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459 records · Page 26

Development and Testing of Pulse Guns for Combustion Instability Testing

To test liquid rocket engines (LREs) for combustion instabilities, devices such as bombs are often used to create pressure wave disturbances. Bombs, while effective, are inherently dangerous, expensive, and difficult to procure. Over the years, pulse guns have been used as a safer and more cost-effective way to generate controlled pressure disturbances in engine chambers. In anticipation of the need for stability testing of prototype LREs at NASA Marshall Space Flight Center (MSFC), a set of pulse guns have been designed, fabricated, tested, and characterized. The pulse gun program is funded by the RS-25 Engine Program managed out of MSFC and funded by NASA’s Space Launch System (SLS) through the MSFC Liquid Engines Office (LEO). A pulse gun is a simple device – like a traditional gun, it has a breech and barrel section. However, unlike a traditional gun, there is no bullet, as the purpose of the pulse gun is strictly to fire a high pressure pulse. Instead of a firing pin and primer that would normally be used in a traditional gun, an initiator is used to activate the gun powder. The initiator is a highly reliable pyrotechnic initiator. For this study, clone versions of the NASA Standard Initiator (NSI) were used. The initiator is used to ignite a pre-measured amount of gun powder loaded into the breech. When the pressure of the burnt gun powder exceeds the set point of a downstream burst disk, the disk ruptures, allowing the high pressure pulse to travel downstream through the barrel section. A ballistic pressure transducer located in the breech section is used to measure the short duration, high pressure pulse. Some configurations of the pulse gun have barrel sections that include one or more additional ballistic pressure transducers. These additional pressure measurements help track the degradation and damping of the pulse as it travels out of the barrel section. The measurements may also be used to determine the velocity of pulse. The objective of this paper is to present the different variants of this newly-developed pulse gun and characterize performance over a range of parameters. The parameters include breech diameter, barrel length, amount of gun powder used, the way the gun powder is packed, and the pressure setting of the burst disk. There are a total of six pulse guns: three with the 0.25 inch breech and three different length barrels, and three with the 0.40 inch breech and three different length barrels. For both breech sizes, the associated barrels are referred to as standard, one-port, and two-port barrels. The standard barrel has no instrumentation and is likely the barrel that will be used for engine stability testing. The one- and two-port barrels were designed specifically for pulse gun component testing to allow measurements of the magnitude and timing of the high pressure pulse as it makes its way through the pulse gun. The burst disks tested were commercially-available burst disks designed to rupture at 8,000, 16,000, and 24,000 psid. Testing was accomplished by firing the pulse gun into a test chamber pressurized with nitrogen at about 2300 psig. A total of four Model 113B23 High frequency ICP® pressure sensors (10k psi) were mounted in the test chamber, in the same plane as the pulse gun. Two of the sensors had “trimmed” adapters, and two did not. Given the symmetrical configuration of the sensors within the test chamber, different amplitudes of pressure measurements are attributed to the use of trimmed versus untrimmed adapters. The untrimmed adapters, with their narrower passages, tended to amplify the pressure amplitudes by as much as 50%. In total, 41 pulse gun tests have been conducted.Data are still being analyzed, but some trends are apparent. For example, measurements taken within the pulse gun are shown in Figures 3 and 4 for Hot-fires (HFs) # 11 and 39, respectively. Both tests were identical in that the 0.40 inch ID breech, two-port barrel, 8,000 psid burst disk, and same amount of gunpowder (6.639 grains for HF# 11, and 6.576 grains for HF# 39) were used, and the back pressure in the nitrogen test chamber was ~2300 psig. Despite keeping all these variables constant, the results from these two tests look quite different. For both tests, the first pressure peak shown in the P2111 trace within the breech is the firing of the initiator. The second peak and any subsequent peaks are from the combustion of the gunpowder. It should be noted that because the sensors are dynamic, the ~2300 psig baseline static pressure is not shown in the figures. For HF# 11, the burst disk clearly ruptured in a little over 0.1 ms from the time the initiator was fired. This is evident from the abrupt rise in pressure for P2112 followed by another abrupt rise in pressure for P2113. The sensors track the movement of the high pressure pulse through the pulse gun. For HF# 39, however, there was nearly 1 ms delay between the initiator firing and the burst disk rupturing. The only difference between these two tests was the way in which the gunpowder was packed within the breech. For HF# 11, the gunpowder was poured directly into the breech, atop the initiator, and held in place with a vegetable fiber wad appropriately sized for the barrel diameter. Once in place, the wad firmly held the gunpowder in place. For HF# 39, the gunpowder was rolled within cigarette paper with the ends of the paper twisted. The rolled gunpowder was gently pushed down into the breech, toward the initiator, and no wad was used. This method of loading the gunpowder consistently produced longer delays in the rupture of the burst disk, presumably because there was a steady, fuller burning of the gunpowder. The burst disk was rated for 8,000 psid, which means that the burst disk should not have opened until the pressure in the breech reached ~10,300 since there was ~2300 psig back pressure in the test chamber. In the case of HF# 11, the breech pressure only reached roughly 4200 psi before there was leakage either through or past the burst disk, but for HF#39, the breech pressure reached 10,600 psi prior to the burst disk opening. The temperature of the burst disk is certainly a factor in determining when the burst disk will rupture. A sharp rise in the burst disk temperature could weaken it and cause it to stray from the designed set point burst pressure. The burst disks are manufactured of Inconel to try to reduce their sensitivity to temperature, but how the burst disks perform above 900°F is not documented. All burst disks were engineered and tested by the manufacturer for ambient temperature conditions. One theory for why rolling the gunpowder in cigarette paper may produce better results is related to a more controlled temperature environment. The cigarette paper may act as a sheath that protects the burst disk from excessive temperatures during the rapid buildup of pressure in the breech. The ultimate benefit of not breeching the burst disk prematurely is shown in Figures 5 and 6, which graphs the resultant pressure pulse produced in the test chamber. For HF #11, the peak pressure on the P2123 trimmed adapter is about 270 psi, while for HF# 39 it is 343 psi, which is about 1.27 times greater in magnitude. In either case, the pressure pulse is ~20 μs in width once it reaches the test chamber. Another pressure rise about 100 μs later can be seen on the sensors located across from the pulse gun port, P2124 and P2121. These pressures are lower due to attenuation of the pressure wave as it moves across the test chamber. After the wave encounters the wall, it reflects back and forth within the chamber until it completely dissipates.

Pulse Gun↗

Software Tools to Support the Assessment of System Health

This presentation provides an overview of three software tools that were developed by the NASA Glenn Research Center to support the assessment of system health: the Propulsion Diagnostic Method Evaluation Strategy (ProDIMES), the Systematic Sensor Selection Strategy (S4), and the Extended Testability Analysis (ETA) tool. Originally developed to support specific NASA projects in aeronautics and space, these software tools are currently available to U.S. citizens through the NASA Glenn Software Catalog. The ProDiMES software tool was developed to support a uniform comparison of propulsion gas path diagnostic methods. Methods published in the open literature are typically applied to dissimilar platforms with different levels of complexity. They often address different diagnostic problems and use inconsistent metrics for evaluating performance. As a result, it is difficult to perform a one ]to ]one comparison of the various diagnostic methods. ProDIMES solves this problem by serving as a theme problem to aid in propulsion gas path diagnostic technology development and evaluation. The overall goal is to provide a tool that will serve as an industry standard, and will truly facilitate the development and evaluation of significant Engine Health Management (EHM) capabilities. ProDiMES has been developed under a collaborative project of The Technical Cooperation Program (TTCP) based on feedback provided by individuals within the aircraft engine health management community. The S4 software tool provides a framework that supports the optimal selection of sensors for health management assessments. S4 is structured to accommodate user ]defined applications, diagnostic systems, search techniques, and system requirements/constraints. One or more sensor suites that maximize this performance while meeting other user ]defined system requirements that are presumed to exist. S4 provides a systematic approach for evaluating combinations of sensors to determine the set or sets of sensors that optimally meet the performance goals and the constraints. It identifies optimal sensor suite solutions by utilizing a merit (i.e., cost) function with one of several available optimization approaches. As part of its analysis, S4 can expose fault conditions that are difficult to diagnose due to an incomplete diagnostic philosophy and/or a lack of sensors. S4 was originally developed and applied to liquid rocket engines. It was subsequently used to study the optimized selection of sensors for a simulation ]based aircraft engine diagnostic system. The ETA Tool is a software ]based analysis tool that augments the testability analysis and reporting capabilities of a commercial ]off ]the ]shelf (COTS) package. An initial diagnostic assessment is performed by the COTS software using a user ]developed, qualitative, directed ]graph model of the system being analyzed. The ETA Tool accesses system design information captured within the model and the associated testability analysis output to create a series of six reports for various system engineering needs. These reports are highlighted in the presentation. The ETA Tool was developed by NASA to support the verification of fault management requirements early in the Launch Vehicle process. Due to their early development during the design process, the TEAMS ]based diagnostic model and the ETA Tool were able to positively influence the system design by highlighting gaps in failure detection, fault isolation, and failure recovery.

Melcher, Kevin J.↗

Incorporation of Human Risk Directed Acyclic Graphs (DAG) With Mishap Investigations to Un-Silo Knowledge

NASA’s Human System Risk Board (HSRB) has been a central driver in efforts to understand, mitigate, and communicate the 29 human systems risks monitored by the board. As a result of the collaboration between research, operations, and technical authorities, large bodies of knowledge have been collected and digested to represent the current understanding of the risks. As a part of these bodies of knowledge, directed acyclic graphs (DAGs) have been developed to communicate the current understanding of the causal relationship of the hazards, contributing factors, countermeasures, other risks, and outcomes that contribute to the overall risk. This risk knowledge is applied in a theoretical sense for potential incidents during exploration even while informed by surveillance data. However, there have been mishaps and close calls during past space exploration that intersect with one or more of the Human System Risks DAGs and knowledge bases. The purpose of this exercise was to un-silo this risk knowledge and connect it to the close call of EVA 23 through the development of a DAG representing the intersection of the HSRB Risks and the events of the close call. The development of the DAG occurred through an iterative process, with each iteration expanding and/or refining the nodes and connections described by the source materials. In addition to the risk documentation developed by the HSRB, lessons learned and other mishap investigation documents were utilized to understand the events that led to water entering the helmet of a crewmember on the EVA. New nodes specific to the events of EVA 23 were interconnected with existing HSRB DAG nodes and edges. Nodes within the DAG were defined within a “DAG-tionary” with any updates to a definition that may have previously existed from the HSRB DAGs, and edges were recorded in a matrix. Both the DAG-tionary and matrix describe where nodes and edges are present across the Risk and Mishap DAG. This DAG will then be reviewed by experts outside of HSRB and HRP to confirm that interpretations of the non-health related events (such as the engineering nodes) are represented accurately. DISCUSSION This process highlighted a method by which the knowledge generated among the contributing members of the HSRB Risks can be effectively adapted and utilized through the tools employed by the Risk Custodian teams. By leveraging these tools, new context and insights to the information at hand can be brought forward to address current spaceflight challenges. Moreover, un-siloing this knowledge through future DAGs and other efforts can drive interprofessional collaboration and foster communication. This will enable teams to work together more effectively, leveraging their diverse expertise to tackle the complex challenges of space exploration and human research. Ultimately, this collaboration will bring NASA closer to achieve agency goals and contribute to the overall shared mission and vision.

Samuel Jacobs↗

NASA Tech Briefs, May 2010

Topics covered include: Instrument for Analysis of Greenland's Glacier Mills Cryogenic Moisture Apparatus; A Transportable Gravity Gradiometer Based on Atom Interferometry; Three Methods of Detection of Hydrazines; Crossed, Small-Deflection Energy Analyzer for Wind/Temperature Spectrometer; Wavefront Correction for Large, Flexible Antenna Reflector; Novel Micro Strip-to-Waveguide Feed Employing a Double-Y Junction; Thin-Film Ferro Electric-Coupled Microstripline Phase Shifters With Reduced Device Hysteresis; Two-Stage, 90-GHz, Low-Noise Amplifier; A 311-GHz Fundamental Oscillator Using InP HBT Technology; FPGA Coprocessor Design for an Onboard Multi-Angle Spectro-Polarimetric Imager; Serrating Nozzle Surfaces for Complete Transfer of Droplets; Turbomolecular Pumps for Holding Gases in Open Containers; Triaxial Swirl Injector Element for Liquid-Fueled Engines; Integrated Budget Office Toolbox; PLOT3D Export Tool for Tecplot; Math Description Engine Software Development Kit; Astronaut Office Scheduling System Software; ISS Solar Array Management; Probabilistic Structural Analysis Program; SPOT Program; Integrated Hybrid System Architecture for Risk Analysis; System for Packaging Planetary Samples for Return to Earth; Offset Compound Gear Drive; Low-Dead-Volume Inlet for Vacuum Chamber; Simple Check Valves for Microfluidic Devices; A Capillary-Based Static Phase Separator for Highly Variable Wetting Conditions; Gimballing Spacecraft Thruster; Finned Carbon-Carbon Heat Pipe with Potassium Working Fluid; Lightweight Heat Pipes Made from Magnesium; Ceramic Rail-Race Ball Bearings; Improved OTEC System for a Submarine Robot; Reflector Surface Error Compensation in Dual-Reflector Antennas; Enriched Storable Oxidizers for Rocket Engines; Planar Submillimeter-Wave Mixer Technology with Integrated Antenna; Widely Tunable Mode-Hop-Free External-Cavity Quantum Cascade Laser; Non-Geiger-Mode Single-Photon Avalanche Detector with Low Excess Noise; Using Whispering-Gallery-Mode Resonators for Refractometry; RF Device for Acquiring Images of the Human Body; Reactive Collision Avoidance Algorithm; Fast Solution in Sparse LDA for Binary Classification; Modeling Common-Sense Decisions in Artificial Intelligence; Graph-Based Path-Planning for Titan Balloons; Nanolaminate Membranes as Cylindrical Telescope Reflectors; Air-Sea Spray Airborne Radar Profiler Characterizes Energy Fluxes in Hurricanes; Large Telescope Segmented Primary Mirror Alignment; and Simplified Night Sky Display System.

Source record↗

Active Control of Rotating Stall Demonstrated for a Multistage Compressor With Inlet Distortion

Aircraft compressors can suffer debilitating consequences as a result of rotating stall and surge events caused by inlet distortions. This is particularly true of aircraft during takeoff, when the compressor is operating at peak performance close to the surge line. Significant research has been conducted by the NASA Glenn Research Center in the area of compressor stability enhancement through active and passive control methods. Most recently, an experiment was conducted at the Wright Patterson Air Force Base Research Laboratory on a two-stage fan with inlet guide vanes and inlet distortion. In this joint Small Business Innovation Research effort between Scientific Systems and Glenn, control of rotating stall was demonstrated in a multistage transonic fan. This twostage fan with inlet guide vanes was tested under clean and distorted inlet conditions. The compressor was also configured with a circumferential distortion screen capable of 180 of distortion and with 14 high-velocity injectors upstream of the first rotor. Twelve of these injectors could oscillate up to frequencies of 450 Hz. The additional two injectors were located next to each other and were used in concert with each other as a single, on/off, high-authority actuator. In a first test of injection in this multistage environment, 12 of the valves were opened 50 percent of their full stroke to assess steady injection through the compressor. This baseline injection is shown in the compressor characteristic of the following figure, and stall margin improvements are tracked from this baseline condition. The compressor was then tested with clean inlet conditions using 12 injectors and active control. Pressure disturbances were tracked before rotating stall, and a constant gain control scheme reduced the stalling mass flow by 10.8 percent over the baseline. With the distortion screen present in the inlet, a pole-zero cancellation control scheme was used to achieve a 6.4-percent decrease in stalling mass flow. These improvements also are shown in the figure. In a final experiment, actively controlled, high-frequency injection from the 12 valves was used in conjunction with the high-authority actuators. In this test, the stalling mass flow of the compressor was reduced by 27 percent as indicated in the graph. These results were obtained by injecting less than 2 percent of the total compressor throughflow into the rotor tip region via 14 injection ports. These results mark the first successful demonstration of actively controlled air injection as a stall-control strategy for multistage compressors operating at speeds typical of an actual gas turbine engine. A goal of continuing research is to determine the combination of air-injection parameters and control strategies that are most effective in providing stall control for both clean and distorted inlet flow conditions for multistage environments. Other goals include the demonstration of stall control at many locations along the core compressor and development and application of active stall control strategies that will be integral flightworthy components of onboard engine hardware.

VanSchalkwyk, Christian↗

Linearized Aeroelastic Solver Applied to the Flutter Prediction of Real Configurations

A fast-running unsteady aerodynamics code, LINFLUX, was previously developed for predicting turbomachinery flutter. This linearized code, based on a frequency domain method, models the effects of steady blade loading through a nonlinear steady flow field. The LINFLUX code, which is 6 to 7 times faster than the corresponding nonlinear time domain code, is suitable for use in the initial design phase. Earlier, this code was verified through application to a research fan, and it was shown that the predictions of work per cycle and flutter compared well with those from a nonlinear time-marching aeroelastic code, TURBO-AE. Now, the LINFLUX code has been applied to real configurations: fans developed under the Energy Efficient Engine (E-cubed) Program and the Quiet Aircraft Technology (QAT) project. The LINFLUX code starts with a steady nonlinear aerodynamic flow field and solves the unsteady linearized Euler equations to calculate the unsteady aerodynamic forces on the turbomachinery blades. First, a steady aerodynamic solution is computed for given operating conditions using the nonlinear unsteady aerodynamic code TURBO-AE. A blade vibration analysis is done to determine the frequencies and mode shapes of the vibrating blades, and an interface code is used to convert the steady aerodynamic solution to a form required by LINFLUX. A preprocessor is used to interpolate the mode shapes from the structural dynamics mesh onto the computational fluid dynamics mesh. Then, LINFLUX is used to calculate the unsteady aerodynamic pressure distribution for a given vibration mode, frequency, and interblade phase angle. Finally, a post-processor uses the unsteady pressures to calculate the generalized aerodynamic forces, eigenvalues, an esponse amplitudes. The eigenvalues determine the flutter frequency and damping. Results of flutter calculations from the LINFLUX code are presented for (1) the E-cubed fan developed under the E-cubed program and (2) the Quiet High Speed Fan (QHSF) developed under the Quiet Aircraft Technology project. The results are compared with those obtained from the TURBO-AE code. A graph of the work done per vibration cycle for the first vibration mode of the E-cubed fan is shown. It can be seen that the LINFLUX results show a very good comparison with TURBO-AE results over the entire range of interblade phase angle. The work done per vibration cycle for the first vibration mode of the QHSF fan is shown. Once again, the LINFLUX results compare very well with the results from the TURBOAE code.

Reddy, Tondapu S.↗

Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.

Decker, Arthur J.↗

Environmental Stress Testing of the Single Sample Cylinder: A Proven Consensus Standard for Internal Gas Analysis (IGA) or Residual Gas Analysis (RGA)

In August 2008, Schuessler Consulting was contracted by NASA GSFC in support of the NASA Electronic Parts and Packaging (NEPP) program to perform two separate studies on moisture laden air in a stainless steel cylinder that had been designed to become a consensus standard for Test Method 1018. This Test Method was originally released for hybrids under Mil. Std. 883 but was quickly utilized on other microelectronic devices under the auspice of Mil. Std. 750. The cylinder had subsequently been fabricated for the 750 community. It was back-filled with moist air and subsequently analyzed over a period of time under a previous NASA contract. It had been shown that moisture in the 4000 - 5000 ppm range could be analyzed rather precisely with a mass spectrometer, commonly referred to as a Residual Gas Analyzer (RGA). The scope of this study was to ascertain if the composition and precision varied as a function of thermal shock at sub-zero temperatures and whether there was consensus when the standard was submitted to other RGA units. It was demonstrated and published that the consensus standard would yield precise RGA data for moisture within +/- 1% when optimized for a given RGA unit. It has been subsequently shown in this study at Oneida Research Services, that sub-zero storage did not affect that precision when a well-defined protocol for the analysis was followed. The consensus standard was taken to a second facility for analysis where it was found that moisture adsorption on the transfer lines caused precision to drop to +/- 12%. The Single Sample Cylinder (SSC) is a one liter stainless steel cylinder with associated sampling valves and has considerable weight and volume. But this considerable size allows for approximately 300 gas samples of the same composition to be delivered to any RGA unit. Lastly, a smaller cylinder, approximately 75 cc, of a second consensus standard was fabricated and tested with a different mix of fixed gases where moisture was kept in the 100 ppm range. This second standard has the potential of providing 30 gaseous samples and can be readily shipped to any analytical facility that desires to generate comparison RGA data. A series of comparison residual gas analyses was performed at the Honeywell Federal Manufacturing & Technologies facility in the National Nuclear Facility Administration s plant in Kansas City to complete this project. It was shown that improvements in the precision of a given RGA unit can be done by controlling the cycle time for each analysis and increasing analysis temperatures to minimize moisture adsorption. It was also found that a "one time event" in the subzero storage of the large SSC did not effect the units ability to continuously supply precise samples of the same chemistry, however the "event" caused a permanent +8% shift in the reported value of the moisture content. Lastly, a set of SSC RGA results was plotted on a common graph with DSCC "correlation study" RGA data. The result demonstrates the ability of the SSC to remove many of the individual variances that single, individual samples introduce. The consensus standards are now in storage at Oneida Research Services, one of the DSCC certified houses that does RGA to Military Standards, where they await future studies. The analytical data and the operational parameters of the instruments used are provided in the following discussion. Limitations and suggested means for improvement of both precision and accuracy are provided.

Schuessler, Philipp WH↗

A Machine Learning Approach to Improve Air Traffic Management Initiatives

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.

Farzan Masrour Shalmani↗