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Software & Computing Systems

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Lifecycle Integrated Modeling and Simulation
Lifecycle Integrated Modeling and Simulation

Lifecycle integrated modeling and simulation enables rapid and thorough exploration of trade spaces during early mission design, as well as validated high-fidelity science and engineering simulations. It targets the development of a formal framework of model verification and validation that includes quantification of uncertainties in model parameters to assess and establish performance margins.


A deep space or earth science mission always starts with a set of questions about natural phenomena, which then evolves into specific measurement objectives and science-return requirements. These objectives and science requirements drive the mission, spacecraft system architecture, and instrument systems. As mission capabilities advance, so do the complexities of the overall system. Testing and validating mission concepts and systems using conventional testbeds is becoming progressively more challenging and, in some cases, infeasible if testing on the ground is not possible. When validated from available test data, large-scale, high-fidelity models and predictive simulations provide an important complementary testbed approach, enabling greater depth and breadth for exploring system designs and conducting engineering analyses, as well as driving selective physical testing. Similarly, predictive scientific simulations that assimilate observed data can be used to develop and assess instrument systems under development.

Advanced modeling and simulation are essential to present and future JPL missions. As an interdisciplinary activity, it enables more thorough instrument engineering, design trade-space analyses, improved science understanding, better representation of operational environments, public outreach product generation, and predictive capabilities of system performance based on quantifiable margins and uncertainties. The "trade space" can be defined as the set of program and system parameters, attributes, and characteristics required in order to be in compliance with with performance standards. These pervasive techniques contribute directly to JPL’s overall mission success.
A best case scenario would be a capability that could directly tie engineering parameters associated with flight and ground systems to advance science return, especially those parameters that drive scheduling and cost. Specific technology challenges in the area of lifecycle integrated modeling and simulation include:

  1. determining the degree and coupling needed or feasible for model integration
  2. developing software integration between scalable (parallelizable) codes/tools and portability of code across multiple platforms;
  3. developing mathematical, computational, and multi-scale modeling scalability;
  4. verifying (assessment of the numerical correctness of the code) and validating (assessment of simulation results with experiments) models; and
  5. reusing models and codes libraries, including domain-specific analyses plus audits for consistency and completeness


Current Research Efforts

JPL seeks new ways to propose compelling new mission and instrument concepts while understanding and containing risks in their development. Testing and validation of these concepts as well as system designs using conventional testbeds are becoming progressively more challenging, and in some cases, infeasible. As a consequence, investment in lifecycle integrated modeling and high-fidelity simulation technologies and capabilities is needed to enable greater depth and breadth in exploring system designs and conducting engineering and scientific analyses.

Trade-Space Exploration
Examples of advanced technologies in trade-space exploration include the following:

  • Engineering design modeling
  • Modeling for engineering and science
  • Performance and operation modeling
  • Visualization for design decisions

These technologies are used for early-mission design trade-space exploration, system trades, design validation and optimization, and requirement validation for broad analyses. Optimization and simulation tools are needed to both analyze and visualize new mission architectural solutions. Immediate performance targets include multi-parameter design with rapid turnaround; systems trades that model and simulate nonlinear systems; design validation/optimization; scientific (phenomenology) modeling for in-situ remote sensing; spacecraft, instrument, and trajectory performance modeling for landers and orbiters; and incorporation of advanced visualization into the design optimization decision process.

Coupled and Integrated Physics-Based Modeling
Physically realistic models of scientific phenomena, instruments, and spacecraft are essential for Earth, planetary, and astrophysics system simulations. However, most large apertures cannot be fully tested on the ground; therefore, technologists must rely on high-fidelity simulations, including a quantification of the margins and uncertainties in the simulation results, to fully model large apertures.

High-Fidelity Model Verification and Validation
Effective large-scale, high-fidelity simulations require that model codes be rigorously verified and test-validated. Model verification is the process of determining the degree to which a computational model accurately represents the underlying assumed mathematical model and its solution. Model validation, which usually follows model verification, is the process of determining the degree to which the assumed mathematical model represents an accurate representation of the real world from the perspective of the intended application of the model. Verification of complex, integrated modeling codes is necessary to ensure that uncertainty in simulation results from software errors and numerical effects is minimized or eliminated. Extrapolation outside a limited test domain means that the models themselves have to get the right answer for the right reason. A traditional “tuning” or “calibration” of the model can make it fit a given test on the ground, but that does not mean the model is any more credible for another application. Consequently, investments in new methods and technologies are needed to verify and validate models so they can be relied upon to extensions from ground tests to flight. Performance targets include inverse statistical analyses for extrapolating model uncertainty beyond a test to another test with different inputs, and demonstration of specific methods on coupled, integrated high-fidelity models of complex systems.

Model Integration
Science and engineering models today do not transfer easily or well across mission phases or projects. One goal in model integration is to create an environment in which models are shared among multiple missions and are readily transferred from phase to phase in an integrated, synergistic fashion, covering a broad range of multidisciplinary problems associated with science and engineering.

These technologies are considered cross-cutting as they are needed for both broad analyses involved in early trade space exploration as well as deep analyses required for detailed design. Current modeling languages, environments, tools, and standards are restrictive in some aspects of expressiveness, lack formal semantics (which impedes the ability to integrate information), and lack mechanisms of model validation. Advances in formal specifications and ontologies provide the basis for robust and sound model sharing. Performance targets against specific capabilities include a semantically rich model integration framework that can integrate representations from a dozen or so models, coupled with high-fidelity and predictive simulations and the ability to connect the models through a full project lifecycle. These models would represent varying degrees of fidelity in a multidisciplinary context.


Engineering Systems Development Plan

JPL’s engineering system plan encompasses many areas of modeling and simulation development will take place over the next decade. JPL is working to further improve upon its state-of-the-art software capabilities to simulate, model, and plan for future missions.

lifecycle diagram
Current and future states of integrated modeling and simulation at JPL.


Integrated Model-Centric Engineering

The JPL Engineering & Science Directorate intends to establish integrated model-centric engineering as "the way of doing business" in order to reduce duplication of information and promote the definition, control and verification & validation of streamlined and high-quality systems engineering products across both systems and subsystems.

Integrated Model-Centric Engineering (IMCE) is the JPL initiative to formalize and accelerate the application and integration of models across disciplines (mechanical, electrical, telecommunication, etc.), across engineering levels (system, subsystems and components), and across the full project life cycle (formulation, design, integration, verification and operations). The objective is to advance from document-centric engineering practices to one in which structural, behavioral, physics and simulation-based models representing the technical designs are integrated and evolve throughout the life-cycle, supporting trade studies, requirements analyses, design verification, and system V&V. The IMCE initiative facilitates this transition by providing: modeling development environment and standards; reusable models repository; tool/model integration; training; user community support; and partnership.


Select Research Projects

Dynamics And Real-Time Simulation (DARTS)
DARTS is a high-performance computational engine for flexible multi-body dynamics. It is an advanced high-fidelity, multi-mission simulation tool for the closed-loop development and testing of spaceflight systems. The DARTS simulator is based on algorithms from the Spatial Operator Algebra mathematical framework. It has been used for many of JPL’s successful missions, such as Galileo, Cassini, Mars Exploration Rover (MER), Stardust and the Mars Science Laboratory (MSL) mission.

Model-Based Systems Engineering and Architectures
Model-based systems for engineering and architectures is an analysis capability that integrates modeling, mission simulation, data analysis, computational technologies, data management, and visualization to help formulate and validate advanced instruments and associated payload systems. The goal of this new, yet maturing, area is to establish a simulation-oriented basis for developing, validating, and adopting new smart-instrument technologies and associated flight architectures for the next generation of Earth and space exploration missions.

High-Capability Computing and Modeling
Researchers in this area provide expertise in implementing legacy applications or in developing new software on large-scale distributed-computing platforms such as cluster computers. The capability has been demonstrated on legacy applications from engineering such as antenna modeling and trajectory design software, and from science such as atmospheric chemistry, radiative transfer, tsunami, and gravity-inversion models.

Large Aperture Models
Integrated multi-physics analysis using a common model approach for Siderostat testbed mirror temperatures and precision optical deformations.

Corps Battle Simulation (CBS)
CBS supports training of Army Division & Corps commanders and their staffs in dynamic, stressing combat situations. CBS is part of an infrastructure that senior Army commanders and their staffs use in “wargame” operations. These games allow commanders to learn lessons from simulations that they can apply to real battle scenarios.

CI OSSE Field Experiment
The Cyberinfrastructure (CI) component of the Ocean Observing System (OOI) is conducting an Observing System Simulation Experiment (OSSE) to test the capabilities of the OOI CI to support field operations in a distributed ocean observatory in the Mid-Atlantic Bight. The CI OSSE goal is to provide a real oceanographic test bed in which the CI will support field operations of ships and mobile platforms, aggregate data from fixed platforms, shore-based radars, and satellites and offer these data streams to data assimilation and forecast models. For the first time, this CI OSSE will construct a multi-model ensemble forecast that will be used to guide the glider deployment and trigger satellite imaging over the region of interests.

quake simulations
Line-of-sight interferograms of surface deformation where color bands represent the (3D) surface deformation projected into the line of sight of the radar instrument.
Integrated Modeling and Simulation for Large Apertures (IMSLA)
IMSLA is a system for advanced computational simulation that integrates heat-transfer, structural-analysis, parallel-computing and optical-aberration capabilities within the Cielo code environment, producing models in which these characteristics are integrated. Such models are vital to the development of future precision, space-based, large-aperture optical systems such as the James Webb Space Telescope (JWST). These instruments will be deployed in microgravity, cryogenic environments and will be expected to operate at extraordinarily high levels of optical precision under automated thermal and mechanical control, but are impractical to fully ground-test prior to launch.

Quakesim
QuakeSim is a project to develop a solid Earth science framework for modeling and understanding earthquake and tectonic processes. The multi-scale nature of earthquakes requires integrating many data types and models to fully simulate and understand the earthquake process. QuakeSim focuses on modeling the inter-seismic process through various boundary element, finite element, and analytic applications, which run on various platforms including desktop and high-end computers. Such work is essential to full realization of the science value in future earth-sensing missions such as interferometric radar.


Contacts

Larry Bergman - Management Contact
E-Mail: Larry.A.Bergman@jpl.nasa.gov
Phone: 818.393.5314

Charles Norton - Technical Contact
E-Mail: Charles.D.Norton@jpl.nasa.gov
Phone: 818.393.3920

Joe Provenzano - Technical Contact
E-Mail: Joseph.P.Provenzano@jpl.nasa.gov
Phone: 818.354.8960


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