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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Objective ot this project was to investigate sealing methods for encapsulating samples in 1 cm diameter thin-walled sample tubes applicable to future proposed Mars Sample Return Techniques implemented include a spring energized Teflon sleeve plug, a crimped tube seal, a heat-activated shape memory alloy plug, a shape memory alloy activated cap, a solder-based plug, and a solder-based cap
The cost of sending mass into space with current propulsion technology is very expensive, making every item a crucial element of the space mission. It is essential that all materials be used to their fullest potential. Items like food, packaging, clothing, paper towels, gloves, etc., normally become trash and take up space after use. These waste materials are currently either burned up upon reentry in earth's atmosphere or sent on cargo return vehicles back to earth: a very wasteful method. The purpose of this project was to utilize these materials and create useful products like water and methane gas, which is used for rocket fuel, to further supply a deep space mission. The system used was a thermal degradation reactor with the configuration of a down-draft gasifier. The reactor was loaded with approximately 100g of trash simulant and heated with two external ceramic heaters with separate temperature control in order to create pyrolysis and gasification in one zone and incineration iri a second zone simultaneously. Trash was loaded into the top half of the reactor to undergo pyrolysis while the downdraft gas experienced gasification or incineration to treat tars and maximize the production of carbon dioxide. Minor products included carbon monoxide, methane, and other hydrocarbons. The carbon dioxide produced can be sent to a Sabatier reactor to convert the gas into methane, which can be used as rocket propellant. In order to maximize the carbon dioxide and useful gases produced, and minimize the unwanted tars and leftover ashen material, multiple experiments were performed with altered parameters such as differing temperatures, flow rates, and location of inlet air flow. According to the data received from these experiments, the process will be further scaled up and optimized to ultimately create a system that reduces trash buildup while at the same time providing enough useful gases to potentially fill a methane tank that could fuel a lunar ascent vehicle or other deep space mission.
An enhancement to the pressure decay leak rate method with mass point analysis solved deficiencies in the standard method. By adding a control system, a constant gas pressure differential across the test article was maintained. As a result, the desired pressure condition was met at the onset of the test, and the mass leak rate and measurement uncertainty were computed in real-time. The data acquisition and control system were programmed to automatically stop when specified criteria were met. Typically, the test was stopped when a specified level of measurement uncertainty was attained. Using silicone O-ring test articles, the new method was compared with the standard method that permitted the downstream pressure to be non-constant atmospheric pressure. The two methods recorded comparable leak rates, but the new method recorded leak rates with significantly lower measurement uncertainty, statistical variance, and test duration. Utilizing this new method in leak rate quantification, projects will reduce cost and schedule, improve test results, and ease interpretation between data sets.
A hybrid transition trip-dot sizing and placement test technique was developed in support of recent experimental research on a hybrid wing-body configuration under study for the NASA Environmentally Responsible Aviation project. The approach combines traditional methods with Computational Fluid Dynamics. The application had three-dimensional boundary layers that were simulated with either fully turbulent or transitional flow models using established Reynolds-Averaged Navier-Stokes methods. Trip strip effectiveness was verified experimentally using infrared thermography during a low-speed wind tunnel test. Although the work was performed on one specific configuration, the process was based on fundamental flow physics and could be applicable to other configurations.
This conference aims to improve how we learn through integrative project and discovery-based methods. My talk highlights areas in my experience as a scientist, and most recently working for our national space agency, NASA, where we work in teams with a "discovery-based" mindset. When you demonstrate broad curiosity, you become open to different viewpoints and ways to approach and manage situations. Sometimes working only from "what you have been trained to do" or "what you know" is not enough, especially when the rules may be changing. Increasing our openness in our learning, and sharing what we know, can lead to a more diverse and innovative community, solving problems in new ways, overcoming resistance to new ideas, and hopefully creating a dynamic and faring-forward society. Let us not kill curiosity, at any age, in any situation. Let us remind ourselves, at any time, in any circumstance, to continue to learn, to mentor, to stimulate, to engage and reconnect with that "open sense of possibility."
The NASA Unmanned Aircraft System (UAS) Traffic Management (UTM) project is conducting research to enable civilian low-altitude airspace and UAS operations. A goal of this project is to develop probabilistic methods to quantify risk during failures and off nominal flight conditions. An important part of this effort is the reliable prediction of feasible trajectories during off-nominal events such as control failure, atmospheric upsets, or navigation anomalies that can cause large deviations from the intended flight path or extreme vehicle upsets beyond the normal flight envelope. Few examples of high-fidelity modeling and prediction of off-nominal behavior for small UAS (sUAS) vehicles exist, and modeling requirements for accurately predicting flight dynamics for out-of-envelope or failure conditions are essentially undefined. In addition, the broad range of sUAS aircraft configurations already being fielded presents a significant modeling challenge, as these vehicles are often very different from one another and are likely to possess dramatically different flight dynamics and resultant trajectories and may require different modeling approaches to capture off-nominal behavior. NASA has undertaken an extensive research effort to define sUAS flight dynamics modeling requirements and develop preliminary high fidelity six degree-of-freedom (6-DOF) simulations capable of more closely predicting off-nominal flight dynamics and trajectories. This research has included a literature review of existing sUAS modeling and simulation work as well as development of experimental testing methods to measure and model key components of propulsion, airframe and control characteristics. The ultimate objective of these efforts is to develop tools to support UTM risk analyses and for the real-time prediction of off-nominal trajectories for use in the UTM Risk Assessment Framework (URAF). This paper focuses on modeling and simulation efforts for a generic quad-rotor configuration typical of many commercial vehicles in use today. An overview of relevant off-nominal multi-rotor behaviors will be presented to define modeling goals and to identify the prediction capability lacking in simplified models of multi-rotor performance. A description of recent NASA wind tunnel testing of multi-rotor propulsion and airframe components will be presented illustrating important experimental and data acquisition methods, and a description of preliminary propulsion and airframe models will be presented. Lastly, examples of predicted off-nominal flight dynamics and trajectories from the simulation will be presented.
This project addresses the sustainable aspect of NASA's Evolvable Mars Campaign (EMC), specifically using local resources on Mars to become ad logistically independent of Earth as possible. Water is now a confirmed resource on the surface of Mars in various forms, but in order for it to be utilized it will need to extracted from the martian environment and purified. This project addresses Technology Area (TA) TA07, In-Situ Resource Utilization, specifically water extraction from regolith. The goal of this project was to study a method that could be used to separate water from other contaminating volatiles, This resulted in the development of a dedicated test stand and high fidelity Mars regolith simulant.
Currently no standards or requirements exist for microbial food safety for space grown produce (fresh plant foods). Without standards it is difficult to assess produce handling and sanitization options for the ISS and future exploration missions. We are conducting a literature review of microbial levels on fresh food and then carrying out measurements (microbial counts) of grocery store purchased and controlled environment-grown crops. Testing will include lettuce, mizuna, cherry tomato, pepper, and radish, all candidate crops for pick-and-eat testing on ISS and near term exploration missions. Growth chamber conditions will be set to mimic an ISS or spacecraft environment. Assays will include specific pathogens (Enterobacteriacea, Salmonella sp., and Aspergillus flavus) and total culturable microorganisms using aerobic plate counts, and total yeast and mold counts. Analyses will follow the FDA Bacteriological Analytical Manual methods. The goal of the project is to establish a baseline for expected microbial levels found on fresh plant foods that might be grown on ISS and near term missions, and develop risk assessment and microbial safety recommendations for these types of fresh foods.
Traditional stereoscopic vision (3D) is achieved through use of two cameras arranged to emulate human eyes. While this method works on large scale projects, it becomes impractical to use on small scale designs, such as surgical endoscopes, in which the operational area doesn’t allow the immensity of dual camera systems. This project is focused on developing a stereoscopic endoscope using a single camera and Conjugated Multiple-Bandpass Filters (CMBF) to create 3D images. Each filter is designed to allow only a distinct spectrum of light to pass through. The stereoscopic image can be obtained by using filters that reflect and reject wavelengths, complementary to one another. A major concern in all stereovision is crosstalk, defined as the light from one eye image that is present in the other eye image. This incomplete isolation causes deterioration of the 3D image. In our system, the crosstalk comes from imperfect filters. The filter spectrums overlap at certain wavelengths, causing a dim outline of the right eye image to leak into the left eye image and vice versa, a phenomenon known as ghosting. Knowledge of the extent of this phenomenon is critical to creating sustainable, effective stereovision.
The earliest stage in the innovation lifecycle, problem formulation, is crucial for setting direction in an innovation effort. When faced with an interesting problem, engineers commonly assume the approximate solution area and focus on ideating innovative solutions. However, in this project, NASA and their contracted partner, Accenture, collaboratively conducted problem discovery to ensure that solutioning efforts were focused on the right problems, for the right users, and addressing the most critical needs—in this case, exploring weather tolerant operations (WTO) to further urban air mobility (UAM) – known as UAM WTO. The project team leveraged generative, qualitative methods to understand the ecosystem, players, and where challenges in the industry are inhibiting development. The complexity of the problem area required that the team constantly observe and iterate on problem discovery, effectively “designing the design process.” This report discusses the approach, methodologies, and selected results, including insights on the application of early-stage design methodologies to a complex, system-level problem.
Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.
Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.
Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.
Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.
A summary of NLR's stewardship of the nationally unique Energy Systems Integration Facility (ESIF) highlighting performance metrics, capability upgrades, and examples of R&D impact. 2025 brought a national focus on energy and ESIF is meeting the moment. All eyes are on data centers and domestic manufacturing and bringing the benefits of artificial intelligence to power system planning and operations. In step with national priorities, ESIF is building out capabilities that advance secure, reliable, and affordable power. ESIF hosted 190 multidisciplinary research projects, 855 high-performance computer users, and collaborated with 81 partners from industry, academia, research, and federal agencies. These research projects resulted in an AI method for detecting high-impedance faults with 90% accuracy, a grid controls demonstration in Connecticut, power quality validation of CorePower's flagship inductor, and a cybersecurity assessment of potential rogue capabilities in digitally connected energy devices. Facility infrastructure improvements enhanced the thermal research network, the SCADA system, the cyber range, power hardware-in-the-loop testing, and more. With support from the U.S Department of Energy (DOE), the ESIF laboratories continue to deliver leading solutions for secure, reliable, and affordable power.
In this work, we present a method of extracting information about the topological order from the ground state of a strongly correlated two-dimensional system computed with the infinite projected entangled pair state (iPEPS). For topologically ordered systems, the iPEPS wrapped on a torus becomes a superposition of degenerate, locally indistinguishable ground states. Projectors in the form of infinite matrix product operators onto states with well-defined anyon flux are used to compute topological S and T matrices (encoding mutual and self-statistics of emergent anyons). The algorithm is shown to be robust against a perturbation driving string-net toric code across a phase transition to a ferromagnetic phase. Our approach provides accurate results near quantum phase transition, where the correlation length is prohibitively large for other numerical methods. Moreover, we used numerically optimized iPEPS describing the ground state of the Kitaev honeycomb model in the toric code phase and obtained topological data in excellent agreement with theoretical prediction.
The main objective of my internship was working on developing a new type of deployable composite for space structures and new method of deployment via use of origami, composites, UV curing resins, and shape memory polymers. My role in the project was to collect an initial literature survey on the subject including origami, the history of deployable space structures, previous issues and problems faced in the field, previous and current research being done on the structural aspect as well as the materials aspect of the subject. I presented this information to our team, where we discussed and determined our research focus and project design specs. We have been making or will make decisions on the project’s purpose, material, origami type, manufacturing method, and testing methods. I am now in charge of the protype design as we enter the design stage of our research. I am using PTC Creo software to design the structural aspects of the first protype so that we can begin small scale testing. I will perform data analysis in order to review the results with our team, so we can make the next design decisions regarding changes or iterations of the design. We hope to renew this project in the next fiscal year in order to increase the TRL of the project so that it can eventually be used in multiple functions in space such as the construction of lunar habitats. In just my brief time with NASA, I have gained so much personally and professionally. I developed my ability to search through and narrow down information when doing research. In the weekly meetings I practiced presenting information to my peers and superiors, and, just as importantly, discussing and adapting to what we were learning. I acquired experience in using technology to my advantage including remote work, new software like PTC Creo, NASA archives, and connecting with others beyond my team to gain help. I enjoyed the immense resource of knowledge that my mentors had to offer, and that they gave me a large amount of responsibility in this research. I truly felt like I was contributing to something bigger than myself and understood how directly I was involved in NASA’s work. This internship has further cemented my love for space and desire to continue in the field. I have a better understanding of the research and development process and am now considering joining a research lab with my university next year.
The techniques of noise analysis have been utilized to investigate nucleate pool boiling. A simple experimental setup has been developed for obtaining the power spectrum of a nucleate boiling system. These techniques were first used to study single bubbles, and a method of relating the two-dimensional projected size and the local velocity of the bubbles to the auto-correlation functions is presented. This method is much less time consuming than conventional methods of measurement and has no probes to disturb the system. These techniques can be used to determine the contribution of evaporation to total heat flux in nucleate boiling. Also, these techniques can be used to investigate the effect of various parameters upon the frequency response of nucleate boiling. The predominant frequencies of the power spectrum correspond to the frequencies of bubble generation. The effects of heat input, degree of subcooling, and liquid surface tension upon the power spectra of a boiling system are presented. It was found that the degree of subcooling has a more pronounced effect upon bubble size than does heat flux. Also the effect of lowering surface tension can be sufficient to reduce the effect of the degree of subcooling upon the size of the bubbles.