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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Multi-level Hierarchical Poly Tree computer architectures

Based on the concept of hierarchical substructuring, this paper develops an optimal multi-level Hierarchical Poly Tree (HPT) parallel computer architecture scheme which is applicable to the solution of finite element and difference simulations. Emphasis is given to minimizing computational effort, in-core/out-of-core memory requirements, and the data transfer between processors. In addition, a simplified communications network that reduces the number of I/O channels between processors is presented. HPT configurations that yield optimal superlinearities are also demonstrated. Moreover, to generalize the scope of applicability, special attention is given to developing: (1) multi-level reduction trees which provide an orderly/optimal procedure by which model densification/simplification can be achieved, as well as (2) methodologies enabling processor grading that yields architectures with varying types of multi-level granularity.

Padovan, Joe↗
NASA NTRS1998

I/O Parallelization for the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

The National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) at the Goddard Space Flight Center (GSFC) has developed the GEOS DAS, a data assimilation system that provides production support for NASA missions and will support NASA's Earth Observing System (EOS) in the coming years. The GEOS DAS will be used to provide background fields of meteorological quantities to EOS satellite instrument teams for use in their data algorithms as well as providing assimilated data sets for climate studies on decadal time scales. The DAO has been involved in prototyping parallel implementations of the GEOS DAS for a number of years and is now embarking on an effort to convert the production version from shared-memory parallelism to distributed-memory parallelism using the portable Message-Passing Interface (MPI). The GEOS DAS consists of two main components, an atmospheric General Circulation Model (GCM) and a Physical-space Statistical Analysis System (PSAS). The GCM operates on data that are stored on a regular grid while PSAS works with observational data that are scattered irregularly throughout the atmosphere. As a result, the two components have different data decompositions. The GCM is decomposed horizontally as a checkerboard with all vertical levels of each box existing on the same processing element(PE). The dynamical core of the GCM can also operate on a rotated grid, which requires communication-intensive grid transformations during GCM integration. PSAS groups observations on PEs in a more irregular and dynamic fashion.

Lucchesi, Rob↗
NASA NTRS2018

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗
NASA NTRS2021

An Assurance Case with a Model at its Core

We describe our pilot study development of an Assurance Case arguing the robustness of a spacecraft demonstration of optical communication. Our Assurance Case addresses the concern that optical communication may be interrupted by the presence of cloud cover, threatening the success of the demonstration. Central to our Assurance Case is its use of a model of atmospheric attenuation to support a key portion of the robustness argument. The conclusion for the demonstration is that its schedule slack plus ability to store data for transmission later accommodates occasional weather-caused atmospheric attenuation.We indicate how the overall structure of the Assurance Case derives from the Objectives Hierarchy set forth in the NASA Reliability and Maintainability standard. We then present the portions of the Assurance Case that argue (1) the atmospheric attenuation model is sufficiently accurate, (2) application of the model shows the desired robustness of the demonstration, and (3) all the model assumptions are satisfied in its application. Lastly, we suggest how an engineering model of the demonstration system and its use could inform the development of the Assurance Case encompassing additional plausible hazards.

DiVenti, Anthony↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
NASA NTRS2017

Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

space archtiecture↗
DOE OSTI2024

QRCODE: Massively parallelized real-time time-dependent density functional theory for periodic systems

We present a new software module, QRCODE (Quantum Research for Calculating Optically Driven Excitations), for massively parallelized real-time time-dependent density functional theory (RT-TDDFT) calculations of periodic systems in the open-source Qbox software package. Our approach utilizes a custom implementation of a fast Fourier transformation scheme that significantly reduces inter-node message passing interface (MPI) communication of the major computational kernel and shows impressive scaling up to 16,344 CPU cores. In addition to improving computational performance, QRCODE contains a suite of various time propagators for accurate RT-TDDFT calculations. As benchmark applications of QRCODE, we calculate the current density and optical absorption spectra of hexagonal boron nitride (h-BN) and photo-driven reaction dynamics of the ozone-oxygen reaction. We also calculate the second and higher harmonic generation of monolayer and multi-layer boron nitride structures as examples of large material systems. Our optimized implementation of RT-TDDFT in QRCODE enables large-scale calculations of real-time electron dynamics of chemical and material systems with enhanced computational performance and impressive scaling across several thousand CPU cores.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Digital data command bus

Command bus constructed from coaxial cable has short segments of its outer jacket and shield removed and replaced with small ferrite cores carrying multiturn windings connected to decoder. Device reduces number of wire pairs required to communicate command data to systems and subsystems.

Milligan, G. C.↗
NASA NTRS2022

Cross-Cutting Flight Infrastructure Improvements on M2020

Mars2020 (M2020) was formulated as a mission that leveraged as much Mars Science Laboratory (MSL) heritage as possible, while focusing major new development efforts on the original and unique elements needed to accomplish the different mission objectives. Well publicized examples of high profile new developments include precision landing, the sampling and caching system, the specific instrument suite, improved mobility via Autonomous Navigation, and later the addition of the Ingenuity helicopter. Less well known are the refinements to the core flight infrastructure, primarily in the cross-cutting functions of Telecom, Avionics, Data Management, Communications Behaviors, and Parameter Management. These enhancements are introduced predominately via flight software, and represent increases in capability that justified their inclusion in an otherwise heritage-focused project environment.Perseverance’s cross-cutting flight infrastructure improvements fall into and across the following five categories. First is a trimming of the software footprint of infrastructure modules, in order to make room for memory demands elsewhere in the system. Second is the minimization of data volume to be downlinked, through various methods such as the incorporation of new compression options. Third is the maximization of the available downlink bandwidth for data, by curtailing content-less data (fill) and introducing an improved UHF proximity link protocol. Fourth is a reduction in vulnerabilities, through increased file system redundancy, robustness, and software process monitoring. Fifth is an increase in operations efficiency by lowering file system mount times, improving parallelism between simultaneous events, minimizing the time to recover from file system errors, streamlining the purging of obsolete data, and reducing the number of commands to service parameters by a factor of 100.Individually, none of the cross-cutting infrastructure improvements are likely to garner headlines, but collectively they appreciably improve the safety and operability of Perseverance over its predecessor. This paper will describe the improvements, their promise, and where applicable, their actual impact in operations.

Bohannon, Emily↗
DOE OSTI2025

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC

Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2024

SmartFuse: Reconfigurable Smart Switches to Accelerate Fused Collectives in HPC Applications

Communication switches have sometimes been augmented to process collectives (e.g., the IBM BlueGene project and the Mellanox SHArP switch). In this work, we find that there is a great acceleration opportunity through the further augmentation of switches to accelerate more complex functions that combine communication with computation. We consider three types of such functions. The first is fully-fused collectives built by fusing multiple existing collectives like Allreduce with Alltoall. The second is semi-fused collectives built by combining a collective with another computation. The third we refer to as higher-order collectives built by combining multiple computations and communications, such as to perform matrix-matrix multiply (PGEMM). In this work, we propose a framework called SmartFuse to accelerate fused collective functions. The core of SmartFuse is a reconfigurable smart switch to support these operations. The semi/fully fused collectives are implemented with a CGRAlike architecture, while higher-order collectives are implemented with a more specialized computational unit that can also schedule communication. Supporting our framework is software to evaluate and translate relevant parts of the input program, compile them into a control data flow graph, and then map this graph to the switch hardware. The proposed framework, once deployed, has the strong potential to accelerate existing HPC applications transparently by encapsulation within an MPI implementation. Experimental results show that this approach improves the performance of the PGEMM kernel, MINIFE, and AMG by, on average, 94%, 15%, and 13%, respectively.

Haghi, Pouya↗
DOE OSTI2025

Low spontaneous Brillouin scattering in anti-resonant hollow-core fibers in GHz frequency range

Brillouin light scattering (BLS) is a powerful experimental tool that can be used to gain insights into the fundamental and applied properties of matter, like dispersions of quasiparticles in a solid, as well as their spatiotemporal dynamics. Many applications of light scattering favor the use of optical fibers in place of free-space optics. In this study, we compare the performance of anti-resonant hollow core fibers to that of conventional solid core fused silica fibers for BLS experiments in the GHz frequency range. Conventional fibers are barely suitable for low-noise measurements because of the spontaneous scattering of photons on various phononic modes present in the core and cladding. In the case of the hollow-core fiber, we identify a range of discrete phononic modes and associate them with the various acoustic modes of the structure surrounding the hollow core using finite-element numerical simulations. The measured relative intensity of the spontaneous BLS signal from these modes is orders of magnitude smaller than that of a solid-core fiber, making anti-resonant hollow-core fibers one of the best solutions for the single-mode light guidance for BLS and potentially other low-noise photonic experiments.

36 MATERIALS SCIENCE↗
NASA NTRS2019

UAS Service Supplier Checkout: How UTM Confirmed Readiness of Flight Tests with UAS Service Suppliers

NASA collaborated with industry partners to develop and test the small Unmanned Aircraft System (sUAS) Traffic Management (UTM) research platform, a software prototype used for developing airspace integration requirements for sUAS operations. The lessons learned from these activities will help inform the Federal Aviation Administration (FAA) on what is needed to safely manage sUAS operations. A core component of the UTM platform is the UAS Service Supplier (USS), which acts as a communications bridge to meet the regulatory and operational requirements. As the UTM partners began USS flight tests, NASA found that it was difficult to get all USSs functioning at comparable quality levels to ensure successful flight tests. Also, NASA anticipated that the FAA would encounter similar challenges when they begin to register USSs for operational use. These realizations led to the development of USS Checkout, a set of processes and tools designed to increase flight test efficiency. We learned that a good USS Checkout process is balanced for simplicity versus test coverage, and is amenable to automation. We also learned that when USS Checkout is a USS prerequisite for flight tests, flight tests were more efficient and effective.

Smith, Irene Skupniewicz↗
NASA NTRS2023

Development of Two High-Energy Bus ‘Cores’ for Rapid Support of Low-TRL and Educational Payloads: A Software-Configured EPS Combined with Flexible C&DH

For several years the TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has relied upon an in-house developed unit to serve both EPS (Electrical Power System) and C&DH (Command and Data Handling) roles along with low data-rate telemetry functions, i.e., serving as the ‘core’ of the spacecraft bus. This ‘core’ has a considerable task given the rapid cadence of the TES program and the typically low-TRL of payloads; configurability and compatibility are key to prevent mission-specific hardware. However, at only 15 watts the current core has become insufficient to support the program’s growing missions and increasingly demanding payloads. To this end, the NOW program is developing new cores to support two TES mission classes: a single-PCB ‘MiniCore’ designed to support 80-watt missions 6U or smaller in LEO, and a three-PCB, radiation-tolerant ‘StackCore’ designed to support 6U and larger missions over 500 watts in LEO and beyond. The ‘MiniCore’ design consists of three main segments: a processor-agnostic C&DH, a software-configured EPS, and a backup low data-rate radio. The design philosophy was to enable rapid-manufacture in a turbulent supply chain, hence the design consists of COTS parts with a focus on those able to be drop-in replaced with radiation-tolerant versions when demanded by the mission. As a single PC-104 sized circuit board, power density and ease of integration also dominated design, demanding the use of modern features such as single-point USB-C for easy charging and monitoring of the spacecraft on the ground. The ‘MiniCore’ can support 80 watts of load, 140 watt-hours of storage, and over 20 watts of optimized solar generation with extensive power monitoring throughout. The ‘MiniCore’ supports one battery pack, six solar-panels, six loads, five actuators, Iridium SBD, and an internal 802.15.4 network. Additionally, the processor-agnostic design can accept any PJRC Teensy 3.x or Adafruit Feather microcontroller unit to enable processor scaling with mission requirements or environment. It is expected a development unit of this design will be completed before conference. The ‘StackCore’ design consists of three stacked PC-104 sized circuit boards: one dedicated to power generation and storage, one dedicated to power distribution, and one dedicated to C&DH tasks. This delineation is necessary to support the transition from highly integrated ICs to discrete analog circuitry, enabling a primarily analog control power system able to operate without software in a radiation environment with finer monitoring compared to the ‘MiniCore’ design. The planned base architecture supports over 500 watts of load, 250 watt-hours of storage, and over 80 watts of optimized solar generation. The power distribution board allows for the use of daughter cards hosting custom converters or interfaces for payloads, in addition to the software-configured supplies used on the ‘MiniCore’. This core stack will be managed by a Vorago ARM M4 microcontroller and support the same wireless communications as the ‘MiniCore’, with optional integration of a NOW S-band radio and attitude determination sensors for ‘black box’ functionality. It is expected the prototype will still be in development during conference.

Spacecraft↗
NASA NTRSDate not supplied

Development of Two High-Energy Bus ‘Cores’ for Rapid Support of Low-TRL and Educational Payloads: A Software-Configured EPS Combined with Flexible C&DH

For several years the TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has relied upon an in-house developed unit to serve both EPS (Electrical Power System) and C&DH (Command and Data Handling) roles along with low data-rate telemetry functions, i.e., serving as the ‘core’ of the spacecraft bus. This ‘core’ has a considerable task given the rapid cadence of the TES program and the typically low-TRL of payloads; configurability and compatibility are key to prevent mission-specific hardware. However, at only 15 watts the current core has become insufficient to support the program’s growing missions and increasingly demanding payloads. To this end, the NOW program is developing new cores to support two TES mission classes: a single-PCB ‘MiniCore’ designed to support 80-watt missions 6U or smaller in LEO, and a three-PCB, radiation-tolerant ‘StackCore’ designed to support 6U and larger missions over 500 watts in LEO and beyond. The ‘MiniCore’ design consists of three main segments: a processor-agnostic C&DH, a software-configured EPS, and a backup low data-rate radio. The design philosophy was to enable rapid-manufacture in a turbulent supply chain, hence the design consists of COTS parts with a focus on those able to be drop-in replaced with radiation-tolerant versions when demanded by the mission. As a single PC-104 sized circuit board, power density and ease of integration also dominated design, demanding the use of modern features such as single-point USB-C for easy charging and monitoring of the spacecraft on the ground. The ‘MiniCore’ can support 80 watts of load, 140 watt-hours of storage, and over 20 watts of optimized solar generation with extensive power monitoring throughout. The ‘MiniCore’ supports one battery pack, six solar-panels, six loads, five actuators, Iridium SBD, and an internal 802.15.4 network. Additionally, the processor-agnostic design can accept any PJRC Teensy 3.x or Adafruit Feather microcontroller unit to enable processor scaling with mission requirements or environment. It is expected a development unit of this design will be completed before conference. The ‘StackCore’ design consists of three stacked PC-104 sized circuit boards: one dedicated to power generation and storage, one dedicated to power distribution, and one dedicated to C&DH tasks. This delineation is necessary to support the transition from highly integrated ICs to discrete analog circuitry, enabling a primarily analog control power system able to operate without software in a radiation environment with finer monitoring compared to the ‘MiniCore’ design. The planned base architecture supports over 500 watts of load, 250 watt-hours of storage, and over 80 watts of optimized solar generation. The power distribution board allows for the use of daughter cards hosting custom converters or interfaces for payloads, in addition to the software-configured supplies used on the ‘MiniCore’. This core stack will be managed by a Vorago ARM M4 microcontroller and support the same wireless communications as the ‘MiniCore’, with optional integration of a NOW S-band radio and attitude determination sensors for ‘black box’ functionality. It is expected the prototype will still be in development during conference.

Spacecraft↗
NASA NTRSDate not supplied

Development of Two High-Energy Bus ‘Cores’

For several years the TechEdSat flight series (TES-n), developed by the Nano Orbital Workshop (NOW) group at NASA Ames, has relied upon an in-house developed unit to serve both EPS (Electrical Power System) and C&DH (Command and Data Handling) roles along with low data-rate telemetry functions, i.e., serving as the ‘core’ of the spacecraft bus. This ‘core’ has a considerable task given the rapid cadence of the TES program and the typically low-TRL of payloads; configurability and compatibility are key to prevent mission-specific hardware. However, at only 15 watts the current core has become insufficient to support the program’s growing missions and increasingly demanding payloads. To this end, the NOW program is developing new cores to support two TES mission classes: a single-PCB ‘MiniCore’ designed to support 80-watt missions 6U or smaller in LEO, and a three-PCB, radiation-tolerant ‘StackCore’ designed to support 6U and larger missions over 500 watts in LEO and beyond. The ‘MiniCore’ design consists of three main segments: a processor-agnostic C&DH, a software-configured EPS, and a backup low data-rate radio. The design philosophy was to enable rapid-manufacture in a turbulent supply chain, hence the design consists of COTS parts with a focus on those able to be drop-in replaced with radiation-tolerant versions when demanded by the mission. As a single PC-104 sized circuit board, power density and ease of integration also dominated design, demanding the use of modern features such as single-point USB-C for easy charging and monitoring of the spacecraft on the ground. The ‘MiniCore’ can support 80 watts of load, 140 watt-hours of storage, and over 20 watts of optimized solar generation with extensive power monitoring throughout. The ‘MiniCore’ supports one battery pack, six solar-panels, six loads, five actuators, Iridium SBD, and an internal 802.15.4 network. Additionally, the processor-agnostic design can accept any PJRC Teensy 3.x or Adafruit Feather microcontroller unit to enable processor scaling with mission requirements or environment. It is expected a development unit of this design will be completed before conference. The ‘StackCore’ design consists of three stacked PC-104 sized circuit boards: one dedicated to power generation and storage, one dedicated to power distribution, and one dedicated to C&DH tasks. This delineation is necessary to support the transition from highly integrated ICs to discrete analog circuitry, enabling a primarily analog control power system able to operate without software in a radiation environment with finer monitoring compared to the ‘MiniCore’ design. The planned base architecture supports over 500 watts of load, 250 watt-hours of storage, and over 80 watts of optimized solar generation. The power distribution board allows for the use of daughter cards hosting custom converters or interfaces for payloads, in addition to the software-configured supplies used on the ‘MiniCore’. This core stack will be managed by a Vorago ARM M4 microcontroller and support the same wireless communications as the ‘MiniCore’, with optional integration of a NOW S-band radio and attitude determination sensors for ‘black box’ functionality. It is expected the prototype will still be in development during conference.

Avery Brock↗
NASA NTRS2024

Core Body Temperature Predictions Using Metabolic Energy Expenditure and Heart Rate During Simulated Extravehicular Activity

Long duration spaceflight missions will require crew to become more autonomous in conducting extravehicular activities (EVA) without direct communication with Mission Control for biomedical support. To enable such autonomy, we are developing a Crew State and Risk Model (CSRM) as a collection of key physiology domains that drive EVA crew capabilities and workloads. One model component of CSRM is human thermal regulation. In this paper, customized development of a baseline model to predict core body temperature is presented using physiology inputs of heart rate and metabolic rate. The model development dataset included a baseline study where participants (n=6, equal male and female) performed a 5-hour EVA in a two-part session while wearing a hybrid space suit simulator (HS3). The first session included an end-to-end EVA traversing 1500 meters to a geology site and traversing back to a habitat conducting geology, payload relocation, and maintenance operations every 500 meters. The second session included standalone tasks of a 2000-meter traverse followed by geology tasks. Thermal measures of core body temperature, local skin temperature, liquid cooling garment temperature, heart rate, and metabolic rate were collected through the test duration. Multiple regression was used to build a linear equation to predict core body temperature from inputs of heart rate and metabolic rate. Heat storage was calculated via predicted core body temperature plus LCG and skin temperatures. The model was tested against a dataset from a pressurized suited test (n=6, equal male and female) in the NASA Active Response Gravity Offload System (ARGOS) conducting similar end-to-end EVA and standalone tasks. Predicted error of the model against the raw test cases was 0.2±0.15 °C. The baseline prediction of core body temperature using heart rates and metabolic rates allows for simple real-time tracking from data collected in-flight to monitor crew consumables and thermal flight limits during EVA.

Bradley Hoffmann↗
NASA NTRS2005

An Approach for Autonomy: A Collaborative Communication Framework for Multi-Agent Systems

Research done during the last three years has studied the emersion properties of Complex Adaptive Systems (CAS). The deployment of Artificial Intelligence (AI) techniques applied to remote Unmanned Aerial Vehicles has led the author to investigate applications of CAS within the field of Autonomous Multi-Agent Systems. The core objective of current research efforts is focused on the simplicity of Intelligent Agents (IA) and the modeling of these agents within complex systems. This research effort looks at the communication, interaction, and adaptability of multi-agents as applied to complex systems control. The embodiment concept applied to robotics has application possibilities within multi-agent frameworks. A new framework for agent awareness within a virtual 3D world concept is possible where the vehicle is composed of collaborative agents. This approach has many possibilities for applications to complex systems. This paper describes the development of an approach to apply this virtual framework to the NASA Goddard Space Flight Center (GSFC) tetrahedron structure developed under the Autonomous Nano Technology Swarm (ANTS) program and the Super Miniaturized Addressable Reconfigurable Technology (SMART) architecture program. These projects represent an innovative set of novel concepts deploying adaptable, self-organizing structures composed of many tetrahedrons. This technology is pushing current applied Agents Concepts to new levels of requirements and adaptability.

Dufrene, Warren Russell, Jr.↗