Results for “data predictive control” — Page 55

NASA NTRS2006

Phase II Testing of Liquid Cooling Garments Using a Sweating Manikin, Controlled by a Human Physiological Model

An Advanced Automotive Manikin (ADAM) developed at the National Renewable Energy Laboratory (NREL) is used to evaluate NASA's liquid cooling garments (LCGs) used in advanced space suits for extravehicular applications. The manikin has 120 separate heated/sweating zones and is controlled by a finite element physiological model of the human thermoregulatory system. Previous testing showed the thermal sensation and comfort followed the expected trends as the LCG inlet fluid temperature was changed. The Phase II test data demonstrates the repeatability of ADAM by retesting the baseline LCG. Skin and core temperature predictions using ADAM in an LCG/Arctic suit combination are compared to NASA physiological data to validate the manikin/model. Additional LCG configurations are assessed using the manikin and compared to the baseline LCG. Results can extend to other personal protective clothing, including HAZMAT suits, nuclear/biological/chemical protective suits, and fire protection suits.

Paul, Heather↗

NASA NTRS2015

Adaptive State Predictor Based Human Operator Modeling on Longitudinal and Lateral Control

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to categorize these interactions of the pilot with an adaptive controller compensating during control surface failures. A general linear in-parameter model structure is used to represent a pilot. Three different estimation methods are explored. A gradient descent estimator (GDE), a least squares estimator with exponential forgetting (LSEEF), and a least squares estimator with bounded gain forgetting (LSEBGF) used the experiment data to predict pilot stick input. Previous results have found that the GDE and LSEEF methods are fairly accurate in predicting longitudinal stick input from commanded pitch. This paper discusses the accuracy of each of the three methods - GDE, LSEEF, and LSEBGF - to predict both pilot longitudinal and lateral stick input from the flight director's commanded pitch and bank attitudes.

Trujillo, Anna C.↗

NASA NTRS2016

Modeling the Exo-Brake and the Development of Strategies for De-Orbit Drag Modulation

The Exo-Brake is a simple, non-propulsive means of de-orbiting small payloads from orbital platforms such as the International Space Station (ISS). Two de-orbiting experiments with fixed surface area Exo-Brakes have been successfully conducted in the last two years on the TechEdSat-3 and -4 nano-satellite missions. The development of the free molecular flow aerodynamic data-base is presented in terms of angle of attack, projected front surface area variation, and altitude. Altitudes are considered ranging from the 400km ISS jettison altitude to 90km. Trajectory tools are then used to predict de-orbit/entry corridors with the inclusion of the key atmospheric and geomagnetic uncertainties. Control system strategies are discussed which will be applied to the next two planned TechEdSat-5 and -6 nano-satellite missions - thus increasing the targeting accuracy at the Von Karman altitude through the proposed drag modulation technique.

Exo-Brake↗

NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Neural Networks↗

NASA NTRS2018

Modeling the Distribution and Type of High-Latitude Natural Wetlands for Methane Studies

High latitude (>50N) natural wetlands emit a substantial amount of methane to the atmosphere, and are located in a region of amplified warming. Northern hemisphere high latitudes are characterized by cold climates, extensive permafrost, poor drainage, short growing seasons, and slow decay rates. Under these conditions, organic carbon accumulates in the soil, sequestering CO2 from the atmosphere. Methanogens produce methane from this carbon reservoir, converting stored carbon into a powerful greenhouse gas. Methane emission from wetland ecosystems depends on vegetation type, climate characteristics (e.g, precipitation amount and seasonality, temperature, snow cover, etc.), and geophysical variables (e.g., permafrost, soil type, and landscape slope). To understand how wetland methane dynamics in this critical region will respond to climate change, we have to first understand how wetlands themselves will change and therefore, what the primary controllers of wetland distribution and type are. Understanding these relationships permits data-anchored, physically-based modeling of wetland distribution and type in other climate scenarios, such as paleoclimates or future climates, a necessary first step toward modeling wetland methane emissions in these scenarios. We investigate techniques and datasets for predicting the distribution and type of high latitude (>50N) natural wetlands from a suite of geophysical and climate predictors. Hierarchical clustering is used to derive an empirical methane-centric wetland model. The model is applied in a multistep process first to predict the distribution of wetlands from relevant geophysical parameters, and then, given the predicted wetland distribution, to classify the wetlands into methane-relevant types using an expanded suite of climate and biogeophysical variables. As the optimum set of predictor variables is not known a priori, the model is applied iteratively, and each simulation is evaluated with respect to observed high-latitude wetlands.

Romanski, Joy↗

NASA NTRS2018

Status Report on Aeroelasticity in the Vehicle Development for X-57 Maxwell

Risk reduction is the objective of the X-57 Maxwell aeroelasticity team. The X-57, NASA’s experimental electric propulsion aircraft, has a long thin wing with primary propulsion systems located at the wing tips and high lift motors distributed along the span. Many of the classical aeroelastic concerns associated with such a configuration were addressed through early design decisions. The as-designed intermediate flight vehicle configurations show flutter mechanisms associated with flexible models of control surface systems –the stabilator, flaps and ailerons. Improvements to the analytical models, based on ground test data and project decisions about flight operations, show improved prospects of the vehicle being aeroelastically stable throughout the flight envelope. On-going ground testing and further analyses will lend credibility to the flutter predictions and vehicle safety.

Heeg, Jennifer↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗

NASA NTRS2007

Separation Control Over A Wall-Mounted Hump

Separation control by means of steady suction or zero efflux oscillatory jets is known to be effective in a wide variety of flows under different flow conditions. Control is effective when applied in a nominally two-dimensional manner, for example, at the leading-edge of a wing or at the shoulder of a deflected flap. Despite intuitive understanding of the flow, at present there is no accepted theoretical model that can adequately explain or describe the observed effects of the leading parameters such as reduced suction-rate, or frequency and momentum input. This difficulty stems partly from the turbulent nature of the flows combined with superimposed coherent structures, which are usually driven by at least one instability mechanism. The ever increasing technological importance of these flows has spurned an urgent need to develop turbulence models with a predictive capability. Present attempts to develop such models are hampered in one way or another by incomplete data sets, uncertain or undocumented inflow and boundary conditions, or inadequate flow-field measurements. This paper attempts to address these issues by conducting an experimental investigation of a lowspeed separated flow over a wall-mounted hump model. The model geometry was designed by Seifert & Pack, who measured static and dynamic pressures on the model for a wide range of Reynolds and Mach numbers and control conditions. This paper describes the present experimental setup, as well as the types and range of data acquired. Sample data is presented and future work is discussed.

Greenblatt, D.↗

NASA NTRS1997

Quantitative Measurement of Oxygen in Microgravity Combustion

A low-gravity environment, in space or in ground-based facilities such as drop towers, provides a unique setting for studying combustion mechanisms. Understanding the physical phenomena controlling the ignition and spread of flames in microgravity has importance for space safety as well as for better characterization of dynamical and chemical combustion processes which are normally masked by buoyancy and other gravity-related effects. Due to restrictions associated with performing measurements in reduced gravity, diagnostic methods which have been applied to microgravity combustion studies have generally been limited to capture of flame emissions on film or video, laser Schlieren imaging and (intrusive) temperature measurements using thermocouples. Given the development of detailed theoretical models, more sophisticated diagnostic methods are needed to provide the kind of quantitative data necessary to characterize the properties of microgravity combustion processes as well as provide accurate feedback to improve the predictive capabilities of the models. When the demands of space flight are considered, the need for improved diagnostic systems which are rugged, compact, reliable, and operate at low power becomes apparent. The objective of this research is twofold. First, we want to develop a better understanding of the relative roles of diffusion and reaction of oxygen in microgravity combustion. As the primary oxidizer species, oxygen plays a major role in controlling the observed properties of flames, including flame front speed (in solid or liquid flames), extinguishment characteristics, flame size and flame temperature. The second objective is to develop better diagnostics based on diode laser absorption which can be of real value in both microgravity combustion research and as a sensor on-board Spacelab as either an air quality monitor or as part of a fire detection system. In our prior microgravity work, an eight line-of-sight fiber optic system measured water vapor mole fractions in the NASA Lewis 2.2-sec Drop Tower. In that system, the laser and all electronics resided at the top of the drop tower and was connected via a fiber optic cable to the rig, on which a 'pitch and catch' set of fiber collimating lenses were used to transmit the laser beam across a jet diffusion flame. This system required eight independent detection/demodulation units and had poor spatial resolution. This research builds on this earlier work, resulting in an improved capability for quantitative, nonintrusive measurement of major combustion species. A vertical cavity surface-emitting diode laser (VCSEL) and a continuous spatial scanning method permit the measurement of temporal and spatial profiles of the concentrations and temperatures of molecular oxygen. High detection sensitivity is achieved with wavelength modulation spectroscopy (WMS). One-g experiments are performed using a slot diffusion flame. Microgravity measurements on a solid fuel (cellulose sheet) system are planned for the NASA Lewis 2.2-second Drop Tower Facility.

Silver, Joel A.↗

NASA NTRS1977

Economic consequences of improved temperature forecasts: An experiment with the Florida citrus growers (control group results)

A demonstration experiment is being planned to show that frost and freeze prediction improvements are possible utilizing timely Synchronous Meteorological Satellite temperature measurements and that this information can affect Florida citrus grower operations and decisions. An economic experiment was carried out which will monitor citrus growers' decisions, actions, costs and losses, and meteorological forecasts and actual weather events and will establish the economic benefits of improved temperature forecasts. A summary is given of the economic experiment, the results obtained to date, and the work which still remains to be done. Specifically, the experiment design is described in detail as are the developed data collection methodology and procedures, sampling plan, data reduction techniques, cost and loss models, establishment of frost severity measures, data obtained from citrus growers, National Weather Service, and Federal Crop Insurance Corp., resulting protection costs and crop losses for the control group sample, extrapolation of results of control group to the Florida citrus industry and the method for normalization of these results to a normal or average frost season so that results may be compared with anticipated similar results from test group measurements.

Source record↗

NASA NTRS2016

An Integrated Gate Turnaround Management Concept Leveraging Big Data Analytics for NAS Performance Improvements

"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance.

Efficent ATM systems↗

NASA NTRS1962

NASA Scout ST-1 Flight-Test Results and Analyses, Launch Operations, and Test Vehicle Description

The first of a series of flight tests for the development of the four-stage, solid-propellant Scout vehicle was conducted at the NASA Wallops Station under the direction of the Langley Research Center. Vehicle designation for the test was NASA Scout ST-1. Performance characteristics of the vehicle and components were recorded during a high-altitude probe mission. Flight-simulation studies are presented and show that the accuracy of the guidance system during the flight was within control-system design specifications. The control system functioned normally during the flight with the exception of an overpowering of the reaction-control roll jets near burnout of the third-stage rocket motor. The resulting roll displacement of the vehicle is shown to have caused the monitor tracking radar which had been erroneously tracking a radar beacon in the vehicle on a side lobe to reorient to the major lobe of the receiving antenna. This tracking switch falsely indicated a violent turning maneuver on the monitor plot board and resulted in a hold-fire decision for the fourth-stage rocket motor. Although data for the final thrusting and coast phase of the flight were not obtained, the majority of the test objectives were achieved. In-flight thrust misalignment angles for the second- and third-stage rocket motors derived from control-system error data and for the first-stage motor determined from flight-simulation studies are presented. All rocket-motor thrust misalignment angles were well within the tolerances used for control-system design. Rocket-motor flight performance is presented, and velocity increments attained from the first three stages substantiated the predicted nominal performance. Operation of the rocket motors was satisfactory with the exception of high-level vibrations which were encountered during third-stage motor burning. Rolling moments which overpowered the reaction-control jets are also attributed to the burning characteristics of the third-stage motor. A discussion of the premature loss of the third-stage heat shield is given and shows that the heat-shield latching mechanism failed from pressure loads as the vehicle entered the transonic speed range. Although venting was provided to relieve the high negative pressures known to exist on the heat shield at these speeds, a field modification of the wiring tunnel had the same effect as opening the inside of the heat shield to ambient pressures. Consequently, the latching mechanism failed from pressure loads which were of about the same magnitude as the latching-mechanism yield loads. Skin temperatures were recorded at several locations on the vehicle and were generally in good agreement with theoretical values. Aerodynamic heating presented no problem during the flight since the maximum temperatures recorded during the flight were only about half the design values because of the high-launch-angle trajectory. Environmental vibrations recorded in the vicinity of the guidance package showed that no significant continuous amplitude levels above the general instrumentation noise level were present during first- and second-stage burning. Large vibration amplitudes were recorded during third-stage burning which coincided with the large roll disturbance experienced by the vehicle near burnout of the third-stage motor.

Scout Launch Vehicle↗

NASA NTRS1972

A wind-tunnel evaluation of analytical techniques for predicting static stability and control characteristics of flexible aircraft

An experimental evaluation of analytical techniques for predicting certain stability and control characteristics of a large flexible aircraft is presented. Analytical methods based on both the model approach and flexibility influence coefficients are developed to predict the aerodynamic characteristics of a flexible airplane. These methods are then applied to a flexibly scaled model of a supersonic transport configuration. Comparisons of wind-tunnel data, calculations based on the model approach, and flexibility influence coefficients are presented over the Mach number range from 0.6 to 2.7. An examination of the results obtained from this study indicates that both analytical techniques predict reasonably well the effect of flexibility on the basic longitudinal characteristics and that both techniques give generally comparable results.

Abel, I.↗

NASA NTRS1974

A study of Minnesota forests and lakes using data from Earth Resources Technology Satellites

Highlights of research and practical benefits are discussed for the following projects which utilized ERTS 1 data to provide municipal, state, federal, and industrial users with environmental resource information for the state of Minnesota: (1) forest disease detection and control; (2) evaluation of water quality by remote sensing techniques; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) snowmelt flood prediction; (6) remote sensing applications to hydrology; (7) Rice Creek watershed project; (8) water quality in Lake Superior and the Duluth Superior Harbor; and (9) determination of Lake Superior currents from turbidity patterns.

Source record↗

NASA NTRS1974

Experimental investigation and model development of the Skylab ATM secondary nickel-cadmium batteries

The performance of a Ni-Cd battery was evaluated and the cyclic battery controls to be used on the ATM batteries were determined. Cell and battery performance models were developed which to predict the performance of the secondary Ni-Cd batteries on the ATM. Capacity degradation and charge acceptance characteristics were considered. Mathematical models for each of these operating areas were developed based on data from two separate test programs. The capacity degradation model describes the expected usable battery capacity as a function of time, temperature, and depth-of-discharge. The charge acceptance model describes the cell charge acceptance as a function of charge rate, temperature, and state of charge. The models were designed for computer use and to facilitate possible battery modification. They can be used to predict expected performance or to compare real time performance during flight.

Kirsch, W. W.↗

NASA NTRS1976

A general unified approach to modelling switching-converter power stages

A method for modeling switching-converter power stages is developed, whose starting point is the unified state-space representation of the switched networks and whose end result is either a complete state-space description or its equivalent small-signal low-frequency linear circuit model. A new canonical circuit model is proposed, whose fixed topology contains all the essential input-output and control properties of any dc-to-dc switching converter, regardless of its detailed configuration, and by which different converters can be characterized in the form of a table conveniently stored in a computer data bank to provide a useful tool for computer aided design and optimization. The new canonical circuit model predicts that, in general, switching action introduces both zeros and poles into the duty ratio to output transfer function in addition to those from the effective filter network.

Middlebrook, R. D.↗