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Drag Modulated Aerocapture (DMA) for Venus orbiter missions relies on the deployable aeroshell ADEPT to perform orbit insertion Delta-V
NASA Ames Entry Systems and Technology Division has been leading development and testing of the Adaptable, Deployable, Entry and Placement Technology (ADEPT). A teaming partnership with JPL has been established to study and develop a compelling application of the ADEPT mechanically deployable hypersonic decelerator as part of the Venus Drag Modulated Aerocapture (VDMA) JPL R&TD effort. The primary work of this activity is to develop a design tool for the ADEPT drag skirt (e.g. a CFD-anchored CBAERO model) that enables rapid estimation of aerothermal environments for a range of candidate trajectories. This capability will enable rapid feasibility trades and design assessments for early stage mission concept evaluation for ADEPT Drag Modulated Aerocapture applications. Results of using TPSizer provided an ADEPT carbon fabric design thickness of the required number of fabric layers needed for predicted aerothermal environments and still have sufficient fabric remaining for overall vehicle integrity. This resulted in aerothermal design closure of the ADEPT drag skirt. In addition, the similar methodology for Venus DMA was used to assess a similar DMA operation at Earth as part of a possible Technology Demonstration Mission (TDM) for ADEPT DMA. This analysis showed that there is a close similarity of design approaches for an Earth DMA TDM that would be applicable to Venus DMA mission designs.
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Results of a comprehensive computer simulation of the behavior of the earth's inner magnetosphere during a substorm-type event are reported. It is pointed out that the computer model self-consistently computes electric fields, currents, and plasma distributions and velocities in the inner-magnetosphere/ionosphere system; parallel electric fields and ionospheric neutral winds, however, are not included. The basic equations of the model are derived, and the inputs are described. An overview of the results is also given. The first appendix contains derivations of general, useful laws of bounce-averaged gradient, curvature, and E x B drifts in a plasma with isotropic pitch angle distribution. The second appendix gives a description of the numerical method used in the simulation.
Computational fluid dynamics (CFD) enables modeling, analysis, and visualization of phenomena that would usually be impossible or extremely expensive to measure experimentally.
The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.
Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this paper, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: 1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and 2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. Here, we validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of effects of dataset size on IRL.
Computer simulations that demonstrate the value of novel approaches are crucial to developing more flexible and robust power systems operations with high penetrations of renewable energy at multiple geographic and temporal scales. However, optimization-based simulations that depend on forecast data often face challenges in evaluating performance, reproducing results, and testing under realistic simulation scenarios. In this paper, we develop scientific computing best-practices for the validation and reproduction of power systems operational models. We then employ two case studies to demonstrate the proposed validation and reproduction framework.
Several computer models of the thermosphere are discussed. J70MM, the Jacchia 1970 model with matrix and matrix mean output, and J703X, the Jacchia 1970, 1971, and 1977 models, are of primary interest. The subroutines in these programs were studied in detail, and several mistakes were found and corrected. It is proposed that a weighted average of the three-hour geomagnetic index be used in the models rather than a single index value. The densities (for a given date and time) generated by the J703X program (all three models), using both a single value of the index and weighted averages for three different lag times, are tabulated and discussed. Most of the equations used in the models are the results of empirical curve fits. An attempt was made to generate a theoretical prediction of the thermospheric temperature profile based on a simple physical model of atmospheric heat conduction in a spherically-symmetric shell. The exponential heating theory and the temperature dependence of k are discussed.
A computer model has been developed that simulates light propagating through an optical data storage system. A model of a laser beam that originates at a laser diode, propagates through an optical system, interacts with a optical disk, reflects back from the optical disk into the system, and propagates to data and servo detectors is discussed.
A computer model is under continuing development at NASA Glenn Research Center that enables first-order assessments of space power technology. The model, an evolution of NASA Glenn's Array Design Assessment Model (ADAM), is an Excel workbook that consists of numerous spreadsheets containing power technology performance data and sizing algorithms. Underlying the model is a number of databases that contain default values for various power generation, energy storage and power management and distribution component parameters. These databases are actively maintained by a team of systems analysts so that they contain state-of-art data as well as the most recent technology performance projections. Sizing of the power subsystems can be accomplished either by using an assumed mass specific power (W/kg) or energy (Wh/kg) or by a bottoms-up calculation that accounts for individual component performance and masses. The power generation, energy storage and power management and distribution subsystems are sized for given mission requirements for a baseline case and up to three alternatives. This allows four different power systems to be sized and compared using consistent assumptions and sizing algorithms. The component sizing models contained in the workbook are modular so that they can be easily maintained and updated. All significant input values have default values loaded from the databases that can be over-written by the user. The default data and sizing algorithms for each of the power subsystems are described in some detail. The user interface and workbook navigational features are also discussed. Finally, an example study case that illustrates the model's capability is presented.
A detailed space suit computational model is being developed at the Langley Research Center for radiation exposure evaluation studies. The details of the construction of the space suit are critical to estimation of exposures and assessing the risk to the astronaut on EVA. Past evaluations of space suit shielding properties assumed the basic fabric layup (Thermal Micrometeoroid Garment, fabric restraints, and pressure envelope) and LCVG could be homogenized as a single layer overestimating the protective properties over 60 percent of the fabric area. The present space suit model represents the inhomogeneous distributions of LCVG materials (mainly the water filled cooling tubes). An experimental test is performed using a 34-MeV proton beam and high-resolution detectors to compare with model-predicted transmission factors. Some suggestions are made on possible improved construction methods to improve the space suit s protection properties.
With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.