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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

LV software for supersonic flow analysis

The NASA Lewis Research Center (LeRC) maintains a leadership position in research into advanced aerospace propulsion systems. For the next generation of aircraft, engine designs continue to involve complex, high-speed flows. Performing the detailed flow diagnostics to properly evaluate these designs requires advanced instrumentation to probe these highly turbulent flows. The hostile flow environment often requires nonintrusive measurement techniques such as the laser velocimeter (LV). Since the LV is a proven instrument for nonintrusive flow measurement, it can provide quantitative velocity data with minimal interference to the flow. Based on anticipated flow conditions, laser velocimeter systems were procured from TSI, Inc. The initial system utilized counter processor technology, but later procurements this past year include a more advanced, correlator-based processor, which significantly improves the overall LV performance. To meet the needs of advanced research into propulsion, this instrument must be integrated into an existing VAX/VMS computer system for data acquisition, processing, and presentation. The work done under this grant before this period concentrated on developing the software required to setup and acquire data from the TSI MI-990 multichannel interface, and the RMR 1989 rotating machinery resolver. With the basis established for controlling the operation of the LV system, software development this past year shifted in emphasis from instrumentation control and data acquisition to data analysis and presentation. The progress of the program is reported.

Bell, William A.↗
DOE OSTI2022

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the “glue” that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5 T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Herein our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1986

A computer modeling methodology and tool for assessing design concepts for the Space Station Data Management System

A computer modeling tool is being developed to assess candidate designs for the Space Station Data Management System (DMS). The DMS is to be a complex distributed computer system including the processor, storage devices, local area networks, and software that will support all processing functions onboard the Space Station. The modeling tool will allow a candidate design for the DMS, or for other subsystems that use the DMS, to be evaluated in terms of parameters. The tool and its associated modeling methodology are intended for use by DMS and subsystem designers to perform tradeoff analyses between design concepts using varied architectures and technologies.

Jones, W. R.↗
DOE OSTI2020

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-physics Modeling of Radiative Heat Transfer and Fluid Flow for the Reactor Cavity Cooling System

High-temperature gas-cooled reactors (HTGRs) are notable for their high thermal efficiency and potential for combined heat and power applications. These reactors are particularly appealing due to their advanced passive safety features. HTGRs utilize passive safety systems that function without requiring active components like pumps or compressors during emergencies. These reactor designs depend on a Reactor Cavity Cooling System (RCCS) to manage decay heat removal from the reactor pressure vessel (RPV) during accident conditions. The RCCS consists of vertical rectangular channels known as "risers" or riser ducts positioned around the RPV. These risers receive heat from the RPV through both convective and radiative heat transfer mechanisms. Understanding the interplay of multiple physical phenomena, such as fluid dynamics, heat transfer, and neutron interactions, is essential for the effective design and operation of nuclear reactors, particularly for systems like the RCCS. Multi-physics simulations provide a comprehensive approach to studying these interactions, offering detailed insights and enhancing accuracy. They are especially important in RCCS designs, where the interaction between radiative and convective heat transfer can significantly impact system performance. By leveraging multi-physics simulations, complex reactor behaviors can be modeled without compromising the fidelity of the underlying physical processes. This work aims to establish a robust methodology for coupling multiple physical processes in an air-cooled RCCS. By focusing on validating this multi-physics approach, the study involves designing test cases that simulate various conditions to verify the numerical models employed. The outcomes of this research will provide critical insights for accurately modeling and optimizing complex nuclear systems like the RCCS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System

NASA↗
NASA NTRS1996

Review of NASA's Planned Mars Program

The exploration of Mars has long been a prime scientific objective of the U.S. planetary exploration program. Yet no U.S. spacecraft has successfully made measurements at Mars since the Viking missions of the late 1970s. Mars Observer, which was designed to conduct global observations from orbit, failed just before orbit insertion in 1993. The Russian spacecraft Phobos 2 did succeed in making some observations of the planet in 1989, but it was designed primarily to observe Phobos, the innermost satellite of Mars; the spacecraft failed 2 months after insertion into Mars orbit during the complex maneuvers required to rendezvous with the martian satellite. In fall 1996 NASA plans to launch Mars Pathfinder for a landing on the martian surface in mid-1997. This spacecraft is one of the first two missions in NASA's Discovery program that inaugurates a new style of planetary exploration in which missions are low-cost (less than $150 million) and have very focused science objectives. As can be seen in the comparative data presented in Box 1, this mission is considerably smaller in terms of cost, mass, and scope than NASA's previous Mars missions. NASA's FY 1995 budget initiated a continuing Mars exploration program, called Mars Surveyor, that involves multiple launches of spacecraft as small as or smaller than Mars Pathfinder to Mars over the next several launch opportunities, which recur roughly every 26 months. The first mission in the program, Mars Global Surveyor, set for launch late in 1996, is intended to accomplish many of the objectives of the failed Mars Observer. Like the Discovery program, Mars Surveyor is a continuing series of low-cost missions, each of which has highly focused science objectives. See Box 1 for comparative details of those Surveyor missions currently defined. Around the same time that the Mars Surveyor series was chosen as the centerpiece of NASA's solar system exploration program, the Committee on Planetary and Lunar Exploration (COMPLEX) designated Mars as one of four scientific targets for emphasis in future studies. It was against this background that the Space Studies Board charged COMPLEX to review whether the Mars Pathfinder and Surveyor programs, as presently conceived, satisfy the highest priorities for understanding Mars as provided in its report, An Integrated Strategy for the Planetary Sciences: 1995-2010. The present document is COMPLEX's assessment of the scientific potential of NASA's new approach to Mars exploration. This assessment considers how well the scientific objectives of the Mars Surveyor program match those of the Integrated Strategy; it also addresses some advantages and disadvantages of the smaller-faster-cheaper approach to the exploration of Mars. The capabilities of the various instruments are not discussed in detail since the Mars Observer instruments, all of which are scheduled for reflight, have already been assessed by COMPLEX2 and later instruments are, in general, not yet well defined.

Source record↗
NASA NTRS2004

Spitzer Space Telescope in-orbit checkout and science verification operations

Spitzer Space Telescope, the fourth and final of NASA's great observatories, and the first mission in NASA's Origins Program was launched 25 August 2003 into an Earth-trailing solar orbit. The observatory was designed to probe and explore the universe in the infrared. Before science data could be acquired, however, the observatory had to be initialized, characterized, calibrated, and commissioned. A two phased operations approach was defined to complete this work. These phases were identified as In-Orbit Checkout (IOC) and Science Verification (SV). Because the observatory lifetime is cryogen-limited these operations had to be highly efficient. The IOC/SV operations design accommodated a pre-defined distributed organizational structure and a complex, cryogenic flight system. Many checkout activities were inter-dependent, and therefore the operations concept and ground data system had to provide the flexibility required for a 'short turn-around' environment. This paper describes the adaptive operations system design and evolution, implementation, and lessons-learned from the completion of IOC/SV.

space verification↗
DOE OSTI2020

Grand canonical inverse design of multicomponent colloidal crystals

Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. We present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Towards an integral computer environment supporting system operations analysis and conceptual design

VITROCISET has in house developed a prototype tool named System Dynamic Analysis Environment (SDAE) to support system engineering activities in the initial definition phase of a complex space system. The SDAE goal is to provide powerful means for the definition, analysis, and trade-off of operations and design concepts for the space and ground elements involved in a mission. For this purpose SDAE implements a dedicated modeling methodology based on the integration of different modern (static and dynamic) analysis and simulation techniques. The resulting 'system model' is capable of representing all the operational, functional, and behavioral aspects of the system elements which are part of a mission. The execution of customized model simulations enables: the validation of selected concepts with respect to mission requirements; the in-depth investigation of mission specific operational and/or architectural aspects; and the early assessment of performances required by the system elements to cope with mission constraints and objectives. Due to its characteristics, SDAE is particularly tailored for nonconventional or highly complex systems, which require a great analysis effort in their early definition stages. SDAE runs under PC-Windows and is currently used by VITROCISET system engineering group. This paper describes the SDAE main features, showing some tool output examples.

Barro, E.↗
DOE OSTI2020

A Novel AC to AC Wireless Power Transfer System for EV Charging Applications

In this paper, a novel ac to ac wireless power transfer (WPT) system is introduced for electric vehicle (EV) charging applications to reduce cost and design complexity. The presented wireless power transfer concept achieves unity power factor (UPF) on the grid-side by using a hybrid frequency ac / ac converter without an additional converter stage and closed loop control compared to the conventional systems. Due to inherent merit of the proposed ac / ac converter, ac input with 60 Hz grid frequency can be directly transferred to the load by superimposing with high frequency switching signal through the wireless coils. To validate the theoretical analysis of the proposed WPT system, the experimental results of the proposed converter are provided for 650 W output power by using 6 inches air gap between the couplers with the input of 110 VRMS ac source. The system overall efficiency is measured 89 % achieving 0.99 power factor (PF) and 1.5 % current total harmonic distortion (THD).

Asa, Erdem↗
NASA NTRS1986

Space station data management system assessment methodology

A computer-aided modeling tool and methodology was developed and is currently being used to assess candidate designs for the Space Station Data Management System (DMS). The DMS will be a complex distributed computer system including processors, storage devices, local area networks, and software that will support all processing functions on board the Space Station. The methodology produces assessments of the performance, reliability, cost, and physical attributes of the candidate designs. This paper describes the architecture and design of the modeling tool and presents the modeling methodology.

Jones, W. R.↗
NASA NTRS2023

Mars Sample Return and The Capture, Containment, and Return System New Design and Path To 2027 Launch

The Mars Sample Return (MSR) campaign is one of the most ambitious and complex exoplanet scientific exploration missions currently underway. With the participation of NASA, ESA, and a large number of industry partners, MSR aims to bring back Martian rock and atmosphere samples with the goal of answering key questions about Mars’ geological, climatological, and (potentially) biological evolution. To accomplish this ambitious goal, the campaign relies on three distinct flight elements and a ground element. The Earth Return Orbiter (ERO) mission that would host the Capture, Containment, and Return System (CCRS) is the last element of the trio. The mission would capture the orbiting sample in low Mars orbit (launched into orbit by another mission), contain it and return it to Earth, landing it at the Utah Test and Training Range (UTTR). The CCRS continues its development. Since its early architecture, several changes were adopted by the project in order to improve overall payload efficiency and reduce mass. CCRS is currently on track to achieve the next milestone, Critical Design Review, expected in late 2024. This paper will discuss the CCRS design, how the current architecture contributes to an improved mission baseline, and the next critical steps of the mission towards its launch.

Mars mission↗
NASA NTRS1992

An intelligent decomposition approach for efficient design of non-hierarchic systems

The design process associated with large engineering systems requires an initial decomposition of the complex systems into subsystem modules which are coupled through transference of output data. The implementation of such a decomposition approach assumes the ability exists to determine what subsystems and interactions exist and what order of execution will be imposed during the analysis process. Unfortunately, this is quite often an extremely complex task which may be beyond human ability to efficiently achieve. Further, in optimizing such a coupled system, it is essential to be able to determine which interactions figure prominently enough to significantly affect the accuracy of the optimal solution. The ability to determine 'weak' versus 'strong' coupling strengths would aid the designer in deciding which couplings could be permanently removed from consideration or which could be temporarily suspended so as to achieve computational savings with minimal loss in solution accuracy. An approach that uses normalized sensitivities to quantify coupling strengths is presented. The approach is applied to a coupled system composed of analysis equations for verification purposes.

Bloebaum, Christina L.↗