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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Operational Concept for the Smart Landing Facility (SLF)

The purpose of this document is to describe an operational concept for the Smart Landing Facility (SLF). The SLF is proposed as a component of the Small Aircraft Transportation System (SATS) and is envisioned to utilize Communication, Navigation, Surveillance and Air Traffic Management (CNS/ATM) technologies to support higher-volume air traffic operations in a wider variety of weather conditions than are currently possible at airports without an Air Traffic Control Tower (ATCT) or Terminal Radar Approach Control (TRACON). In order to accomplish this, the SLF will provide aircraft sequencing and separation within its terminal airspace (the SLF traffic area) and on the airport surface. The approach taken in this report is to first define and describe the SLF environment and the type of operations and aircraft that must be supported. Services currently provided by an ATCT and TRACON are reviewed and assembled into a set of high-level operational functions. A description of the applicable CNS/ATM technologies that have been deployed in the NAS (National Airspace System) or have been demonstrated to be operationally feasible is presented. A candidate SLF system concept that employs the CNS/ATM technologies is described. This is followed by SLF operational scenarios for minimally-equipped aircraft and for aircraft fully-equipped to make full use of SLF services. An assessment is made of the SLF technology and key research issues are identified.

Thompson, S. D.↗
NASA NTRS2003

A Framework for Intelligent Rocket Test Facilities with Smart Sensors

A long-term center goal at the John C. Stennis Space Center (SSC) is the formulation and implementation of a framework for an Intelligent Rocket Test Facility (IRTF), which incorporates distributed smart sensor elements. The IRTF is to provide reliable, high-confident measurements. Specific objectives include: 1. Definition of a framework and architecture that supports implementation of highly autonomous methodologies founded on basic physical principles and embedded knowledge. 2. Modeling of autonomous sensors and processes as self-sufficient, evolutionary elements. 3. Development of appropriate communications protocols to enable the complex interactions that must take place to allow timely and high-quality flow of of information among all the autonomous elements of the system. 4. Development of lab-scale prototypes of key system elements. Though our application is next-generation rocket test facilities, applications for the approach are much wider and include monitoring of shuttle launch operations, air and spacecraft operations and health monitoring, and other large-scale industrial system operations such as found in processing and manufacturing plans. Elements of prototype IRTF have been implemented in preparation for advanced development and validation using rocket test stand facilities as SSC. This work has identified issues that are important to further development of complex network and should be of interest to other working with sensor networks.

Figueroa, Fernando↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

JPL Innovation Foundry

NASA supports the community of mission principal investigators by helping them ideate, mature, and propose concepts for new missions. As NASA's Federally Funded Research and Development Center (FFRDC), JPL is a primary resource for providing this service. The environmental context for the formulation lifecycle evolves continuously. Contemporary trends include: more competitors; more-complex mission ideas; scarcer formulation resources; and higher standards for technical evaluation. Derived requirements for formulation support include: stable, clear, reliable methods tailored for each stage of the formulation lifecycle; on-demand access to standout technical and programmatic subject-matter experts; optimized, outfitted facilities; smart access to learning embodied in a vast oeuvre of prior formulation work; hands-on method coaching. JPL has retooled its provision of integrated formulation lifecycle support to PIs, teams, and program offices in response to this need. This mission formulation enterprise is the JPL Innovation Foundry.

foundry↗
DOE OSTI2024

Smart Labs: Improving Lab Facilities to Meet Decarbonization Goals

Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. The Smart Labs Toolkit describes a systematic process to help laboratory owners and operators plan, assess, optimize, and manage laboratory facilities. This half-day workshop reviews the four phases of the Smart Labs Toolkit along with an interactive activity to complete a Laboratory Ventilation Risk Assessment. This session will also highlight the Lab Benchmarking Tool, which compares the energy use, emissions, and operational practices of lab buildings with similar facilities. Attendees will also discuss application as well as sharing tools and best practice guides.

assessment↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2001

Structural Acoustic Prediction and Interior Noise Control Technology

This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

Mathur, G. P.↗
NASA NTRS2004

An Architecture for Intelligent Systems Based on Smart Sensors

Based on requirements for a next-generation rocket test facility, elements of a prototype Intelligent Rocket Test Facility (IRTF) have been implemented. A key component is distributed smart sensor elements integrated using a knowledgeware environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

Schmalzel, John↗
DOE OSTI2024

SMART SiC Power ICs: Scalable, Manufacturable, and Robust Technology for SiC Power Integrated Circuits (Final Technical Report)

This collaborative project was initiated with the goal of developing Scalable, Manufacturable, and Robust Technology for SiC Power Integrated Circuits (SMART SiC Power ICs). In pursuit of this objective, innovative designs and fabrication processes were implemented, enabling the development of large-scale (>1 cm²) SiC Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits and high-voltage (400–600 V) lateral power MOSFETs (HV-LDMOS) on 150 mm 4H-SiC substrates. The resulting SMART SiC Power ICs are tailored to support a wide range of applications requiring diverse voltage and power levels, including automotive systems, industrial equipment, electronic data processing, energy harvesting, and power conditioning. To achieve the proposed ‘SMART’ technology for SiC ICs, the team focused on 1) the Development of highly scalable CMOS (with high channel mobilities for n-type and p-type MOSFETs), LDMOS (~600V, 10A rated), and IC technologies, 2) Establishment of a manufacturable process baseline in a production-grade-, 150mm, SiC fabrication facility, and 3) Demonstration of SMART SiC ICs. The project initially comprised of fabricating 5 lots. In lot 1 monolithic integration using a single process was achieved. Here, we were able to successfully accomplish Integrated HV NMOSFET with LV CMOS on N-epi/N+ Substrate. The HV NMOS demonstrated a Breakdown Voltage (BV) more than 600V. Circuit demonstration of CMOS was also another achievement from this lot. In lot 2, priority was in place for isolation and integration. Here we addressed the isolation concerns and integrated the HV NMOS and LV CMOS using the N-epi/P-epi/N+ substrate. Similar to the lot 1, we were able to achieve a BV of 600 V for HV NMOS. Optimized gate oxide process with high channel mobilities, better gate oxide reliability, development of SPICE models, successful ohmic process development, novel wafer area saving design layouts, P+ isolation schemes with channeling implantations and high temperature operational circuits demonstrations are some of the key highlights from lot 1 and lot2. In lot 3, discrete device performances of HV NMOS with a BV ~700V and reliable LV CMOS performances were achieved. Also, novel architectural solutions were successfully implemented to suppress the electric field crowding at the gate oxide for reliable operations. In lot 4, half bridge power driver ICs with a conversion efficiency of (target 90% to 95%) in the 1-5MHz switching frequency range for output power between 25 W to 3 kW have been included in. However, due to the unfortunate events of sudden foundry shutdown (SiCamore Semi) the processing of lot 4 wafers came to a complete stop (January 2024). Arrangements have recently been made to shift the fabrication to another foundry, General Electric Aerospace. The fabrication process now on course (as of December 2024). Characterizations are delayed due to this unfortunate circumstance. The proposed trench architectural-based devices and ICs (lot 5) underwent modifications from the original project proposal. This change was necessitated by limitations in the availability of trench-based processes at commercial production-grade fabrication facilities in the US. Apart from above achievements, a Process Development Kit (PDK) was successfully developed for planar type SiC CMOS/LDMOS.

42 ENGINEERING↗
NASA NTRS2011

NASA's Space Launch System Progress Report

Exploration beyond Earth will be an enduring legacy for future generations, confirming America's commitment to explore, learn, and progress. NASA's Space Launch System (SLS) Program, managed at the Marshall Space Flight Center, is responsible for designing and developing the first exploration-class rocket since the Apollo Program's Saturn V that sent Americans to the Moon. The SLS offers a flexible design that may be configured for the MultiPurpose Crew Vehicle and associated equipment, or may be outfitted with a payload fairing that will accommodate flagship science instruments and a variety of high-priority experiments. Both options support a national capability that will pay dividends for future generations. Building on legacy systems, facilities, and expertise, the SLS will have an initial lift capability of 70 metric tons (mT) and will be evolvable to 130 mT. While commercial launch vehicle providers service the International Space Station market, this capability will surpass all vehicles, past and present, providing the means to do entirely new missions, such as human exploration of asteroids and Mars. With its superior lift capability, the SLS can expand the interplanetary highway to many possible destinations, conducting revolutionary missions that will change the way we view ourselves, our planet and its place in the cosmos. To perform missions such as these, the SLS will be the largest launch vehicle ever built. It is being designed for safety and affordability - to sustain our journey into the space age. Current plans include launching the first flight, without crew, later this decade, with crewed flights beginning early next decade. Development work now in progress is based on heritage space systems and working knowledge, allowing for a relatively quick start and for maturing the SLS rocket as future technologies become available. Together, NASA and the U.S. aerospace industry are partnering to develop this one-of-a-kind asset. Many of NASA's space centers across the country will provide their unique expertise to the Space Launch System endeavor. Unique infrastructure to be used includes the Michoud Assembly Facility for tank manufacturing, Stennis Space Center for engine testing, and Kennedy Space Center for processing and launch. As this panel will discuss, the SLS team is dedicated to doing things differently-from applying lean oversight/insight models to smartly using legacy hardware and existing facilities. Building on the foundation laid by over 50 years of human and scientific space flight--and on the lessons learned from the Apollo, Space Shuttle, and Constellation Programs-the SLS team has delivered both technical trade studies and business case analyses to ensure that the SLS architecture will be safe, affordable, reliable, and sustainable.

Singer, Joan A.↗
NASA NTRS2015

SMART Rotor Wind Tunnel Test Report

The Boeing Company, Mesa Arizona, has been developing smart material actuated rotor technology (SMART) under in-house, DARPA (Defense Advanced Research Projects Agency), NASA and Army funding. A whirl tower test of the SMART active flap rotor system was successfully conducted at the remote test facility (RTF) in Mesa, Arizona, in 2003. Under DARPA and NASA funding, the SMART rotor system was tested in the NASA Ames National Full Scale Aerodynamic Complex (NFAC) 40- by 80-Foot Wind Tunnel in 2008. The DARPA program objectives were to demonstrate the active flap impact on rotor acoustics in forward flight and establish a validation database for noise prediction tools. Under NASA funding, additional wind tunnel tests were conducted, with the objective to demonstrate and quantify vibration, noise, and performance improvements.Wind tunnel testing was successfully and safely concluded, meeting all high priority objectives. The authority, effectiveness, and reliability of the flap actuation system were demonstrated in 65 hours of testing at up to 155 knots and 7,700 pound thrust. Validation data was successfully acquired for four test conditions; blade loads were too high for the high speed condition. The effectiveness of the flap for noise and vibration control was demonstrated conclusively, with results showing significant reductions in BVI (Blade-Vortex Interaction) and in-plane noise as well as vibratory hub loads. The impact of the flap on control power and rotor smoothing was also demonstrated. Data evaluating any benefits in aerodynamic performance and impact on flight controls were acquired, but will need more detailed evaluation. Both open loop control and closed loop feedback control, using continuous time and higher harmonic controllers, were applied.The purpose of the Test Report is to provide a comprehensive document that describes the preparation for and conduct of the wind tunnel test and summarizes the processing and evaluation of wind tunnel test data for the DARPA and the NASA portion of the test. An overview of the wind tunnel test and results can be found in Reference 1. Details on the testing and results for BVI noise, in-plane noise, and vibrations can be found in References 2-4, respectively. These references are four papers, presented at the American Helicopter Society Annual Forum in 2009. A brief description of the delivered electronic data is provided in Reference 5.

Rotor↗
DOE OSTI2024

Key Grid-Interactive Efficient Building Technologies for Federal and Commercial Facilities

This report serves as a resource for building owners and managers interested in deploying GEB technologies in federal and commercial facilities. This document also provides an overview of smart buildings and the prioritization and categorization of GEB technologies that have a high potential to provide grid services.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

A smart end-effector for assembly of space truss structures

A unique facility, the Automated Structures Research Laboratory, is being used to investigate robotic assembly of truss structures. A special-purpose end-effector is used to assemble structural elements into an eight meter diameter structure. To expand the capabilities of the facility to include construction of structures with curved surfaces from straight structural elements of different lengths, a new end-effector has been designed and fabricated. This end-effector contains an integrated microprocessor to monitor actuator operations through sensor feedback. This paper provides an overview of the automated assembly tasks required by this end-effector and a description of the new end-effector's hardware and control software.

Doggett, William R.↗
NASA NTRS2014

Smart Coatings for Launch Site Corrosion Protection

Smart, environmentally friendly paint system for early corrosion detection, mitigation, and healing that will enable supportability in KSC launch facilities and ground systems through their operational life cycles. KSC's Corrosion Technology Laboratory is developing a smart, self-healing coating that can detect and repair corrosion at an early stage. This coating is being developed using microcapsules specifically designed to deliver the contents of their core when corrosion starts.

TechPort Portfolio System↗
NASA NTRS1988

Cost effective development of a national test bed

For several years, the Marshall Space Flight Center has pursued the coordinated development of a Large Space Structures (LSS) National Test Bed for the investigation of numerous technical issues involved in the use of LSS in space. The origins of this development, the current status of the various test facilities and the plans laid down for the next five years' activities are described. Particular emphasis on the control and structural interaction issues has been paid so far; however, immediately emerging are user applications (such as the proposed pinhole occulter facility). In the immediate future, such emerging technologies as smart robots and multibody interactions will be studied. These areas are covered.

Waites, H. B.↗