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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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This document summarizes the energy and cost-savings benefits of wireless lighting sensors, provides an overview of where and how they can be used in buildings, and recommends best practices for different room types.
Researchers at West Virginia University (WVU) propose to demonstrate inexpensive wireless, high-temperature sensors for real-time monitoring of the temperature and corrosion of metal components, which are commonly used in coal-fired boilers. This study presents the development of cost-effective wireless high-temperature sensors for real-time temperature and corrosion monitoring in coal-fired boilers' metal components. The focus is on fabricating and evaluating chipless radio-frequency identification (RFID) sensors capable of operating between 25-1300 ºC. Efforts were directed towards designing passive RFID sensor and interrogator antenna with a broad frequency range, optimizing a microstrip patch antenna sensor integrated into a "peel-and-stick" format for efficient application to various metal specimens without altering their geometry. Additionally, this research aimed to assess the sensor responses under accelerated high-temperature conditions, correlating corrosion and cracking mechanisms with sensor data. An investigation of through-wall data acquisition techniques was also planned, facilitating unobtrusive monitoring of sensor responses housed within metal enclosures. Ultimately, this work sought to establish a robust passive wireless sensor system for the continuous health monitoring of metal components in operational settings, thereby contributing to enhanced safety and efficiency in coal-fired power plants.
Funded by NASA's Constellation Universities Institutes Project (CUIP), we have been developing and testing a system to wirelessly power and collect data from sensors on space platforms in general and, in particular, the harsh environment of spacecraft re-entry. The elimination of wires and associated failures such as chafing, sparking, ageing, and connector issues can increase reliability and design flexibility while reducing costs. These factors present an appealing case for the pursuit of wireless solutions for harsh environments, particularly for their use in space and on spacecraft. We have designed and built a prototype wireless sensor system. The system, with capabilities similar to that of a wired sensor system, was tested in NASA Ames Research Center s Aerodynamic Heating Facility and Interaction Heating Facility. This paper discusses the overall development effort, testing results, as well as future directions.
Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. In conclusion, combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.
This report focuses on integrating TEG and WSN simulators with DC-DC converter as an interface. Here DC stands for direct current. DC-DC converter is essential to balance a wide range of analog, digital, and radio loads acting on the energy source. Also, the voltage level generated by TEGs under varying temperature conditions could be low, irregular, and insufficient to operate WSN, therefore DC-DC is required to boost up the voltage to a desired level. Most of the main problems of DC-DC converters used in TEG system are related to impedance matching between the internal resistance of TEG and the input resistance of DC-DC converter. This report would address the issue associated with dynamic impedance matching under varying temperature conditions in the effort to integrate TEG and WSN. In this effort, dynamic impedance matching algorithms like perturb and observe (P&O) and extremum seeking control (ESC) algorithms will de implemented and compared to achieve maximum peak power tracking (MPPT). In addition, the report will summarize the experimental study performed at BSU on profiling behavior of WSN prototype.
The research objective of this project is to design and demonstrate a low-cost, compact, easy-to-deploy, maintenance-free sensor node technology, and a network of such sensors, which enable the monitoring of multiphysical parameters and can transform today’s ordinary buildings into smart buildings with environmental awareness. We develop the sensor node and network via engineering and integration of existing technologies, including high-efficiency mechanical energy harvesting, and ultralow-power integrated circuits (ICs) for sensing and wireless communication. Through integration and innovative power management via specifically designed low-power control circuits for wireless sensing applications, and tailoring energy-harvesting components to indoor applications, the target products will have smaller volume, higher efficiency, and much lower cost (in both manufacturing and maintenance) than the baseline technology. Our development and commercialization objective is to create prototypes for our target products under the CWRU-Intwine collaboration.
This report presents the development a TEG-powered WSN prototype that could be used in nuclear applications. Different design considerations that are important in assembling different components of a WSN were carefully taken into consideration. In designed WSN is compatible to operate with low-power generating TEG and high-power generating TEG based on application requirements. Also, to maintain appropriate power level across different sensor node components, direct current-to-direct current converter with maximum peak power tracking algorithm is implemented. Storage device with real-time clock is embedded to enable local data storage and real-time time stamping. The wireless communication module selected and implemented is Zig Bee protocol. To accommodate different operating conditions and any loss of power from TEG due to unforeseen reasons, backup battery system is also implemented. Entire assembly is performed on a 2-layer printed circuit board and tested for operation. The outcome was successful and it also provided data to validate the mathematical model developed to estimate average power consumption of a WSN under different operating conditions.
This paper reports on the development of key components required for a self-powered oscillator unit designed to wirelessly transmit its signal under full insertion in high-temperature (HT) harsh-environments (HE), such as those present in power plants and industrial settings (metallurgic, oil extraction, molding, and aerospace). The oscillator employed a silicon carbide (SiC) power transistor and HT passive components on a screen-printed alumina circuit board capable of operation beyond 300 °C. The HT oscillator circuit was powered solely by in-situ energy scavenging thermoelectric generator (TEG) modules using passive cooling, eliminating the need for an external power supply or active cooling. In addition, a silicon-based external booster circuit was used to achieve the required TEG voltage regulation to test the TEG-powered HT oscillator circuit. The TEG-powered oscillator circuit was tested inside a non-metallic furnace from room temperature to over 300 °C for transmission of a wireless signal, which was detected outside the furnace at 11 ft (3.4 m). Such a wireless transmitting system powered only by in-situ TEGs, with no requirement for external power or active cooling, is very attractive for flexible, mobile stand-alone control and sensor units targeted for operation in HT HE conditions found in power plants and industrial settings.
Reliable and efficient sensing becomes increasingly difficult in harsher environments. A sensing module for high-temperature conditions utilizes a digital, rather than analog, implementation on a wireless platform to achieve good quality data transmission. The module comprises a sensor, integrated circuit, and antenna. The integrated circuit includes an amplifier, A/D converter, decimation filter, and digital transmitter. To operate, an analog signal is received by the sensor, amplified by the amplifier, converted into a digital signal by the A/D converter, filtered by the decimation filter to address the quantization error, and output in digital format by the digital transmitter and antenna.
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In-situ monitoring of physical parameters in fossil energy (FE) systems is challenging because of their high operating temperatures and oxidizing environments. This research aims utilize Aerosol Jet (AJ) nanoparticle printing, an advanced additive manufacturing method, to fabricate high temperature sensors for FE applications. Sensor films were fabricated by the AJ printing method with an excellent control over film microstructure and properties, in addition to creating less waste. Printing of six different materials, namely, silver (Ag), nickel (Ni), nickel-chromium (NiCr) alloy, copper (Cu), copper-nickel (CuNi), and carbon nanotubes (CNTs) was carried out for their evaluation of as sensor films. The films were then characterized using impedance spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS). Film microstructure was correlated with the sensor electrical and mechanical behavior at different temperatures (24-500 °C) and frequencies (0.02 - 300 kHz). This knowledge was then used to fabricate, test, and characterize strain and temperature sensors for high temperature applications. The strain sensors were shown to have a very low thermal strain up to 500 °C and a 57% higher gauge factor compared to the commercially available counterparts. This enhance performance was attributed to the inherent porosity in the sensor films that allowed the films to exhibit a metamaterial-like behavior when deformed. The AJ printed temperature sensors were shown to have the highest Seebeck coefficient amongst all the film-based temperature sensors. In addition, fundamental aspects of crystal structure and phase transformations in Ba(Fe0.7Ta0.3)O3-δ (BFTO), a high temperature sensor material, was investigated along with 3D printed antennas that can transmit sensor signals at high temperatures. Two PhD students, one from first generation college-going underrepresented minority community (URM), two postdoctoral fellows, and several masters and undergraduate students were supported by this award. This work opens up the possibility of realizing low-cost high-performance sensor devices using advanced manufacturing techniques for high temperature FE applications.
The Arctic region is a complex and dynamic system, where changes in temperature and partitioning between water in solid, liquid and vapor phase are fundamentally reshaping hydro-biogeochemical fluxes from the bedrock to the atmosphere. The increasing temperature and frequency of extreme heat, precipitation and fire events (Wang et al., 2017; Meredith et al., 2019) have critical consequences for the Arctic ecosystems and people, as well as for the global climate system. Although many observational and remote sensing activities have been on-going, detailed ecosystem processes – particularly related to controls on and impacts of permafrost thaw – are difficult to detect or measure. To improve the understanding and prediction of these extreme events and their consequences, we need a revolution in the way we collect ground-based observations from hillslope to pan-Arctic scales and couple them with satellite imagery and models to deliver rich, actionable, and scale-appropriate data.
This report presents machine learning (ML) analysis of temperature-strain relationships for structural health monitoring of nuclear reactor stainless steel (SS) pipes with the strain gauge sensor directly printed on the pipe with a 3D conformal aerosol jet printer. We investigate correlations for two sensor pairs installed on the same SS304 pipe: commercial K-type thermocouple with a printed gold strain gauge (TC3-SG3), and commercial K-type thermocouple with commercial Kyowa strain gauge (TC0-SG0). The temperature ranges for the sensor pairs TC0-SG0 and TC3-SG3 are 20.00°C to 266.37°C and 39.95°C to 219.28°C respectively. ML algorithms in this study include Linear Regression (baseline method), Ridge Regression, Lasso Regression, and Gradient Boosting. Performance evaluation metrics include Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), R 2 Score, and Explained Variance. Using advanced feature engineering techniques, we extracted 27 temperature-based features and 30 strategic inclusion features. The best performance was obtained with the Gradient Boosting method, which achieves prediction accuracy of R 2 = 0.9999 and RMSE = 7.69 μStrain for TC0-SG0, and R 2 = 0.9998 and RMSE = 18.03 μStrain for TC3-SG3. While the temperature-strain correlations are weaker for the gauge directly printed on the pipe than for the commercial strain gauge, deployment-ready performance exceeding industry standards is achieved for both sensor pairs.
Collaborative strategies for mobile sensor nodes ensure the efficiency and the robustness of data processing, while limiting the required communication bandwidth. In order to solve the problem of pipeline inspection and oil leakage monitoring, a collaborative weighted mobile sensing scheme is proposed. By adopting a weighted mobile sensing scheme, the adaptive collaborative clustering protocol can realize an even distribution of energy load among the mobile sensor nodes in each round, and make the best use of battery energy. A detailed theoretical analysis and experimental results revealed that the proposed protocol is an energy efficient collaborative strategy such that the sensor nodes can communicate with a fusion center and produce high power gain.