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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 289 records · Page 16

Preparation of Smart Materials by Additive Manufacturing Technologies: A Review

Over the last few decades, advanced manufacturing and additive printing technologies have made incredible inroads into the fields of engineering, transportation, and healthcare. Among additive manufacturing technologies, 3D printing is gradually emerging as a powerful technique owing to a combination of attractive features, such as fast prototyping, fabrication of complex designs/structures, minimization of waste generation, and easy mass customization. Of late, 4D printing has also been initiated, which is the sophisticated version of the 3D printing. It has an extra advantageous feature: retaining shape memory and being able to provide instructions to the printed parts on how to move or adapt under some environmental conditions, such as, water, wind, light, temperature, or other environmental stimuli. This advanced printing utilizes the response of smart manufactured materials, which offer the capability of changing shapes postproduction over application of any forms of energy. The potential application of 4D printing in the biomedical field is huge. Here, the technology could be applied to tissue engineering, medicine, and configuration of smart biomedical devices. Various characteristics of next generation additive printings, namely 3D and 4D printings, and their use in enhancing the manufacturing domain, their development, and some of the applications have been discussed. Special materials with piezoelectric properties and shape-changing characteristics have also been discussed in comparison with conventional material options for additive printing.

36 MATERIALS SCIENCE↗

Superconductivity Devices: Commercial Use of Space

The work described in this report covers various aspects of the Rainbow solid-state actuator and sensor technologies. It is presented in five parts dealing with sensor applications, nonlinear properties, stress-optic and electrooptic properties, stacks and arrays, and publications. The Rainbow actuator technology is a relatively new materials development which had its inception in 1992. It involves a new processing technique for preparing pre-stressed, high lead containing piezoelectric and electrostrictive ceramic materials. Ceramics fabricated by this method produce bending-mode actuator devices which possess several times more displacement and load bearing capacity than present-day benders. Since they can also be used in sensor applications, Rainbows are part of the family of materials known as smart ceramics. During this period, PLZT Rainbow ceramics were characterized with respect to their piezoelectric properties for potential use in stress sensor applications. Studies of the nonlinear and stress-optic/electrooptic birefringent properties were also initiated during this period. Various means for increasing the utility of stress-enhanced Rainbow actuators are presently under investigation.

Haertling, Gene↗

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗

Performance Efficient Launch Vehicle Recovery and Reuse

For decades, economic reuse of launch vehicles has been an elusive goal. Recent attempts at demonstrating elements of launch vehicle recovery for reuse have invigorated a debate over the merits of different approaches. The parameter most often used to assess the cost of access to space is dollars-per-kilogram to orbit. When comparing reusable vs. expendable launch vehicles, that ratio has been shown to be most sensitive to the performance lost as a result of enabling the reusability. This paper will briefly review the historical background and results of recent attempts to recover launch vehicle assets for reuse. The business case for reuse will be reviewed, with emphasis on the performance expended to recover those assets, and the practicality of the most ambitious reuse concept, namely propulsive return to the launch site. In 2015, United Launch Alliance (ULA) announced its Sensible, Modular, Autonomous Return Technology (SMART) reuse plan for recovery of the booster module for its new Vulcan launch vehicle. That plan employs a non-propulsive approach where atmospheric entry, descent and landing (EDL) technologies are utilized. Elements of such a system have a wide variety of applications, from recovery of launch vehicle elements in suborbital trajectories all the way to human space exploration. This paper will include an update on ULA's booster module recovery approach, which relies on Hypersonic Inflatable Aerodynamic Decelerator (HIAD) and Mid-Air Retrieval (MAR) technologies, including its concept of operations (ConOps). The HIAD design, as well as parafoil staging and MAR concepts, will be discussed. Recent HIAD development activities and near term plans including scalability, next generation materials for the inflatable structure and heat shield, and gas generator inflation systems will be provided. MAR topics will include the ConOps for recovery, helicopter selection and staging, and the state of the art of parachute recovery systems using large parafoils for space asset recovery and high altitude deployment. The next proposed HIAD flight demonstration is called HULA (for HIAD on ULA), and will feature a 6m diameter HIAD. An update for the HULA concept will be provided in this paper. As proposed, this demonstration will fly as a secondary payload on an Atlas mission. The Centaur upper stage provides the reentry pointing, deorbit burn, and entry vehicle spin up. The flight test will culminate with a recovery of the HIAD using MAR. HULA will provide data from a Low Earth Orbit (LEO) return aeroheating environment that enables predictive model correlation and refinement. The resultant reduction in performance uncertainties should lead to design efficiencies that are increasingly significant at larger scales. Relevance to human scale Mars EDL using a HIAD will also be presented, and the applicability of the data generated from both HULA and SMART Vulcan flights, and its value for NASA's human exploration efforts will be discussed. A summary and conclusion will follow.

Reed, John G.↗

A study of mechanoelectrical transduction behavior in polyvinyl chloride (PVC) gel as smart sensors

Abstract Polyvinyl chloride (PVC) gels are soft electroactive polymers (EAPs) being researched for soft robotic applications. Sensing properties of these EAPs have not been investigated in detail in regard to fundamental mechanoelectrical transduction behavior, but this smart material has been shown to exhibit a detectable response to external stimuli. This study shows PVC gels to be an extremely sensitive material when undergoing mechanoelectrical transduction and explores some response dependencies and proposes a theoretical framework for mechanoelectrical transduction within the gel. The work presented here also uncovers a very interesting phenomena under extremely low compressive loads during the initial contact with the gel. This phenomenon is attributed to a surface tension creeping motion onto the loading surface with an accompanying polarity inversion in the sensing signal relative to fully loaded gels in compression. Experimental work on hysteresis was also completed showing very little memory in steady state mechanoelectrical response to repeated stepped loading cycles. This study demonstrates the mechanoelectric ability of PVC gels to perform in sensing experiments and acts as a fundamental framework to further broaden the applications of PVC gel sensors.

Instruments & Instrumentation↗

Micro- and nano-scale techniques to explore bacteria and fungi interactions with silicate minerals

Biogeochemical interfaces such as bacteria-mineral, fungi-mineral, and water-mineral are hot spots for important processes and reactions in all environments. Studying these interfaces are useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro- and nano-scale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state-of-art of analytical techniques allows to study solid, liquid, and gas phases and their interactions with biological components and often permits simultaneous study of chemical and physical properties in a site-specific manner. Improved techniques not only increased spatial and temporal resolution with site specificity but also decreased sample preparation, artifact generation, sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe-mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offer some solutions. This review chapter focuses on a subset of the available micro- and nano-scale techniques: electron and atomic force microscopy, and X-ray based spectroscopy of traditional and synchrotron-based applications.

Slicate minerals, microscopy, microbes↗

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High stability of flexible perovskite transparent conductive oxide film via van der Waals heteroepitaxy

Transparent conducting oxides (TCOs), such as indium-tin oxide (ITO) and indium gallium zinc oxide (IGZO), are essential components in the design of flexible optoelectronic devices. However, it is still a challenge to prepare low-cost flexible transparent conductive film for achieving the flexible functionality together with high optical transparency and electrical conductivity. Herein, fluorphlogopite (mica) is selected as the bendable and transparent substrate for the fabrication of low-cost perovskite CaVO 3 TCO epitaxial film. Benefitting from thin mica substrate with high Young’s modulus of 200 GPa, flexible CaVO 3 TCO epitaxial film exhibits high mechanical stability in terms of electrical conductivity and optical transparency after bending up to 10 5 cycles. The change of resistance can reflect the bending curvature and strain state. Finally, our results provide a new pathway to design superior mechanical flexibility and stability CaVO 3 TCO films for application in flexible, light-weight, and portable smart optoelectronic devices.

36 MATERIALS SCIENCE↗

Prediction of Power Measurements Using Adaptive Filters

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyberattack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System 9EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be complex. In signal processing, time-series solutions do not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

Hamad, Khaled↗

Visualizing energy transfer between redox-active colloids

Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficient D CT of the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.

Qu, Alan Subing [Univ. of Illinois at Urbana-Champ↗

Midwest Industrial Assessment Center (Final Technical Report)

The Midwest IAC’s prime center was at the University of Missouri (MU) and the satellite center was at Wichita State University, Kansas. During the 5-year period we conducted at least 96 energy assessments, with recommended energy cost savings of $10.3 million of which $6.5 million was implemented savings. The implemented energy savings were 0.63 Terra BTU and CO2 emissions reduction was 400,000 tons. Thirty-five students completed requirements for a certificate of completion from DOE. IAC students received training in cybersecurity basics and application to manufacturing, fundamentals of energy efficiency, ISO50001, smart manufacturing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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↗

Robotic control and inspection verification

Three areas of possible commercialization involving robots at the Kennedy Space Center (KSC) are discussed: a six degree-of-freedom target tracking system for remote umbilical operations; an intelligent torque sensing end effector for operating hand valves in hazardous locations; and an automatic radiator inspection device, a 13 by 65 foot robotic mechanism involving completely redundant motors, drives, and controls. Aspects concerning the first two innovations can be integrated to enable robots or teleoperators to perform tasks involving orientation and panal actuation operations that can be done with existing technology rather than waiting for telerobots to incorporate artificial intelligence (AI) to perform 'smart' autonomous operations. The third robot involves the application of complete control hardware redundancy to enable performance of work over and near expensive Space Shuttle hardware. The consumer marketplace may wish to explore commercialization of similiar component redundancy techniques for applications when a robot would not normally be used because of reliability concerns.

Davis, Virgil Leon↗

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed.

Cooper, Tommy G.↗

Layerwise Mechanics and Finite Elements for Smart Composite Structures with Piezoelectric Actuators and Sensors

Recent developments on layerwise mechanics for the analysis of composite laminates and structures with piezoelectric actuators and sensors are reviewed. The mechanics implement layerwise representations of displacements and electric potential, and can model both the global and local electromechanical response of smart composite structures. The corresponding finite-element implementations for the static and dynamic analysis of smart piezoelectric composite structures are also summarized. Select application illustrate the accuracy, robustness and capability of the developed mechanics to capture the global and local dynamic response of thin and/or thick laminated piezoelectric plates.

Saravanos, Dimitris A.↗

Distributed sensing of Composite Over-wrapped Pressure Vessels using Fiber-Bragg Gratings

The increasing use of advanced composite materials in the wide range of applications including Space Structures is a great impetus to the development of smart materials. These materials offer a wide range of possibilities within the space program. But before they can be reliably incorporated into space flight applications, additional understanding is required in the area of damage tolerance of these materials. Efforts to enhance our understanding of failure modes, mechanical properties, long and short term environmental effects, cyclic damage accumulation and residual strength are needed. Thus we have employed the use of fiber optical sensors which offers an excellent opportunity exploit these materials through monitoring and characterizing their mechanical properties and thus the integrity of structures made from such materials during their life cycle. Use of these optical innovations provides an insight into structures that have not been available in the past, as well as the technology available to provide real time health monitoring throughout its life cycle. The embedded fiber optical sensor shows a clearly detectable sensitivity to changes in the near strain and stress fields of the host structure promoted by mechanical or thermal loading or, in certain conditions, structural damage. The last ten years have seen a large increase in the use of FBG based monitoring systems in a broad range of applications. Fiber Bragg gratings are use to monitor the structural properties of composite pressure vessels. These gratings optically inscribed into the core of a single mode fiber are used as a tool to monitor the stress strain relation in composite structures. The fiber Bragg sensors are both embedded within the composite laminates and bonded to the surface of the vessel with varying orientations with respect to the carbon fiber in the epoxy matrix. The response of these fiber-optic sensors is investigated by pressurizing the cylinder up to its burst pressure of around 4400 psi. This is done at both ambient and cryogenic temperatures using water and liquid nitrogen. The recorded response is compared with the response from conventional strain gauge also present on the vessel. Additionally, several vessels were tested that had been damaged to simulate different type of events, such as cut tow, delimitation and impact damage.

Grant, Joseph↗