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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 127 records · Page 7

Reduction of Iron (III) Oxide in Microwaves Toward Gasification Studies

The purpose of this study is to reduce hematite (Fe2O3) using hydrogen (H2), carbon monoxide (CO), and methane (CH4) gases using microwave irradiation. Conventional temperature programmed reduction of iron phase transformation under H2, CO, and CH4 agree with the literature. Furthermore, the activation energy for the iron phase transformation in a conventional reactor was in the order of H2>CO>CH4, suggesting that CH4 was the best reductant to initiate the reduction at a much lower temperature than CO, and CO was significantly better than H2 atmosphere. Due to the uneven temperature distribution, the apparent activation energy of microwave reduction under H2 was approximately 1/4th when compared to the conventional reduction in H2. X-ray diffraction (XRD) analysis showed a mixed oxide phase in microwave reduction as opposed to the clear phase transformation in conventional studies. To combat the temperature measurement issues in microwave studies, fiber optic sensors and Forward Looking InfraRed (FLIR) sensors were used to acquire the actual temperature distribution in the catalyst bed. Therefore, a reasonably accurate activation energy estimation was achieved in the catalyst bed. Combining these with the characterization and quantification of the different iron phases in microwave reduction will provide a blueprint for future microwave-assisted gasification studies.

Aireddy, Divakar Reddy↗

Machine-Learning Architecture for Ultrasonic Thermometry

Temperature distribution in solids can be inverted from the speed of sound (SOS) measurements, as has been shown feasible by timing the propagation of the excitation pulse and the train of echoes in ultrasonically segmented waveguides (WGs) and metal components. However, complicated geometries and closely-space echogenic features (EFs) create complex waveforms, from which the segmental time of flights (TOFs) are impossible to estimate using traditional methods. This work describes a machine learning architecture shown to extract temperature information from complex ultrasonic waveforms without explicit measurements of segmental TOFs. We accomplish this by using an autoencoder neural network (NN) to map ultrasonic waveforms into a low-dimensional latent space. A second NN then maps the latent space into unknown temperature distribution along the WG. The proposed architecture was tested in simulations and experimentally. The autoencoder accurately reconstructs the waveforms from their latent representation, several orders of magnitude lower in dimensionality. The obtained latent space was successfully mapped into the temperature of the propagation path.

John, Mason↗

Cross-Scale Land-Atmosphere Experiment (CSLAEX) (Final Report)

It is now well recognized that land-atmosphere interactions play an important role in both weather and climate. These interactions take place over a wide range of spatial and temporal scales. Most of the current formulations of the momentum, heat and moisture transport laws are informed by observations with a limited temporal or spatial scale, and hence may have limited applicability. The objective of this work is to improve the multi-scale representation of the near-surface heat transfer in the ground and in the atmosphere using a combination of modeling and field observations. Our knowledge and predictive capacity in land-atmosphere coupling is limited by our capacity to observe the full-scale spectrum at play. In order to bridge this fundamental gap in our observation capacity I will work to: create the first long-term Integrated Cross-Scale land-Atmosphere Experiment (CSLAEX) since I will work to: measure the components of the surface energy balance at a spatial scale relevant to remote sensing and meteorological applications (5km), along with observations of the nested subscale processes with Distributed Temperature Sensors (DTS). This proposal aims to fill the gaps in modeling and observing land-atmosphere interactions across scales via a novel Cross-Scale Land-Atmosphere Experiment (CSLAEX) to be implemented as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site near Lamont, Oklahoma. The proposed research effort aims at exploring land-atmosphere processes across multiple spatial scales and to investigate the effect of landscape heterogeneity using Distributed Temperature Sensor measurements. The novel instrumentation of the Surface Energy Balance and simultaneous observations of the atmospheric state (boundary layer, cloud, convection) available at the DOE ARM SGP site will be used to create CSLAEX. This dataset will be used to frame and evaluate new transport laws and parameterizations for the boundary layer and the land surface, which can be implemented into the Weather and Research Forecasting (WRF) model and Community Atmospheric Model (CAM). The result of this study should impact multiple fields: meteorology, climate, and hydrology.

54 ENVIRONMENTAL SCIENCES↗

Thermal Management System for an Electric Machine with Additively Manufactured Hollow Conductors with Integrated Heat Pipes: Preprint

This paper discusses steps taken to size a thermal management system for an aircraft propulsion electric machine containing additively manufactured coils integrated with heat pipes aimed at boosting its specific power. Experimental setups are used to size and characterize heat pipes for the application and 3D thermal FEA is used to determine optimum heat transfer coefficient of convective boundaries. Geometric details of fin-based surface area enhancement required to reach target combined overall heat transfer coefficient (U) and surface area (A) performance (UA) in W/K, is worked out for relevant boundaries and the resulting UA is verified in 3D thermal FEA. Thermal management system's UA (by extension specific power) sensitivity to coolant temperature is explored and temperature distribution plots of optimized machine components are presented and discussed.

additive manufacturing↗

Reconstructing 3-D Asymmetries in Laser-Direct-Drive Implosions on OMEGA

Three-dimensional reconstruction algorithms have been developed which determine the hot-spot velocity, hot-spot apparent ion temperature distribution, and fuel areal-density distribution present in laser-direct- drive inertial confinement fusion implosions on the OMEGA laser. These reconstructions rely on multiple independent measurements of the neutron energy spectrum emitted from the fusing plasma. Measurements of the neutron energy spectrum on OMEGA are made using a suite of quasi-orthogonal neutron time-of-flight detectors and a magnetic recoil spectrometer. These spectrometers are positioned strategically around the OMEGA target chamber to provide unique 3-D measurements of the conditions of the fusing hot spot and compressed fuel near peak compression. The uncertainties involved in these 3-D reconstructions are discussed and are used to identify a new nTOF diagnostic line of sight which when built will reduce the uncertainty in the hot-spot apparent ion temperature distribution from 700 to <400 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Reconstructing 3D asymmetries in laser-direct-drive implosions on OMEGA

Three-dimensional reconstruction algorithms have been developed which determine the hot-spot velocity, hot-spot apparent ion temperature distribution, and fuel areal density distribution present in laser-direct-drive inertial con finement fusion implosions on the OMEGA laser. These reconstructions rely on multiple independent measurements of the neutron energy spectrum emitted from the fusing plasma. Measurements of the neutron energy spectrum on OMEGA are made using a suite of quasi-orthogonal neutron time-of-flight detectors and a magnetic recoil spectrometer. These spectrometers are positioned strategically around the OMEGA target chamber to provide unique 3-D measurements of the conditions of the fusing hot spot and compressed fuel near peak compression. Here, the uncertainties involved in these 3-D reconstructions are discussed and are used to identify a new nTOF diagnostic line of sight which when built will reduce the uncertainty in the hot-spot apparent ion temperature distribution from 0.7 to < 0.4 keV.

47 OTHER INSTRUMENTATION↗

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Construction & Building Technology↗

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗

Satellite-Based Ranking of the World’s Hottest and Coldest Cities Reveals Inequitable Distribution of Temperature Extremes

The identification of the world’s hottest and coldest cities fascinates both the public and the scientific community. However, the ranking of city temperatures, especially from the perspective of human discomfort, has been difficult. Here we estimated the monthly mean maximum and minimum 1-km resolution urban temperatures of 13,135 cities worldwide (2003–19) from the thermal discomfort perspective by combining in situ measurements, satellite-based land surface temperatures, fine-resolution intracity data, and reanalysis data. Manama, Bahrain, was identified as the hottest city (48.18° ± 1.31°C) and Yakutsk, Russia (–42.96° ± 0.72°C), as the coldest city. The global city temperatures followed a power-law pattern, characterized by cities with <0.3 million inhabitants covering 80% of the top 20% global cities with extreme temperatures. Our study reveals an inequitable pattern of global city temperature extremes and highlights the urgency of developing appropriate strategies to reduce climate change risks in small- and medium-sized cities with low development levels.

54 ENVIRONMENTAL SCIENCES↗

Abstract for CRADA between National Energy Technology Laboratory and Colonial Pipeline Company

The National Energy Technology Laboratory (NETL) and Colonial Pipeline Company (Participant) will collaborate in the field demonstration of optical fiber sensor systems developed at NETL on Participant’s fuel pipeline. The optical fiber sensor technologies are capable of distributed temperature and strain sensing, distributed acoustic sensing, and ultrasensitive acoustic sensing. Real-time monitoring of these parameters enables pipeline integrity monitoring, security monitoring, flow rate monitoring, etc. Successful demonstration on a real fuel pipeline at Participant’s facilities will validate the sensor technologies and installation methods at a real scale. This effort aligns with NETL’s mission in reliable and sustainable energy and reducing environmental effects due to pipeline failures.

42 ENGINEERING↗

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic stimulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporting electrical resistivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies. Pre-stimulation characterization of the groundwater conditions identified only two hydraulically significant fractures, neither of which transects the central portion of the testbed. Flow and pressure monitoring indicated low preexisting pore pressure conditions likely affected by the mine openings.

15 GEOTHERMAL ENERGY↗

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic simulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporing electrical resisivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies.

15 GEOTHERMAL ENERGY↗

Distributed sensing of electromagnetic components using optical fiber based methods

An OFDR based fiber-optics sensor for distributed real-time temperature rise monitoring of a transformer in operation has been disclosed. The fiber-optic sensor provides an effective solution to monitoring the physical structures of the transformer core, as well as accurately detecting the non-uniform temperature distribution inside the transformer, and thus provides innovative feedback to the transformer design by minimizing the core losses. Additionally, the method may be responsive to the presence of magnetic and electric fields, as well as responsive to various chemical species. The method allows novel approaches to real-time asset monitoring of power transformers while operational.

Ohodnicki, Paul R.↗

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A multi-scale temperature-based strategy to map hydrologic exchange flows in highly dynamic systems

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

54 ENVIRONMENTAL SCIENCES↗

Measuring thermal profiles in high explosives using neural networks

We present a new method for calculating the temperature profile of high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in an experiment would require inserting a large number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use numerical simulation of the heating process to calculate the temperature distribution and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15 °C, which is ∼12% of the range of temperatures in the experiment. We also investigate how the algorithm’s accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. In addition, it has implications for a range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes such as combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operations.

97 MATHEMATICS AND COMPUTING↗