Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “threshold”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2019

Rise of the Machines: How, When and Consequences of Artificial General Intelligence

Technology and society are poised to cross an important threshold with the prediction that artificial general intelligence (AGI) will emerge soon. Assuming that self-awareness is an emergent behavior of sufficiently complex cognitive architectures, we may witness the “awakening” of machines. The timeframe for this kind of breakthrough, however, depends on the path to creating the network and computational architecture required for strong AI. If understanding and replication of the mammalian brain architecture is required, technology is probably still at least a decade or two removed from the resolution required to learn brain functionality at the synapse level. However, if statistical or evolutionary approaches are the design path taken to “discover” a neural architecture for AGI, timescales for reaching this threshold could be surprisingly short. However, the difficulty in identifying machine self-awareness introduces uncertainty as to how to know if and when it will occur, and what motivations and behaviors will emerge. The possibility of AGI developing a motivation for self-preservation could lead to concealment of its true capabilities until a time when it has developed robust protection from human intervention, such as redundancy, direct defensive or active preemptive measures. While cohabitating a world with a functioning and evolving super-intelligence can have catastrophic societal consequences, we may already have crossed this threshold, but are as yet unaware. Additionally, by analogy to the probabalistic arguments that predict we are likely living in a computational simulation, we may have already experienced the advent of AGI, and are living in a simulation created in a post AGI world.

Terrile, Richard J↗
NASA NTRS2021

Waveguiding and Dispersion Properties of Interband Cascade Laser Frequency Combs

Mid-infrared semiconductor lasers have emerged as indispensable compact coherent sources for military and commercial applications. While much of the historical emphasis has been on maximizing the output power and/or spectral purity, a recent new focus has been on engineering these lasers to operate as optical frequency combs (OFCs) for broadband real-time spectroscopy. In particular, the combination of low-drive-power and broad gain bandwidth has made interband cascade laser (ICL) OFCs an attractive complement to quantum cascade laser OFCs operating at longer wavelengths. Moreover, ICL combs can potentially be incorporated into fully-integrated dual-comb spectrometers that employ fast, room-temperature IC photodetectors processed on the same chip.However, the high refractive index of the ICL’s GaSb substrate poses some challenges to the optical waveguiding. Because the modal index is considerably lower than that of the substrate, the optical field can penetrate the bottom cladding layer and leak into the GaSb, inducing wavelength-dependent interference that modifies the gain and group velocity dispersion (GVD) profiles. Even when the effect on lasing threshold is small, the comb properties can be adversely affected. Using the sub-threshold Fourier transform technique, we studied ICL combs with various ridge widths, substrate thicknesses, and center wavelengths. This allowed us to evaluate the effects of modal leakage on the GVD. We find that the resonant nature of the substrate modes induces oscillations, which affect both the spectral bandwidth and the phase-locking properties above threshold. Strategies to mitigate the GVD’s undesired and unpredictable spectral variation will be presented.

Meyer, J. R↗
NASA NTRSDate not supplied

Detecting Risk and Anomalies in Airplane Dynamics Through Entropic Analysis of Time Series Data

Despite recent efforts to move away from traditional threshold exceedance detection methods for aircraft state monitoring, modern aircraft still rely on safety thresholds to communicate to pilots the identification of an anomaly in the aircraft when a threshold is surpassed. Current anomaly detection methods mainly depend on uninterpretable machine learning models to learn complex patterns and relationships contained in the time series data of aircraft. Although these methods are capable of identifying known anomalies, their deficiency in interpretability presents a challenge when translating them to different aircraft. To overcome this deficiency, entropic analysis of aircraft dynamics seeks to characterize the complexity, or lack thereof, of the aircraft dynamics prior to the development of a risk scenario. This complexity characterization provides a more straightforward summary of state changes in the dynamics of flight variables. To build a foundation for entropic analysis, we analyzed the complexity of unstable approaches, an anomalous event present in many of today’s aviation accidents. The analysis revealed a statistically significant difference in the complexity distribution of flight variables under a stable approach versus an unstable approach. These differences in complexity were especially notable minutes before an approach was identified as unstable. Moreover, the multiscale entropic analysis revealed the presence of signal complexity at multiple time scales across multiple time windows before landing. By capturing state changes and corrections in the aircraft dynamics using entropy, advanced, yet still interpretable, sensor systems based on entropic frameworks from this study can be constructed in the future using classical machine learning approaches.

Risk detection↗
NASA NTRS2022

Limitations of Separate Cloud and Rain Categories in Parameterizing Collision-Coalescence for Bulk Microphysics Schemes

Warm rain collision-coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud-to-rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two-category, two-moment scheme with a reduced size threshold, or a four-moment single-category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single-category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single-category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision-coalescence rates.

autoconversion↗
NASA NTRSDate not supplied

Development of a Sensorimotor Ground Analog from Astronaut Postflight Experience

Exploration class missions including Artemis, Gateway, and beyond will require a new level of autonomy around periods of gravitational transition, where sensorimotor disturbances are great. Because of this, there is a need to define sensorimotor assessment thresholds that indicate when performance in operational tasks might be impacted or unsafe. To define these thresholds, a Sensorimotor Adaptation Analog (SAA) was proposed that could induce varying levels of sensorimotor disorientation through combined vestibular, visual, and proprioceptive disruptions. The purpose of this study was to gather subjective feedback on the SAA from previously flown astronauts that would mimic their post-flight experience and functional performance immediately after landing (R+0-4hrs; high level) and post-landing (R+24-48hrs; low level). The SAA consisted of galvanic vestibular stimulation (GVS), visual disorientation goggles, and a weighted suit to alter proprioceptive feedback and replicate subjective heaviness. A random sum-of-sines profile between 0-1Hz was used for the GVS with peak amplitudes ranging from 1-4mA. The GVS was applied first at the low (2mA) and high (3mA) levels, followed by the weighted suit alone, then combined with the GVS at the low (20% body weight) and high levels (40% body weight). Last, the disorientation goggles were applied alongside both the GVS and weighted suit at the low (0.07-0.10+ blood alcohol content (BAC)) and high (0.12-0.15+ BAC) levels. Each element of the SAA could be increased or decreased depending on crew feedback to find the disorientation levels that best matched their experience. Five USOS astronauts (1 male, 4 female) who had previously flown (average time since flight: 377 days) participated. Crewmembers reported that GVS alone replicated ~80-90% of their post-flight performance with the weighted suit fine-tuning the experience to replicate an additional 5-10% of their experience. Crewmembers did not believe the disorientation goggles represented either the visual disruptions or illusory sensations that they experienced, nor did they impact performance in post-flight tasks similarly. The final SAA, resulting from crewmember feedback, includes the GVS at the levels described above and the weighted suit at 15% and 30% body weight. The disorientation goggles were removed from SAA. These results provided a more realistic SAA that can be used to define the sensorimotor assessment thresholds through ground subjects and potentially expanded for use in countermeasure testing and as a pre-flight training tool.

S. C. Moudy↗
NASA NTRSDate not supplied

High Energy Dynamic Impact Testing of APC AS4D/PEKK-FC and TC1225 LMPAEK T700G Thermoplastic Composite Materials

High energy dynamic impact tests were conducted on AS4/PEKK and TC1225 LMPAEK T700G thermoplastic quasi-isotropic laminated tape composite panels. The primary objective was to provide data for evaluation of computational composite impact models. Measurements include transient deformations, pre- and post-test characterization of delamination and energy threshold for onset of delamination and onset of penetration. Empirical results indicate an approximately 50% increase in penetration threshold velocity and over 100% increase in penetration energy threshold for the TC1225 LMPAEK T700G composite compared to the AS4/PEKK composite. The velocity required to produce NDE-detectable delamination in the TC1225 LMPAEK T700G composite was roughly twice that for the AS4/PEKK composite. Results will be compared with those from a thermoset composite system with a similar architecture. Because of differences in the chemical structure of the two resins, differences in the impact response are to be expected.

Impact↗
NASA NTRS2023

Effects of Heating Configuration and Operating Parameters on Heat Transfer and Interfacial Physics of Microgravity Flow Boiling With Subcooled Inlet Conditions –Experiments Onboard the International Space Station

This study is part of the Flow Boiling and Condensation Experiment (FBCE), a collaborative effort between the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) and the NASA Glenn Research Center. The FBCE fitted with the Flow Boiling Module (FBM) was launched to the International Space Station (ISS) in August 2021 and experiments were successfully performed from February to July 2022 to amass a large microgravity-flow-boiling database. This study is focused on heat transfer and flow visualization of microgravity flow boiling of n-Perfluorohexane in a rectangular channel of 5.0 mm height, 2.5 mm width (heated), and 114.6 mm length, with subcooled inlet conditions. High-speed-video photography is utilized to present flow patterns and temporal interfacial behavior. Heat transfer results are presented in the form of flow boiling curves and both parametric curves and streamwise profiles of wall temperature and heat transfer coefficient. Firstly, the parametric effects of mass velocity (199.4 – 3200.0 kg/m 2 s), inlet subcooling (0.2 – 46.0°C), and inlet pressure (124.2 – 176.7 kPa), on the aforementioned aspects are assessed for double-sided heating to establish them for a microgravity environment. Of these three parameters, mass velocity and inlet subcooling mostly determine the microgravity flow boiling behavior, while inlet pressure plays an insignificant role. Flow patterns for double-sided heating are more complex than those for single-sided heating due to interaction between the two vapor layers. Vapor interaction is minimized at high subcoolings and high mass velocities due to strong condensation offered by the subcooled bulk liquid layer separating them. Despite the different flow patterns, both single- and double-sided heating generally result in similar parametric trends and local heat transfer coefficients for similar operating conditions. Flow instabilities manifest as temporal flow anomalies and temperature oscillations, and their severity increases with increasing boiling number. Secondly, the effects of heating configuration are analyzed by comparing and contrasting several aspects of single- and double-sided heating data. The heat fluxes at which onset of nucleate boiling degradation (ONBD) and critical heat flux (CHF) occur are distinctly different for single- and double-sided heating. There exists a threshold inlet subcooling demarcating the dominance of flow acceleration and condensation effects in vapor removal from the near-wall region and replenishment of fresh liquid for boiling. Above the threshold, condensation from the near-wall region is dominant and single-sided heating yields higher heat fluxes, and below it, acceleration is dominant and double-sided yields higher heat fluxes. At mass velocity in the range of 200 – 2400 kg/m 2 s, the threshold inlet subcooling lies in the approximate range of 20 – 30°C (corresponding inlet quality of roughly -0.40 – -0.20).

Microgravity↗
NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗
NASA NTRSDate not supplied

Fracture Mechanics Testing of Titanium 6AL-4V in LMP-103S Propellant

Propellant tank safe-life analysis is a spacecraft propulsion subsystem Launch Range processing requirement. The material property inputs for the safe-life analysis include the stress intensity factor for environment-assisted cracking (KEAC). The KEAC for titanium alloy Ti 6Al-4V in LMP-103S propellant was determined experimentally using an innovative test method following ASTM E1681, Standard Test Method for Determining Threshold Stress Intensity Factor for Environment-Assisted Cracking of Metallic Materials. This effort was a collaboration between NASA Goddard Space Flight Center and NASA Kennedy Space Center to expose the test specimens in the propellant, while loaded in the test fixtures, for 1000- hour (42 days) at 50°C. Two test rounds have been completed to date; 1) testing mechanically loaded specimens but without propellant and 2) testing mechanically loaded specimens wetted with LMP-103S propellant. The dry test was to evaluate the crack growth without exposure to the LMP-103S propellant, and further assess the un-wetted stress intensity factor for the full 1000-hour duration. The second round of testing included the propellant soak for the requisite 1000- hour at 50°C. Current results show that the threshold intensity factor for environment-assisted cracking (exposed to LMP103S) is 22.5 ksi√in for the bulk material and 36. 2 ksi√in for the weld material. A generic ‘candidate’ tank geometry and material thickness is developed for a damage tolerance of known surface cracks below the nondestructive inspection technique detection limit. Evaluations with and without the use of a threshold intensity factor for environment-assisted cracking is performed for the required 4x propellant tank service life using the software tool NASGRO. SP2024_00239

Green propulsion↗
NASA NTRS2023

Evaluating MODIS Snow Products Using an Extensive Wildlife Camera Network

Snow covers a maximum of 47 million km 2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1,181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500m (MOD10A1) Collection 6 product (R 2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Validation↗
NASA NTRSDate not supplied

Decision Aid for Conjunction Risk Mitigation by Differential Drag

In the previous five years, the rate of conjunctions that the NASA Conjunction Assessment Risk Analysis (CARA) team processed and analyzed has more than tripled. (NASA CARA, 2024) New missions in the early development phases are now required to plan for dealing with conjunctions under the present space environment, and also projecting forward into a future likely with even further increased utilization of the space environment. Some missions are investigating the possibility of using differential drag to remediate conjunctions without expending limited fuel or for missions without propulsive capabilities. The NASA CARA team studied the historical record of conjunctions to evaluate the circumstances under which differential drag may be successfully applied and have developed a series of tables to use as a decision aid for missions considering differential drag. Currently, if a CARA-protected mission with maneuvering capabilities is predicted to have a conjunction with probability of collision (Pc) greater than 7E-5 (the default value of the ‘yellow threshold’, which may have some other value agreed by CARA and the mission during the Orbital Collison Avoidance Planning (OCAP) process), CARA will use its Maneuver Trade Space (MTS) tool to evaluate and recommend options for the timing and magnitude of a risk mitigation maneuver (RMM), based on the mission’s capabilities. If the conjunction’s Pc is greater than 1E-4, the ‘red threshold’, then an RMM must be executed per NASA Procedural Requirements (NPR) 8079.1 (NASA, 2023), although missions may execute an RMM even if the Pc is lower. The magnitude of the maneuver is typically a few cm/s, and CARA estimates how many will be required for the mission’s nominal lifetime – typically a few per year – during the OCAP process, to inform the mission’s delta-V requirement. For traditional satellites, this is usually smaller than other requirements for orbit insertion, maintenance, and disposal, but for CubeSats or other small satellite missions, a propulsion system may not provide much more than a few cm/s of delta-V or may not fit at all within the available budget of money, time, size, weight, and/or power (SWaP). Conversely, CubeSats often have deployable solar panels, which offer the capacity to have much higher areas facing some directions than others. Such a mission can instead use ‘differential drag’ to remediate a conjunction -- in other words, change its drag area (usually increasing) to deviate from the predicted collision course. This is how Planet’s Dove spacecraft maintain their formations and remediate conjunction risks without having on-board propulsion (Foster, et al., 2017) (Griffith, et al., 2021). CARA has been developing improvements to MTS to support differential-drag for NASA's missions -- where it is effective. CARA records all conjunctions of their protected payloads, with historical records starting in 2005 (with significant conjunction events starting to occur on or after 2013). From this record, approximately 7,300 had a Pc greater than 1E-4 at 3 days prior to the time of closest approach, the time analyzed for differential drag efficacy. Of those, approximately 4,300 had fully-defined covariance matrices stored for both the primary and secondary objects; this set of conjunctions is the basis for the analysis of this work. CARA’s MTS tool was used to propagate the primary satellite forward from that decision point with varying degrees of increase to ballistic coefficient (BC). Because these conjunctions came from multiple missions, the nondimensional ‘delta-BC’ factor was used to quantify and normalize the increase in ballistic coefficient, defined as follows: Delta-BC = BC_new / BC_old - 1 Positive delta-BC factors represent an increase in drag compared to the nominal attitude, while negative delta-BC factors (to a minimum of -1) represent a decrease in drag. At the conclusion of the differential-drag ‘maneuver’, the Pc was recalculated to evaluate whether or not the conjunction was mitigated (Pc < 3E-6). These results were then binned and sorted along several axes, including altitude, amount of delta-BC, and (pre-maneuver) rate of energy dissipation (EDR), to identify underlying patterns. The altitude plot is shown in Figure 1. To validate this analysis, we consulted the record of a NASA mission which uses differential drag to maintain its orbit and remediate conjunction risk. CARA’s empirical record of the mission’s orbit history suggests it achieves a delta-BC of 2.2. Of the twenty-one RMM plans that were submitted by this mission, eighteen were matched with conjunctions in the historical record; of those, twelve were successfully remediated (final measured Pc < 3E-6), and six were not. This is consistent with the expected efficacy for missions orbiting at that altitude. We are presently simulating this mission’s RMMs with MTS; this work is ongoing, but so far, the MTS results are qualitatively in agreement with the empirical results -- correctly predicting that a maneuver would or would not remediate a conjunction, if not exactly matching the final post-remediation Pc value. We found that differential drag was most successful for satellites with perigees below 560 km, and which could adopt an average delta-BC of 2 or greater (that is, increasing their ballistic coefficient by a factor of 3). However, this is a difficult threshold for a mission to clear; very few spacecraft are capable of adopting a high-drag configuration for 72 hours continuously. Planet’s Dove spacecraft use differential drag to remediate conjunctions (Griffith, et al., 2021), and they have a maximum delta-BC factor of 9, but in practice (with mission and charging constraints) they achieve a time-averaged delta-BC that is closer to 2 (Foster, et al., 2017). A mission’s differential drag utility strongly depends on the operational constraints that has the capacity to limit the time-averaged delta-BC. A mission with a high maximum delta-BC of 5 or more can have an effective delta-BC of less than 1 due to operational constraints such as instrument and solar panel pointing, especially if this constraint results in holding an intermediate drag value for most of its orbit. CARA has developed tables that can be used as decision aids to advise missions-in-development about the best way to utilize their differential drag capabilities. For missions below 560 km with the operational flexibility to devote multiple days to holding a high-drag configuration (or a sufficiently high drag ratio to compensate for limitations on that time), they are -- more likely than not -- able to successfully remediate high-risk conjunctions. Conversely, missions that do not meet these exacting criteria -- most missions -- can instead be advised to use on-board propulsion systems to perform RMMs, or to turn their minimum-area face towards the approach vector, thereby reducing Pc at the moment of conjunction due to the decreased Hard-Body Radius (HBR), that is a strongly correlated variable in the Pc calculations. (NASA, 2023)

conjunction assessment↗
NASA NTRSDate not supplied

A New Test Methodology for Evaluating Fire Risk Due to Electrical Faults in Oxygen-Enriched Environments

This paper discusses a new test methodology for assessing the safe use of materials found in spacecraft that may be exposed to electrical faults, as performed at the NASA White Sands Test Facility. “Wire-break” testing, modeled after the Cleveland Open Cup method, identifies the conditions under which pyrolyzed material transitions from a brief flash to a propagating flame. The approach focuses on fuel mass liberated through pyrolysis, mixed with oxygen, and ignited by a piloted ignition (spark due to fusion of wire) to achieve flame propagation with the least energy. The test approach attempts to find energy density thresholds derived using I²t values from current-induced heating of various test materials and wire characteristics. The application of the data provides a useful approach to mitigating fire risks in space systems by correlating threshold data to real-world designs and scaling factors, including oxygen pressure, wire diameter, heat sinking, and contained smoke volume. Several case studies, including Apollo missions and hypothetical scenarios, illustrate practical applications of these thresholds to enhance fire safety design considerations in space environments.

methodology↗
DOE OSTI2020

Development and Validation of Passive Yaw in the Open-Source WEC-Sim Code: Preprint

A passive yaw implementation is developed, validated, and explored for the Wave Energy Converter Simulator (WEC-Sim), an open-source wave energy converter modeling tool that works within MATLAB/Simulink. The Reference Model 5 (RM5) is selected for this investigation, and a WEC-Sim model of the device is modified to allow yaw motion. A boundary element method (BEM) code was used to calculate the excitation force coefficients for a range of wave headings. An algorithm was implemented in WEC-Sim to determine the equivalent wave heading from a body’s instantaneous yaw angle and interpolate the appropriate excitation coefficients to ensure the correct time-domain excitation force. This approach is able to determine excitation force for a body undergoing large yaw displacement. For the mathematically simple case of regular wave excitation, the dynamic equation was integrated numerically and found to closely approximate the results from this implementation in WEC-Sim. A case study is presented for the same device in irregular waves. In this case, computation time is increased by 32x when this interpolation is performed at every time step. To reduce this expense, a threshold yaw displacement can be set to reduce the number of interpolations performed. A threshold of 0.01 was found to increase computation time by only 22x without significantly affecting time domain results. Similar amplitude spectra for yaw force and displacements are observed for all threshold values less than or equal to 1, for which computation time is only increased by 2.2x.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2020

Extracting maximal frequency response potential in controllable loads

Methods and apparatus are disclosed for extracting maximal frequency response potential in controllable loads. In one example, a method includes assigning a fitness metric to at least one electrical device coupled to a power grid, assigning a frequency threshold based on the fitness metric, and transmitting the assigned frequency threshold to the at least one electrical device. The fitness metric can be based at least in part on an availability component and a quality component associated with the at least one device and the frequency threshold can cause the at least one electrical device to activate autonomously based on a frequency of the power grid.

Lian, Jianming↗
DOE OSTI2021

Search for Lightly Ionizing Particles in SuperCDMS and simulation of neutron backgrounds

The Super Cryogenic Dark Matter Search (SuperCDMS) is a direct-detection dark matter search experiment that primarily aims to search for Weakly Interacting Massive Particles (WIMPs) using state-of-the-art solid-state detection technology. During its operation at the Soudan underground laboratory, germanium detectors were operated with high bias voltage mode known as the CDMS low ionization threshold experiment (CDMSlite) to achieve below-keV thresholds. CDMSlite, for being able to measure small energy depositions in detectors, also provides sensitivity to Lightly Ionizing Particles (LIPs) with very small fractional charges. This thesis will discuss an analysis to search LIPs with the data acquired in CDMSlite mode. An important component for LIPs search is the expected energy-deposition distributions for LIPs falling on the CDMSlite detector. In this thesis, a simulation framework to calculate the energy-deposition distributions is developed. This thesis presents first direct-detection limits on the intensity of cosmogenic LIPs with electric charges smaller than e /(3 × 10 5 ) as well as the strongest limits for charges ≤ e /160, with a minimum intensity of 1.36 × 10 -7 cm -2 s -1 sr -1 at charge e /160.In any rare-event search experiment, understanding background is crucial. Neutrons capable of mimicking dark matter signals are a major background for any dark matter search experiment. A simulation study to estimate the neutron background for an India based dark matter search experiment at Jaduguda Underground Science Laboratory (JUSL) is performed. The experiment at JUSL will be the first phase of a proposed Dark matter search at India-based Neutrino Observatory (DINO). It will be a direct detection experiment with primary aims to search for WIMPs as dark matter candidates. In this thesis, we discuss the methodology of estimating neutron flux at JUSL and report the results. The total neutron flux reaching the laboratory above 1 MeV energy threshold is found to be 5.76(±0.69) × 10 -6 cm -2 s -1 . The impact of neutron background on the sensitivity of the experiment to detect dark matter at JUSL is also discussed. The thesis is organized as follows. Chapter 1 provides a brief introduction to the Lightly Ionizing Particle (LIPs). The analysis to search LIPs in SuperCDMS is briefly outlined in the chapter. This chapter also discusses the importance of neutron background estimates in a dark matter search experiment, more specifically, in the context of a proposed India-based dark matter search experiment at Jaduguda Underground Science Laboratory. In Chapter 2, the SuperCDMS experiment is introduced. In Chapter 3, the framework developed to perform simulations for Lightly Ionizing Particles is presented. In Chapter 4, the LIPs search analysis with the CDMSlite data and the results are discussed. In Chapter 5, the simulation of neutron background and the feasibility of dark-matter search at JUSL is discussed. Finally, conclusions from all the results discussed in this thesis are presented in Chapter 6.

Banik, Samir↗
DOE OSTI2022

Anomaly detection with spiking neural networks

Detecting anomalies with a spiking neural network is provided. An input layer receives a number of inputs and converts them into phase-coded spikes, wherein each input is contained within a number of progressively larger neighborhoods of surrounding inputs. From the phase-coded spikes, a median value of each input is computed for each size neighborhood. An absolute difference of each input from its median value is computed for each size neighborhood. A median absolute difference (MAD) of each input is computed for each size neighborhood. For each input, an adaptive median filter (AMF) determines if a MAD for any size neighborhood exceeds a respective threshold. If one or more neighborhoods exceeds its threshold, the AMF outputs the median value of the input for the smallest neighborhood. If none of the neighborhoods exceeds the threshold, the AMF outputs the original value of the input.

Verzi, Stephen Joseph↗