SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Battery of tests determines the primary dimensions of perceptual-motor performance. Eighteen basic measures range from simple tests to sophisticated electronic devices. Improved system has one unit for the subject containing test display and response elements, and one for the experimenter where test setups, programming, and scoring are accomplished.
U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.
As missions progress into deep space, communication delays and disruptions will disenable the crew’s reliance on Earth experts. There are also limitations in the amount of data that can be downlinked to the ground. It is prudent to assume that critical, complex vehicle or habitat sub-systems will malfunction at a time when a lunar or Mars’ crew cannot rely on the Earth-Support team to detect, diagnose and resolve the problem and it is impractical to expect a small crew to step-in with the same level of expertise as 50+ authorities. The crew will need novel processes and advanced technological support to independently identify and resolve safety- and time-critical anomalies. That a self-reliant crew is unable to respond appropriately to time-critical anomalies is a significant risk to crew safety and mission success. This risk is driven by several factors; novel and unanticipated anomalies would not have been trained pre-flight, the crew could forget their pre-flight training or spaceflight stressors could impair the crew’s problem-solving ability. At last year’s IWS, Beard reported that a single spaceflight stressor (elevated CO2) could undermine the crew’s ability to independently respond to emergencies. Concept of Operations (ConOps) provide a common view of future system functions to all stakeholders. For the current project, a ConOps was developed that describes the operational processes, practices and capabilities needed by a crew of astronauts on deep space missions to autonomously respond to anticipated and unanticipated anomalies. It is crucial to recognize that, as of August 2018 existing technologies are unable to effectively support crew anomaly response to unanticipated events. “Intelligent technology” has not reached a maturity level that permits generalizing a solution to novel situations. For example, to train intelligent technology requires volumes of data that do not exist. The complexities involved in a manned mission to Mars cannot be compared to sending rovers to Mars using scripted software. This ConOps proposes a Crew Performance Support System (CPSS) that will push NASA and its industry partners toward what will be required for a safe and successful manned mission to Mars. Anomaly resolution during a deep space mission will take place within a dynamic, or changing, context. The figure to the left shows five broad contextual variables: the organizational culture, mission context, system characteristics, team characteristics and individual characteristics. The yellow arrow indicates that spaceflight and task-related stressors can affect system, team and individual crewmember characteristics and therefore anomaly response potential. The figure depicts a protective umbrella of Human-System Integration (HSI) principles that should be instituted during CPSS development including a balanced workload, shared situation awareness and building an appropriate level of trust in the automation. The figure also depicts two interrelated and cooperative components, an HSI Data System and other Enabling Capabilities will be required to support crew anomaly response and Earth-Support situation awareness. As we journey from ISS to Gateway to Mars, multiple, simultaneous and integrated research and development efforts (i.e., support systems co-evolution) must be implemented to meet the problem-solving challenges a self-reliant crew will face on a Mars’ mission. The crossovers between the capabilities are just as important as the discrete capabilities themselves. As the capabilities mature, the lines between the support subdomains will blur and an integrated system will emerge. The ConOps summarizes current knowledge about how highly trained people solve anomalies in safety- and time-critical situations, describes a group of capabilities that could help to reduce the extant risk and documents requirements levied on additional systems that provides critical inputs to the CPSS. Scenarios are used to promote a shared understanding of processes, practices and technological goals needed for safe and productive manned missions beyond LEO.
The Exploration Medical Capability (ExMC) Element Systems Engineering (SE) goals include defining the technical system needed to support medical capabilities for a Mars exploration mission. A draft medical system architecture was developed based on stakeholder needs, system goals, and system behaviors, as captured in an ExMC concept of operations document and a system model. This talk will discuss a high-level view of the medical system, as part of a larger crew health and performance system, both of which will support crew during Deep Space Transport missions. Other mission components, such as the flight system, ground system, caregiver, and patient, will be discussed as aspects of the context because the medical system will have important interactions with each. Additionally, important interactions with other aspects of the crew health and performance system are anticipated, such as health & wellness, mission task performance support, and environmental protection. This talk will highlight areas in which we are working with other disciplines to understand these interactions.
The performance of the Random Multiple Access (RMA) technique, applied to a direct terminal-to-terminal link with a large number of potential users, is determined. The average signal-to-noise ratio (SNR) is derived. Under Gaussian assumption, the approximation of the probability of error is given. The analysis shows that the system performance is affected by the sequence length, the number of simultaneous users, and the number of cochannel symbols, but is not sensitive to the thermal noise. The performance of using very small aperture antenna for both transmitting and receiving without a hub station is given.
The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.
The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.
The electric power system is a crucial element of any mission for the human exploration of the Martian surface. The bulk of the power generated will be delivered to crew life support systems, extravehicular activity suits, robotic vehicles, and predeployed in situ resource utilization (ISRU) equipment. In one mission scenario, before the crew departs for Mars, the ISRU plant operates for 435 days producing liquefied methane and oxygen for ascent-stage propellants and water for crew life support. About 200 days after ISRU production is completed, the crew arrives for a 500-day surface stay. In this scenario, the power system must operate for a total of 1130 days (equivalent to 1100 Martian "sols"), providing 400 MW-hr of energy to the ISRU plant and up to 18 kW of daytime user power. A photovoltaic power-generation system with regenerative fuel cell (RFC) energy storage has been under study at the NASA Glenn Research Center at Lewis Field. The conceptual power system is dominated by the 4000- m2 class photovoltaic array that is deployed orthogonally as four tent structures, each approximately 5 m on a side and 100-m long. The structures are composed of composite members deployed by an articulating mast, an inflatable boom, or rover vehicles, and are subsequently anchored to the ground. Array panels consist of thin polymer membranes with thin-film solar cells. The array is divided into eight independent electrical sections with solar cell strings operating at 600 V. Energy storage is provided by regenerative fuel cells based on hydrogen-oxygen proton exchange membrane technology. Hydrogen and oxygen reactants are stored in gaseous form at 3000 psi, and the water produced is stored at 14.7 psi. The fuel cell operating temperature is maintained by a 40-m2 deployable pumped-fluid loop radiator that uses water as the working fluid. The power management and distribution (PMAD) architecture features eight independent, regulated 600-Vdc channels. Power management and distribution power cables use various gauges of copper conductors with ethylene tetrafluoroethylene insulation. To assess power system design options and sizing, we developed a dedicated Fortran code to predict detailed power system performance and estimate system mass. This code also modeled the requisite Mars surface environments: solar insolation, Sun angles, dust storms, dust deposition, and thermal and ultraviolet radiation. Using this code, trade studies were performed to assess performance and mass sensitivities to power system design parameters (photovoltaic array geometry and orientation) and mission parameters (landing date and landing site latitude, terrain slope, and dust storm activity). Mission analysis cases were also run. Power results are shown in this graph for an analysis case with a September 1, 2012, landing date; 18.95 North latitude landing site; two seasonal dusts storms; and tent arrays. To meet user load requirements and the ISRU energy requirement, an 8-metric ton (MT) power system and 4000-m2 photovoltaic array area were required for the assumed advanced CuInS2 thin-film solar cell technology. In this figure, the top curve is the average daytime photovoltaic array power, the middle curve is average daytime user load power, and the bottom curve is nighttime power. At mission day 1, daytime user power exceeds 120 kW before falling off to 80 kW at the end of the mission. Throughout the mission, nighttime user power is set to the nighttime power requirement. In this analysis, "nighttime" is defined as the 13- to 15-hr period when array power output is below the daytime power requirement. During dust storms, power system capability falls off dramatically so that by mission day 900, a daily energy balance cannot be maintained. Under these conditions, the ISRU plant is placed in standby mode, and the regenerative fuel cell energy storage is gradually discharged to meet user loads.
It is noted that if computing system performance is degradable then system evaluation must deal simultaneously with aspects of both performance and reliability. One approach is the evaluation of a system's performability which, relative to a specified performance variable Y, generally requires solution of the probability distribution function of Y. The feasibility of closed-form solutions of performability when Y is continuous are examined. In particular, the modeling of a degradable buffer/multiprocessor system is considered whose performance Y is the (normalized) average throughput rate realized during a bounded interval of time. Employing an approximate decomposition of the model, it is shown that a closed-form solution can indeed be obtained.
In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).
A model of a high temperature electrolyzer/fuel cell, hydrogen/oxygen, thermally regenerative power cycle is developed and used to simulate system performance for varying system parameters. Initial estimates of system efficiency, weight, and volume are provided for a one KWe module assuming specific electrolyzer and fuel cell characteristics, both current and future. Specific interest is placed on examining the system responses to changes in device voltage versus current density operating curves, and the associated optimum operating ranges. The performance of a solar-powered, space based system in low earth orbit is examined in terms of the light-dark periods requiring storage. The storage design tradeoffs between thermal energy, electrical energy, and hydrogen/oxygen mass storage are examined. The current technology module is based on the 1000 C solid oxide electrolyzer cell and the alkaline fuel cell. The Future Technology system examines benefits involved with developing a 1800K electrolyzer operating with an advanced fuel cell.
Optimal role of man in space, allocation of men and machines in aerospace systems, and descriptive model for determining optimal human performance
A series of optical communication experiments between a high altitude aircraft at 18.3 km (60,000 ft) and a ground station are planned by NASA in the summer of 1972. The basic concept is that an optical tracker and transmitter will be located in each terminal. The aircraft transceiver consists of a 5-mW HeNe laser transmitter with a 30-megabit modulator. The ground station beacon is an argon laser operating at 488 nm. A separate pulsed laser radar is used for initial acquisition. The objective of the experiment is to obtain engineering data on the precision tracking and communication system performance at both terminals. Atmospheric effects on the system performance are of prime importance.
This document is a compilation of papers presented at the YF-12 Experiments Symposium held at the NASA Dryden Flight Research Center, September 13-15, 1978. Papers were presented by personnel from the Dryden Flight Research Center, the Lewis Research Center, and the Ames Research Center. Topics covered propulsion system performance, inlet time varying distortion, structures, aircraft controls, propulsion controls, and aerodynamics. The reports were based on analytical studies, laboratory experiments, wind tunnel tests, and extensive flight research with two YF - 12 airplanes.
The phase control system is the fundamental element in the forming, steering and control of the solar power satellite (SPS) microwave power beam. This system must in essence automatically adjust the phase at each of the transmitter's 101,552 power amplifiers to compensate for differences in transmission path lengths to the earth-based receiving antenna (rectenna). SPS phase control system requirements are discussed, taking into account system concepts, a reference system description, reference system performance, ground based phase control concepts, and ionosphere considerations. It is pointed out that the importance of determining the ionospheric effects cannot be overemphasized. The permissable power density limit through the ionosphere is a critical SPS sizing factor and the phase control system must be able to accommodate errors induced by a heated ionosphere.
This paper presents an overview of the flight test objectives and performance of the Orion Launch Abort System during Exploration Flight Test-1. Exploration Flight Test-1, the first flight test of the Orion spacecraft, was managed and led by the Orion prime contractor, Lockheed Martin, and launched atop a United Launch Alliance Delta IV Heavy rocket. This flight test was a two-orbit, high-apogee, high-energy entry, low-inclination test mission used to validate and test systems critical to crew safety. This test included the first flight test of the Launch Abort System performing Orion nominal flight mission critical objectives. Although the Orion Program has tested a number of the critical systems of the Orion spacecraft on the ground, the launch environment cannot be replicated completely on Earth. Data from this flight will be used to verify the function of the jettison motor to separate the Launch Abort System from the crew module so it can continue on with the mission. Selected Launch Abort System flight test data is presented and discussed in the paper. Through flight test data, Launch Abort System performance trends have been derived that will prove valuable to future flights as well as the manned space program.
As future space exploration missions will involve larger number of spacecraft and more complex systems, theoretical analysis alone may have limitations on characterizing system performance and interactions among the systems. Simulation tools can be useful for system performance characterization through detailed modeling and simulation of the systems and its environment...This paper reports the simulation of the Orion (Crew Exploration Vehicle) to the International Space Station (ISS) mission where Orion is launched by Ares into orbit on a 14-day mission to rendezvous with the ISS. Communications services for the mission are provided by the Space Communication and Navigation (SCaN) network infrastructure which includes the NASA Space Network (SN), Ground Network (GN) and NASA Integrated Services Network (NISN). The objectives of the simulation are to determine whether SCaN can meet the communications needs of the mission, to demonstrate the benefit of using QoS prioritization, and to evaluate network-key parameters of interest such as delay and throughout.
As future space exploration missions will involve larger number of spacecraft and more complex systems, theoretical analysis alone may have limitations on characterizing system performance and interactions among the systems. Simulation tools can be useful for system performance characterization through detailed modeling and simulation of the systems and its environment.