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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 NTRS2010

Reinventing User Applications for Mission Control

In 2006, NASA Ames Research Center's (ARC) Intelligent Systems Division, and NASA Johnson Space Centers (JSC) Mission Operations Directorate (MOD) began a collaboration to move user applications for JSC's mission control center to a new software architecture, intended to replace the existing user applications being used for the Space Shuttle and the International Space Station. It must also carry NASA/JSC mission operations forward to the future, meeting the needs for NASA's exploration programs beyond low Earth orbit. Key requirements for the new architecture, called Mission Control Technologies (MCT) are that end users must be able to compose and build their own software displays without the need for programming, or direct support and approval from a platform services organization. Developers must be able to build MCT components using industry standard languages and tools. Each component of MCT must be interoperable with other components, regardless of what organization develops them. For platform service providers and MOD management, MCT must be cost effective, maintainable and evolvable. MCT software is built from components that are presented to users as composable user objects. A user object is an entity that represents a domain object such as a telemetry point, a command, a timeline, an activity, or a step in a procedure. User objects may be composed and reused, for example a telemetry point may be used in a traditional monitoring display, and that same telemetry user object may be composed into a procedure step. In either display, that same telemetry point may be shown in different views, such as a plot, an alpha numeric, or a meta-data view and those views may be changed live and in place. MCT presents users with a single unified user environment that contains all the objects required to perform applicable flight controller tasks, thus users do not have to use multiple applications, the traditional boundaries that exist between multiple heterogeneous applications disappear, leaving open the possibility of new operations concepts that are not constrained by the traditional applications paradigm.

Trimble, Jay Phillip↗
NASA NTRS1992

Spaceflight optical disk recorder development

Mass memory systems based on rewriteable optical disk media are expected to play an important role in meeting the data system requirements for future NASA spaceflight missions. NASA has established a program to develop a high performance (high rate, large capacity) optical disk recorder focused on use aboard unmanned Earth orbiting platforms. An expandable, adaptable system concept is proposed based on disk drive modules and a modular controller. Drive performance goals are 10 gigabyte capacity, 300 megabit/s transfer rate, 10 exp -12 corrected bit error rate, and 150 millisec access time. This performance is achieved by writing eight data tracks in parallel on both sides of a 14 in. optical disk using two independent heads. System goals are 160 gigabyte capacity, 1.2 gigabits/s data rate with concurrent I/O, 250 millisec access time, and two to five year operating life on orbit. The system can be configured to meet various applications. This versatility is provided by the controller. The controller provides command processing, multiple drive synchronization, data buffering, basic file management, error processing, and status reporting. Technology developments, design concepts, current status including a computer model of the system and a Controller breadboard, and future plans for the Drive and Controller are presented.

Jurczyk, Stephen G.↗
NASA NTRS2004

Implementation of a Message Passing Interface into a Cloud-Resolving Model for Massively Parallel Computing

The capability for massively parallel programming (MPP) using a message passing interface (MPI) has been implemented into a three-dimensional version of the Goddard Cumulus Ensemble (GCE) model. The design for the MPP with MPI uses the concept of maintaining similar code structure between the whole domain as well as the portions after decomposition. Hence the model follows the same integration for single and multiple tasks (CPUs). Also, it provides for minimal changes to the original code, so it is easily modified and/or managed by the model developers and users who have little knowledge of MPP. The entire model domain could be sliced into one- or two-dimensional decomposition with a halo regime, which is overlaid on partial domains. The halo regime requires that no data be fetched across tasks during the computational stage, but it must be updated before the next computational stage through data exchange via MPI. For reproducible purposes, transposing data among tasks is required for spectral transform (Fast Fourier Transform, FFT), which is used in the anelastic version of the model for solving the pressure equation. The performance of the MPI-implemented codes (i.e., the compressible and anelastic versions) was tested on three different computing platforms. The major results are: 1) both versions have speedups of about 99% up to 256 tasks but not for 512 tasks; 2) the anelastic version has better speedup and efficiency because it requires more computations than that of the compressible version; 3) equal or approximately-equal numbers of slices between the x- and y- directions provide the fastest integration due to fewer data exchanges; and 4) one-dimensional slices in the x-direction result in the slowest integration due to the need for more memory relocation for computation.

Juang, Hann-Ming Henry↗
NASA NTRSDate not supplied

Astrobee: Completed, Current, and Future Research using Free Flying Robots on the International Space Station

After four years on the International Space Station (ISS), the Astrobee Research Facility, has completed over 130 Test Sessions logging over 1000 hours of operations. Managed by the NASA ISS Program OZ office and supported by NASA Ames Research Center (ARC) in California, the Astrobee Team maintains three identical free-flying Astrobee robots for research on the ISS. As a technology demonstration platform, the Astrobee Robots are available for Guest Scientists to use for a spectrum of research capabilities. Astrobee, propelled by battery-operated fans, is designed to autonomously operate throughout most of the USOS (US Orbital Segment), with the objective of minimizing astronaut support. Astrobee carries a suite of six cameras, a two degree-of-freedom (DOF) arm with a gripper that can grasp ISS handrails and other objects, and three payload bays that provide power and data for guest science hardware. Astrobee can autonomously execute hours-long flight plans or be teleoperated from the ground or by astronauts. While the Astrobee Team continues to improve mapping and autonomous flight capabilities, one of the main goals of Astrobee Robots is to provide research opportunities for Guest Scientists. The Astrobee Robot Software (ARS) makes extensive use of the open-source Robot Operating System (ROS). The ARS can be used interchangeably with an Astrobee Simulator or as Astrobee’s onboard software. ARS features include autonomous docking and perching, real-time teleoperations from the ground, plan based autonomous tasks, multi Astrobee communication, among other capabilities. Through simulation software and ground testing laboratories, the Astrobee Team is available to support Guest Scientists during development and testing and lead real-time ISS operations. Guest Scientists can participate in this research opportunity following the Guest Science Lifecycle (GSL) shown in Figure 1: Guest Science Lifecycle below. The Astrobee Team and Guest Scientists have complete research including Gecko materials studies, RFID and sound sensing capabilities, student Robotics Programming Challenges, and Free Flyer formation flight investigations. Current science with the Astrobee Robots includes Free Flyer self-toss studies, new docking capabilities, advanced mapping resolution capabilities, and high resolution panoramic imagery. Future Guest scientists and Astrobee Team research will focus on robotics applications for future NASA missions such as Gateway and Artemis and potential experiments involving human-robot interactions. This presentation will focus on four main subjects, 1) completed, current, and future planned research using the Astrobee robots, 2) how Guest Scientist get from conception to the ISS, 3) Astrobee Facility resources available for Guest Science ground testing and real-time ISS operations support, and 4) lessons learned from four years of ISS operations.

Astrobee↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
DOE OSTI2025

An Innovative High Throughput Genome Releaser for Rapid and Efficient PCR Screening

High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.

Yuan, Guoliang [BATTELLE (PACIFIC NW LAB)]↗
NASA NTRS1994

Development of a Power Electronics Unit for the Space Station Plasma Contactor

A hollow cathode plasma contactor has been baselined as a charge control device for the Space Station (SS) to prevent deleterious interactions of coated structural components with the ambient plasma. NASA LeRC Work Package 4 initiated the development of a plasma contactor system comprised of a Power Electronics Unit (PEU), an Expellant Management Unit (EMU), a command and data interface, and a Plasma Contactor Unit (PCU). A breadboard PEU was designed and fabricated. The breadboard PEU contains a cathode heater and discharge power supply, which were required to operate the PCU, a control and auxiliary power converter, an EMU interface, a command and telemetry interface, and a controller. The cathode heater and discharge supplies utilized a push-pull topology with a switching frequency of 20 kHz and pulse-width-modulated (PWM) control. A pulse ignition circuit derived from that used in arcjet power processors was incorporated in the discharge supply for discharge ignition. An 8088 based microcontroller was utilized in the breadboard model to provide a flexible platform for controller development with a simple command/data interface incorporating a direct connection to SS Mulitplexer/Demultiplexer (MDM) analog and digital I/O cards. Incorporating this in the flight model would eliminate the hardware and software overhead associated with a 1553 serial interface. The PEU autonomously operated the plasma contactor based on command inputs and was successfully integrated with a prototype plasma contactor unit demonstrating reliable ignition of the discharge and steady-state operation.

Hamley, John A.↗
NASA NTRS2019

Connecting Space to Village: Tethys Adoption Within SERVIR

The SERVIR program, a joint development initiative of National Aeronautics and Space Administration (NASA) and United States Agency for International Development (USAID), works in partnership with leading regional organizations world-wide to help developing countries use information provided by Earth observing satellites and geospatial technologies for managing climate risks and land use. SERVIR empowers decision-makers with tools, products, and services to act locally on critical issues related to four key areas: Food Security and Agriculture, Water and Water-related Disasters, Land Use/Land Cover and Ecosystems, Weather and Climate. SERVIR is improving awareness, increasing access to information, and supporting analysis to help people in West Africa, Eastern and Southern Africa, Hindu Kush-Himalaya (HKH), the Lower Mekong, South America and Mesoamerica. NASA funds an Applied Sciences Team (AST), composed of experts from universities and research institutions around the United States. Through AST, SERVIR adopted the Tethys platform introduced by Brigham Young University (BYU) for the implementation of tools to deliver scientific data to stakeholders in the Hindu Kush-Himalaya region. Several Tethys applications developed within SERVIR have raised interest amongst decision makers in the countries served by our regional hubs. They see these new apps as key vehicles to disseminate complex information related to weather models, hydrological models, and air quality monitoring, and as such the scope of our Tethys efforts continue to expand to other regions. As of July 2019, each one of the SERVIR hubs (with the exception of Amazonia, which has only recently started), have stood up Tethys portals and have at least one individual trained in the implementation and maintenance of a Tethys portal and in the creation of basic applications. HKH, with BYU's valuable support, has implemented very sophisticated applications that showcase the possibilities and the benefits that the Tethys platform brings.

Tethys↗
NASA NTRS1988

Power systems for production, construction, life support and operations in space

As one looks to man's future in space it becomes obvious that unprecedented amounts of power are required for the exploration, colonization, and exploitation of space. Activities envisioned include interplanetary travel and LEO to GEO transport using electric propulsion, Earth and lunar observatories, advance space stations, free-flying manufacturing platforms, communications platforms, and eventually evolutionary lunar and Mars bases. These latter bases would start as camps with modest power requirements (kWes) and evolve to large bases as manufacturing, food production, and life support materials are developed from lunar raw materials. These latter activities require very robust power supplies (MWes). The advanced power system technologies being pursued by NASA to fulfill these future needs are described. Technologies discussed will include nuclear, photovoltaic, and solar dynamic space power systems, including energy storage, power conditioning, power transmission, and thermal management. The state-of-the-art and gains to be made by technology advancements will be discussed. Mission requirements for a variety of applications (LEO, GEO, lunar, and Martian) will be treated, and data for power systems ranging from a few kilowatts to megawatt power systems will be represented. In addition the space power technologies being initiated under NASA's new Civilian Space Technology Initiative (CSTI) and Space Leadership Planning Group Activities will be discussed.

Sovie, Ronald J.↗
NASA NTRS1988

Power systems for production, construction, life support, and operations in space

As one looks to man's future in space it becomes obvious that unprecedented amounts of power are required for the exploration, colonization, and exploitation of space. Activities envisioned include interplanetary travel and LEO to GEO transport using electric propulsion, earth and lunar observatories, advance space stations, free-flying manufacturing platforms, communications platforms, and eventually evolutionary lunar and Mars bases. These latter bases would start as camps with modest power requirements (kWes) and evolve to large bases as manufacturing, food production, and life support materials are developed from lunar raw materials. These latter activities require very robust power supplies (MWes). The advanced power system technologies being pursued by NASA to fulfill these future needs are described. Technologies discussed will include nuclear, photovoltaic, and solar dynamic space power systems, including energy storage, power conditioning, power transmission, and thermal management. The state-of-the-art and gains to be made by technology advancements will be discussed. Mission requirements for a variety of applications (LEO, GEO, lunar, and Martian) will be treated, and data for power systems ranging from a few kilowatts to megawatt power systems will be represented. In addition the space power technologies being initiated under NASA's new Civilian Space Technology Initiative (CSTI) and Space Leadership Planning Group Activities will be discussed.

Sovie, Ronald J.↗
NASA NTRS2021

Population Viability Analysis (PVA) as a Platform for Predicting Outcomes of Management Options for the Florida Scrub-Jay in Brevard County

The Florida scrub-jay (FSJ) is a species in decline because of extinction debt caused by habitat fragmentation and degradation. Understanding and managing the species and its habitats is challenging due to the complex interactions among the social system, a dynamic mosaic of scrub habitat, and active management. To provide insights into the possible fates of the FSJ populations of mainland, cape, and island sections of Brevard County, we modeled the population dynamics using the Vortex population viability analysis (PVA) software. Vortex is an individual-based simulation that allowed us to include such factors as demographic rates dependent on habitat state, impact of helpers on breeding success, and helper to breeder transition probabilities responding to availability of nearby vacant optimal habitat and vacancies due to the death of breeders. Detailed modeling of the FSJ was possible only because a lot of data about the species and its habitats have been gathered over decades of intensive research. We followed a phased approach to constructing population models that incorporated the best available science and data to address a variety of conservation actions. The first phase focused on constructing a model that incorporates sociobiology and source-sink habitat dynamics. This model allowed us to address some of the most important questions about population size and habitat quality. Once the model framework was in place, we considered the real landscapes and actual local populations, rather than just generic representations of typical FSJ dynamics. After examining the viability of the metapopulations under current conditions, we explored the likely consequences of various management actions that might slow or reverse population declines.

Population Viability Analysis↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

TrustDER: Trusted, Private and Scalable Coordination of Distributed Energy Resources

In this project, the Stanford and SLAC Teams have developed a Trusted, Private and Scalable platform for coordinating Coordination of Distributed Energy Resources (TrustDER). This is a layered system that ensures private, trusted and scalable coordination and monitoring of DERs. It accommodates a variety of resources, such as solar generation, gensets and loads, with a particular focus on battery systems-based resources, as they are a transformational technology experiencing fast growth in adoption by large critical facilities. The platform can be used as standalone or added to existing aggregation systems to enable trust, privacy and resilience. TrustDER consists of layers that address each of the shortcomings of the existing state of the art. Each layer in the platform can operate independently but provides information to the layers above it to enable a novel form of overall coordination architecture. The project consists of several tasks, with each task dedicated to the design of each layer. Task 2 Resource Virtualization defined a software abstraction layer for distributed energy resources (DERs). The goal of this abstraction was to simplify the implementation of algorithms utilizing cooperation of DERs resources in a variety of use cases. Task 3 is on Secure ID for Asset Authentication. Identity Management Systems (IDMS) are a foundational infrastructure for interactions between entities (organizations, users, devices, and services). Secure ID is blockchain-based a distributed identity management system allowing (1) identity provisioning, (2) authentication, (3) authorization, and (4) identity data sharing for IoT-enabled assets on the electricity grid. In this project, the SLAC team focused on designing and testing Keymaker, a protocol for authenticating device identity managed by Secure ID. Task 5 Private and Safe Integration is focused on the design and evaluation of a DER cooperation scheme which allows for the aggregation of DERs without impacting network reliability. The approach is designed based on realistic assumptions regarding data availability, communication infrastructure limitations, and privacy. Task 6 Scalable Distributed Privacy for Information explored how virtualized batteries could be managed privately. Specifically, it examined the case in which a principal provides a partitioned battery to multiple clients. Task 7 Use Cases was to ensure that this technology was applied in relevant situations and scenarios. Primarily, this means that virtualization needed to be employed in a manner that either improved flexibility, bolstered security or privacy, or decreased costs.

25 ENERGY STORAGE↗
NASA NTRS2009

Distributed Health Monitoring System for Reusable Liquid Rocket Engines

The ability to correctly detect and identify any possible failure in the systems, subsystems, or sensors within a reusable liquid rocket engine is a major goal at NASA John C. Stennis Space Center (SSC). A health management (HM) system is required to provide an on-ground operation crew with an integrated awareness of the condition of every element of interest by determining anomalies, examining their causes, and making predictive statements. However, the complexity associated with relevant systems, and the large amount of data typically necessary for proper interpretation and analysis, presents difficulties in implementing complete failure detection, identification, and prognostics (FDI&P). As such, this paper presents a Distributed Health Monitoring System for Reusable Liquid Rocket Engines as a solution to these problems through the use of highly intelligent algorithms for real-time FDI&P, and efficient and embedded processing at multiple levels. The end result is the ability to successfully incorporate a comprehensive HM platform despite the complexity of the systems under consideration.

Lin, C. F.↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

PrismTech Data Distribution Service Java API Evaluation

My internship duties with Launch Control Systems required me to start performance testing of an Object Management Group's (OMG) Data Distribution Service (DDS) specification implementation by PrismTech Limited through the Java programming language application programming interface (API). DDS is a networking middleware for Real-Time Data Distribution. The performance testing involves latency, redundant publishers, extended duration, redundant failover, and read performance. Time constraints allowed only for a data throughput test. I have designed the testing applications to perform all performance tests when time is allowed. Performance evaluation data such as megabits per second and central processing unit (CPU) time consumption were not easily attainable through the Java programming language; they required new methods and classes created in the test applications. Evaluation of this product showed the rate that data can be sent across the network. Performance rates are better on Linux platforms than AIX and Sun platforms. Compared to previous C++ programming language API, the performance evaluation also shows the language differences for the implementation. The Java API of the DDS has a lower throughput performance than the C++ API.

Riggs, Cortney↗
NASA NTRS2003

An Architectural Concept for Intrusion Tolerance in Air Traffic Networks

The goal of an intrusion tolerant network is to continue to provide predictable and reliable communication in the presence of a limited num ber of compromised network components. The behavior of a compromised network component ranges from a node that no longer responds to a nod e that is under the control of a malicious entity that is actively tr ying to cause other nodes to fail. Most current data communication ne tworks do not include support for tolerating unconstrained misbehavio r of components in the network. However, the fault tolerance communit y has developed protocols that provide both predictable and reliable communication in the presence of the worst possible behavior of a limited number of nodes in the system. One may view a malicious entity in a communication network as a node that has failed and is behaving in an arbitrary manner. NASA/Langley Research Center has developed one such fault-tolerant computing platform called SPIDER (Scalable Proces sor-Independent Design for Electromagnetic Resilience). The protocols and interconnection mechanisms of SPIDER may be adapted to large-sca le, distributed communication networks such as would be required for future Air Traffic Management systems. The predictability and reliabi lity guarantees provided by the SPIDER protocols have been formally v erified. This analysis can be readily adapted to similar network stru ctures.

Maddalon, Jeffrey M.↗