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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Load driven by two motors continuously draws power from a varying source (solar cells) and steady auxiliary source (utility company). Power-factor controller apportions electrical load between two sources to maintain motor speed. This novel application of power-factor controller would regulate input of auxiliary energy to a solar-powered system in response to availability of Sunlight.
Sun-tracking solar energy-focusing devices involving a central receiver, thermionic conversion, or a distributed solar thermal collector system are described. The Perkins solar collector uses a fixed focal point about which an 18 m-diameter parabolic dish moves on tracks. The elevation axis also moves on a circular track. A microprocessor manipulates sun sensor information and sun ephemeris data to ensure correct placement. Stepper motors are digital devices which provide direct interface with digital electronics and a wide dynamic range, and could easily be associated with the microprocessors. Design philosophy, performance criteria, wind load analysis, and control system requirements are also discussed.
The spin and recovery characteristics of the Curtiss-Wright XP-87 airplane, as well as the spin-recovery parachute requirements, the control forces that would be encountered in the spin, and the best method for the crew to attempt an emergency escape, are presented in this report. The characteristics were estimated rather than determined by model tests because the XP-87 dimensional and mass characteristics were considered to be noncritical and because data were available from model tests of several similar airplanes. The study indicated that the recovery characteristics of the airplane will be satisfactory for all loadings if the controls are reversed fully and rapidly. The control forces, however, will probably be beyond the capabilities of the pilot unless some additional balance or a booster is used. A 6-foot tail parachute or a 3.5-foot wing-tip parachute with a drag coefficient of 0.7 will be a satisfactory, emergency spin-recovery device for spin demonstrations. If it is necessary for the crew to abandon the spinning airplane, they should leave from the outboard side of the cockpit.
Computer networks use congestion feedback from the routers and destinations to control the transmission load. Delivering timely congestion feedback is essential to the performance of networks. Reaction to the congestion can be more effective if faster feedback is provided. Current TCP/IP networks use timeout, duplicate Acknowledgement Packets (ACKs) and explicit congestion notification (ECN) to deliver the congestion feedback, each provides a faster feedback than the previous method. In this paper, we propose a markfront strategy that delivers an even faster congestion feedback. With analytical and simulation results, we show that mark-front strategy reduces buffer size requirement, improves link efficiency and provides better fairness among users. Keywords: Explicit Congestion Notification, mark-front, congestion control, buffer size requirement, fairness.
The control of structural vibration is helicopters is discussed. Active control concepts, wind tunnel tests, rotor vibratory loads, systems integration, and engine control are among the topics discussed.
A photovoltaic power system in an Arizona Indian village was installed. The control subsystem of this photovoltaic power system was analyzed. The four major functions of the control subsystem are: (1) voltage regulation; (2) load management; (3) water pump control; and (4) system protection. The control subsystem functions flowcharts for the control subsystem operation, and a computer program that models the control subsystem are presented.
Integrated-circuit microcomputers process signals among sensors, external controls, and end-effector motor. Multiple-microprocessor system within end effector of remote manipulator accepts data from sensors in end effector, commumnicates with external control system, and controls end-effector motor. External system includes central control computer (which interacts with operator) and display. Local dataprocessing system in end effector relieves external control system of some of its computational load and minimizes data communication load with external higher level controller.
A variable conductance heat pipe (VCHP) with feedback control was fabricated with a reservoir-condenser volume ratio of 10 and an axially grooved action section. Tests of the heat transport capability were greater than or equal to the analytical predictions for the no gas case. When gas was added, the pipe performance degraded by 18% at zero tilt as was expected. The placement of the reservoir heater and the test fixture cooling fins are believed to have caused a superheated vapor condition in the reservoir. Erroneously high reservoir temperature indications resulted from this condition. The observed temperature gradients in the reservoir lend support to this theory. The net result was higher than predicted reservoir temperatures. Also, significant increases in minimum heat load resulted for controller set point temperatures higher than 0 C. At 30 C, control within the tolerance band was maintained, but high reservoir heater power was required. Analyses showed that control is not possible for reasonably low reservoir heater power. This is supported by the observation of a significant reservoir heat leak through the condenser.
Day of launch I-load uplink (DOLILU) and a corresponding day of launch trajectory and loads evaluation are being considered for the Constellation Program. Abort effectiveness, controllability, and ascent structural loads all improve with some of the flight software I-loads designed using current measured atmospheric conditions. While a generalized DOLILU framework has been assumed, specific implementation details have not yet been discussed in depth nor documented. Operational improvements can be leveraged from Space Shuttle experience to create a more cost-effective and streamlined process while maintaining the integrity of the results. A preliminary discussion of DOLILU design methodology is presented followed by some suggestions and implementation details for Constellation Program DOLILU operations
Motivation - Higher loading on Low-Pressure Turbine (LPT) airfoils: Reduce airfoil count, weight, cost. Increase efficiency, and Limited by suction side separation. Growing understanding of transition, separation, wake effects: Improved models. Take advantage of wakes. Higher lift airfoils in use. Further loading increases may require flow control: Passive: trips, dimples, etc. Active: plasma actuators, vortex generator jets (VGJs). Can increased loading offset higher losses on high lift airfoils. Objectives: Advance knowledge of boundary layer separation and transition under LPT conditions. Demonstrate, improve understanding of separation control with pulsed VGJs. Produce detailed experimental data base. Test and develop computational models.
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.
Damping device checks the axial motion of a rotating shaft by exerting a controllable, radial frictional load to the outer race of the ball bearing in which the shaft is mounted. The device can be used as a soft bearing mount to damp resonant frequencies at critical shaft speed.
Recommended procedures for stability, control, and structural loading analysis of large liquid fueled rocket booster sloshing parameters
Wind tunnel test data obtained from a 1/4.622 Froude scale Boeing Model 222 with a full span, two prop, tilt rotor, powered model in the Boeing V/STOL wind tunnel are reported. Data were taken in transition and cruise flight conditions and include performance, stability and control and blade loads information. The effects of the rotors, tail surfaces and airframe on the performance and stability are isolated as are the effects of the airframe on the rotors.
The rotor system designed for the Boeing Model 222 tilt rotor aircraft is a soft-in-plane hingeless rotor design, 26 feet in diameter. This rotor has completed two test programs in the NASA Ames 40' X 80' wind tunnel. The first test was a windmilling rotor test on two dynamic wing test stands. The rotor was tested up to an advance ratio equivalence of 400 knots. The second test used the NASA powered propeller test rig and data were obtained in hover, transition and low speed cruise flight. Test data were obtained in the areas of wing-rotor dynamics, rotor loads, stability and control, feedback controls, and performance to meet the test objectives. These data are presented.
Recently developed experimental techniques permit evaluating thermal performance and structural integrity of full-scale panel concepts applicable to reentry and hypersonic vehicles in the Langley 8-foot high-performance structures tunnel. Rate and magnitude of surface heating and differential-pressure loading are independently controlled. Realistic temperature distributions are radiantly preheated into the panel prior to aerodynamic heating, and stream conditions are preselected to sustain the preheat surface heating input during aerodynamic exposure. During tunnel start and shutdown, panels are shielded outside the stream from potentially damaging transient acoustics and buffeting and are then rapidly inserted into the hypersonic flow. Infrared radiometry provides detailed surveys of surface temperatures.
Performance capabilities of electrohydrodynamic (EHD) flat heat pipes were investigated using Freon 113 and Freon 11 as working fluids. All of the pipes employed straight rod electrodes to form axial liquid flow channels and tranverse grooves for capillary surface wetting. Results show: (1) the EHD pipe will prime under load, (2) voltage controlled conductance can be achieved by varying the active area of the evaporator, and (3) the average evaporator conductances measured in these experiments were consistent with those obtained in other experiments with heat pipes of similar surface geometry using the same or similar working fluids.
The potential applications of active controls are examined for improving the characteristics of transport type aircraft used in short-haul service ( 1,000-kilometer range capability). The types of aircraft to meet future needs (quiet operation, congestion alleviation, fuel conservation, operating economy, and traveler acceptance) are identified as helicopters for shorter stage lengths and fixed wing aircraft of reduced field-length capability for longer stage lengths. Likely uses for active controls for these aircraft are examined regarding payoffs which can be expected and problems and constraints which must be dealt with. Uses showing significant benefits include augmented stability and control, gust-load alleviation, and ride smoothing. Gust-load alleviation is particularly effective for low-wing-loading aircraft employing conventional lift. Ride-smoothing systems are indicated to be the furthest advanced and ready for production commitment for those applications where they can be shown to have payoff.