Mid-Infrared Laser-Absorption-Spectroscopy Measurements of Temperature, Pressure, and NO at 500 kHz in Shock-Heated Air.
Abstract not provided.
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Abstract not provided.
Multi-scale problems are commonly known in many scientific and engineering fields where microscopic behaviors are coupled with macroscopic processes. This is also an unsolved problem in low-temperature plasma chemistry where hundreds of chemical species are involved in thousands of chemical reactions. To address this problem, a physics-informed data-driven modeling is developed to solve such a multi-scale problem using the experimental Fourier-transform infrared spectroscopy (FTIR) measurements of several species’ concentrations. The modeling based on modern machine learning techniques provides concentrations of other relevant species along with the electron temperature and gas temperature at the location of FTIR measurements. For example, the concentrations O, OH, and H 2 O 2 play key roles in plasma-based cancer therapy. This approach overcomes the multi-scale difficulties of microscopic low-temperature plasma chemistry coupling with macroscopic gas flow and allows the acquisition of a full picture of output species concentrations. Presented here for the helium-air jet at atmospheric pressure, the ML-based modeling can be used to describe and possibly control multiscale systems using partial experimental data sets.
The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management.
The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management. Raw data.
The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management.
Abstract not provided.
Abstract not provided.
This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.
Hourly mixing heights, inversion heights and surface pressure gradients are available for Ponca, OK and Oklahoma City, OK from 1/1/24 - 1/6/25. Hourly mixing heights, inversion heights, air mass types and surface pressure gradients are available for Plymouth, MA and six sites offshore of New England.
Abstract Although using an air cathode is the goal for superoxide‐based potassium‐oxygen (K‐O 2 ) batteries, prior studies were limited to pure oxygen. Now, the first K‐air (dry) battery based on reversible superoxide electrochemistry is presented. Spectroscopic and gas chromatography analyses are applied to evaluate the reactivity of KO 2 in ambient air. Although KO 2 reacts with water vapor and CO 2 to form KHCO 3 , it is highly stable in dry air. With this knowledge, rechargeable K‐air (dry) batteries were successfully demonstrated by employing dry air cathode. The reduced partial pressure of oxygen plays a critical role in boosting battery lifespan. With a more stable environment for the K anode, a K‐air (dry) battery delivers over 100 cycles (>500 h) with low round‐trip overpotentials and high coulombic efficiencies as opposed to traditional K‐O 2 battery that fails early. This work sheds light on the benefits and restrictions of employing the air cathode in superoxide‐based batteries.
Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O 3 , N 2 O, and NO 2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. In conclusion, this methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.
New routes for transforming nitrogen into ammonia at ambient conditions would be a milestone toward an energy efficient and economically attractive production route in comparison to the traditional Haber-Bosch process. Recently, the synthesis of ammonia from water and nitrogen at room temperature and atmospheric pressure has been reported to be catalyzed by Fe 3 O 4 at the air-water interface. By integrating ambient pressure X-ray photoelectron spectroscopy and ab initio molecular dynamics and free energy calculations, we investigate the underlying thermodynamic mechanisms governing ammonia and hydrazine formation at the water-Fe 3 O 4 -nanoparticle interface. Here, we find that, unlike pure Fe 3 O 4 where N 2 can only interact with a limited number of Fe sites, hydroxylated species introduce large and diverse adsorption geometries where N 2 can bind through either Fe sites or Fe-OH groups, each of which are capable of independently facilitating proton-coupled electron transfer.
A new method for predicting the pressure drop across High Efficiency Particulate Air (HEPA) filter media is proposed based upon mass deposited onto the filter and known physical characteristics of the filter media. Detailed are the methods used in conjunction with current filter loading models to predict the pressure drop, as well as tests conducted to validate the methods. The benefit of a prediction model for practical use lies in the manufacturing and service life of nuclear grade HEPA filters. HEPA filters for use in nuclear facilities have a prescribed expiration date and a maximum allowable operating pressure drop. Therefore, the ability to predict the pressure drop across a filter and relate it to an expected length of service time can enable a reduction in wasted filters. This will allow for more informed decisions to be made based upon the dictated life cycle of the filters. Also, understanding and predicting how the pressure drop of HEPA filter media behaves as a function of physical characteristics and loaded mass can assist in future design and manufacturing of filter media. Currently, most existing pressure drop models are either computationally based or analytical methods relying on data gathered during tests. Neither are practical for prediction; the computational methods are difficult to implement, and the current analytical methods are more useful as tools for analysis. The current analytical model developed by Bergman et al. is based upon the pressure drop from each media fiber and modeling the deposited particles as newly formed fibers. Using Bergman's model and the media properties, air properties, and known aerosol particle size distribution, a pressure drop curve as a function of loaded mass can be created. This curve implies initial loading in the depth of the filter media with a transition to pure surface loading on the filter face, and a sensitivity to an evolving particle size distribution as mass continues to load onto the filter. To implement the predictive model, knowledge of the mean filter fiber diameter and porosity of the filter media is required. Traditionally, the mean fiber diameter is calculated as an effective diameter from prior media testing, however, in this study a Scanning Electron Microscope (SEM) technique is used to acquire this variable. Validation of the predictive model is provided by flat sheet media tests under a controlled environment with a measured particle size distribution of the challenge aerosol and shows reasonable preliminary agreement.
Condensation dehumidification in conventional air conditioning technologies is energy-intensive, accounting for up to 50% of building cooling energy used in some climates. Selective vacuum membrane dehumidification (VMD) is one of the leading alternative dehumidification technologies due to its potential for significant energy savings, and the “dual-module humidity pump” is one of the most promising VMD concepts. Here, this work is the first to provide experimental proof-of-concept for the dual-module humidity pump system and provides the first thermodynamic modeling framework that accounts for realistic steady-state operating limitations, both of which are lacking in the current literature. Additionally, this work is the first to provide a system design solution that overcomes practical challenges associated with air accumulation in the vacuum channels. The experimental results in this work show that the current prototype can remove up to 45% of the water vapor in the air stream, and the vapor pressure difference in the vapor rejection module needs to be approximately 2–4 times greater than that of the dehumidification module in order to maintain balanced mass transfer. The thermodynamic model applied to typical air conditioning conditions shows that the ideal dehumidification (latent) COPs can reach up to 40, but practical COPs are limited to approximately 10. Furthermore, the model shows that the overall energy efficiency increases as the membrane air selectivity increases, though this improvement gradually starts to diminish when the membrane selectivity is increased above 10,000.
Illuminating past hydrological and climatological conditions in arid regions may provide insights into future trends in groundwater availability. The main objective of this study is to explore paleorecharge processes in the deep regional Nubian Sandstone Aquifer (NSA), which stretches below the hyperarid deserts of the Sinai Peninsula (Egypt) and the Negev (Israel), using dissolved noble gas data. Extremely high amounts of dissolved excess air (Ne concentrations up to 4 × solubility equilibrium) were observed in the ancient ( 81 Kr-depleted) groundwater that was recharged during past pluvial epochs in the basin. Here, the observed unique excess air signal and the clear spatiotemporal structure of the noble gas compositions in the aquifer are hypothesized to reflect two major characteristics of the groundwater system: (a) large-scale, long-term rises in the water table that facilitate the entrapment and dissolution of air bubbles under increased hydrostatic pressure, and (b) the geological settings in the southern recharge area in Sinai, including an almost horizontal position of the layers and intercalation of low-permeability rock formations within the aquifer, which inefficiently reject air bubbles from groundwater, thereby allowing for substantial entrapment of bubbles that dissolve as the water table rises. Enhanced dissolution of entrapped air was also observed in other paleo-groundwater-containing regional sandstone aquifers across North Africa, which, together with the recent findings from the NSA, suggest that largescale water table fluctuations have likely extensively occurred during past pluvial periods over these (and possibly other) arid regions. A preliminary assessment of noble gas recharge temperatures (NGTs) indicates an apparent decoupling of surface and water table temperatures in the case of deep aquifers with a thick (hundreds of m) unsaturated zone. This observed decoupling calls for a re-evaluation of previously obtained NGT records and a need for future work to consider the modification of NGTs due to soil air fractionation and geothermal heating of the underlying deep unsaturated zones.
Abstract not provided.
Carbon dioxide (CO 2 ) is the most abundant of all greenhouse gases (GHGs). CO 2 levels in the atmosphere are 50% higher than in the preindustrial era, trapping heat. CO 2 removal from the atmosphere by direct air capture (DAC) is needed to achieve the internationally agreed global temperature goals. The most common CO 2 capture technology is absorption by aminebased solvents in packed columns. Amino acid solutions have recently gained attention due to their advantages over traditional amine solvents. To be implemented effectively, DAC industrial processes need to handle large airflow rates in separation absorbers. The large air and solvent flow rates preclude the use of countercurrent columns due to high-pressure drops and the occurrence of flooding. Crossflow air−liquid absorbers are used to handle large air and liquid volumes due to their lower-pressure drop. The objective of this work is to study the influence of crossflow absorber geometric parameters and operating conditions on product formation and process efficiency. An already derived theoretical model for countercurrent absorbers has been modified to simulate the operation of a crossflow DAC absorber. The predictive model was implemented into a computer code that was used to study the efficiency of the processes as geometric equipment dimensions and operating parameters vary. Practical suggestions are made to design more efficient DAC processes.