Search NASA⌕ Search

SEARCH · Search NASA

Results for “Space Mission Models Design”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Distributed Space Mission Design for Earth Observation Using Model-Based Performance Evaluation

Distributed Space Missions (DSMs) are gaining momentum in their application to earth observation missions owing to their unique ability to increase observation sampling in multiple dimensions. DSM design is a complex problem with many design variables, multiple objectives determining performance and cost and emergent, often unexpected, behaviors. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal design. This paper presents a software tool that can multiple DSM architectures based on pre-defined design variable ranges and size those architectures in terms of predefined science and cost metrics. The tool will help a user select Pareto optimal DSM designs based on design of experiments techniques. The tool will be applied to some earth observation examples to demonstrate its applicability in making some key decisions between different performance metrics and cost metrics early in the design lifecycle.

Constellation↗
NASA NTRS2008

Conceptual Design Methods and the Application of a Tradespace Modeling Tool for Deep Space Missions

Concept studies for deep space missions are typically time-consuming and costly, given the variety of missions and uniqueness of each design. Yet, in an increasingly cost-constrained environment, it is critical to identify the most scientifically valuable and cost-effective designs early in the design process. Modeling is an integral part in helping to identify the most desirable design option. While some spacecraft design models currently exist for Earth-orbiting spacecraft, there has been less success with deep space missions. Instead, these missions require a modified design and modeling approach to enable the same construction of a comprehensive, yet credible, mission tradespace. This paper presents an approach for efficiently constructing such a mission tradespace. In addition to a proposed design and modeling approach, three case study missions are presented including a solar orbiter, a Europa orbiter, and a near-Earth asteroid (NEA) sample return mission.

space vehicles↗
NASA NTRS1999

Reinventing the Design Process: Teams and Models

The future of space mission designing will be dramatically different from the past. Formerly, performance-driven paradigms emphasized data return with cost and schedule being secondary issues.

Design↗
NASA NTRS1999

Reinventing The Design Process: Teams and Models

The future of space mission designing will be dramatically different from the past. Formerly, performance-driven paradigms emphasized data return with cost and schedule being secondary issues. Now and in the future, costs are capped and schedules fixed-these two variables must be treated as independent in the design process. Accordingly, JPL has redesigned its design process. At the conceptual level, design times have been reduced by properly defining the required design depth, improving the linkages between tools, and managing team dynamics. In implementation-phase design, system requirements will be held in crosscutting models, linked to subsystem design tools through a central database that captures the design and supplies needed configuration management and control. Mission goals will then be captured in timelining software that drives the models, testing their capability to execute the goals. Metrics are used to measure and control both processes and to ensure that design parameters converge through the design process within schedule constraints. This methodology manages margins controlled by acceptable risk levels. Thus, teams can evolve risk tolerance (and cost) as they would any engineering parameter. This new approach allows more design freedom for a longer time, which tends to encourage revolutionary and unexpected improvements in design.

Wall, Stephen D.↗
DOE OSTI2022

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

42 ENGINEERING↗
NASA NTRS2021

Mission Concept Design for Autonomous Space Missions using Mission-Level Modeling and Simulation

NASA’s Europa Lander mission is to search for biosignatures on Europa based on in-situ science using a lander architecture. This mission presents a set of challenges that requires a high level of autonomy on the lander system, leading to the need for a new operational paradigm that supports better collaboration and coordination between the lander and the ground operations team. M\&S is used for both designing the onboard system-level autonomy and the ground operations paradigm that allows effective and efficient collaboration and coordination between the lander and the ground operations team. In this paper, M\&S as applied to the design of new mission and operational concepts will be discussed. At its current early stage of the mission development for Europa Lander, the M\&S is used to explore different mission concepts and gain insights for design (formative) rather than to verify and validate fully designed mission concepts quantitatively (summative). Organically, we established a new approach to mission and operational concept exploration using high-fidelity modeling and simulation. M\&S has been an integral part of the approach of defining constraints and assertions, designing mission concepts, assessing (i.e., simulating them), and discovering insights, which feeds back to the definition an design steps. This organically-established approach provided important benefits to the project at its early phase of the development by enabling the project team to be able to build shared understanding of impacts from design characteristics, constraints, and their interactions on the mission performance.

Ye, Sean↗
NASA NTRS2003

Models Required to Mitigate Impacts of Space Weather on Space Systems

This viewgraph presentation attempts to develop a model of factors which need to be considered in the design and construction of spacecraft to lessen the effects of space weather on these vehicles. Topics considered include: space environments and effects, radiation environments and effects, space weather drivers, space weather models, climate models, solar proton activity and mission design for the GOES mission. The authors conclude that space environment models need to address issues from mission planning through operations and a program to develop and validate authoritative space environment models for application to spacecraft design does not exist at this time.

Barth, Janet L.↗
NASA NTRS2002

The StarLight Space Interferometer: Optical design and performance modeling

The StarLight mission aimed to place the first formation flying optical interferometer into space in year 2006. Utilizing two spacecraft to form a long baseline Michelson interferometer, it would measure white light fringes on a number of partially resolved stars of magnitudes >5 in the wavelength range 600 to 1000nm. The interferometer baseline is variable between 30 and 125 m, and also has a fixed 1 m mode. The spacecraft are flown in a parabolic geometry which requires an optical delay line to build up more than 14m of delay on one arm of the interferometer. To obtain high fringe visibility, starlight wavefront, pointing and intensity must be preserved through 22 reflections from mirrors and beamsplitters. The alignment of a total of 27 optics is maintained through careful thermal design and the use of two actuated mirrors on each arm. This paper describes the optical layout in depth, including the beam combiner design which allows star tracking, optical system alignment and fringe formation on a single CCD. The effects of diffraction of the starlight transferred from a distant spacecraft and from optical surface imperfections are modeled. Other contributors to the visibility budget and the resulting variation of fringe visibility across the focal plane are discussed .

StarLight↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
NASA NTRS2005

The SMART Theory and Modeling Team: An Integrated Element of Mission Development and Science Analysis

When targeting physical understanding of space plasmas, our focus is gradually shifting away from discovery-type investigations to missions and studies that address our basic understanding of processes we know to be important. For these studies, theory and models provide physical predictions that need to be verified or falsified by empirical evidence. Within this paradigm, a tight integration between theory, modeling, and space flight mission design and execution is essential. NASA's Magnetospheric MultiScale (MMS) mission is a pathfinder in this new era of space research. The prime objective of MMS is to understand magnetic reconnection, arguably the most fundamental of plasma processes. In particular, MMS targets the microphysical processes, which permit magnetic reconnection to operate in the collisionless plasmas that permeate space and astrophysical systems. More specifically, MMS will provide closure to such elemental questions as how particles become demagnetized in the reconnection diffusion region, which effects determine the reconnection rate, and how reconnection is coupled to environmental conditions such as magnetic shear angles. Solutions to these problems have remained elusive in past and present spacecraft missions primarily due to instrumental limitations - yet they are fundamental to the large-scale dynamics of collisionless plasmas. Owing to the lack of measurements, most of our present knowledge of these processes is based on results from modern theory and modeling studies of the reconnection process. Proper design and execution of a mission targeting magnetic reconnection should include this knowledge and have to ensure that all relevant scales and effects can be resolved by mission measurements. The SMART mission has responded to this need through a tight integration between instrument and theory and modeling teams. Input from theory and modeling is fed into all aspects of science mission design, and theory and modeling activities are tailored to SMART needs during mission development and science analysis. In this presentation, we will present an overview of SMART theory and modeling team activities. In particular, we will provide examples of science objectives derived from state-of-the art models, and of recent research results that continue to be utilized in SMART mission development.

Hesse, Michael↗
NASA NTRS2004

The Role of Theory and Modeling in the International Living with a Star Program

Today, theory and modeling play a critical role in our quest to understand the connection between solar eruptive phenomena, and their impacts in interplanetary space and in the near-Earth space environment. This new role is based on two developments, one related to the goal of basic physical understanding, and the other to space weather-related applications. When targeting physical our focus is shifting away from investigations aiming at basic discoveries, to missions and studies that address our basic understanding of processes we know to be important. For these studies, theory and models provide physical explanations that need to be verified or falsified by empirical evidence. Within this paradigm, a much more tight integration between theory modeling, and space flight mission design and execution is not only beneficial, but essential. One of the prime objectives of space weather research, on the other hand, is the prediction of space environmental conditions for the benefit of humans and their assets in near-Earth space and on the ground, as well as on solar system bodies like Mars that are of interest to exploration by humans. By its very nature, prediction requires modeling, which, in turn, requires understanding. We will present an overview of the role of theory and modeling within the International Living With a Star program. Specifically, we will focus on an assessment of present-day and future capabilities, as well as on strategies for tight integration of theory and modeling in space science investigations.

Hesse, M.↗