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At least 307 records · Page 17

Atmospheric and wind modeling for ATC

The section on atmospheric modeling covers the following topics: the standard atmosphere, atmospheric variations, atmosphere requirements for ATC, and implementation of a software model for Center/Tracon Advisory System (CTAS). The section on wind modeling covers the following topics: wind data -- NOAA profiler system; wind profile estimation; incorporation of various data types into filtering scheme; spatial and temporal variation; and software implementation into CTAS. The appendices contain Matlab codes for atmospheric routines and for wind estimation.

Slater, Gary L.↗

ISS Propulsion Module Crew Systems Interface Analysis in the Intelligent Synthesis Environment

ERGO, a human modeling software for ergonomic assessment and task analysis, was used for the crew systems interface analysis of the International Space Station (ISS) Propulsion Module (PM). The objective of analysis was to alleviate passageway size concerns. Three basic passageway configuration concepts: (1) 45" clear passageway without centerline offset (2) 50" clear passageway, 12" centerline offset, (3) 50" clear passageway, no centerline offset, and were reviewed. 95 percentile male and female models which were provided by the software performed crew system analysis from an anthropometric point of view. Four scenarios in which the crew floats in microgravity through a 50" no-offset passageway as they carry a 16" x 20" x 30" avionics box were simulated in the 10-weeks of intensive study. From the results of the analysis, concept (3) was the preferred option. A full scale, three-dimensional virtual model of the ISS Propulsion Module was created to experience the sense of the Intelligent Synthesis Environment and to evaluate the usability and applicability of the software.

Chen, Di-Wen↗

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗

Evaluation of Energy Storage Potential of Unconventional Shale Reservoirs Using Numerical Simulation of Cyclic Gas Injection

Compressed air energy storage (CAES) stores energy as compressed air in underground formations, typically salt dome caverns. When electricity demand grows, the compressed air is released through a turbine to produce electricity. CAES in the US is limited to one plant built in 1991, due in part to the inherent risk and uncertainty of developing subsurface storage reservoirs. As an alternative to CAES, we propose using some of the hundreds of thousands of hydraulically fractured horizontal wells to store energy as compressed natural gas in unconventional shale reservoirs. To store energy, produced or “sales” natural gas is injected back into the formation using excess electricity and is later produced through an expander to generate electricity. To evaluate this concept, we performed numerical simulations of cyclic natural gas injection into unconventional shale reservoirs using cmg-gem commercial reservoir modeling software. We tested short-term (diurnal) and long-term (seasonal) energy storage potential by modeling well injection and production gas flowrates as a function of bottom-hole pressure. First, we developed a conceptual model of a single fracture stage in an unconventional shale reservoir to characterize reservoir behavior during cyclic injection and production. Next, we modeled cyclic injection in the Marcellus shale gas play using published data. Results indicate that Marcellus unconventional shale reservoirs could support both short- and long-term energy storage at capacities of 100–1000 kWe per well. The results indicate that energy storage in unconventional shale gas wells may be feasible and warrants further investigation.

25 ENERGY STORAGE↗

General MACOS Interface for Modeling and Analysis for Controlled Optical Systems

The General MACOS Interface (GMI) for Modeling and Analysis for Controlled Optical Systems (MACOS) enables the use of MATLAB as a front-end for JPL s critical optical modeling package, MACOS. MACOS is JPL s in-house optical modeling software, which has proven to be a superb tool for advanced systems engineering of optical systems. GMI, coupled with MACOS, allows for seamless interfacing with modeling tools from other disciplines to make possible integration of dynamics, structures, and thermal models with the addition of control systems for deformable optics and other actuated optics. This software package is designed as a tool for analysts to quickly and easily use MACOS without needing to be an expert at programming MACOS. The strength of MACOS is its ability to interface with various modeling/development platforms, allowing evaluation of system performance with thermal, mechanical, and optical modeling parameter variations. GMI provides an improved means for accessing selected key MACOS functionalities. The main objective of GMI is to marry the vast mathematical and graphical capabilities of MATLAB with the powerful optical analysis engine of MACOS, thereby providing a useful tool to anyone who can program in MATLAB. GMI also improves modeling efficiency by eliminating the need to write an interface function for each task/project, reducing error sources, speeding up user/modeling tasks, and making MACOS well suited for fast prototyping.

Sigrist, Norbert↗

1.2.2.404 – Improving the Representation of Hydropower in Production Cost Models

This project's goal is to improve hydropower's representation in power system models by actively coupling river basin (hydrologic) models with grid operations (production cost) models. Near term, this work provides a foundation that allows improved available flexibility and operational constraints representation. Longer term, the work will provide a template that can be used by commercial production cost modeling software vendors to capture the nuances of hydropower operations in their software offerings.

HYDRO ENERGY↗

NASA Orbital Debris Engineering Model ORDEM 3.1 - Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.1 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.1. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.1 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.1, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.1 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.1 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Vavrin, Andrew B.↗

NASA Orbital Debris Engineering Model ORDEM 3.2 – Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.2 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.2. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.2 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.2, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.2 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.2 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Andrew Vavrin↗

Simulation of Aircraft Behaviour on and Close to the Ground

A guide to the current state of the technology of simulating fixed-wing aircraft handling qualities and performance on or close to the ground is presented and pitfalls which may prevent an adequate implementation are indicated. The scope of possible applications in both aircraft design work and pilot training is considered and the requirements for mathematical model definitions and implementations are discussed. The current requirements for visual and motion systems, cockpit cueing, and software modelling are also reviewed, and illustrated with specific examples in areas of aircraft research and development studies and pilot training uses. Needs for further improvements and additional data acquisition are identified.

Barnes, A. G.↗

Generalized Ocean Color Inversion Model for Retrieving Marine Inherent Optical Properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.

Werdell, P. Jeremy↗

MPCV Exercise Operational Volume Analysis

In order to minimize the loss of bone and muscle mass during spaceflight, the Multi-purpose Crew Vehicle (MPCV) will include an exercise device and enough free space within the cabin for astronauts to use the device effectively. The NASA Digital Astronaut Project (DAP) has been tasked with using computational modeling to aid in determining whether or not the available operational volume is sufficient for in-flight exercise.Motion capture data was acquired using a 12-camera Smart DX system (BTS Bioengineering, Brooklyn, NY), while exercisers performed 9 resistive exercises without volume restrictions in a 1g environment. Data were collected from two male subjects, one being in the 99th percentile of height and the other in the 50th percentile of height, using between 25 and 60 motion capture markers. Motion capture data was also recorded as a third subject, also near the 50th percentile in height, performed aerobic rowing during a parabolic flight. A motion capture system and algorithms developed previously and presented at last years HRP-IWS were utilized to collect and process the data from the parabolic flight [1]. These motions were applied to a scaled version of a biomechanical model within the biomechanical modeling software OpenSim [2], and the volume sweeps of the motions were visually assessed against an imported CAD model of the operational volume. Further numerical analysis was performed using Matlab (Mathworks, Natick, MA) and the OpenSim API. This analysis determined the location of every marker in space over the duration of the exercise motion, and the distance of each marker to the nearest surface of the volume. Containment of the exercise motions within the operational volume was determined on a per-exercise and per-subject basis. The orientation of the exerciser and the angle of the footplate were two important factors upon which containment was dependent. Regions where the exercise motion exceeds the bounds of the operational volume have been identified by determining which markers from the motion capture exceed the operational volume and by how much. A credibility assessment of this analysis was performed in accordance with NASA-STD-7009 prior to delivery to the MPCV program.

mathematical models↗

Geothermal Operational Optimization with Machine Learning

The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.

40 EE - Geothermal Technologies Office (EE-4G)↗

Display system software for the integration of an ADAGE 3000 programmable display generator into the solid modeling package C.A.D. software

A software system that integrates an ADAGE 3000 Programmable Display Generator into a C.A.D. software package known as the Solid Modeling Program is described. The Solid Modeling Program (SMP) is an interactive program that is used to model complex solid object through the composition of primitive geomeentities. In addition, SMP provides extensive facilities for model editing and display. The ADAGE 3000 Programmable Display Generator (PDG) is a color, raster scan, programmable display generator with a 32-bit bit-slice, bipolar microprocessor (BPS). The modularity of the system architecture and the width and speed of the system bus allow for additional co-processors in the system. These co-processors combine to provide efficient operations on and rendering of graphics entities. The resulting software system takes advantage of the graphics capabilities of the PDG in the operation of SMP by distributing its processing modules between the host and the PDG. Initially, the target host computer was a PRIME 850, which was later substituted with a VAX-11/785. Two versions of the software system were developed, a phase 1 and a phase 2. In phase 1, the ADAGE 3000 is used as a frame buffer. In phase II, SMP was functionally partitioned and some of its functions were implemented in the ADAGE 3000 by means of ADAGE's SOLID 3000 software package.

Montoya, R. J.↗

Viscoelastic Material Characterization and Modeling of Photovoltaic Module Packaging Materials for Direct Finite-Element Method Input

Numerical tools, such as the finite-element method, are increasingly used to design and evaluate the photovoltaic (PV) modules, providing for the reduction of development time and improved performance and reliability. However, high-fidelity material models are necessary to accurately model the complex structural behavior of the involved packaging materials. A common simplification used in recent years is to model the polymer materials (i.e., encapsulant and backsheet) as linear elastic, which will lead to inaccurate results. Therefore, in this work, we present a thorough characterization of the time- and temperature-dependent mechanical response of predominant PV module encapsulant and backsheet materials. Based on this material characterization, we developed and experimentally validated generalized Maxwell models to describe each material's viscoelastic response. In addition, we included measurements of the coefficient of thermal expansion and presented all material models in such a fashion for direct input into commercial finite-element method modeling software.

FEM↗

Thermal Management and Thermal Protection Systems

During my internship in the Thermal Design Branch (ES3), I contributed to two main projects: i) novel passive thermal management system for future human exploration, ii) AVCOAT undercut thermal analysis. i) As NASA prepares to further expand human and robotic presence in space, it is well known that spacecraft architectures will be challenged with unprecedented thermal environments. Future exploration activities will have the need of thermal management systems that can provide higher reliability, mass and power reduction and increased performance. In an effort to start addressing the current technical gaps the NASA Johnson Space Center Passive Thermal Discipline has engaged in technology development activities. One of these activities was done through an in-house Passive Thermal Management System (PTMS) design for a lunar lander. The proposed PTMS, functional in both microgravity and gravity environments, consists of three main components: a heat spreader, a novel hybrid wick Variable Conductance Heat Pipe (VCHP), and a radiator. The aim of this PTMS is to keep electronics on a vehicle within their temperature limits (0 and 50 C for the current design) during all mission phases including multiple lunar day/night cycles. The VCHP was tested to verify its thermal performance. I created a thermal math model using Thermal Desktop (TD) and analyzed it to predict the PTMS performance. After testing, the test data provided a means to correlate the thermal math model. This correlation took into account conduction and convection heat transfer, representing the actual benchtop test. Since this PTMS is proposed for space missions, a vacuum test will be taking place to provide confidence that the system is functional in space environments. Therefore, the model was modified to include a vacuum chamber with a liquid nitrogen shroud while taking into account conduction and radiation heat transfer. Infrared Lamps were modelled and introduced into the model to simulate the sun's rays directly impinging on the system. Heating rate of the lamps were calculated by knowing fraction of emitted energy in a wavelength interval and the filament temperature. This version of the model can be used to predict performance of the system under vacuum with extreme cold or hot conditions. Initial testing of the PTMS showed promise, and the thermal math model predicts even better performance in thermal vacuum testing. ii) Thermal Protection Systems (TPS) are required for vehicles which enter earth's atmosphere to protect from aerodynamic heating caused by the friction between the vehicle and atmospheric gases. Orion's heat shield design has two aspects which needed to be analyzed thermally: i) a small excess of adhesive used to bond the outer AVCOAT layer to the inner composite structure tends to seep from under the AVCOAT and form a small bead in between two bricks of AVCOAT, ii) a silicone rubber with different thermophysical properties than AVCOAT fills the gap between two bricks of AVCOAT. I created a thermal model using TD to determine temperature differences that are caused by these two features. To prevent false results, all TD models must be verified against something known. In this case, the TD model was correlated to CHAR, an ablation modelling software used to analyze TPS. Analyzing a node far from the concerning features, we saw that the TD model data match CHAR data, verifying the TD model. Next, the temperature of the silicone rubber as well as the bead of adhesive were analyzed to determine if they exceeded allowable temperatures. It was determined that these two features do not have a significant effect on the max temperature of the heat shield. This model can be modified to check temperatures at various locations of the heat shield where the composite thickness varies.

Hasnain, Aqib↗

Propulsion System Simulation Using the Toolbox for the Modeling and Analysis of Thermodynamic System T-MATS

A simulation toolbox has been developed for the creation of both steady-state and dynamic thermodynamic software models. This paper describes the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), which combines generic thermodynamic and controls modeling libraries with a numerical iterative solver to create a framework for the development of thermodynamic system simulations, such as gas turbine engines. The objective of this paper is to present an overview of T-MATS, the theory used in the creation of the module sets, and a possible propulsion simulation architecture. A model comparison was conducted by matching steady-state performance results from a T-MATS developed gas turbine simulation to a well-documented steady-state simulation. Transient modeling capabilities are then demonstrated when the steady-state T-MATS model is updated to run dynamically.

gas path dynamics↗

Propulsion System Simulation Using the Toolbox for the Modeling and Analysis of Thermodynamic System (T-MATS)

A simulation toolbox has been developed for the creation of both steady-state and dynamic thermodynamic software models. This presentation describes the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), which combines generic thermodynamic and controls modeling libraries with a numerical iterative solver to create a framework for the development of thermodynamic system simulations, such as gas turbine engines. The objective of this presentation is to present an overview of T-MATS, the theory used in the creation of the module sets, and a possible propulsion simulation architecture.

aerothermodynamics↗

Propulsion System Simulation Using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS)

A simulation toolbox has been developed for the creation of both steady-state and dynamic thermodynamic software models. This paper describes the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), which combines generic thermodynamic and controls modeling libraries with a numerical iterative solver to create a framework for the development of thermodynamic system simulations, such as gas turbine engines. The objective of this paper is to present an overview of T-MATS, the theory used in the creation of the module sets, and a possible propulsion simulation architecture. A model comparison was conducted by matching steady-state performance results from a T-MATS developed gas turbine simulation to a well-documented steady-state simulation. Transient modeling capabilities are then demonstrated when the steady-state T-MATS model is updated to run dynamically.

aerothermodynamics↗