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This work represents a part of a project to develop an atmospheric general circulation model based on the semi-Lagrangian advection of potential vorticity (PC) with divergence as the companion prognostic variable.
Solid rocket motor (SRM) slag has been identified as a potential source of man-made orbital debris. The possibility that SRMs (in addition to generating dust particles in the sub-millimeter range) may generate particles up to centimeters in size has caused concern regarding their contribution to the debris environment. Returned surfaces from space do not have sufficient area or exposure time to provide a clear picture of the SRM millimeter and centimeter debris population. Currently, radar observation is probably the only way to collect data showing the debris contribution from SRMs. Such observation is used to sample the debris environment, but it is difficult to obtain accurate orbital elements for the detected debris objects. NASA has developed several models to describe the different orbital debris populations, based on assumed debris production mechanisms to create clouds of debris objects that can be propagated in time. The NASA model, LEGEND (LEO-to-GEO Environment Debris), functions as a time-tested debris model for most debris sources. However, the current LEGEND model does not include contributions from the SRM population. An SRM model has recently been developed by NASA, based on purely theoretical details of SRM production and known SRM launches, but verification with hard data is needed. Because the detections of individual SRM objects cannot be deterministically separated from the total debris observed by radar, the validation of the SRM model can only be done by combining it with the LEGEND breakup model and comparing it with data. By applying observational constraints, the degree of SRM slag contribution to the environment may be estimated. This serves as an observationally sound method from which to calibrate a purely theoretical model into something more realistic. For this study, we use the populations observed by the Haystack radar from 1996 to present. For the SRM debris, we use a historical database of SRM launches, propellant masses, and estimated locations and times of tailoff to produce and propagate the SRM debris clouds. Comparisons with radar data from the ensuing years were made, and the SRM model was altered with respect to size and mass production of slag particles to reflect the populations estimated from the data. The result is a model SRM population that fits within the bounds of the observed environment and estimates of the production and contribution of SRM debris to the environment.
To enable effective management, planning, and operations for future missions that involve a crewed space habitat, operational support must be migrated from Earth to the habitat. Intelligent System Health Management technologies (ISHM) promise to enable the future space habitats to increase the safety and mission success while minimizing operational risks. In this paper, Water Recycling System (WRS) deployed at NASA Ames Research Center's Sustainability Base is used for verification and validation of the proposed solution. Our work includes the development of the WRS simulation model based on its dynamic physical characteristics and the design of Automatic Contingency Management (ACM) framework that integrates fault diagnosis and optimization. In WRS modeling, a nominal model with fault injectors is developed. Fault detection and isolation techniques are then developed for isolating causes and identifying the severity of the faults. Dynamic Programming (DP) based fault mitigation strategies are designed to accommodate the faults in the system. A series of simulations are presented with different fault modes and the results indicate that the proposed ACM system can alleviate the fault in the WRS optimally regarding energy consumption and effects of the fault.
The Lunar Atmosphere Dust Environment Explorer (LADEE) mission will orbit the moon in order to measure the density, composition and time variability of the lunar dust environment. The ground-side and onboard flight software for the mission is being developed using a Model-Based Software methodology. In this technique, models of the spacecraft and flight software are developed in a graphical dynamics modeling package. Flight Software requirements are prototyped and refined using the simulated models. After the model is shown to work as desired in this simulation framework, C-code software is automatically generated from the models. The generated software is then tested in real time Processor-in-the-Loop and Hardware-in-the-Loop test beds. Travelling Road Show test beds were used for early integration tests with payloads and other subsystems. Traditional techniques for verifying computational sciences models are used to characterize the spacecraft simulation. A lightweight set of formal methods analysis, static analysis, formal inspection and code coverage analyses are utilized to further reduce defects in the onboard flight software artifacts. These techniques are applied early and often in the development process, iteratively increasing the capabilities of the software and the fidelity of the vehicle models and test beds.
This report documents research performed by Purdue University under subcontract to the George Mason University (GMU) for the Metroplex Operations effort sponsored by NASA's Airportal Project. Purdue University conducted two tasks in support of the larger efforts led by GMU: a) a literature review on metroplex operations followed by identification and analysis of metroplex dependencies, and b) the analysis of impacts of metroplex operations on the larger U.S. domestic airline service network. The tasks are linked in that the ultimate goal is an understanding of the role of dependencies among airports in a metroplex in causing delays both locally and network-wide. The Purdue team has formulated a system-of-systems framework to analyze metroplex dependencies (including simple metrics to quantify them) and develop compact models to predict delays based on network structure. These metrics and models were developed to provide insights for planners to formulate tailored policies and operational strategies that streamline metroplex operations and mitigate delays and congestion.
Simulations serve as important tools throughout the design and operation of engineering systems. In the context of sys-tems health management, simulations serve many uses. For one, the underlying physical models can be used by model-based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can there-fore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.
Several battery models are under development at JPL based on first principles. The recent models are based on NiH2 and NiMH chemistries. Performance results and computation requirements are discussed.
Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.
The Lunar Atmosphere Dust Environment Explorer (LADEE) mission orbited the moon in order to measure the density, composition, and time variability of the lunar dust environment. The successful mission launched September 7, 2013 and was de-orbited and impacted the moon's surface on April 17, 2014. The ground-side and onboard flight software for the mission was developed using a “Model-Based Software Engineering” (MBSE) methodology combined with strong reuse of Government and Commercial Off-The Shelf (G/COTS) components. Models of the spacecraft and flight software were developed in a graphical dynamics modeling package. Flight Software requirements were prototyped and refined using the simulated models. After the model was shown to work as desired in the simulation framework, C-code software was automatically generated from the models. The auto-generated software was then tested in real-time Processor-in-the-Loop and Hardware-in-the-Loop test beds. “Traveling Road Show” test beds were used for early integration tests with payloads and other subsystems. Traditional techniques for verifying computational sciences models were used to characterize the spacecraft simulation. A lightweight set of formal methods analysis, static analysis, formal inspection, and code coverage analyses were utilized to further reduce defects in the onboard flight software artifacts. These techniques were applied early and often in the development process, iteratively increasing the capabilities of software and fidelity of vehicle models and test beds.
The research program of the aerodynamics, aerothermodynamics and plasmadynamics discipline of NASA's Hypersonic Project is reviewed. Details are provided for each of its three components: 1) development of physics-based models of non-equilibrium chemistry, surface catalytic effects, turbulence, transition and radiation; 2) development of advanced simulation tools to enable increased spatial and time accuracy, increased geometrical complexity, grid adaptation, increased physical-processes complexity, uncertainty quantification and error control; and 3) establishment of experimental databases from ground and flight experiments to develop better understanding of high-speed flows and to provide data to validate and guide the development of simulation tools.
High intensity acoustic radiation occurs when turbulence convects past airframe trailing edges. A mathematical model is developed to predict this acoustic radiation. The model is dependent on the local flow and turbulent statistics above the trailing edge of the flight vehicle airframe. These quantities are dependent on the jet and flight vehicle Mach numbers and jet temperature. A term in the model approximates the turbulent statistics of single-stream heated jet flows and is developed based upon measurement. The developed model is valid for a wide range of jet Mach numbers, jet temperature ratios, and flight vehicle Mach numbers. The model predicts traditional trailing edge noise if the jet is not interacting with the airframe. Predictions of mean-flow quantities and the cross-spectrum of static pressure near the airframe trailing edge are compared with measurement. Finally, predictions of acoustic intensity are compared with measurement and the model is shown to accurately capture the phenomenon.
Ecosystems worldwide are impacted by altered environment conditions resulting from climate, drought, and land use changes. Gaps in the science knowledge base regarding plant community response to these novel and rapid changes limit both science understanding and management of ecosystems. We describe how CE Technologies have enabled the rapid supply of gap-filling science, development of ecosystem simulation models, and remote sensing assessment tools to provide science-informed, adaptive management methods in the impacted aquatic ecosystem of the California Sacramento-San Joaquin River Delta. The Delta is the hub for California's water, supplying Southern California agriculture and urban communities as well as the San Francisco Bay area. The changes in environmental conditions including temperature, light, and water quality and associated expansion of invasive aquatic plants negatively impact water distribution and ecology of the San Francisco Bay/Delta complex. CE technologies define changes in resource use efficiencies, photosynthetic productivity, evapotranspiration, phenology, reproductive strategies, and spectral reflectance modifications in native and invasive species in response to altered conditions. We will discuss how the CE technologies play an enabling role in filling knowledge gaps regarding plant response to altered environments, parameterization and validation of ecosystem models, development of satellite-based, remote sensing tools, and operational management strategies.
Designing stable electrolytes with target properties is an important challenge in realizing next generation energy storage devices. Molten salt eutectics-based electrolytes are known for their stability with minimal parasitic reactions when compared to traditional organic electrolytes and are an attractive option for different battery chemistries. The operating temperature of the molten salt batteries depends on the melting temperature of the eutectic and hence there is a necessity to discover novel low melting temperature molten salt eutectic mixtures for energy storage applications. In this work we develop a high throughput computational screening approach for molten salt mixtures using thermodynamic modeling and machine learning (ML). COSMO-SAC model and ML approaches were independently developed based on the existing experimental data and these models were further used to predict the eutectic melting temperature and composition of several new binary, ternary, and quaternary mixtures. We show that combining ML and thermodynamic modeling strategies is effective in exploring the vast design space of molten salt mixtures.
Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.
Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.
Groundwork was laid for computer models which will assist in the design of a manned lunar base. The models, herein described, will provide the following functions for the successful conclusion of that task: strategic planning; sensitivity analyses; impact analyses; and documentation. Topics addressed include: upper level model description; interrelationship matrix; user community; model features; model descriptions; system implementation; model management; and plans for future action.
Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.
No abstract available