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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
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NASA NTRS1992

Identification of time-varying structural dynamic systems - An artificial intelligence approach

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

Glass, B. J.↗
NASA NTRS2019

Physics-Based Modeling and Simulation of Emerging Battery Technologies for Aerospace

Recently there is a growing interest in the aviation sector to reduce air and noise pollution. Electrochemical energy storage devices such as batteries coupled with a distributed electric propulsion system can reduce noise concerns as well as emissions and allow the concept of Urban Air Mobility to come to fruition. The battery performance needs to improve considerably compared from current state-of-art Li-ion battery (about 200Wh/Kg) to realize all-electric passenger jet for short flights (up to 690 miles). NASA is exploring lithium-oxygen battery chemistry to power hybrid (battery powered electrical system) and all-electric aircraft for short distance and long-distance flights. Li-O2 is one of the advanced Li-ion technologies that promise to provide specific energy of more than 750Wh/Kg. For this presentation, we present our work on improving power density of Li-O2 batteries through the use of multiphysics simulations. Next, a path is outlined to port these model to simulate performance for a new battery chemistry for space application, Li-CO2. Li-CO2 uses carbon dioxide as the active material instead of oxygen. Although this technology is in its early development, the offers two benefits: it can be used as a CO2 scrubber, as oxygen is one of the by-products on charging, and as a backup or a standalone battery for various Mars or Venus missions, where the carbon dioxide content in the atmosphere is high and need battery to operate at higher temperatures.

Mehta, Mohit↗
NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software-engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products are shown for liftoff and booster separation dynamics that provide in-depth insight to the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. These patterns and techniques for SLS dynamics tool construction can be applied for other aerospace simulations.

6DOF↗
NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid, iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced and divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products for liftoff and booster separation dynamics are shown in order to provide in-depth insight into the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. The flexibility and versatility of this tool chain in supporting analyses of such a diverse range of aerospace applications demonstrates the feasibility of applying these patterns and techniques for tool construction to other aerospace simulations.

6DOF↗
NASA NTRSDate not supplied

Comparison of Physical Workload Across Eva-Simulation Analog Environments: Hybrid Space Suit Simulator and Pressurized Suit Testing

NASA conducts research, testing, and training across a variety of analog environments to support characterization of human performance during Extravehicular Activities (EVAs). Time utilizing pressurized suits in an offloaded environment is both limited and expensive, making it challenging to carry out extensive research with these suits. The Human Physiology, Performance, Protection, and Operations (H-3PO) Laboratory at NASA Johnson Space Center designed a Hybrid Space Suit Simulator (HS3) as a low-cost, workload approximator and easy access research tool to provide relevant physical and cognitive workloads during simulated EVAs. A pilot study was conducted in a 1g analog environment where six healthy subjects (3 male, 3 female) underwent simulated 5-hour EVAs in the HS3. This study used a COSMED K5 portable metabolic analyzer and a Polar H10 heart rate monitor to evaluate physical workload during the EVAs. For direct comparison, EVA tasks and timelines were modeled after a similar study conducted in pressurized suits (Mark-III spacesuit, n=3 male; small xPGS spacesuit, n=3 female) at NASA’s Active Response Gravity Offload System (ARGOS) offloaded to Lunar gravity (1/6 g). The 1g HS3 data demonstrated increased metabolic rate when compared to pressurized, Lunar-offloaded suited simulated EVAs for certain tasks including the kneeling scoop (HS3: 1194±199 BTU/hr, ARGOS: 860±170 BTU/hr, p < 0.05) and 20 lb object relocation (HS3: 1651±136 BTU/hr, ARGOS: 1048±252.8 BTU/hr, p < 0.05), but it did not demonstrate any significant differences in heart rate (p > 0.05). Other simulated EVA tasks, such as a 500 m traverse (HS3: 1431±177 BTU/hr, ARGOS: 1212±294 BTU/hr, p > 0.05), showed similar workload profiles with no significant workload differences between HS3 and ARGOS. The HS3 is a useful research tool for increasing workloads in EVA research without pressurized suits, though it may overestimate workload during certain EVA tasks when used in 1g environments.

Zachary Wusk↗
NASA NTRS2012

Zero-Fidelity Simulation: Engaging Team Coordination without Physical, Functional, or Psychological Re-Creation

Team coordination is essential across domains, enabling efficiency and safety. As technology improves, our temptation is to simulate with ever-higher fidelity, by making simulators re-create reality through their physical interfaces, functionality, and by making participants believe they are undertaking the simulated task. However, high-fidelity simulations often miss salient human-human work practices. We introduce the concept of zero-fidelity simulation (ZFS), a move away from literal high-fidelity mimesis of the concrete environment. ZFS alternatively models cooperation and communication as the basis of simulation. The ZFS Team Coordination Game (TeC) is developed from observation of fire emergency response work practice. We identify ways in which team members are mutually dependent on one another for information, and use these as the basis for the ZFS game design. The design creates a need for cooperation by restricting individual activity and requiring communication. The present research analyzes the design of interdependence in the validated ZFS TeC game. We successfully simulate interdependence between roles in emergency response without simulating the concrete environment.

Toups, Zachary O.↗
NASA NTRS1995

Dynamic analysis of astronaut motions in microgravity: Applications for Extravehicular Activity (EVA)

Simulations of astronaut motions during extravehicular activity (EVA) tasks were performed using computational multibody dynamics methods. The application of computational dynamic simulation to EVA was prompted by the realization that physical microgravity simulators have inherent limitations: viscosity in neutral buoyancy tanks; friction in air bearing floors; short duration for parabolic aircraft; and inertia and friction in suspension mechanisms. These limitations can mask critical dynamic effects that later cause problems during actual EVA's performed in space. Methods of formulating dynamic equations of motion for multibody systems are discussed with emphasis on Kane's method, which forms the basis of the simulations presented herein. Formulation of the equations of motion for a two degree of freedom arm is presented as an explicit example. The four basic steps in creating the computational simulations were: system description, in which the geometry, mass properties, and interconnection of system bodies are input to the computer; equation formulation based on the system description; inverse kinematics, in which the angles, velocities, and accelerations of joints are calculated for prescribed motion of the endpoint (hand) of the arm; and inverse dynamics, in which joint torques are calculated for a prescribed motion. A graphical animation and data plotting program, EVADS (EVA Dynamics Simulation), was developed and used to analyze the results of the simulations that were performed on a Silicon Graphics Indigo2 computer. EVA tasks involving manipulation of the Spartan 204 free flying astronomy payload, as performed during Space Shuttle mission STS-63 (February 1995), served as the subject for two dynamic simulations. An EVA crewmember was modeled as a seven segment system with an eighth segment representing the massive payload attached to the hand. For both simulations, the initial configuration of the lower body (trunk, upper leg, and lower leg) was a neutral microgravity posture. In the first simulation, the payload was manipulated around a circular trajectory of 0.15 m radius in 10 seconds. It was found that the wrist joint theoretically exceeded its ulnal deviation limit by as much as 49. 8 deg and was required to exert torques as high as 26 N-m to accomplish the task, well in excess of the wrist physiological limit of 12 N-m. The largest torque in the first simulation, 52 N-m, occurred in the ankle joint. To avoid these problems, the second simulation placed the arm in a more comfortable initial position and the radius and speed of the circular trajectory were reduced by half. As a result, the joint angles and torques were reduced to values well within their physiological limits. In particular, the maximum wrist torque for the second simulation was only 3 N-m and the maximum ankle torque was only 6 N-m.

Newman, Dava J.↗
NASA NTRS2007

Statistical Analyses of Satellite Cloud Object Data from CERES: Comparison with Cloud-Resolving Model Simulations of Tropical Convective Clouds - Part III

The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium- and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Ni o) and March 2000 (weak La Ni a). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud top height while it overestimates the differences in the observed outgoing longwave radiation and cloud top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and in space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

Luo, Yali↗
NASA NTRS2013

SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

Software-in-the-loop and Hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The creation of an offline framework for verifying and validating supervisory failure prognostics and decision making routines is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.

Verification and Validation↗
NASA NTRS2015

Next-Generation Ion Thruster Design Tool

Computational tools that accurately predict the performance of electric propulsion devices are highly desirable and beneficial to NASA and the broader electric propulsion community. The current state of the art in electric propulsion modeling relies heavily on empirical data and numerous computational "knobs." In Phase I of this project, Tech-X Corporation developed the most detailed ion engine discharge chamber model that currently exists. This kinetic model simulates all particles in the discharge chamber along with a physically correct simulation of the electric fields. In addition, kinetic erosion models are included for modeling the ion-impingement effects on thruster component erosion. In Phase II, Tech-X developed a user-friendly computer program for NASA and other governmental and industry customers. Tech-X has implemented a number of advanced numerical routines to bring the computational time down to a commercially acceptable level. NASA now has a highly sophisticated, user-friendly ion engine discharge chamber modeling tool.

Stolz, Peter↗
NASA NTRS2014

CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

This report documents the results of a study to address the long range, strategic planning required by NASA's Revolutionary Computational Aerosciences (RCA) program in the area of computational fluid dynamics (CFD), including future software and hardware requirements for High Performance Computing (HPC). Specifically, the "Vision 2030" CFD study is to provide a knowledge-based forecast of the future computational capabilities required for turbulent, transitional, and reacting flow simulations across a broad Mach number regime, and to lay the foundation for the development of a future framework and/or environment where physics-based, accurate predictions of complex turbulent flows, including flow separation, can be accomplished routinely and efficiently in cooperation with other physics-based simulations to enable multi-physics analysis and design. Specific technical requirements from the aerospace industrial and scientific communities were obtained to determine critical capability gaps, anticipated technical challenges, and impediments to achieving the target CFD capability in 2030. A preliminary development plan and roadmap were created to help focus investments in technology development to help achieve the CFD vision in 2030.

Jeffrey P Slotnick↗
NASA NTRS1992

Algebraic surface design and finite element meshes

Some of the techniques are summarized which are used in constructing C sup 0 and C sup 1 continuous meshes of low degree, implicitly defined, algebraic surface patches in three dimensional space. These meshes of low degree algebraic surface patches are used to construct accurate computer models of physical objects. These meshes are also used in the finite element simulation of physical phenomena (e.g., heat dissipation, stress/strain distributions, fluid flow characteristics) required in the computer prototyping of both the manufacturability and functionality of the geometric design.

Bajaj, Chandrajit L.↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗