Engineering Papers⌕ Search

NASA NTRS · 20120009624

2011 Mars Science Laboratory Launch Period Design

Abstract

The Mars Science Laboratory mission, set to launch in the fall of 2011, has the primary objective of landing the most advanced rover to date to the surface of Mars to assess whether Mars ever was, or still is today, able to sustain carbon-based life. Arriving at Mars in August 2012, the Mars Science Laboratory will also demonstrate the ability to deliver large payloads to the surface of Mars, land more accurately (than previous missions) in a 20-km by 25-km ellipse, and traverse up to 20 km. Following guided entry and parachute deployment, the spacecraft will descend on a parachute and a Powered Descent Vehicle to safely land the rover on the surface of Mars. The launch/arrival strategy is driven by several key requirements, which include: launch vehicle capability, atmosphere-relative entry speed, communications coverage during Entry, Descent and Landing, latitude accessibility, and dust storm season avoidance. Notable among these requirements is maintaining a telecommunications link from atmospheric entry to landing plus one minute, via a Direct-To-Earth X-band link and via orbital assets using an UHF link, to ensure that any failure during Entry, Descent and Landing can be reconstructed in case of a mission anomaly. Due to concerns related to the lifetime of the relay orbiters, two additional launch/arrival strategies have been developed to improve Entry, Descent, and Landing communications. This paper discusses the final launch/arrival strategy selected prior to the launch period down-selection that is scheduled to occur in August 2011. It is also important to note that this paper is an update to Ref. 1 in that it includes two new Type 1 launch periods and drops the Type 2 launch period that is no longer considered.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abilleira, Fernando. 2011-07-31. 2011 Mars Science Laboratory Launch Period Design. https://ntrs.nasa.gov/citations/20120009624

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail.

mission design↗

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗

Thermal Protection System Materials for Sample Return Missions

This white paper from the Thermal Protection System (TPS) community to the NRC Decadal Survey Sub-Panels provides an overview of TPS materials needed for future Sample Return (SR) missions. We consider the capability of heritage TPS material used by recent SR missions and identify appropriate materials for future SR missions. A prime conclusion is that the current TPS materials, if properly maintained, offer good low-density solutions for lower velocity (<13.5 km/s) sample return missions without planetary protection back-protection concerns. Furthermore, missions that have a larger capsule, a higher entry speed (>13.5 km/s), or back-protection concerns will leverage recently developed mid-density TPS materials. To maintain NASA’s Sample Return capabilities in the coming decade, we recommend that NASA continue to invest in sustainment of relevant TPS materials, as well as ground-test facilities, predictive entry modeling, and flight instrumentation.

mission design↗