Richard Weidner

Estimate of carbonyl sulfide tropical oceanic surface fluxes using Aura Tropospheric Emission Spectrometer observations

Author(s): Kuai, L; Worden, JR; Campbell, JE; Kulawik, SS; Li, KF; Lee, M; Weidner, RJ; Montzka, SA; Moore, FL; Berry, JA; Baker, I; Denning, AS; Bian, H; Bowman, KW; Liu, J; Yung, YL | Abstract:

Deep Space 1 mission and observation of comet Borrelly

NASAs Deep Space 1 mission was enabled by several break-through technologies including autonomous optical navigation (AutoNav), miniature integrated camera and spectrometer (MICAS), on-board data processing, model-based science observation sequence planning, and science information analysis. This paper describes a few notable challenges for the MICAS system with respect to flight calibration and observations of comet Borrelly.

Design-based mission operation

The Virtual Mission project led by the Mission Simulation and Instrument Modeling Group at JPL has been playing an active role in the NASA-wide information technology infusion programs, such as, Information System Technology, Next-Generation Infrastructure Technology, and Intelligent Synthesis Environment. The goal of the Virtual Mission project is to enable automated design space exploration, progressive design optimization, and lifecycle-wide design validation to ensure mission success. Design-based mission operation has been a major part of the research effort in order to establish system-wide as well as lifecycle-wide impact analysis as an integral part of the mission design process. The design-based mission operation is approached by implementing Virtual Mission Lifecycle (VML), modeling and simulation tools and system engineering processes for building a virtual mission system that can perform a realistic mission operation during the design phase of a mission. As in the real mission lifecycle convention, the VML is composed of design, development, integration and test, and operation phases. This paper describes the four phases of the VML addressing a major challenge per phase, mission model framework, virtual prototyping, agent-based mission system integration, and virtual mission operation.

A component based implementation of agents and brokers for design coordination

NASAs mission design coordination has been based on expert opinion of parametric data presented in Excel of PowerPoint. Common access is required to more powerful design tools supporting performance simulation and analysis. Components provide the means for inexpensively adding the desired functionality. An Information exchange was developed to provide the physical models required to perform performance analysis of mission designs. The exchange product is continuous polymorphic model data over finite time intervals. Using Distributed Component Object Model (DCOM) components, information brokers were developed to provide controlled access to the products. Each access is a persistent contextual transaction specific to a design team. Information agent Dynamic Link Libraries (DLLs) were developed to translate information requests, search among the brokers, retrieve the exchange products and ultimately generate high level model information such as state or attitude. An in-process dual component interface was developed to provide direct access from office productivity tools to the agent DLLs.

Mission lifecycle modeling and simulation

Mission synthesis and simulation research at JPL addresses mission model taxonomy, progressive lifecycle representation, model-based design, and simulation-in-the-loop design. The Virtual Mission (VM) project integrates the research activities and implements a virtual mission lifecycle to enable a globally optimal mission. The VM is composed of three interacting modeling and simulation layers: a mission model architecture layer, a mission system simulation layer, and a mission operation simulation layer. The three layers collectively simulate the development, integration, and operation phases of the mission cycle for comprehensive validation of mission design products. The VM was applied for the MICAS (Miniature Imaging Camera And Spectrometer) payload system of the Deep Space 1 mission (DS1) to validate the integrated systems performance to ensure the desired science return. The VM is being applied to design and to validate calibration scenarios and science observation scenarios for the extended science mission of the DS1 project.

Virtual Mission Systems for Multi-Disciplinary Engineering System Design

To ensure the science return while minimizing the cost and risk of a mission, a lifecyclewide concurrent engineering design process must be established. This paper describes the Virtual Mission Operation Framework (VMOF), which provides distributed modeling, simulation, and visualization services. The goal of the VMOF is to enable concurrent design of a mission system and operation of that system by providing operational perspective to the system designers and system perspective to the operation designers. Concurrent development of the both perspectives requires interdisciplinary modeling and simulation of the mission-operation activities and the system architecture. The VMOF employs virtual mission systems to capture the dynamic interdependencies of the spacecraft system, the science payload, and the physical world throughout the lifecycle phases. A space science mission starts with a set of science questions about natural phenomena, and it transforms into specific measurement objectives and science-return requirements. The measurement objectives and science-return requirements drive the requirements for the mission operation, spacecraft system architecture, and instrument systems. To ensure the desired quantity and quality of the science data products while minimizing the cost and risk, a lifecycle-wide model-based engineering process that can easily adapt to a mission-specific science traceability matrix must be established. The lifecycle-wide model-based engineering process must also provide concurrent and collaborative system engineering among the instrument system, the spacecraft system, and the mission operation [1]. Mission lifecycle consists of three major phases, formulation, implementation, and operation. During the formulation phase, system-level designs are developed by performing various trade analyses among a wide range of options to ensure the optimality of the design with respect to the mission objectives and project resources. During the implementation phase, the subsystems are designed, developed and verified against the system-level design. The subsystems are then integrated and tested for operation readiness. During the operation phase, the flight system is launched and the spacecraft is navigated, controlled, and monitored toward the science targets. After a successful encounter, various telemetry data are collected from the science payload systems, engineering and science data products are developed, and finally scientific discoveries are shared with the general public. In order for a mission to be successful, everyone must work together toward the same mission objectives and clearly understand his/her role in achieving those objectives. The challenges in providing a lifecycle-wide continuity to all mission teams are enormous due to the typical long duration of a mission lifecycle, the multiple engineering teams

Science-return modeling and simulation

In this paper, an on-going effort to integrate science-return modeling and simulation with JPLs institutional mission system design process will be presented.

Juno Mission Simulation

The Juno spacecraft is planned to launch in August of 2012 and would arrive at Jupiter four years later. The spacecraft would spend more than one year orbiting the planet and investigating the existence of an ice-rock core; determining the amount of global water and ammonia present in the atmosphere, studying convection and deep- wind profiles in the atmosphere; investigating the origin of the Jovian magnetic field, and exploring the polar magnetosphere. Juno mission management is responsible for mission and navigation design, mission operation planning, and ground-data-system development. In order to ensure successful mission management from initial checkout to final de-orbit, it is critical to share a common vision of the entire mission operation phases with the rest of the project teams. Two major challenges are 1) how to develop a shared vision that can be appreciated by all of the project teams of diverse disciplines and expertise, and 2) how to continuously evolve a shared vision as the project lifecycle progresses from formulation phase to operation phase. The Juno mission simulation team addresses these challenges by developing agile and progressive mission models, operation simulations, and real-time visualization products. This paper presents mission simulation visualization network (MSVN) technology that has enabled a comprehensive mission simulation suite (MSVN-Juno) for the Juno project.

Sensor-web Operations Explorer (SOX) for Earth Science Air Quality Mission Concepts

Future air quality missions will face significant measurement strategy design and implementation challenges. Characterizing the atmospheric state and its impact on air quality requires observations of trace gases (e.g., ozone [O3], carbon monoxide [CO], nitrogen dioxide [NO2], sulfur dioxide [SO2]), aerosols (e.g., size and shape distributions, composition), clouds (e.g., type, height, sky coverage), and physical parameters (e.g., temperature, pressure, humidity) across temporal and spatial scales that range from minutes to days and from meters to > 10,000 km. Validating satellite measurements is another major challenge, and it requires well organized and orchestrated sub-orbital sensor web deployments. No single sensor, instrument, platform, or network can provide all of the information necessary to address this issue. Constellations of spacecraft, integrated air-borne campaigns, and distributed sensor networks have been actively pursued to achieve the needed multi-dimensional observation coverage. However, these complicated sensor webs must address how to formulate the complex design trade space, how to explore the trade space rapidly, how to establish evaluation metrics, and how to coordinate observations optimally. The Sensor-web Operations Explorer (SOX) research task under the NASA Earth Science Technology Office addresses these challenges by creating a virtual sensor-web experiment framework that can support orbital and sub-orbital observation system simulation experiment.

In-Situ Site Knowledge System [Mars technology program]

In support of the transition from human-based in-situ mission operations to automated smart operations, the mission simulation and instrument modeling group at JPL has been developing a progressive in-situ automation framework. The In-Situ Site Knowledge System (ISSKS) presented in this paper is the first phase of the automation framework, which addresses flexible virtual site construction, instrument-generic measurement simulation, and intelligent operation service agents. The ISSKS is developed in three tiers. The first tier is composed of site property modeling and synthesis, for construction of a virtual in-situ environment. The second tier is composed of sensor system models and mobility platform models, for simulating in-situ exploration and science data products. The third tier is the operation interface, supporting the mobility platform simulation clients with surface interaction and operation planning activity clients with the observed site phenomena. The ISSKS currently supports several research tasks under the Mars technology program including the Smart Landing Systems, the Advanced EDL (Entry, Descent, and Landing), and the Rover technology.