Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “fleets”

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.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Planning and Scheduling for Fleets of Earth Observing Satellites

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.

Frank, Jeremy↗
NASA NTRS2003

A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

Globus, Al↗
NASA NTRS2005

Development of the Architectural Simulation Model for Future Launch Systems and its Application to an Existing Launch Fleet

A significant portion of lifecycle costs for launch vehicles are generated during the operations phase. Research indicates that operations costs can account for a large percentage of the total life-cycle costs of reusable space transportation systems. These costs are largely determined by decisions made early during conceptual design. Therefore, operational considerations are an important part of vehicle design and concept analysis process that needs to be modeled and studied early in the design phase. However, this is a difficult and challenging task due to uncertainties of operations definitions, the dynamic and combinatorial nature of the processes, and lack of analytical models and the scarcity of historical data during the conceptual design phase. Ultimately, NASA would like to know the best mix of launch vehicle concepts that would meet the missions launch dates at the minimum cost. To answer this question, we first need to develop a model to estimate the total cost, including the operational cost, to accomplish this set of missions. In this project, we have developed and implemented a discrete-event simulation model using ARENA (a simulation modeling environment) to determine this cost assessment. Discrete-event simulation is widely used in modeling complex systems, including transportation systems, due to its flexibility, and ability to capture the dynamics of the system. The simulation model accepts manifest inputs including the set of missions that need to be accomplished over a period of time, the clients (e.g., NASA or DoD) who wish to transport the payload to space, the payload weights, and their destinations (e.g., International Space Station, LEO, or GEO). A user of the simulation model can define an architecture of reusable or expendable launch vehicles to achieve these missions. Launch vehicles may belong to different families where each family may have it own set of resources, processing times, and cost factors. The goal is to capture the required resource levels of the major launch elements and their required facilities. The model s output can show whether or not a certain architecture of vehicles can meet the launch dates, and if not, how much the delay cost would be. It will also produce aggregate figures of missions cost based on element procurement cost, processing cost, cargo integration cost, delay cost, and mission support cost. One of the most useful features of this model is that it is stochastic where it accepts statistical distributions to represent the processing times mimicking the stochastic nature of real systems.

Rabadi, Ghaith↗
NASA NTRS2013

Continued Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet

This paper details analyses of condition indicator performance for the helicopter nose gearbox within the U.S. Army's Condition-Based Maintenance Program. Ten nose gearbox data sets underwent two specific analyses. A mean condition indicator level analysis was performed where condition indicator performance was based on a 'batting average' measured before and after part replacement. Two specific condition indicators, Diagnostic Algorithm 1 and Sideband Index, were found to perform well for the data sets studied. A condition indicator versus gear wear analysis was also performed, where gear wear photographs and descriptions from Army tear-down analyses were categorized based on ANSI/AGMA 1010-E95 standards. Seven nose gearbox data sets were analyzed and correlated with condition indicators Diagnostic Algorithm 1 and Sideband Index. Both were found to be most responsive to gear wear cases of micropitting and spalling. Input pinion nose gear box condition indicators were found to be more responsive to part replacement during overhaul than their corresponding output gear nose gear box condition indicators.

Delgado, Irebert R.↗
NASA NTRS2020

FLEET Velocimetry in the Common Research Model’s Wing Wake

Femtosecond laser electronic excitation tagging was used to make velocity measurements in the wake of the wing of the Common Research Model (CRM). Experiments were performed in the NASA Langley Research Center’s National Transonic Facility over a range of tunnel operating conditions. Pressures ranged from 205 to 411 kPa, temperatures from 278 to 323 K, and Mach from 0.1 to 0.9. Velocity was also determined over a range of model angles of attack. Time-averaged velocity results were obtained in both air and nitrogen, while single-shot velocity was obtained under certain tunnel operating conditions. Spatially-resolved, two dimensional, single component velocity measurements were achieved using a newly-developed laser scanning technique, which proved sufficiently sensitive to measure an approximately 5% velocity deficit in the wing wake region of the CRM.

Daniel T Reese↗
NASA NTRS2021

An Efficient Approach for Scheduling Imaging Tasks Across a Fleet of Satellites

Dynamically retasking satellites in response to scientific alerts is challenging because the tasks and opportunities of one satellite can influence this of another. This abstract focuses on our high-level approach for scheduling imaging tasks across a constellation of satellite, which is subject to orbital and other practical constraints such as finding a feasible up-/down- link schedule. Our approach is inspired by combining insights from two existing approaches about the structure of these problems to create an efficient, new approach. We show that our approach stacks up favorably against two baselines–an optimal solver as well as a naive, greedy approach.

Maillard, Adrien↗
NASA NTRSDate not supplied

Comparative Analysis for EMU Fleet Latent Loading Characterization in Support of US EVA 80 Failure

During United States Extravehicular Activity 80 (US EVA 80), water was observed in the helmet of an Extravehicular Mobility Unit (EMU) during cabin repressurization. One of the primary mechanisms that can cause water in the helmet of an EMU is integrated performance induced sublimator carryover. The sublimator is a heat exchanger that removes heat and humidity from the ventilation loop. Water vapor is condensed from the gas and removed by slurper holes in the sublimator. Sublimator carryover is caused by the inability of the EMU sublimator to remove all of the condensed water vapor, resulting in liquid water entering the helmet. To determine if sublimator carryover was a likely cause of the US EVA 80 failure, a comparative analysis of numerous historical EVAs was conducted to calculate the total latent load (total water vapor generated by the crewmember) for numerous historical EVAs and ground tests using the Systems Improved Numerical Differencing Analyzer EMU (SINDA EMU) model. The analysis showed that US EVA 80 was associated with a comparatively high latent load when compared to other EVAs that did not present water in the helmet. Further, this analysis showed that other historical EVAs which had visible water in the helmet were also associated with higher latent loads. This analysis provides evidence that the likely cause of the US EVA 80 water in the helmet event was not the failure of an individual component, but rather sublimator carryover caused by excessive production of water vapor by the crewmember. The evidence from this analysis agrees with results from the Test, Teardown, and Evaluation (TT&E) of the EMU which did not show any failure of individual components of the EMU that would lead to water in the helmet.

Noah Andersen↗