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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mathematical modeling of novel porous transport layer architectures for proton exchange membrane electrolysis cells

Thin foil based porous transport layers (PTLs) that contain highly structured pore arrays have shown promise as anode PTLs in proton exchange membrane electrolysis cells. These novel PTLs, fabricated with advanced manufacturing techniques, produce thin, tunable, multifunctional layers with reduced flow and interfacial resistances and high thermal and electric conductivities. To further optimize their design, it is important to understand their fundamental impact on the transport of protons, electrons, and liquid/vapor mixtures in the electrode. In this work, we develop a two-dimensional multiphysics model to simulate the coupled electrochemistry and multiphase transport in an electrolysis cell operated with the novel PTL architecture. The results show that larger pores improve access of water to the anode catalyst layer, which is beneficial for both the oxygen evolution reaction and membrane hydration. Larger pore sizes also improve oxygen gas transport from the catalyst layer, because generated oxygen gas is forced to travel in-plane through the anode catalyst layer until it reaches a pore opening that is connected to a channel. The discussed results confirm that the proposed thin foil based PTLs are fundamentally different from conventional PTLs, such as felts or layered meshes. The model developed in this work also provides generalizable insight into fundamental PEMEC phenomena, such as the competition between liquid and gas phase transport, membrane hydration and water management, and nonuniform electrochemical reactions, which are processes relevant to all PEMEC designs.

25 ENERGY STORAGE↗
NASA NTRS2014

NASA Laboratory Analysis for Manned Exploration Missions

The Exploration Laboratory Analysis (ELA) project supports the Exploration Medical Capability Element under the NASA Human Research Program. ELA instrumentation is identified as an essential capability for future exploration missions to diagnose and treat evidence-based medical conditions. However, mission architecture limits the medical equipment, consumables, and procedures that will be available to treat medical conditions during human exploration missions. Allocated resources such as mass, power, volume, and crew time must be used efficiently to optimize the delivery of in-flight medical care. Although commercial instruments can provide the blood and urine based measurements required for exploration missions, these commercial-off-the-shelf devices are prohibitive for deployment in the space environment. The objective of the ELA project is to close the technology gap of current minimally invasive laboratory capabilities and analytical measurements in a manner that the mission architecture constraints impose on exploration missions. Besides micro gravity and radiation tolerances, other principal issues that generally fail to meet NASA requirements include excessive mass, volume, power and consumables, and nominal reagent shelf-life. Though manned exploration missions will not occur for nearly a decade, NASA has already taken strides towards meeting the development of ELA medical diagnostics by developing mission requirements and concepts of operations that are coupled with strategic investments and partnerships towards meeting these challenges. This paper focuses on the remote environment, its challenges, biomedical diagnostics requirements and candidate technologies that may lead to successful blood-urine chemistry and biomolecular measurements in future space exploration missions.

Laboratory Analysis↗
NASA NTRS2014

NASA Laboratory Analysis for Manned Exploration Missions

The Exploration Laboratory Analysis (ELA) project supports the Exploration Medical Capability Element under the NASA Human Research Program. ELA instrumentation is identified as an essential capability for future exploration missions to diagnose and treat evidence-based medical conditions. However, mission architecture limits the medical equipment, consumables, and procedures that will be available to treat medical conditions during human exploration missions. Allocated resources such as mass, power, volume, and crew time must be used efficiently to optimize the delivery of in-flight medical care. Although commercial instruments can provide the blood and urine based measurements required for exploration missions, these commercial-off-the-shelf devices are prohibitive for deployment in the space environment. The objective of the ELA project is to close the technology gap of current minimally invasive laboratory capabilities and analytical measurements in a manner that the mission architecture constraints impose on exploration missions. Besides micro gravity and radiation tolerances, other principal issues that generally fail to meet NASA requirements include excessive mass, volume, power and consumables, and nominal reagent shelf-life. Though manned exploration missions will not occur for nearly a decade, NASA has already taken strides towards meeting the development of ELA medical diagnostics by developing mission requirements and concepts of operations that are coupled with strategic investments and partnerships towards meeting these challenges. This paper focuses on the remote environment, its challenges, biomedical diagnostics requirements and candidate technologies that may lead to successful blood/urine chemistry and biomolecular measurements in future space exploration missions. SUMMARY The NASA Exploration Laboratory Analysis project seeks to develop capability to diagnose anticipated space exploration medical conditions on future manned missions. To achieve this goal, NASA will leverage existing point-of-care technology to provide clinical laboratory measurements in space. This approach will place the project on a path to minimize sample, reagent consumption, mass, volume and power. For successful use in the space environment, NASA specific conditions such as micro gravity and radiation, for example, will also need to be addressed.

Exploration Medical Capability↗
DOE OSTI2024

Modeling and simulation of multiphase flows

This presentation provides an overview of the National Energy Technology Laboratory’s (NETL) multiphase computational fluid dynamics codes. The highly successful Multiphase Flows with Interphase eXchanges (MFIX) suite has been used to model a wide range of applications including post-combustion carbon capture, bioreactor optimization, and bio-FCC regeneration. MFIX-Exa, a state-of-the-art CFD code, developed under DOE’s Exascale Computing Project, is built on the AMReX software framework (https://amrex-codes.github.io/) and is designed to leverage modern accelerator-based compute architectures. This presentation further reviews the underlying physical models of both MFIX and MFIX-Exa and contrasts their similarities and differences. Examples of past and present CFD simulations will illustrate how scientific computing at NETL is being used not only for scientific exploration but also for design, optimization and scale-up of multiphase flow devices.

Musser, Jordan [NETL]↗
DOE OSTI2022

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

42 ENGINEERING↗
DOE OSTI2019

Computational strategies for design and discovery of nanostructured thermoelectrics

Abstract The contribution of theoretical calculations and predictions in the development of advanced high-performance thermoelectrics has been increasingly significant and has successfully guided experiments to understand as well as achieve record-breaking results. In this review, recent developments in high-performance nanostructured bulk thermoelectric materials are discussed from the viewpoint of theoretical calculations. An effective emerging strategy for boosting thermoelectric performance involves minimizing electron scattering while maximizing heat-carrying phonon scattering on many length scales. We present several important strategies and key examples that highlight the contributions of first-principles-based calculations in revealing the intricate but tractable relationships for this synergistic optimization of thermoelectric performance. The integrated optimization approach results in a fourfold design strategy for improved materials: (1) a significant reduction of the lattice thermal conductivity through multiscale hierarchical architecturing, (2) a large enhancement of the Seebeck coefficient through intramatrix electronic band convergence engineering, (3) control of the carrier mobility through band alignment between the host and second phases, and (4) design of intrinsically low-thermal-conductivity materials by maximizing vibrational anharmonicity and acoustic-mode Gruneisen parameters. These combined effects serve to enhance the power factor while reducing the lattice thermal conductivity. This review provides an improved understanding of how theory is impacting the current state of this field and helps to guide the future search for high-performance thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum utility in simulating the real-time dynamics of the Fermi–Hubbard model using superconducting quantum computers

The Fermi–Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum many-body systems. In this work, we demonstrate the quantum simulation of the one-dimensional Fermi–Hubbard model using IBM's superconducting quantum computers, employing over 100 qubits. We introduce a first-order Trotterization scheme and extend it to an optimized second-order Trotterization for the time evolution in the Fermi–Hubbard model, specifically tailored for the limited qubit connectivity of quantum architectures, such as IBM's platforms. Notably, both Trotterization approaches are scalable and maintain a constant circuit depth at each Trotter step, regardless of the qubit count, enabling us to precisely investigate the relaxation dynamics in the Fermi–Hubbard model by measuring the expectation value of the Néel observable (staggered magnetization) for time-evolved quantum states. Lastly, our successful measurement of expectation values in such large-scale quantum many-body systems, especially at longer time scales with larger entanglement, highlights the quantum utility of superconducting quantum platforms over conventional classical approximation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Autoregressive neural quantum states of Fermi Hubbard models

Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz. Published by the American Physical Society 2025

Ibarra-García-Padilla, Eduardo (ORCID:000000019165↗
DOE OSTI2021

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Finding MIDDLE Ground: Scalable and Secure Distributed Learning

Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.

Bornstein, Marc I.↗
DOE OSTI2020

Noise Erasure in Quantum-Limited Current Amplifiers

Superconducting quantum interference devices (SQUIDs) are extraordinarily sensitive to magnetic flux and thus make excellent current amplifiers for cryogenic applications. One such application of high interest to Sandia is the set-up and state read-out of quantum dot based qubits, where a qubit state is read out from a short current pulse (microseconds to milliseconds long) of approximately 100 pA, a signal that is easily corrupted by noise in the environment. A Parametric SQUID Amplifier can be high bandwidth (in the GHz range), low power dissipation (less than 1pW), and can be easily incorporated into multi-qubit systems. In this SAIL LDRD, we will characterize the noise performance of the parametric amplifier front end -- the SQUID -- in an architecture specific to current readout for spin qubits. Noise is a key metric in amplification, and identifying noise sources will allow us to optimize the system to reduce its effects, resulting in higher fidelity readout. This effort represents a critical step in creating the building blocks of a high speed, low power, parametric SQUID current amplifier that will be needed in the near term as quantum systems with many qubits begin to come on line in the next few years.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2009

Evolutionary Computing Methods for Spectral Retrieval

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Terrile, Richard↗
NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗
NASA NTRS2018

Improvements to Integrated Tradespace Analysis of Communications Architectures (ITACA) Network Loading Analysis Tool

NASA's SCENIC project aims to simplify and reduce the cost of space mission planning by replicating the analysis capabilities of commercially licensed software which are integrated with relevant analysis parameters specific to SCaN assets and SCaN supported user missions. SCENIC differs from current tools that perform similar analyses in that it 1) does not require any licensing fees, 2) will provide an all-in-one package for various analysis capabilities that normally requires add-ons or multiple tools to complete. As part of SCENIC's capabilities, the ITACA network loading analysis tool will be responsible for assessing the loading on a given network architecture and generating a network service schedule. ITACA will allow users to evaluate the quality of service of a given network architecture and determine whether or not the architecture will satisfy the mission's requirements. ITACA is currently under development, and the following improvements were made during the fall of 2017: optimization of runtime, augmentation of network asset pre-service configuration time, augmentation of Brent's method of root finding, augmentation of network asset FOV restrictions, augmentation of mission lifetimes, and the integration of a SCaN link budget calculation tool. The improvements resulted in (a) 25% reduction in runtime, (b) more accurate contact window predictions when compared to STK(Registered Trademark) contact window predictions, and (c) increased fidelity through the use of specific SCaN asset parameters.

analysis↗
NASA NTRS1993

On-board B-ISDN fast packet switching architectures. Phase 1: Study

The broadband integrate services digital network (B-ISDN) is an emerging telecommunications technology that will meet most of the telecommunications networking needs in the mid-1990's to early next century. The satellite-based system is well positioned for providing B-ISDN service with its inherent capabilities of point-to-multipoint and broadcast transmission, virtually unlimited connectivity between any two points within a beam coverage, short deployment time of communications facility, flexible and dynamic reallocation of space segment capacity, and distance insensitive cost. On-board processing satellites, particularly in a multiple spot beam environment, will provide enhanced connectivity, better performance, optimized access and transmission link design, and lower user service cost. The following are described: the user and network aspects of broadband services; the current development status in broadband services; various satellite network architectures including system design issues; and various fast packet switch architectures and their detail designs.

Faris, Faris↗
NASA NTRS2014

Conceptual Launch Vehicle and Spacecraft Design for Risk Assessment

One of the most challenging aspects of developing human space launch and exploration systems is minimizing and mitigating the many potential risk factors to ensure the safest possible design while also meeting the required cost, weight, and performance criteria. In order to accomplish this, effective risk analyses and trade studies are needed to identify key risk drivers, dependencies, and sensitivities as the design evolves. The Engineering Risk Assessment (ERA) team at NASA Ames Research Center (ARC) develops advanced risk analysis approaches, models, and tools to provide such meaningful risk and reliability data throughout vehicle development. The goal of the project presented in this memorandum is to design a generic launch 7 vehicle and spacecraft architecture that can be used to develop and demonstrate these new risk analysis techniques without relying on other proprietary or sensitive vehicle designs. To accomplish this, initial spacecraft and launch vehicle (LV) designs were established using historical sizing relationships for a mission delivering four crewmembers and equipment to the International Space Station (ISS). Mass-estimating relationships (MERs) were used to size the crew capsule and launch vehicle, and a combination of optimization techniques and iterative design processes were employed to determine a possible two-stage-to-orbit (TSTO) launch trajectory into a 350-kilometer orbit. Primary subsystems were also designed for the crewed capsule architecture, based on a 24-hour on-orbit mission with a 7-day contingency. Safety analysis was also performed to identify major risks to crew survivability and assess the system's overall reliability. These procedures and analyses validate that the architecture's basic design and performance are reasonable to be used for risk trade studies. While the vehicle designs presented are not intended to represent a viable architecture, they will provide a valuable initial platform for developing and demonstrating innovative risk assessment capabilities.

Launch Vehicle↗