient plant growth on Earth and in space.

David Fehr – University of Iowa

In recent years, research into radiation-hard, high-temperature, and high-voltage devices made from wide bandgap semiconductors such as SiC, GaN, and Ga2O3 in the spaceflight industry has only increased due to their lower cost, volume, weight, and power consumption when compared with silicon chips. These advantages translate to more efficient spacecraft with greater functionality and promise to substantially impact the monetary costs associated with launching payloads. However, despite their intrinsic radiation hardness, the irradiation of planetary environments inevitably results in the formation of deep-level defects in the crystal structure which reduces the operating temperature, voltage, and carrier mobility, eventually resulting in device failure. These unfavorable outcomes necessitate a nondestructive method to quantify the type and density of deep-level defects in wide bandgap semiconductor devices.
The goal of this project is to validate our model of radiation-induced deep-level defects containing unpaired electrons (i.e. spin centers), which will provide a foundation for a simulation tool to identify the type and density of radiation-induced defects in wide bandgap semiconductors. Spin centers can be modeled as synthetic atoms with spin states sensitive to magnetic fields, and their optical or electrical properties have a characteristic response to a Zeeman magnetic field. This makes magnetic resonance simulations and corresponding measurements an attractive choice to characterize these defects. The proposed project would first develop a quantitative theory for identifying optically-active spin centers (i.e. color centers) by simulating optically detected magnetic resonance (ODMR), and then address identifying optically-inactive spin centers by simulating electrically detected magnetic resonance (EDMR).

Hannah Blumhoefer – Iowa State University

The primary objective of this research project is to improve the efficiency of computational fluid dynamics (CFD) modeling by integrating deep learning and optimizing computational expenses through enhanced shock detection and shock capture methods. These enhancements will advance CFD’s diagnostic and design capabilities in research and commercial use. These objectives motivate the focus of this project, which involves the development of both a modified convolution-based and a multigrid-based shock detector technique utilizing a hierarchy of grids with varying levels of fidelity. These detectors will be implemented into the Air Force Research Laboratory’s high-fidelity flow solver FDL3DI. The final task in this project will be extending both detectors into a multilayer perceptron (MLP) algorithm to differentiate between shockwave and turbulence conditions in 3D space.
Preliminary research in pursuit of this project has been centered on developing a strided convolution-based shock detection system. This endeavor involved creating and evaluating this system using the high-fidelity flow solver FDL3DI, made available by the Air Force Research Laboratory. To assess the effectiveness of the shock detection system, a straightforward test case, the 3D Explosion test, was selected. In this scenario, a spherical diaphragm separates a high-pressure, high-density region from a lower-pressure, lower-density region. At time zero, the diaphragm ruptures, causing an expanding shock wave to propagate in all directions. The Density Gradient Magnitude indicates the region that should be identified as a shock sometime after the diaphragm’s rupture. The strided convolution shock detector exhibits a more precise detection of the relevant shock, delineating a narrower flagged region, whereas the compressibility shock detector identifies a broader area extending beyond the critical shock point. Since shock regions necessitate reduced-order shock-capturing techniques to mitigate oscillations, these regions lose their high-order characteristics compared to non-flagged areas. The extended shock region identified by the compressibility shock detector implies that regions where reduced-order shock capturing is unnecessary will still undergo calculation using these techniques.

Abigail Whittemore – Drake University

A big concern for the health of astronauts and space crews is exposure to radiation and NASA lists “Risk of Radiation Carcinogenesis from Space Radiation” as one of its top research priorities. On Earth, we are protected from most space radiation by the atmosphere, but radiation increasingly affects those outside of Earth’s atmosphere and beyond low Earth orbit. Ionizing radiation damages DNA most commonly via phosphodiester backbone breakage. Humans can repair these damages, but prolonged exposure can lead to genomic instability and cancer. Thus, it is important to understand and monitor genome stability of astronauts during space travel. Therefore, our main goal is to develop biomarkers of radiation-induced DNA damage for monitoring genome stability of astronauts during and after space travel, thus contributing to safer space exploration. Previously, we have analyzed signal transduction DNA damage repair pathway genes in human mammary tumor cell lines. However, skin cancer is also a major concern due to radiation exposure, and understanding DNA repair pathways in skin cancer is equally important. Therefore, using the human skin cancer cell line HTB-72 and doxorubicin as a radiomimetic model, we will investigate how the oncoprotein BRAF responds to radiation-induced DNA damage. The data produced from this project will help us understand differences between the development of breast cancer and skin cancer.

Jonathan Percy – University of Iowa

Medical imaging serves a crucial role in establishing diagnosis, determining severity, monitoring progression, and uncovering the pathophysiology associated with many diseases. This research, led by Dr. Sean Fain, explores novel applications of hyperpolarized (HP) 129Xe magnetic resonance imaging (MRI) in populations including those suffering from Long Covid, cystic fibrosis, interstitial lung disease, and radiation-induced pulmonary changes associated with radiation therapy. This technique differs from standard imaging practices today while measuring lung function more directly than conventional pulmonary tests delivering quantitative measures of ventilation, perfusion, and gas exchange of the lungs. After more research, these novel biomarkers could possibly guide adjustments to treatment and deployment of potential therapies to improve outcomes.

Emma Pellegrino – University of Northern Iowa

Moonmilk is a white substance mainly composed of calcium carbonate (CaCO3) that is often found in limestone caves. It is one of many mineral composites in Wind Cave National Park South Dakota and is thought to be formed by microbial activity. Some of the evidence supporting this hypothesis lies in the association of metabolically active microorganisms in moonmilk such as Macromonas bipunctata, a bacterium first discovered in caves over a hundred years ago. More recently, there is evidence that dry versus wet moonmilk harbor different bacterial communities (Shanae et al., 2020). We have collected our own samples of moonmilk from different areas within Wind Cave using sterile supplies to avoid cross-contamination. Each site had varying degrees of human exposure, from public tour routes to caverns only occasionally visited by park rangers. By extracting environmental DNA from these samples, we will then be able to measure microbial diversity. The resulting data set will allow us to estimate how much of the human microbiome transfers to moonmilk and whether there is a correlation between microbial diversity and the level of hydration in Wind Cave moonmilk. DNA will be extracted using the Qiagen PowerSoil Pro DNA Extraction kit followed by 16S rDNA PCR amplification and clone library construction to identify microbes. The results of this study will not only offer insights into the composition of microbial species in moonmilk, as well as their role in its formation, but will add to the larger genetic map of Wind Cave being constructed by the other students on my research team.

Gage MacLin – University of Iowa

This project aims to design and implement the guidance and control systems for various autonomous vehicles. Multi loop control will be used to calculate a trajectory that leads to a desired position, and then calculates the required actuation commands to reach that desired position. The work ahead lies in verifying the mathematical model of the vehicles, and then implementing various control theories and algorithms including adaptive control and trajectory tracking control. Once the control system is completed, it will be simulated extensively using MATLAB Simulink to prove robustness. Then, the control system will be implemented onto different autonomous vehicles for further testing.

Jeff Leiberton – University of Iowa

X-ray telescopes differ from optical telescopes in the sense that they must utilize grazing incidence mirrors to “skip” X-rays to the detector. To meet the performance requirements necessary to achieve NASA’s science goals, we must stack many thin (<1mm) X-ray mirrors together. However, thin mirrors are susceptible to stray loads, which distort the mirror and diminish its sensitivity. To address this problem, our group studies the fabrication and implementation of thin, adjustable X-ray mirrors. These mirrors are micro-electro-mechanical systems (MEMS) that utilize an array of addressable cells to correct distortions introduced in spaceflight and production. My research focuses primarily on the development and optimization of the algorithms used to correct the mirror distortions. These algorithms are essentially nonlinear, least-squares optimization solvers. I will use these algorithms to predict which voltages we must apply to each cell in order to optimize the performance of the mirror. We will then use these predictions to apply and measure corrections to a mirror prototype available to us at the University of Iowa.

Yajatra Kulkarni – University of Iowa

This project aims to extend strain-based kinematic shape sensing from 1-D beams to 2-D plates.

Kinematic shape sensing methods use discrete measurements of strain to infer structural deformations using purely geometric/kinematic models. This is computationally efficient, independent of structural material properties, and extremely accurate when applied to certain geometries. Previous efforts have focused upon the deformations of long, slender, beam-like bodies, which were dubbed “shape-sensing spars”. These spars were used as sensors by inserting them into flexible wings to track deformations and perform novel fluid-structure interaction experiments. A next step in novel shape sensing would be to extend the instrumentation from beam-like structures to 2-dimensional plates, which would allow structures like ship hulls, tank walls, or airplane fuselage panels to be monitored with unprecedented rigor.

This research focuses on extending a kinematic reconstruction algorithm to 2D applications and investigating optimal sensor placement and produce a prototype of an instrumented panel. Experiments will be performed to validate the kinematic reconstruction of both static and dynamic deformations imposed on the plate with known boundary conditions.