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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Marine Alternative Fuel Pricing, Supply, and Demand

The International Maritime Organization low sulfur fuel rules and greenhouse gas-reduction strategy may create a significant perturbation to global refinery operations, impacting volumes and prices of marine fuel globally and within the United States and incentivizing the transition to alternative fuel use. The goals of this project are to (1) enhance understanding of how very low-sulfur fuel-primarily fuel oil and diesel-and low carbon fuel requirements and promising biofuel processes will affect the marine fuel supply chain; and (2) explore how these perturbations interact with and impact indicators such as pricing, number of trips, and demand behavior, along with the potential to meet low sulfur and carbon fuel demand with biofuel supply chains, (3) merge innovative thinking in the area of marine fuels within research centers of DOE (NREL) and the U.S. Department of Transportation (Volpe). These goals will be accomplished through the combination of detailed refinery, marine fuel burn, system dynamics, and geospatially explicit linear programming models.

biofuel↗
DOE OSTI2022

Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption

Improving the performance of windows has the potential to reduce U.S. annual energy use by 1.7% and CO2 emissions by 1.9% in 2050. Beyond static windows, dynamic technologies could reduce U.S. annual energy use by 1.6 quads and CO2 emissions by 68 million metric tons. In addition, the widespread adoption of static and dynamic technologies would substantially reduce peak electricity demand from buildings. Beyond energy use and CO2 emissions reductions, improving window performance will increase occupant comfort and well-being. In a new report, Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption, the U.S. Department of Energy (DOE) outlines multiple avenues for technology development, deployment, and adoption to increase the impact that windows can have on decarbonizing America's buildings.

building energy efficiency↗
DOE OSTI2022

Cross-Cutting Metrology Tools for In Operando Characterization of Carrier Dynamics in Photovoltaic Devices (Final Technical Report)

Understanding the nature of recombination and its dependence on defects and interfaces is essential for engineering materials and contacts for higher V oc and photovoltaic (PV) efficiency. Time-resolved photoluminescence (TRPL) has conventionally been used to evaluate recombination, but not all materials are strongly emissive or otherwise suitable. Time-resolved terahertz spectroscopy is a pump – probe method that presents valuable complementary information, wherein an optical pump pulse photoexcites carriers within the absorber and the transient photoconductivity is probed with a terahertz pulse. Until now, experimental constraints have prohibited the use of terahertz probes to interrogate full device stacks, and measurements were instead made on exfoliated films or films grown on unconventional substrates. However, interfaces are critical to the behavior of photoexcited carriers in solar cells, and the substrates themselves often influence the film growth and bulk properties. Therefore, it is important to probe the behavior of PV absorbers as close as possible to their normal operating conditions. Here we have developed cross-cutting metrology that enable in operando characterization of carrier dynamics and recombination mechanisms in working PV devices. Through a combination of complementary non-contact TRTS and TRPL experiments and modeling, we have obtained key parameters including photoexcited carrier bulk lifetime and interface and back surface recombination velocities with greater precision and accuracy than can be achieved with conventional TRPL alone. By varying pulsed photoexcitation conditions, the contributions of interface and bulk recombination mechanisms can be determined. We have developed and refined this characterization approach by investigating CdTe PVs, which is a well-established technology but with significant margin for further improvements in efficiency. Results were published in Journal of Applied Physics (DOI: 10.1063/5.0064730) and Proceedings of the IEEE PVSC (DOI: 10.1109/PVSC43889.2021.9518559). After validating the approach, we have applied it to correlate recombination rates to processing conditions and material properties, including composition and defects as well as interfaces and surface treatments. Such feedback can inform processing and design choices to enable higher PV efficiency, not only for CdTe, but also CdSe x Te 1-x , CIGS, perovskites, kesterites, and future technologies. A perspective on predicting solar cell performance from terahertz spectroscopy was published in Advanced Energy Materials (DOI: 10.1002/aenm.202102776). The cross-cutting metrology tools developed here will enable determination of the locus and mechanism of performance-limiting recombination in thin film PV devices. This feedback can guide the engineering of PV devices with higher V oc and efficiency, thereby leading to reductions in levelized cost of electricity to meet the SunShot 2030 target of $0.03/kWh for utility-scale PV. Lower costs will enable rapid expansion of renewable electricity generation that is nearly free of carbon emissions. Reducing carbon emissions of the electricity sector is one of the most important solutions to climate change. The project’s specific focus on CdTe PV can benefit First Solar, the US company that is the global leader in CdTe technology, through both technology and workforce development.

14 SOLAR ENERGY↗
DOE OSTI2020

Vegetation forcing modulates global land monsoon and water resources in a CO 2 -enriched climate

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO 2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO 2 -enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions.

58 GEOSCIENCES↗
DOE OSTI2023

Historical and future trends in South Asian monsoon low pressure systems in a high-resolution model ensemble

Historical trends in monsoon low pressure systems (LPS), the dominant rain-bearing weather system of South Asia, have been difficult to assess due to changes in the observing network. Future projections have also remained uncertain because prior studies concluded that many coarse-resolution climate models do not accurately simulate LPS. Here, we examine changes in South Asian monsoon LPS simulated by an ensemble of global models, including some with high spatial resolution, that we show skillfully represent LPS. In the ensemble mean, the number of strong LPS (monsoon depressions) decreased over the last 65 years (1950–2014) by about 15% while no trend was detected for weaker LPS (monsoon lows). The reduction in depression counts then moderated, yielding no trend in the periods 1980–2050 or 2015–2050. The ensemble mean projects a shift in genesis from ocean to land and an increase in LPS precipitation of at least 7% K −1 , which together contribute to a projected increase in seasonal mean and extreme precipitation over central India.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Space Shuttle solid rocket booster initial water impact loads and dynamics - Analysis, tests, and flight experience

A series of scale model tests, finite element dynamic response analyses and full scale segment tests have been performed for purposes of developing design criteria for the initial water impact loading conditions applied to the internal stiffener rings located in the aft skirt portion of the Space Shuttle Solid Rocket Booster (SRB). In addition, flight experience has yielded information relative to structural reinforcement requirements. This paper discusses the test and analysis methods and summarizes significant results. It is noted that, although scale model test data are valuable for identifying trends, they have shortcomings concerning definition of full scale design loads criteria. Also, the frequently used static equivalent loads definition approach is not applicable for this type impact loading condition applied to an aft skirt type structure. Various types of ring structural fixes, including the addition of selected types of foam, are presented as well as associated full scale segment test results. Depending on the type and contour shape of the foam, reductions on applied pressures and peak measured strains over 50 percent are noted.

Kross, D. A.↗
NASA NTRS1987

A comparative study of rapidly and slowly rotating dynamical regimes in a terrestrial general circulation model

As a preliminary step in the development of a general circulation model for general planetary use, a simplified vesion of thef GISS Model I GCM has been run at various rotation periods to investigate differences between the dynamical regimes of rapidly and slowly rotating planets. To isolate the dynamical processes, the hydrologic cycle is suppressed and the atmosphere is forced with perpetual annual mean solar heating. All other parameters except the rotation period remain fixed at their terrestrial values. Experiments were conducted for rotation periods of 2/3, 1, 2, 4, 8, 16, 64 and 256 days. The results are in qualitative agreement with similar experiments carried out previously with other GCMs and with certain aspects of one Venus GCM simulation. As rotation rate decreases, the energetics shifts from baroclinc to quasi-barotropic when the Rossby radius of deformation reaches planetary scale. The Hadley cell expands poleward and replaces eddies as the primary mode of large-scale heat transport. Associated with this is a poleward shift of the baroclinic zone and jet stream and a reduction of the equator-pole temperature contrast. Midlatitude jet strength peaks at 8 days period, as does the weak positive equatorial zonal wind which occurs at upper levels at all rotation periods. Eddy momentum transport switches from poleward to equatorward at the same period. Tropospheric mean static stability generally increases in the tropics and decreases in midlatitudes as rotation rate decreases, but the global mean static stability is independent of rotation rate. The peak in the eddy kinetic energy spectrum shifts toward lower wavenumbers, reaching wavenumber 1 at a period of 8 days. Implications of these results for the dynamics of Venus and Titan are discussed. Specifically, it is suggested that the extent of low-level convection determines whether the Gierasch mechanism contributes significantly to equatorial superrotation on these planets.

Del Genio, Anthony D.↗
NASA NTRS2003

Three Dimensional Numerical Simulation of Rocket-based Combined-cycle Engine Response During Mode Transition Events

The GTX program at NASA Glenn Research Center is designed to develop a launch vehicle concept based on rocket-based combined-cycle (RBCC) propulsion. Experimental testing, cycle analysis, and computational fluid dynamics modeling have all demonstrated the viability of the GTX concept, yet significant technical issues and challenges still remain. Our research effort develops a unique capability for dynamic CFD simulation of complete high-speed propulsion devices and focuses this technology toward analysis of the GTX response during critical mode transition events. Our principal attention is focused on Mode 1/Mode 2 operation, in which initial rocket propulsion is transitioned into thermal-throat ramjet propulsion. A critical element of the GTX concept is the use of an Independent Ramjet Stream (IRS) cycle to provide propulsion at Mach numbers less than 3. In the IRS cycle, rocket thrust is initially used for primary power, and the hot rocket plume is used as a flame-holding mechanism for hydrogen fuel injected into the secondary air stream. A critical aspect is the establishment of a thermal throat in the secondary stream through the combination of area reduction effects and combustion-induced heat release. This is a necessity to enable the power-down of the rocket and the eventual shift to ramjet mode. Our focus in this first year of the grant has been in three areas, each progressing directly toward the key initial goal of simulating thermal throat formation during the IRS cycle: CFD algorithm development; simulation of Mode 1 experiments conducted at Glenn's Rig 1 facility; and IRS cycle simulations. The remainder of this report discusses each of these efforts in detail and presents a plan of work for the next year.

Edwards, Jack R.↗
NASA NTRS2016

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

big data↗
NASA NTRS2017

Applying Graph Theory to Problems in Air Traffic Management

Graph theory is used to investigate three different problems arising in air traffic management. First, using a polynomial reduction from a graph partitioning problem, it isshown that both the airspace sectorization problem and its incremental counterpart, the sector combination problem are NP-hard, in general, under several simple workload models. Second, using a polynomial time reduction from maximum independent set in graphs, it is shown that for any fixed e, the problem of finding a solution to the minimum delay scheduling problem in traffic flow management that is guaranteed to be within n1-e of the optimal, where n is the number of aircraft in the problem instance, is NP-hard. Finally, a problem arising in precision arrival scheduling is formulated and solved using graph reachability. These results demonstrate that graph theory provides a powerful framework for modeling, reasoning about, and devising algorithmic solutions to diverse problems arising in air traffic management.

computational complexity↗
DOE OSTI2026

Pathways of bio-jet adoption in the US aviation industry with implications for the overall transportation and energy sectors: an integrated, multi-sectoral analysis of future scenarios

Bio-jet adoption has emerged as an attractive option to complement and supplement the use of refined fossil liquid fuels in the aviation industry in the US. However, there are significant uncertainties surrounding the costs of bio-jet including but not limited to costs of feedstock, transformation costs and the competition with co-products of bio-jet that may be demanded elsewhere in the transportation or energy sectors. This study models alternative trajectories of bio-jet adoption in the US aviation industry by 2050 through the use of a global integrated multi sector dynamics model. Three bio-jet production and consumption pathways are presented- soybean oil to jet, corn ethanol to jet (ETJ) and Fisher–Tropsch-based bio-jet, with each pathway explicitly considering the co-production of renewable diesel and renewable gasoline alongside the bio-jet. Without explicit actions or technology changes to offset the technology cost of bio-jet, scenarios where bio-jet displaces refined liquids result in higher aviation fuel prices (ranging from a 25% increase to 120% increase by mid-century) and lower demand (ranging from −14% to −43%). Corn ethanol will play an important role in the US if large scale amounts of bio-jet are to be produced with smaller effects on demand and prices. While scenarios with high levels of bio-jet availability without the availability of ETJ in the US can significantly reduce emissions in the aviation sector, these reductions are achieved more through the reduction in overall aviation fuel demand rather than technology adoption.

09 BIOMASS FUELS↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
NASA NTRS2017

Structural Design Exploration of an Electric Powered Multi-Propulsor Wing Configuration

Advancements in aircraft electric propulsion may enable an expanded operational envelope for electrically powered vehicles compared to their internal combustion engine counterparts. High aspect ratio wings provide additional lift and drag reduction for a proposed multi-propulsor design, however, the challenge is to reduce the weight of wing structures while maintaining adequate structural and aeroelastic margins. Design exploration using a conventional design-and-build philosophy coupled with a finite element method (FEM)-based design of experiments (DOE) strategy are presented to examine high aspect ratio wing structures that have spanwise distributed electric motors. Multiple leading-edge-mounted engine masses presented a challenge to design a wing within acceptable limits for dynamic and aeroelastic stability. Because the first four primary bending eigenmodes of the proposed wing structure are very sensitive to outboard motor placement, safety-of-flight requirements drove the need for multiple spars, rib attachments, and outboard structural reinforcements in the design. Global aeroelasticity became an increasingly important design constraint during the on-going design process, with outboard motor pod flutter ultimately becoming a primary design constraint. Designers successively generated models to examine stress, dynamics, and aeroelasticity concurrently. This research specifically addressed satisfying multi-disciplinary design criteria to generate fluid-structure interaction solution sets, and produced high aspect ratio primary structure designs for the NASA Scalable Convergent Electric Propulsion Technology and Operations Research (SCEPTOR) project in the Aeronautic Research Mission Directorate at NASA. In this paper, a dynamics-driven, quasi-inverse design methodology is presented to address aerodynamic performance goals and structural challenges encountered for the SCEPTOR demonstrator vehicle. These results are compared with a traditional computer aided design based approach.

Moore, James B.↗
DOE OSTI2023

Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO 2 Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation

Electrocatalytic CO 2 reduction reactions (CO 2 RR) based on scalable and highly efficient catalysis provide an attractive strategy for reducing CO 2 emissions. Here in this work, we combined first-principles density functional theory (DFT) and machine learning (ML) to comprehensively explore the potential of double-atom catalysts (DACs) featuring an inverse sandwich structure anchored on defective graphene (gra) to catalyze CO 2 RR to generate C 1 products. We started with five homonuclear M 2 ⊥gra (M = Co, Ni, Rh, Ir, and Pt), followed by 127 heteronuclear MM'⊥gra (M = Co, Ni, Rh, Ir, and Pt, M' = Sc–Au). Stable DACs were screened by evaluating their binding energy, formation energy, and dissolution potential of metal atoms, as well as conducting first-principles molecular dynamics simulations with and without solvent water molecules. Based on DFT calculations, Rh 2 ⊥gra DAC was found to outperform the other four homonuclear DACs and the Rh-based single- and double-atom catalysts of noninverse sandwich structures. Out of the 127 heteronuclear DACs, 14 were found to be stable and have good catalytic performance. An ML approach was adopted to correlate key factors with the activity and stability of the DACs, including the sum of radii of metal and ligand atoms (d M–M' , d M–C , and d M'–C ), the sum and difference of electronegativity of two metal atoms (P M + P M' , P M – P M '), the sum and difference of first ionization energy of two metal atoms (I M + I M' , I M – I M '), the sum and difference of electron affinity of two metal atoms (A M + A M' , A M – A M '), and the number of d-electrons of the two metal atoms (Nd). The obtained ML models were further used to predict 154 potential electrocatalysts out of 784 possible DACs featuring the same inverse sandwich configuration. Overall, this work not only identified promising CO 2 RR DACs featuring the reported inverse sandwich structure but also provided insights into key atomic characteristics associated with high CO 2 RR activity.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗