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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Determination of critical experiment correlations using the sampler sequence within scale 6.2 - 14602

The validation of neutron transport methods used in nuclear criticality safety analyses is required by consensus American National Standards Institute/American Nuclear Society (ANSI/ANS) standards. In the last decade, there has been an increased interest in correlations among critical experiments used in validation that have shared physical attributes and which impact the independence of each measurement. The statistical methods included in many of the frequently cited guidance documents on performing validation calculations incorporate the assumption that all individual measurements are independent, so little guidance is available to practitioners on the topic. Typical guidance includes recommendations to select experiments from multiple facilities and experiment series in an attempt to minimize the impact of correlations or common-cause errors in experiments. Recent efforts have been made both to determine the magnitude of such correlations between experiments and to develop and apply methods for adjusting the bias and bias uncertainty to account for the correlations. This paper describes recent work performed at Oak Ridge National Laboratory using the Sampler sequence from the SCALE code system to develop experimental correlations using a Monte Carlo sampling technique. Sampler will be available for the first time with the release of SCALE 6.2, and a brief introduction to the methods used to calculate experiment correlations within this new sequence is presented in this paper. Techniques to utilize these correlations in the establishment of upper subcritical limits are the subject of a companion paper and will not be discussed here. Example experimental uncertainties and correlation coefficients are presented for a variety of low-enriched uranium water-moderated lattice experiments selected for use in a benchmark exercise by the Working Party on Nuclear Criticality Safety Subgroup on Uncertainty Analysis in Criticality Safety Analyses. The results include studies on the effect of fuel rod pitch on the correlations, and some observations are also made regarding difficulties in determining experimental correlations using the Monte Carlo sampling technique. (authors)

42 ENGINEERING↗
DOE OSTI2023

Advancements in Validation of TSLs through Inelastic Neutron Scattering and Transmission Measurements [Abstract]

Historically, the free gas approximation has been used to treat the thermal scattering of neutrons with energies below a few electron-volts (eV) in unevaluated materials. However, this method inadequately reproduces neutron scattering at these energies. Until recently, only a limited number of materials had available thermal scattering law (TSL) files/libraries in the ENDF nuclear data libraries in this energy range. With advancements in atomistic modeling techniques, such as molecular dynamics, ab-initio molecular dynamics, and density functional theory, TSL libraries have become available for many more materials. This is particularly relevant due to the rising interest in several advanced reactor systems that require novel moderator and reflector materials. While quasi-integral and integral benchmarks have been designed to validate historically important moderator materials (such as light water and polyethylene), there is currently a lack of standard validation methods for TSLs, especially when multiple conflicting TSLs exist. To address this issue, the Oak Ridge National Lab Nuclear Data group has been working on utilizing inelastic neutron scattering (INS) measurements combined with transmission (i.e., total cross section) measurements to evaluate and validate TSLs for different materials. We plan to demonstrate how this method has worked on materials such as polyethylene, lucite, and polystyrene. In addition, we will compare the newly created libraries to ENDF libraries for these materials and explain why integral benchmarks should not be used for validation when multiple TSLs exist.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

New tools for the analysis and validation of cryo-EM maps and atomic models

Recent advances in the field of electron cryomicroscopy (cryo-EM) have resulted in a rapidly increasing number of atomic models of biomacromolecules that have been solved using this technique and deposited in the Protein Data Bank and the Electron Microscopy Data Bank. Similar to macromolecular crystallography, validation tools for these models and maps are required. While some of these validation tools may be borrowed from crystallography, new methods specifically designed for cryo-EM validation are required. Here, new computational methods and tools implemented in PHENIX are discussed, including d 99 to estimate resolution, phenix.auto_sharpen to improve maps and phenix.mtriage to analyze cryo-EM maps. It is suggested that cryo-EM half-maps and masks should be deposited to facilitate the evaluation and validation of cryo-EM-derived atomic models and maps. The application of these tools to deposited cryo-EM atomic models and maps is also presented.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Survey of DAKOTA's V&V Capabilities in the Simulation of Residual Stresses in a Simple Composite Structure

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses can result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, a process modeling methodology was developed and implemented into Sandia National Laboratories' SIERRA/SolidMechanics code. This method can be used to predict the process-induced stresses in any composite structure, regardless of material composition or geometric complexity. However, to develop confidence in these predictions, they must be rigorously validated. Specifically, sensitivity studies should be completed to define which model parameters are critical to the residual stress predictions. Then, the uncertainty associated with those critical parameters should be quantified and processed through the model to develop stress-state predictions encompassing the most important sources of physical variability. Numerous sensitivity analysis and uncertainty quantification methods exist, each offering specific strengths and weaknesses. Therefore, the objective of this study is to compare the performance of several accepted sensitivity analysis and uncertainty quantification methods during the manufacturing process simulation of a composite structure. The examined methods include simple sampling techniques as well as more sophisticated surrogate approaches. The computational costs are assessed for each of the examined methods, and the results of the study indicate that the surrogate approaches are the most computationally efficient validation methods and are ideal for future residual stress investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Use of machine learning for a helium line intensity ratio method in Magnum-PSI

Optical emission spectroscopy (OES) of helium (He) line intensities has been used to measure the electron density, ne, and temperature, Te, in various plasma devices. In this study, a neural network with five hidden layers is introduced to model the relation between the OES data and ne/Te from laser Thomson scattering in the linear plasma device Magnum-PSI and compared to multiple regression analysis. It is shown that the neural network reduces the residual errors of prediction values (ne and Te) less than half those of the multiple regression analysis. We checked two different data splitting methods for training and validation data, i.e., with and without considering the unit of discharge. A comparison of the splitting methods suggests that the residual error will decrease to ~10% even for a new discharge data when accumulating a sufficient data set.

Kajita, Shin↗
DOE OSTI2015

Multienergy CT acquisition and reconstruction with a stepped tube potential scan

Purpose: Based on an energy-dependent property of matter, one may obtain a pseudomonochromatic attenuation map, a material composition image, an electron-density distribution, and an atomic number image using a dual- or multienergy computed tomography (CT) scan. Dual- and multienergy CT scans broaden the potential of x-ray CT imaging. The development of such systems is very useful in both medical and industrial investigations. In this paper, the authors propose a new dual- and multienergy CT system design (segmental multienergy CT, SegMECT) using an innovative scanning scheme that is conveniently implemented on a conventional single-energy CT system. The two-step-energy dual-energy CT can be regarded as a special case of SegMECT. A special reconstruction method is proposed to support SegMECT. Methods: In their SegMECT, a circular trajectory in a CT scan is angularly divided into several arcs. The x-ray source is set to a different tube voltage for each arc of the trajectory. Thus, the authors only need to make a few step changes to the x-ray energy during the scan to complete a multienergy data acquisition. With such a data set, the image reconstruction might suffer from severe limited-angle artifacts if using conventional reconstruction methods. To solve the problem, they present a new prior-image-based reconstruction technique using a total variance norm of a quotient image constraint. On the one hand, the prior extracts structural information from all of the projection data. On the other hand, the effect from a possibly imprecise intensity level of the prior can be mitigated by minimizing the total variance of a quotient image. Results: The authors present a new scheme for a SegMECT configuration and establish a reconstruction method for such a system. Both numerical simulation and a practical phantom experiment are conducted to validate the proposed reconstruction method and the effectiveness of the system design. The results demonstrate that the proposed SegMECT can provide both attenuation images and material decomposition images of reasonable image quality. Compared to existing methods, the new system configuration demonstrates advantages in simplicity of implementation, system cost, and dose control. Conclusions: This proposed SegMECT imaging approach has great potential for practical applications. It can be readily realized on a conventional CT system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

A review of MRI evaluation of demyelination in cuprizone murine model

The cuprizone mouse model of non-autoimmune demyelination reproduces some phenomena of multiple sclerosis and is appropriate for validation and specification of a new method of non-invasive diagnostics. In the review new data which are collected using the new MRI method are compared with one or more conventional MRI tools. Also the paper reviewed the validation of MRI approaches using histological or immunohistochemical methods. Luxol fast blue histological staining and myelin basic protein immunostaining is widespread. To improve the accuracy of non-invasive conventional MRI, multimodal scanning could be applied. The new quantitative MRI method of fast mapping of the macromolecular proton fraction is a reliable biomarker of myelin in the brain and can be used for research of demyelination in animals. To date, a validation of MPF method on the CPZ mouse model of demyelination is not performed, although this method is probably the best way to evaluate demyelination using MRI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Bayesian model aggregation for ensemble-based estimates of protein pKa values

This paper investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid p$K_a$ predictions. Structure-based p$K_a$ calculations play an important role in the mechanistic interpretation of protein structure and are also used to determine a wide range of protein properties. A diverse set of methods currently exist for p$K_a$ prediction, ranging from empirical statistical models to {\it ab initio} quantum mechanical approaches. However, each of these methods are based on a set of assumptions that have inherent bias and sensitivities that can effect a model's accuracy and generalizability for p$K_a$ prediction in complicated biomolecular systems. We use BMA to combine eleven diverse prediction methods that each estimate pKa values of amino acids in staphylococcal nuclease. These methods are based on work conducted for the pKa Cooperative and the pKa measurements are based on experimental work conducted by the Garc{\'i}a-Moreno lab. Our study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods in our cross-validation study with improvements from 40-70\% over other method classes. This work illustrates a new possible mechanism for improving the accuracy of p$K_a$ prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy.

electrostatics↗
DOE OSTI2015

Implementation of a hybrid particle code with a PIC description in r–z and a gridless description in ϕ into OSIRIS

For many plasma physics problems, three-dimensional and kinetic effects are very important. However, such simulations are very computationally intensive. Fortunately, there is a class of problems for which there is nearly azimuthal symmetry and the dominant three-dimensional physics is captured by the inclusion of only a few azimuthal harmonics. Recently, it was proposed [1] to model one such problem, laser wakefield acceleration, by expanding the fields and currents in azimuthal harmonics and truncating the expansion. The complex amplitudes of the fundamental and first harmonic for the fields were solved on an r–z grid and a procedure for calculating the complex current amplitudes for each particle based on its motion in Cartesian geometry was presented using a Marder's correction to maintain the validity of Gauss's law. In this paper, we describe an implementation of this algorithm into OSIRIS using a rigorous charge conserving current deposition method to maintain the validity of Gauss's law. We show that this algorithm is a hybrid method which uses a particles-in-cell description in r–z and a gridless description in ϕ. We include the ability to keep an arbitrary number of harmonics and higher order particle shapes. Examples for laser wakefield acceleration, plasma wakefield acceleration, and beam loading are also presented and directions for future work are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

1 H NMR Quantification of Aromatic Monomers from Reductive Catalytic Fractionation

Reductive catalytic fractionation (RCF) can produce high yields of aromatic monomers from lignin in native biomass. Quantification of these aromatic monomers is a well-known but demanding task, in part due to the lack of commercially available standards. Here, we demonstrate 1 H NMR spectroscopy as a complementary method to rapidly quantify aromatic monomer concentrations in RCF oils. The method exhibited good agreement with measurements from ultrahigh pressure liquid chromatography (UHPLC) for 96 RCF oils with varying monomer selectivity, with average absolute deviations of individual monomer yields between 0.5 and 1.1 wt % (relative 11–17%) and R 2 values above 0.9 compared to conventional UHPLC quantification. Quantification of S-type monomers, including for 4-ethylsyringol and 4-propenylsyringol, was generally reliable. The validity of G-type monomer quantifications depended on reaction selectivity due to overlap between peaks of 4-ethylguaiacol and 4-(3-hydroxypropyl)-guaiacol. The method could be applied on crude RCF oils without needing to perform the liquid–liquid extraction typically done for RCF reactions, thereby providing a convenient way to quantify lignin extraction and aromatic monomer yield. Overall, 1 H NMR spectroscopy can serve as a rapid primary quantification or secondary validation method for RCF monomer yield and selectivity measurements.

1H NMR spectroscopy↗
DOE OSTI2016

Development plan for the External Hazards Experimental Group. Light Water Reactor Sustainability Program

This report describes the development plan for a new multi-partner External Hazards Experimental Group (EHEG) coordinated by Idaho National Laboratory (INL) within the Risk-Informed Safety Margin Characterization (RISMC) technical pathway of the Light Water Reactor Sustainability Program. Currently, there is limited data available for development and validation of the tools and methods being developed in the RISMC Toolkit. The EHEG is being developed to obtain high-quality, small- and large-scale experimental data validation of RISMC tools and methods in a timely and cost-effective way. The group of universities and national laboratories that will eventually form the EHEG (which is ultimately expected to include both the initial participants and other universities and national laboratories that have been identified) have the expertise and experimental capabilities needed to both obtain and compile existing data archives and perform additional seismic and flooding experiments. The data developed by EHEG will be stored in databases for use within RISMC. These databases will be used to validate the advanced external hazard tools and methods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

SU-E-T-490: Independent Three-Dimensional (3D) Dose Verification of VMAT/SBRT Using EPID and Cloud Computing

Purpose: Dosimetric verification of VMAT/SBRT is currently performed on one or two planes in a phantom with either film or array detectors. A robust and easy-to-use 3D dosimetric tool has been sought since the advent of conformal radiation therapy. Here we present such a strategy for independent 3D VMAT/SBRT plan verification system by a combined use of EPID and cloud-based Monte Carlo (MC) dose calculation. Methods: The 3D dosimetric verification proceeds in two steps. First, the plan was delivered with a high resolution portable EPID mounted on the gantry, and the EPID-captured gantry-angle-resolved VMAT/SBRT field images were converted into fluence by using the EPID pixel response function derived from MC simulations. The fluence was resampled and used as the input for an in-house developed Amazon cloud-based MC software to reconstruct the 3D dose distribution. The accuracy of the developed 3D dosimetric tool was assessed using a Delta4 phantom with various field sizes (square, circular, rectangular, and irregular MLC fields) and different patient cases. The method was applied to validate VMAT/SBRT plans using WFF and FFF photon beams (Varian TrueBeam STX). Results: It was found that the proposed method yielded results consistent with the Delta4 measurements. For points on the two detector planes, a good agreement within 1.5% were found for all the testing fields. Patient VMAT/SBRT plan studies revealed similar level of accuracy: an average γ-index passing rate of 99.2± 0.6% (3mm/3%), 97.4± 2.4% (2mm/2%), and 72.6± 8.4 % ( 1mm/1%). Conclusion: A valuable 3D dosimetric verification strategy has been developed for VMAT/SBRT plan validation. The technique provides a viable solution for a number of intractable dosimetry problems, such as small fields and plans with high dose gradient.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Microscopically based energy density functionals for nuclei using the density matrix expansion. II. Full optimization and validation

Background: Energy density functional methods provide a generic framework to compute properties of atomic nuclei starting from models of nuclear potentials and the rules of quantum mechanics. Until now, the overwhelming majority of functionals have been constructed either from empirical nuclear potentials such as the Skyrme or Gogny forces, or from systematic gradient-like expansions in the spirit of the density functional theory for atoms. Purpose: In this study, we seek to obtain a usable form of the nuclear energy density functional that is rooted in the modern theory of nuclear forces. We thus consider a functional obtained from the density matrix expansion of local nuclear potentials from chiral effective field theory. We propose a parametrization of this functional carefully calibrated and validated on selected ground-state properties that is suitable for large-scale calculations of nuclear properties. Methods: Our energy functional comprises two main components. The first component is a non-local functional of the density and corresponds to the direct part (Hartree term) of the expectation value of local chiral potentials on a Slater determinant. Contributions to the mean field and the energy of this term are computed by expanding the spatial, finite-range components of the chiral potential onto Gaussian functions. The second component is a local functional of the density and is obtained by applying the density matrix expansion to the exchange part (Fock term) of the expectation value of the local chiral potential. We apply the UNEDF2 optimization protocol to determine the coupling constants of this energy functional. Results: We obtain a set of microscopically constrained functionals for local chiral potentials from leading order up to next-to-next-to-leading order with and without three-body forces and contributions from Δ excitations. These functionals are validated on the calculation of nuclear and neutron matter, nuclear mass tables, single-particle shell structure in closed-shell nuclei, and the fission barrier of 240 Pu . Quantitatively, they perform noticeably better than the more phenomenological Skyrme functionals. Conclusions: The inclusion of higher-order terms in the chiral perturbation expansion seems to produce a systematic improvement in predicting nuclear binding energies while the impact on other observables is not really significant. In conclusion, this result is especially promising since all the fits have been performed at the single-reference level of the energy density functional approach, where important collective correlations such as center-of-mass correction, rotational correction, or zero-point vibrational energies have not been taken into account yet.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

A method for evaluating the expectation value of a power spectrum using the probability density function of phases

Here, we present a new method to evaluate the expectation value of the power spectrum of a time series. A statistical approach is adopted to define the method. After its demonstration, it is validated showing that it leads to the known properties of the power spectrum when the time series contains a periodic signal. The approach is also validated in general with numerical simulations. The method puts into evidence the importance that is played by the probability density function of the phases associated to each time stamp for a given frequency, and how this distribution can be perturbed by the uncertainties of the parameters in the pulsar ephemeris. We applied this method to solve the power spectrum in the case the first derivative of the pulsar frequency is unknown and not negligible. We also undertook the study of the most general case of a blind search, in which both the frequency and its first derivative are uncertain. We found the analytical solutions of the above cases invoking the sum of Fresnel's integrals squared.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

D–Q Impedance Based Stability Analysis and Parameter Design of Three-Phase Inverter-Based AC Power Systems

Small-signal stability is an important concern in three-phase inverter-based ac power systems. The impedance-based approach based on the generalized Nyquist stability criterion (GNC) can analyze the stability related with the medium and high-frequency modes of the systems. However,. the GNC involves the right-half-plane (RHP) pole calculation of return-ratio transfer function matrices, which cannot be avoided for stability analysis of complicated ac power systems. Therefore, it necessitates the detailed internal control information of the inverters, which is not normally available for commercial inverters. To address this issue, this paper introduces the component connection method (CCM) in the frequency domain for stability analysis in the synchronous d-q frame, by proposing a method of deriving the impedance matrix of the connection networks of inverter-based ac power systems. Demonstration on a two-area system and a microgrid shows that: The CCM-enabled approach can avoid the RHP pole calculation of return-ratio matrices and enables the stability analysis by using only the impedances of system components, which could be measured without the need for the internal information. A stability analysis method based on d-q impedances, the CCM, and the determinant-based GNC is also proposed to further simplify the analysis process. Inverter controller parameters can be designed as stability regions in parameter spaces, by repetitively applying the proposed stability analysis method. Simulation and experimental results verify the validity of the proposed stability analysis method and the parameter design approach.

42 ENGINEERING↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
DOE OSTI2015

Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy’s resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this framework, the project team has focused on optimizing resource allocation for improving numerical models through further code development and experimentation. Related to further code development, we have developed a code prioritization index (CPI) for coupled numerical models. CPI is implemented to effectively improve the predictive capability of the coupled model by increasing the sophistication of constituent codes. In relation to designing new experiments, we investigated the information gained by the addition of each new experiment used for calibration and bias correction of a simulation model. Additionally, the variability of ‘information gain’ through the design domain has been investigated in order to identify the experiment settings where maximum information gain occurs and thus guide the experimenters in the selection of the experiment settings. This idea was extended to evaluate the information gain from each experiment can be improved by intelligently selecting the experiments, leading to the development of the Batch Sequential Design (BSD) technique. Additionally, we evaluated the importance of sufficiently exploring the domain of applicability in experiment-based validation of high-consequence modeling and simulation by developing a new metric to quantify coverage. This metric has also been incorporated into the design of new experiments. Finally, we have proposed a data-aware calibration approach for the calibration of numerical models. This new method considers the complexity of a numerical model (the number of parameters to be calibrated, parameter uncertainty, and form of the model) and seeks to identify the number of experiments necessary to calibrate the model based on the level of sophistication of the physics. The final component in the project team’s work to improve model calibration and validation methods is the incorporation of robustness to non-probabilistic uncertainty in the input parameters. This is an improvement to model validation and uncertainty quantification stemming beyond the originally proposed scope of the project. We have introduced a new metric for incorporating the concept of robustness into experiment-based validation of numerical models. This project has accounted for the graduation of two Ph.D. students (Kendra Van Buren and Josh Hegenderfer) and two M.S. students (Matthew Egeberg and Parker Shields). One of the doctoral students is now working in the nuclear engineering field and the other one is a post-doctoral fellow at the Los Alamos National Laboratory. Additionally, two more Ph.D. students (Garrison Stevens and Tunc Kulaksiz) who are working towards graduation have been supported by this project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

The Science, Engineering, and Validation of Marine Carbon Dioxide Removal and Storage

Scenarios to stabilize global climate and meet international climate agreements require rapid reductions in human carbon dioxide (CO 2 ) emissions, often augmented by substantial carbon dioxide removal (CDR) from the atmosphere. While some ocean-based removal techniques show potential promise as part of a broader CDR and decarbonization portfolio, no marine approach is ready yet for deployment at scale because of gaps in both scientific and engineering knowledge. Marine CDR spans a wide range of biotic and abiotic methods, with both common and technique-specific limitations. Further targeted research is needed on CDR efficacy, permanence, and additionality as well as on robust validation methods—measurement, monitoring, reporting, and verification—that are essential to demonstrate the safe removal and long-term storage of CO 2 . Engineering studies are needed on constraints including scalability, costs, resource inputs, energy demands, and technical readiness. Research on possible co-benefits, ocean acidification effects, environmental and social impacts, and governance is also required.

climate mitigation↗