Search NASA⌕ Search

SEARCH · Search NASA

Results for “EXHAUST SYSTEM”

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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modularization of Ceramic Hollow Fiber Membrane Technology for Air Separation

This proposed project is aimed at studying high performance and economically competitive ceramic membrane technology for air separation and high-purity oxygen production using hollow fiber ceramic membrane stack and module technology. The design of the single permeate membrane is a hollow fiber substrate-supported thin tri-layer structure. Radially well-aligned micro-channels are embedded in the thick substrate and open at the inner surface of the substrate, enabling facile air/gas diffusion. The tri-layer structure of thin dense membrane layer (~ 10 µm) sandwiched by porous surface layer on either side is then built on the substrate using advanced fabrication process. The tri-layer structure design allows different materials to be used in different layers, where the materials of surface layers have high surface exchange coefficients while the material of dense layer has high bulk diffusivity. Such a synergetic combination leads to high permeation performance. The focus of the proposed project will be on membrane stack/module development using the developed single hollow fiber membranes, including: 1) fabrication and characterization of novel single hollow fiber membranes; 2) membrane stack design and assembly using fabricated single membranes; 3) stack modeling and analysis to guide membrane stack designs; 4) permeation performance testing and characterization of membrane stacks. The hollow fiber feature and simple sealing requirement enable very compact design of membrane stacks, which have excellent flexibility for further modularizations at different scales. The operations of such membrane stack and module may employ the exhaust heat from other components of Integrated Gasification Combined Cycle and oxy-combustion systems. Therefore, modularization of such an air separation membrane technology can be incorporated into the DOE’s REMS (radically engineered modular systems)-gasification skid and support the oxidant feed of an oxygen-blown REMS gasifier scaled to different ranges.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Exergy Analysis of a Convective Heat Pump Dryer Integrated with a Membrane Energy Recovery Ventilator

To increase energy efficiency, heat pump dryers and membrane dryers have been proposed to replace conventional fossil fuel dryers. Both conventional and heat pump dryers require substantial energy for condensing and reheating, while “active” membrane systems require vacuum pumps that are insufficiently developed. Lower temperature dehumidification systems make efficient use of membrane energy recovery ventilators (MERVs) that do not need vacuum pumps, but their high heat losses and lack of vapor selectivity have prevented their use in industrial drying. In this work, we propose an insulating membrane energy recovery ventilator for moisture removal from drying exhaust air, thereby reducing sensible heat loss from the dehumidification process and reheating energy. The second law analysis of the proposed system is carried out and compared with a baseline convective heat pump dryer. Irreversibilities in each component under different ambient temperatures (5–35 °C) and relative humidity (5–95%) are identified. At an ambient temperature of 35 °C, the proposed system substantially reduces sensible heat loss (47–60%) in the dehumidification process, resulting in a large reduction in condenser load (45–50%) compared to the baseline system. The evaporator in the proposed system accounts for up to 59% less irreversibility than the baseline system. A maximum of 24.5% reduction in overall exergy input is also observed. The highest exergy efficiency of 10.2% is obtained at an ambient condition of 35 °C and 5% relative humidity, which is more than twice the efficiency of the baseline system under the same operating condition.

Balaraman, Anand (ORCID:0000000218355788)↗
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Scanning Mobility Particle Sizer (SMPS) Instrument Handbook

The Model 3936 Scanning Mobility Particle Spectrometer (SMPS) measures the size distribution of aerosols ranging from 10 nm up to 1000 nm. The SMPS uses a bipolar aerosol charger to keep particles within a known charge distribution. Charged particles are classified according to their electrical mobility, using a long-column differential mobility analyzer (DMA). Particle concentration is measured with a condensation particle counter (CPC). The SMPS is well-suited for applications including: nanoparticle research, atmospheric aerosol studies, pollution studies, smog chamber evaluations, engine exhaust and combustion studies, materials synthesis, filter efficiency testing, nucleation/condensation studies, and rapidly changing aerosol systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Transition to Petschek Reconnection in Subrelativistic Pair Plasmas: Implications for Particle Acceleration

While relativistic magnetic reconnection in pair plasmas has emerged in recent years as a candidate for the origin of radiation from extreme astrophysical environments, the corresponding subrelativistic pair-plasma regime has remained less explored, leaving open the question of how relativistic physics affects reconnection. In this paper, we investigate the differences between these regimes by contrasting two-dimensional particle-in-cell simulations of reconnection in pair plasmas with relativistic magnetization (σ ≫ 1) and subrelativistic magnetization (σ < 1). By utilizing unprecedentedly large domain sizes and outflow boundary conditions, we demonstrate that lowering the magnetization results in a change in the reconnection geometry from a plasmoid chain to a Petschek geometry, where laminar exhausts bounded by slow-mode shocks emanate from a single diffusion region. We attribute this change to the reduced plasmoid production rate in the low-σ case: When the secondary tearing rate is sufficiently low, plasmoids are too few in number to prevent the system from relaxing into a stable Petschek configuration. This geometric change also affects particle energization: We show that while high-σ plasmoid chains generate power-law energy spectra, low-σ Petschek exhausts merely heat incoming plasma and yield negligible nonthermal acceleration. These results have implications for predicting the global current sheet geometry and the resulting energy spectra in a variety of systems.

Plasma astrophysics↗
DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗
DOE OSTI2026

Validating Protection System Behavior with Machine Learning in a Master State Overseer

As power system protection devices continue the widespread transition from analog to digital, they become increasingly intricate. The internal functions and communication between critical grid components must now be significantly more complex to keep up with the demands of the modern smart grid. This brings increased difficulty in maintenance and monitoring, making it harder to identify potential misoperation, power anomalies, and cyber threats. Such issues are often only pinpointed after an exhaustive and costly post-mortem analysis, when a major outage or damage has already occurred. A solution is needed for validating protection systems as they operate, independently evaluating grid state and confirming whether the protection system is behaving accordingly. As opposed to incident response, this acts as a constant verification mechanism that raises a flag when subtler issues are noticed, catching them earlier and preventing larger incidents. This work presents the implementation of such a system, expanding on the prototype developed by the authors in a previous paper. This is accomplished with a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. Additionally, this system is contextualized within a larger, modular Master State awareness Overseer (MSO) framework, responsible for monitoring, analyzing, and managing an electric grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Suppression of Collisionless Magnetic Reconnection in the High Ion β, Strong Guide Field Limit

Abstract In magnetic reconnection, the ion bulk outflow speed and ion heating have been shown to be set by the available reconnecting magnetic energy, i.e., the energy stored in the reconnecting magnetic field ( B r ). However, recent simulations, observations, and theoretical works have shown that the released magnetic energy is inhibited by upstream ion plasma beta β i —the relative ion thermal pressure normalized to magnetic pressure based on the reconnecting field—for antiparallel magnetic field configurations. Using kinetic theory and hybrid particle-in-cell simulations, we investigate the effects of β i on guide field reconnection. While previous works have suggested that guide field reconnection is uninfluenced by β i , we demonstrate that the reconnection process is modified and the outflow is reduced for sufficiently large β i > ( B r 2 + B g 2 ) / B r 2 . We develop a theoretical framework that shows that this reduction is consistent with an enhanced exhaust pressure gradient, which reduces the outflow speed as v out ∝ 1 / β i . These results apply to systems in which guide field reconnection is embedded in hot plasmas, such as reconnection at the boundary of eddies in fully developed turbulence like the solar wind or the magnetosheath as well as downstream of shocks such as the heliosheath or the mergers of galaxy clusters.

Giai, Carlos A.↗
DOE OSTI2025

Clean Water Production in Cooling Towers

This project developed and demonstrated a novel technology that produces clean water from cooling tower recirculating water by using the natural evaporation and condensation cycle inside cooling towers. The system captures the escaping plume and converts blowdown quality water into high purity water suitable for on-site reuse such as boiler feed. The technology uses electric fields to ionize exhaust plumes, charge the entrained droplets, and direct them toward collection electrodes where they coalesce and flow downward. This allows water recovery at a low energy cost while reducing visible plume emissions. In addition, we developed a complementary software platform that improves overall cooling tower performance. The system uses wireless sensors and physics-based machine learning algorithms to optimize key parameters of the cooling process. For power generation facilities, this increases the thermal efficiency of the cooling loop and condenser, resulting in measurable cycle efficiency gains. Improvements of one percent or more can deliver significant increases in electricity production for the same fuel input.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

System and method for substance removal

In variants, an air treatment module can include a sorption module defining a sorption cavity, an air intake channel, an air exhaust channel, and a target substance exhaust channel. The air treatment module can include a sorbent encapsulated within a sorbent structure (e.g., microencapsulated carbon sorbent) which can sorb carbon dioxide from air passing through the sorption cavity. The air treatment module can desorb carbon dioxide from the air and store the carbon dioxide in long term storage.

Dess, Peter Colin↗
DOE OSTI2025

Pre- and post-launch operation of the Resolve soft X-ray spectrometer onboard the XRISM satellite

Resolve is a high-resolution X-ray spectrometer onboard the X-Ray Imaging and Spectroscopy Mission (XRISM), launched on September 6 (UT), 2023. The Resolve has performed better than its required spectral resolution (7 eV at full width at half maximum at 6 keV), both on the ground and in orbit, and has been confirmed to have comparable performance to the soft X-ray spectrometer onboard the ASTRO-H (Hitomi) satellite. The focal plane is composed of an array of microcalorimeter detectors operated at 50 mK to achieve the required energy resolution, and the cooling system is designed to satisfy the lifetime requirement of over 3 years. The focal plane and cooling system are contained in a vacuum-insulated dewar. The cooling system is equipped with a two-stage adiabatic demagnetization refrigerator (ADR) that uses superfluid liquid helium (LHe) as its heat sink. The system includes a third ADR stage that can be used to provide the heat sink when the helium is exhausted. A Joule–Thomson cooler and several two-stage Stirling coolers are used to reduce the heat load on the LHe. During pre-launch operations, we carried out a superfluid LHe top-off operation. The resultant amount of LHe onboard Resolve was over 35 L before launch, which is sufficient to meet the lifetime requirement. During post-launch operation, the LHe vent valve was opened 5 min after launch during rocket acceleration, and the cryocoolers were turned on after several orbits, as planned, which established stable cooling within the dewar. Pre- and post-launch operations for the Resolve instrument were planned around multiple constraints from launch vehicle operations; all were successfully completed, and the launch requirements were fully met.

X-Ray Imaging and Spectroscopy Mission↗
DOE OSTI2026

Protection System Validation Using Post-Event Anomaly Classification with Machine Learning

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Natural Gas Combined Cycle Power Plants with Carbon Capture and Exhaust Gas Recycle

This is a presentation given at the 3rd International Conference on Energy and Environment. The presentation highlights analysis work conducted by NETL to assess the cost and performance for natural gas combined cycle (NGCC) power plants that incorporate solvent-based post combustion carbon dioxide (CO2) capture and exhaust gas recycle (EGR). Increasing the inlet CO2 concentration by EGR is expected to improve the efficiency of the capture system and reduce its cost, however the analysis results show that the cost improvement is minimal as is the cost of capture reduction.

Hackett, Gregory↗
DOE OSTI2024

Innovative SCR Materials and System for Low Temperature - CRADA 350 (Abstract)

The aim of this CRADA is focused on providing a new enabling SCR catalyst system that will function at very high efficiency to attain the most demanding emissions regulations and thereby facilitate the market introduction of advanced powertrains that will support domestic energy independence and security. Future powertrains, that will be significantly more efficient than currently available technologies, will be needed by automotive manufacturers to meet rapidly increasing CAFE and GHG standards. However, these powertrains cannot enter into the US light duty vehicle market unless they are coupled with an aftertreatment system that will sufficiently remediate tailpipe emissions to meet EPA Tier III and California SULEV emissions standards. The low temperature exhaust associated with these powertrains is especially challenging for any current aftertreatment technology to meet these standards. The key focus of this CRADA is to further develop newly invented materials for the selective catalytic reduction (SCR) of NOx by ammonia (NH3) that show promise for significantly reducing ‘light-off’ temperatures compared to current commercial catalysts. Specifically, the goal of the proposed work is to achieve ‘light-off’ of NH3 SCR at 150 ºC in order to realize conversion efficiencies of 90% at these low temperatures. This will enable deployment of lean combustion powertrains with significantly increased fuel efficiencies but lower exhaust temperatures. To accomplish this overall goal, it will be essential to also identify an appropriate NH3 supply strategy for the SCR aftertreatment device that can controllably deliver NH3 at these low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fluor Solvent Evaluation and Testing New Scope: Techno-economic Assessment of EEMPA Solvent for CO 2 Separations from Natural Gas Combined Cycle Power Plant

In this project, a techno-economic analysis (TEA) and sensitivity studies were conducted to assess the PNNL’s leading water-lean CO 2 capture solvent, EEMPA, for capture CO 2 from a natural gas combined cycle (NGCC) power plant at different levels of capture rate. Process models for the NGCC power plant, integrated with EEMPA carbon capture processes, were developed in Aspen Plus V14 using the most up-to-date property package for EEMPA-H 2 O-CO 2 system. The TEA evaluated EEMPA carbon capture process at normal capture rates (90%, 95% and 97%) against Case B32B (Cansolv) described in NETL Rev4a baseline report, and at higher capture rates aimed at achieving zero or negative emissions from the power plant (400 ppmv, 200 ppmv, and 100 ppmv CO 2 in exhaust gas), compared to typical Direct Air Capture (DAC) technologies. A manuscript was drafted for peer-reviewed publication. The results suggested that the carbon capture cost reaches a minimum of $\$$53.7/tonne CO 2 at 90% capture rate. Compared to Cansolv, one of the industrial benchmarks, EEMPA demonstrates 2-4% cost savings at capture rates up to 95%, but minimal savings at higher capture rate. The water lean-solvent system proves economically attractive for achieving moderate negative emissions (about 200 ppmv CO 2 in exhaust gas, and equivalent to 50% CO 2 removal from air) for NGCC flue gas, with marginal capture costs comparable to direct air capture (DAC) technologies. A sensitivity analysis results reveal that its economic advantage, unaffected by EEMPA price due to low solvent loss and degradation rate. However, the marginal carbon capture cost exceeds $\$$1,000/tonne CO 2 when transitioning from moderate to extreme negative emissions (100 ppmv CO 2 in exhaust gas), suggesting that water-lean solvents may not be economically competitive with other DAC technologies for removing more than 75% CO 2 from air. In addition, initial connection was established with Technology Center Mongstad (TCM) for a potential pilot testing proposal. However, detailed modeling and proposal preparation was not conducted due to the delay of non-disclosure agreement.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid: Preprint

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aimto meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗