Net Zero World Initiative: Accelerating Global Energy System Decarbonization
Overview of Net Zero World Initiative (2024 Update)
ENERGY PLANNING, POLICY, AND ECONOMY↗
SEARCH · Search NASA
Results for “NeTS”
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.
Net Zero World: Transformative Partnerships for Global Decarbonization
Overview of Net Zero World's program capabilities and offerings for partners.
ENERGY PLANNING, POLICY, AND ECONOMY↗
Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation
Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.
Juhong, Aniwat↗
Catalytic Processes to Accelerate Decarbonization in a Net-Zero Carbon World
Reducing carbon dioxide emissions is one of the critical challenges to mitigate global climate change, which is having detrimental impacts on society and the environment. Fossil fuel combustion in transportation, power generation, and industrial processes is the dominant contributor to carbon emissions. Over the past decades, sustainable solutions and strategies have been investigated and developed to enable decarbonization. Catalysis plays an essential role to address this global challenge by increasing energy efficiency, reducing carbon emissions, capturing carbon dioxide, and utilizing clean energy sources to displace fossil fuels. In this Review, the role of catalysis in reducing energy demand was discussed, enhancing process efficiency, displacing carbon-intensive feedstocks and products, and therefore, reducing carbon emissions. Here, recent advances in catalyst development were summarized, focusing on applications to enhance industrial processes efficiency and enable utilization of clean energy sources. Emerging approaches in catalysis were reviewed, including the manufacture of iron and steel, direct air capture of CO 2 , production of ethylene, ammonia, and sustainable aviation fuels, plastic recycling, and the synthesis of biobased plastics. The Review was concluded with suggested research directions to achieve a carbon net-zero world.
36 MATERIALS SCIENCE↗
Determination of Chemical Freeze-Out Parameters from Net-Kaon Fluctuations at RHIC
We calculate the mean-over-variance ratio of the net-kaon fluctuations in the Hadron Resonance Gas (HRG) Model for the five highest energies of the RHIC Beam Energy Scan (BES) for different particle data lists. We compare these results with the latest experimental data from the STAR collaboration in order to extract sets of chemical freeze-out parameters for each list. We focused on the PDG2012 and PDG2016+ particle lists, which differ largely in the number of resonant states. Furthermore, our analysis determines the effect of the amount of resonances included in the HRG on the freeze-out conditions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns
Abstract This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties. In particular, image classification and regression studies are conducted by means of convolutional neural networks (CNNs) and NPs. First, the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, case studies are conducted with the prototype microelastic brittle (PMB), linear peridynamic solid (LPS), and viscoelastic solid (VES) models obtained by using the peridynamic theory. The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB, LBS, and VES models. Finally, a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns. The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs. The training results gradually improve, and the variance ranges decrease to less than 0.035. The main finding of this study is that the NPs enable accurate predictions, even with missing or insufficient training data. The results demonstrate that if the context points are set to the 10th, 100th, 300th, and 784th, the training information is deliberately omitted for the context points of the 10th, 100th, and 300th, and the predictions are different when the context points are significantly lower. However, the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs. Therefore, if the NPs are employed for training, the missing information of the training data can be supplemented to predict the results.
Mathematics↗
Reduced accrual of mineral-associated organic matter after two years of enhanced rock weathering in cropland soils, though no net losses of soil organic carbon
Abstract Enhanced rock weathering (ERW), the application of crushed silicate rock to soil, can remove atmospheric carbon dioxide by converting it to (bi) carbonate ions or solid carbonate minerals. However, few studies have empirically evaluated ERW in field settings. A critical question remains as to whether additions of crushed rock might positively or negatively affect soil organic matter (SOM)—Earth’s largest terrestrial organic carbon (C) pool and a massive reservoir of organic nitrogen (N). Here, in three irrigated cropland field trials in California, USA, we investigated the effect of crushed meta-basalt rock additions on different pools of soil organic carbon and nitrogen (i.e., mineral-associated organic matter, MAOM, and particulate organic matter, POM), active microbial biomass, and microbial community composition. After 2 years of crushed rock additions, MAOM stocks were lower in the upper surface soil (0–10 cm) of plots with crushed rock compared to unamended control plots. At the 2 sites where baseline pre-treatment data were available, neither total SOC nor SON decreased over the 2 years of study in plots with crushed rock or unamended control plots. However, the accrual rate of MAOM-C and MAOM-N at 0–10 cm was lower in plots with crushed rock vs. unamended controls. Before ERW is deployed at large scales, our results suggest that field trials should assess the effects of crushed rock on SOM pools, especially over multi-year time scales and in different environmental contexts, to accurately assess changes in net C and understand the mechanisms driving interactions between ERW and SOM cycling.
58 GEOSCIENCES↗
Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net
Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by ~4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.
Computer science↗
Hydrometeorological sensitivities of net ecosystem carbon dioxide and methane exchange of an Amazonian palm swamp peatland
Here, we report ecosystem-scale CO 2 and CH 4 flux observations for an Amazonian palm swamp peatland over a two-year period in relation to hydrometeorological forcings. Seasonal and short-term variations in hydrometeorological forcing had a strong effect on carbon dioxide and methane fluxes. High air temperature and vapor pressure deficit exerted an important limitation on photosynthesis during the dry season, while latent heat flux appeared to be insensitive to these climate drivers. Evidence from light-response analyses and flux partitioning support that photosynthetic activity was downregulated during dry conditions, while ecosystem respiration was either inhibited or enhanced depending on water table position. The cumulative net ecosystem carbon dioxide exchange indicated that the peatland was a significant carbon dioxide sink ranging from –465 (–279 to –651) g C m -2 y -1 in 2018 to –462 (–277 to –647) g C m -2 y -1 in 2019. The forest was a methane source of 22 (20 to 24) g C m -2 y -1 , similar in magnitude to other tropical peatlands and larger than boreal and arctic peatlands. Furthermore, the annual carbon budget of this Amazonian palm swamp peatland appears to be a major carbon sink under current hydrometeorological conditions, but evidence indicates that the sink strength is vulnerable to future climate change.
54 ENVIRONMENTAL SCIENCES↗
Into the net zero emissions and climate change control: From solid carbon waste to effective solar convertors
Here, in this work, we demonstrate the potential for solar-to-heat conversion of carbon byproducts and their outstanding effects to reduce anthropogenic emissions. Those carbon byproducts are soots (biomass, evaporated carbon, etc.) as well as morphed graphenes. Our results cover optical characterization to demonstrate their suitability as heat capacitors and solar-to-heat convertors. Our raw materials are sustainable and currently considered environmental pollutants that require true attention to minimize global warming effects. Fortunately, they are ideal candidates to reduce the carbon footprints and can contribute towards a true net-zero environmental transition. We explore and demonstrate that soots are among the most competitive materials to transform solar energy into heat due to their absorption capacity, and durability. These materials are more competitive than commercial products and other known solar convertors demonstrating the superior thermosolar benefits. The best candidate is biomass with solar absorptivity, light emission, and cost reduction of 96, 85% and 15-fold, respectively, when compare to commercial products. Biomass and soot have comparable or better solar conversion characteristics, and sustainability than pristine carbon nanostructures and they are up to 3 orders of magnitude cheaper.
14 SOLAR ENERGY↗
The role of the iron and steel sector in achieving net zero U.S. CO 2 emissions by 2050
The U.S. steel sector is a hard-to-abate sector because of its heavy dependence on fossil fuels and its high capital requirements. In 2015, the sector was one of the major carbon emitters, contributing 10 % of the U.S. industrial CO 2 emissions. The ability to decarbonize the U.S. iron and steel sector directly affects the ability of the U.S. to achieve economy-wide net zero CO 2 by 2050. In this paper, we use the Global Change Analysis Model (GCAM) to analyze different U.S. steel sector decarbonization pathways under varying technology, policy, and demand futures. These pathways provide insights on how various low-carbon steelmaking technologies such as those using carbon capture and storage (CCS), hydrogen, or scrap could help reduce U.S. steel emissions by mid-century. In our primary decarbonization pathway, we find that nearly all of the conventional fossil-based steelmaking capacity is fully integrated with CCS by 2050. However, without CCS availability, almost all of the conventional fossil-based steelmaking is phased-out by 2050 and is replaced by hydrogen-based production. Scrap-based production continues to remain vital across both of these decarbonization pathways. Furthermore, we find that demand reduction could help reduce the required levels of CCS and hydrogen-based production in the decarbonization pathways. Implementation of advanced energy efficiency measures could help substantially reduce the sector's energy usage. Finally, we observe that addressing the embodied carbon transfer associated with steel imports will be crucial for fully decarbonizing the U.S. steel sector.
54 ENVIRONMENTAL SCIENCES↗
Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information
Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.
42 ENGINEERING↗
Utilization of an Advanced Sensor network to determine fuel heating value and Real-Time net unit heat rate during transient operation
Coal-fired utility boilers are being increasingly used as variable electricity generation to resolve the imbalance in the energy market from the expansion of intermittent renewable energy. The frequent transient operation required to meet residual energy demand has created a challenge for coal-fired units to operate efficiently. This work utilizes an advanced sensor network (ASN) to calculate net unit heat rate (NUHR) of a coal-fired boiler in real time through combustion calculations and statistical correlations to provide the tools for optimizing dynamic operation. Real-time heating values that were necessary to determine fuel input energy to calculate accurate NUHR were found using both fundamental and data-driven methods. Real-time NUHR shows distinct shifts that reflect changes in process conditions that will improve the ability to optimize transient operation. Data-driven heating value correlations had 24% lower root mean square error (RMSE) than the fundamental combustion calculation approach when compared to daily retrospective proximate analysis. Furthermore, the data-driven method RMSE improved by 7% with the inclusion of ASN data. Future work is to validate by comparing unit performance with and without the inclusion of NUHR as a control parameter for the dynamic neural network.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Investigation of methane mass transfer and sorption in Marcellus shale under variable net-stress
Natural gas in shale exists as free and adsorbed gas, subject to prevailing pore pressures and stress conditions. Accordingly, to accurately estimate/predict the shale gas recovery potential, a central requirement is to represent gas transport and sorption behavior under varying stress conditions. The objective of this work is to facilitate the interpretation of laboratory-scale experiments, at relevant conditions, in an attempt to bridge the gap in scales between laboratory- and field-scale observations. We have conducted a series of high-pressure experiments on a full-diameter core sample from the Marcellus shale. These include gas loading (pressure-decay) and depletion (production) experiments with pure methane (CH 4 ) at variable stress conditions to characterize transport and sorption behavior under reservoir-relevant conditions. Here we have formulated and applied a novel integral model for mass transfer and storage in multi-porosity shale systems that allows us to effectively investigate transport and sorption phenomena: We delineate gas transport by interpreting helium (He) pressure-decay experiments and demonstrate how to use the information gained to calculate the relevant transport coefficients of CH 4 and other gases. A separate measurement of the CH 4 sorption isotherm on a smaller sample (a shale cube) was interpreted and combined with the transport description to predict the production behavior of CH 4 from the experiments with the full-diameter core. Our experiments demonstrate that the representation of sorption hysteresis is crucial for predicting and guiding shale gas production: At the end of both gas production experiments, approximately 20% of the initial gas in place remained in the core. Without accounting for sorption hysteresis, our modeling demonstrates that the CH 4 production could be overestimated by 10%. We demonstrate that our integral, triple-porosity model provides an effective approach for the interpretation and prediction of gas transport and sorption behavior during loading and production experiments on shale cores under variable net-stress conditions. In summary, our work combines measurements and modeling of mass transfer and sorption in shales at different scales to validate a characterization approach that facilitates an improved understanding of shale gas production. Furthermore, the triple-porosity model utilized in our work defines a potential pathway for the translation of laboratory-scale experimentation to larger-scale applications.
58 GEOSCIENCES↗
Development, validation, and verification of multi-pass thermo-mechanical welding simulations using the open-source MOOSE framework: NeT TG4 benchmark weldment
This study develops and validates a sequentially coupled thermo-mechanical welding simulation for the three-pass 316L stainless steel NeT TG4 benchmark weldment using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) and the Nuclear Engineering Material model Library (NEML). A diffused ellipsoidal heat source was calibrated against thermocouple data and weld macrographs to accurately model the fusion zone geometry and transient thermal fields. Material hardening is represented using the Lemaitre-Chaboche mixed isotropic-kinematic hardening model, while four annealing models - no annealing, single-stage at 1050 °C and 1300 °C, and two-stage at 800 °C/1300 °C - were implemented to assess the impact of annealing models on the accuracy of the predicted welding-induced plasticity, distortions, and residual stresses. The predictions were validated against experimental measurements and benchmarked against results from commercial software, demonstrating that thermo-mechanical MOOSE welding simulations achieve comparable accuracy with enhanced computational efficiency. This work highlights the potential of using open-source finite element frameworks like MOOSE for advanced manufacturing simulations.
Ji, Wendy [Australian Nuclear Science and Technolo↗
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.
97 MATHEMATICS AND COMPUTING↗
YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy
Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.
3D instance segmentation↗
Measurements of charged-particle multiplicity dependence of higher-order net-proton cumulants in p + p collisions at $\sqrt{s}$ = 200 GeV from STAR at RHIC
We report on the charged-particle multiplicity dependence of net-proton cumulant ratios up to sixth order from $\sqrt{s}$ = 200 GeV p+p collisions at the Relativistic Heavy Ion Collider (RHIC). The measured ratios C 4 /C 2 , C 5 /C 1 , and C 6 /C 2 decrease with increased charged-particle multiplicity and rapidity acceptance. Neither the Skellam baselines nor PYTHIA8 calculations account for the observed multiplicity dependence. In addition, the ratios C 5 /C 1 and C 6 /C 2 approach negative values in the highest-multiplicity events, which implies that thermalized QCD matter may be formed in p+p collisions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗