SEARCH · Search NASA
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.
Galileo is now nearly one year along its three-year direct Earth-to-Jupiter transfer trajectory following it final interplanetary gravity assist from Earth on December 8, 1992.
Galileo is now nearing the one-year mark along its three-year direct Earth-to-Jupiter transfer trajectory, having received it final interplanetary gravity assist from Earth on December 8, 1992.
In this paper, the feasibility of using an L-band low gain antenna (LGA) as a mobile terminal antenna for helicopters is described. The objective is to select the lowest cost antenna system which can be easily mounted on a helicopter and capable of communicating with a geosynchronous satellite. To ensure that all the antenna options are being considered, the steerable high gain reflector and medium gain array antennas as well as LGA are studied and compared in an exhaustive survey. The high gain reflector antenna in L-band is usually very large in size and heavy in weight. In addition, a bulky and expensive tracking system is needed to steer the antenna beam to the satellite direction. The medium gain antennas (including mechanically and electronically steered arrays) are also more expensive and less reliable than an LGA due to the addition of a beam steering system to track the satellite. The omni-directional LGA is simple, reliable, and inexpensive. It is typically ten times smaller than the medium gain antenna. This makes the position, selection, and mounting on the helicopter relatively easier. Therefore, the LGA is selected as a mobile terminal antenna for helicopters. Among the many LGA's (cross-dipole, helix, spiral, and slot antennas), the helix antenna is the most inexpensive. One can also change the size, shape, or pitch angle of the helix to optimize the gain in the desired direction. Therefore, the helix antenna is selected for further study. Both 2-arm and 4-arm helices are studied theoretically and experimentally to determine the antenna's performance and the scattering effects from the helicopter body and the blades. The multipath, Doppler, and Doppler rate issues as well as the periodic fading effects caused by the helicopter rotor blades will be briefly discussed in the paper.
This innovation provides the user with autonomous on-screen monitoring, embedded computations, and tabulated output for two new processes. The software was originally written for the Continuous Lunar Water Separation Process (CLWSP), but was found to be general enough to be applicable to the Lunar Greenhouse Amplifier (LGA) as well, with minor alterations. The resultant program should have general applicability to many laboratory processes (see figure). The objective for these programs was to create a software application that would provide both autonomous monitoring and data storage, along with manual manipulation. The software also allows operators the ability to input experimental changes and comments in real time without modifying the code itself. Common process elements, such as thermocouples, pressure transducers, and relative humidity sensors, are easily incorporated into the program in various configurations, along with specialized devices such as photodiode sensors. The goal of the CLWSP research project is to design, build, and test a new method to continuously separate, capture, and quantify water from a gas stream. The application is any In-Situ Resource Utilization (ISRU) process that desires to extract or produce water from lunar or planetary regolith. The present work is aimed at circumventing current problems and ultimately producing a system capable of continuous operation at moderate temperatures that can be scaled over a large capacity range depending on the ISRU process. The goal of the LGA research project is to design, build, and test a new type of greenhouse that could be used on the moon or Mars. The LGA uses super greenhouse gases (SGGs) to absorb long-wavelength radiation, thus creating a highly efficient greenhouse at a future lunar or Mars outpost. Silica-based glass, although highly efficient at trapping heat, is heavy, fragile, and not suitable for space greenhouse applications. Plastics are much lighter and resilient, but are not efficient for absorbing longwavelength infrared radiation and therefore will lose more heat to the environment compared to glass. The LGA unit uses a transparent polymer antechamber that surrounds part of the greenhouse and encases the SGGs, thereby minimizing infrared losses through the plastic windows. With ambient temperatures at the lunar poles at 50 C, the LGA should provide a substantial enhancement to currently conceived lunar greenhouses. Positive results obtained from this project could lead to a future large-scale system capable of running autonomously on the Moon, Mars, and beyond. The software for both applications needs to run the entire units and all subprocesses; however, throughout testing, many variables and parameters need to be changed as more is learned about the system operation. The software provides the versatility to permit the software operation to change as the user requirements evolve.
The feasibility of using a low gain antenna (LGA) as a mobile terminal antenna for a helicopter is described in this paper. The objectives are to select the lowest cost antenna system which can be easily mounted on a helicopter and capable of communicating with a satellite, and to determine the best antenna position on the helicopter to mitigate the signal blockage due to rotor blades and the multipath effect from the helicopter's body. The omnidirectional LGA is selected because it is simple, reliable, and low cost. The helix antenna is selected among the many LGA's because it is the most economical one and has the widest elevation beamwidth. Both 2-arm and 4-arm helices are studied experimentally to determine the antenna's performance and the scattering effects from the helicopter's body. It is found that the LGA should be located near the tail section and at least eight inches above the helicopter.
Earth's moon is the largest in the solar system relative to its parent body, the Earth, and can have significant effect on the path of a spacecraft flying close by. This effect, when planned to benefit a specific mission, is called lunar gravity assist (LGA) , and assumes that one aims the spacecraft towards the Moon in such a way that the Moon's gravitational pull will alter the spacecraft's course in a favorable manner. The first application of LGA was in the Apollo program, where the command and lunar modules (and astronauts) were propelled to the Moon such that, if no additional course changes were made, they would swing around the backside of the Moon at a certain altitude and be flung back to Earth to enter the atmosphere at a specified location in the Pacific ocean. This LGA was an essential element saving the lives of the astronauts on Apollo 13. This paper will illustrate the basic mechanics of gravity assist, and list the many applications where it has been used effectively over the past 30 some years. These include missions to the sun and Earth libration points, redirecting a spacecraft from one of these point to a comet encounter, and enhancing payloads by providing an energy boost by the Moon. More recently, studies and actual missions have shown the benefits of LGA in: (1) assisting lunar capture, (2) repositioning geosynchronous communications satellites, (3) boosting spacecraft to Earth escape and departure to planets and other solar system bodies, and (4) allowing small spacecraft to be launched as secondary payloads and released into almost a random orbit from which each may depart and maneuver in space with gravity assists from the Earth and Moon to perform a specific planetary or other mission. This latter application is a recent development by the author and is being applied in 2002 and later years, with piggyback flights on the Ariane 5 which launches comsats to GEO.
LaGuardia (LGA) departure delay was identified by the stakeholders and subject matter experts as a significant bottleneck in the New York metropolitan area. Departure delay at LGA is primarily due to dependency between LGA's arrival and departure runways: LGA departures cannot begin takeoff until arrivals have cleared the runway intersection. If one-in one-out operations are not maintained and a significant arrival-to-departure imbalance occurs, the departure backup can persist through the rest of the day. At NASA Ames Research Center, a solution called "Departure-sensitive Arrival Spacing" (DSAS) was developed to maximize the departure throughput without creating significant delays in the arrival traffic. The concept leverages a Terminal Sequencing and Spacing (TSS) operations that create and manage the arrival schedule to the runway threshold and added an interface enhancement to the traffic manager's timeline to provide the ability to manually adjust inter-arrival spacing to build precise gaps for multiple departures between arrivals. A more complete solution would include a TSS algorithm enhancement that could automatically build these multi-departure gaps. With this set of capabilities, inter-arrival spacing could be controlled for optimal departure throughput. The concept was prototyped in a human-in-the- loop (HITL) simulation environment so that operational requirements such as coordination procedures, timing and magnitude of TSS schedule adjustments, and display features for Tower, TRACON and Traffic Management Unit could be determined. A HITL simulation was conducted in August 2014 to evaluate the concept in terms of feasibility, controller workload impact, and potential benefits. Three conditions were tested, namely a Baseline condition without scheduling, TSS condition that schedules the arrivals to the runway threshold, and TSS+DSAS condition that adjusts the arrival schedule to maximize the departure throughput. The results showed that during high arrival demand period, departure throughput could be incrementally increased under TSS and TSS+DSAS conditions without compromising the arrival throughput. The concept, operational procedures, and summary results were originally published in ATM20151 but detailed results were omitted. This paper expands on the earlier paper to provide the detailed results on throughput, conformance, safety, flight time/distance, etc. that provide extra insights into the feasibility and the potential benefits on the concept.
The Space Communications and Navigation (SCAN) Testbed was a Software Defined Radio (SDR)-based payload launched to the International Space Station (ISS) in July of 2012. The purpose of the SCAN Testbed payload was to investigate the applicability of SDRs to NASA space missions in an operational environment, which means that a proper model for system performance in said operational space environment is a necessary condition. The SCAN Testbed has line-of-sight connections to various ground stations with its S-Band Earth-facing Near-Earth-Network Low Gain Antenna (NEN-LGA). Any previous efforts to characterize the NEN-LGA proved difficult, therefore, the NASA Glenn Research Center built its own S-Band ground station, which became operational in 2015, and has been used successfully to characterize the NEN-LGA's in-situ pattern measurements. This methodology allows for a more realistic characterization of the antenna performance, where the pattern oscillation induced by the complex ISS ground plane, as well as shadowing effects due to ISS structural blockage are included into the final performance model. This paper describes the challenges of characterizing an antenna pattern in this environment. It will also discuss the data processing, present the final antenna pattern measurements and derived model, as well as discuss various lessons learned
Package manufacturers are now offering commercial-off-the-shelf column grid array (COTS CGA) packaging technologies in high-reliability versions. Understanding the process and quality assurance (QA) indicators for reliability are important for low-risk insertion of these advanced electronics packages. The previous reports, released in January of 2012 and January of 2013, presented package test data, assembly information, and reliability evaluation by thermal cycling for CGA packages with 1752, 1517, 1509, and 1272 inputs/outputs (I/Os) and 1-mm pitch. It presented the thermal cycling (-55C either 100C or 125C) test results for up to 200 cycles. This report presents up to 500 thermal cycles with quality assurance and failure analysis evaluation represented by optical photomicrographs, 2D real time X-ray images, dye-and-pry photomicrographs, and optical/scanning electron Microscopy (SEM) cross-sectional images. The report also presents assembly challenge using reflowing by either vapor phase or rework station of CGA and land grid array (LGA) versions of three high I/O packages both ceramic and plastic configuration. A new test vehicle was designed having high density interconnect (HDI) printed circuit board (PCB) with microvia-in-pad to accommodate both LGA packages as well as a large number of fine pitch ball grid arrays (BGAs). The LGAs either were assembled onto HDI PCB as an LGA or were solder paste print and reflow first to form solder dome on pads before assembly. Both plastic BGAs with 1156 I/O and ceramic LGAs were assembled. It also presented the X-ray inspection results as well as failures due to 200 thermal cycles. Lessons learned on assembly of ceramic LGAs are also presented.
The feasibility of using a low gain antenna (LGA) as a mobile terminal antenna for helicopters is described in this paper. The objectives are 1) to select the lowest cost antenna system which can be easily mounted on a helicopter and capable of communicating with a satellite, and 2) to determine the best antenna position on the helicopter to mitigate the signal blockage due to rotor blades and the multipath effect from the helicopter's body. The omni-directional low gain antenna (LGA) is selected because it is simple, reliable, and low cost. The helix antenna is selected among the many LGAs because it is the most economical one and has the widest elevation beamwidth. Both 2-arm and 4-arm helices are studied experimentally to determine the antenna's performance and the scattering effects from the helicopter's body. It is found that the LGA should be located near the tail section and at least 8 inches above the helicopter.
The catalytic conversion of cellulose to levoglucosenone (LGO) was studied using dilute sulfuric acid and propylsulfonic acid functionalized SBA-15 (PS-SBA-15) in tetrahydrofuran (THF). Here we show that the addition of small amounts of a liquid acid catalyst such as sulfuric acid complements the use of a solid acid catalyst for the conversion of cellulose. Sulfuric acid promotes the depolymerization of cellulose into levoglucosan (LGA). The main role of the solid Brønsted acid catalyst is to dehydrate the LGA into LGO. The addition of low concentrations of H 2 SO 4 to PS-SBA-15 resulted in an increase in LGO yield of up to 37% (from 18% obtained using an equivalent amount of H 2 SO 4 only). Our approach provides a novel alternative for a more environmentally friendly production of LGO.
Symbiotic mutualisms of bacteria and animals are ubiquitous in nature, running a continuum from facultative to obligate from the perspectives of both partners. The loss of functions required for living independently but not within a host gives rise to reduced genomes in many symbionts. Although the phenomenon of genome reduction can be explained by existing evolutionary models, the initiation of the process is not well understood. Here, we describe the microbiome associated with the eggs of the beetle Lagria villosa, consisting of multiple bacterial symbionts related to Burkholderia gladioli, including a reduced-genome symbiont thought to be the exclusive producer of the defensive compound lagriamide. We show that the putative lagriamide-producing symbiont is the only member of the microbiome undergoing genome reduction and that it has already lost the majority of its primary metabolism and DNA repair pathways. The key step preceding genome reduction in the symbiont was likely the horizontal acquisition of the putative lagriamide lga biosynthetic gene cluster. Unexpectedly, we uncovered evidence of additional horizontal transfers to the symbiont’s genome while genome reduction was occurring and despite a current lack of genes needed for homologous recombination. These gene gains may have given the genome-reduced symbiont a selective advantage in the microbiome, especially given the maintenance of the large lga gene cluster despite ongoing genome reduction.
The ability to communicate with spacecraft during emergencies is a vital service that NASA's Deep Space Network (DSN) provides to all deep space missions. Emergency communications is characterized by low data rates(typically is approximately10 bps) with the spacecraft using either a low-gain antenna (LGA, including omnidirectional antennas) or,in some cases, a medium-gain antenna (MGA). Because of the use of LGAs/MGAs for emergency communications, the transmitted power requirements both on the spacecraft andon the ground are substantially greater than those required for normal operations on the high-gain antenna (HGA) despite the lower data rates. In this paper, we look at currentand future emergency communications capabilities available to NASA's deep-space missions and discuss their limitations in the context of emergency mode operations requirements.These discussions include the use of the DSN 70-m diameter antennas, the use of the 34-m diameter antennas either alone or arrayed both for the uplink (Earth-to-spacecraft) and the downlink (spacecraft-to-Earth), upgrades to the ground transmitters, and spacecraft power requirements both with unitygain (0 dB) LGAs and with antennas with directivity (>0 dB gain, either LGA or MGA, depending on the gain). Also discussed are the requirements for forward-error-correctingcodes for both the uplink and the downlink. In additional, we introduce a methodology for proper selection of a directionalLGA/MGA for emergency communications.
The Space Communications and Navigation (SCAN) Testbed was a Software Defined Radio (SDR)-based payload launched to the International Space Station (ISS) in July of 2012. The purpose of the SCAN Testbed payload was to investigate the applicability of SDRs to NASA space missions in an operational space environment, which means that a proper model for system performance in said operational space environment is a necessary condition. The SCAN Testbed has line-of-sight connections to various ground stations with its S-Band Earth-facing Near-Earth Network Low Gain Antenna (NEN-LGA). Any previous efforts to characterize the NEN-LGA proved difficult, therefore, the NASA Glenn Research Center built its own S-Band ground station, which became operational in 2015, and has been successfully used to characterize the NEN-LGAs in-situ pattern measurements. This methodology allows for a more realistic characterization of the antenna performance, where the pattern oscillation induced by the complex ISS ground plane, as well as shadowing effects due to ISS structural blockage are included into the final performance model. This paper describes the challenges of characterizing an antenna pattern in this environment. It will also discuss the data processing, present the final antenna pattern measurements and derived model, as well as discuss various lessons learned.
LaGuardia Airport (LGA) in New York has many unique challenges that create excess taxi-out delays. The purpose of this paper is to investigate the potential benefit that could be gained by tactically adjusting the Terminal Sequencing and Spacing (TSS) schedule to precisely manage inter-arrival spacing to maximize the number of departures per arrival pair. Three strategies for dynamically adjusting arrival schedules are proposed in this paper: Delay Control, Delay and Advance, and No Slack Capacity. The benefits of these strategies were examined on actual traffic data at LGA. The results showed that by applying these strategies, a 10 to 60 increase in departures and a reduction in un-utilized departure capacity (gaps) could be achieved during the airports busiest six-hour period. Significant increases in departure throughput would improve air traffic operations by reducing departure delay time. Furthermore, the concept could be used to resolve temporal mismatches between departure capacity and demand which also cause excessive departure delays.
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.