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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Two-stage membrane-based process utilizing highly CO 2 -selective membranes for cost and energy efficient carbon capture from coal flue gas: A process simulation study

Membrane technology for CO 2 capture has become an attractive strategy due to its cost and energy efficiency and low materials costs. In the past decade, membrane-based process designs for post-combustion power plant CO 2 capture have been developed, utilizing existing highly CO 2 -permeable membranes with relatively low CO 2 /N 2 selectivity (<50), and have obtained reasonably economic carbon capture. However, few membrane-based process designs were proposed for moderate to highly CO 2 -selective membranes (CO 2 /N 2 selectivity of 50–300, and >300, respectively), which have vastly emerged in recent years, such as various facilitated transport membranes (FTMs). Herein, we proposed a two-stage membrane-base process design targeting economic carbon capture from coal-fired flue gas. This process design features the utilization of highly CO 2 -selective membranes for one-stage CO 2 enrichment to 95% dry-base purity in the first stage and recycle of the remaining CO 2 by a highly CO 2 -permeable membrane in the second stage, in order to achieve economic CO 2 capture with 90% capture rate and >95% CO 2 product purity. Through an integration-iteration membrane model and the Aspen Plus process simulation, a sensitivity study of operating pressures (feed and permeate pressures) and membrane properties (CO 2 permeance and CO 2 /N 2 selectivity) was conducted. Critical CO 2 /N 2 selectivity of 300–400 was found for the highly CO 2 -selctive membranes to meet the demand for cost and energy efficient results. The lowest possible membrane area of 4.8 × 10 5 m 2 and fractional energy of 19.3% were obtained, which is comparable to or even more attractive than reported membrane-based process designs. Here, this work provides a new membrane process design option for highly CO 2 -selective membranes and gives insights on the influence of membrane performance and operation condition.

42 ENGINEERING↗
DOE OSTI2015

Meson exchange current (MEC) models in neutrino interaction generators

Understanding of the so-called 2 particle-2 hole (2p-2h) effect is an urgent program in neutrino interaction physics for current and future oscillation experiments. Such processes are believed to be responsible for the event excesses observed by recent neutrino experiments. The 2p-2h effect is dominated by the meson exchange current (MEC), and is accompanied by a 2-nucleon emission from the primary vertex, instead of a single nucleon emission from the charged-current quasi-elastic (CCQE) interaction. Current and future high resolution experiments can potentially nail down this effect. For this reason, there are world wide efforts to model and implement this process in neutrino interaction simulations. In these proceedings, I would like to describe how this channel is modeled in neutrino interaction generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Correction to “COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins”

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

Molecular interactions↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Formation of Arctic Stratocumuli Through Atmospheric Radiative Cooling

Stratocumulus clouds are important to the Arctic climate because they are prevalent and exert a strong radiative forcing on the surface. However, relatively little is known about how stratocumulus clouds form in the Arctic. In this study, radiative transfer calculations are used to show that the timescale over which stably stratified Arctic temperature and water vapor profiles cool to saturation is less than typical residence times for individual air parcels in the Arctic. This result is consistent with previous studies in suggesting that elevated stratocumulus can form naturally through clear-sky radiative cooling during all seasons, without assistance from frontal lifting or other atmospheric forcing. Single column model simulations of the cloud formation process, after radiative cooling has resulted in saturation in a stably stratified profile, suggest that stratocumulus cloud properties are sensitive to the characteristics of the environment in which the formation process takes place. For example, sensitivity tests suggest that clouds may attain liquid water paths of over 50 g/m 2 if they form in moist environments but may become locked in a low-liquid water path quasi steady state or dissipate within hours if they form in dry environments. A potential consequence of these sensitivities is that when an Arctic stratocumulus layer forms by radiative cooling, it is more likely to become optically thick, optically thin, or dissipate than it is to obtain an intermediate optical thickness. This could help explain why the cloudy and radiatively clear atmospheric states are so prevalent across the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data and Scripts Associated with "Modeling Ecohydrological Responses of Vegetation to Urban Microclimates Using the E3SM Land Model"

This dataset supports the study of vegetation ecohydrological responses to urban microclimates using the land component of the Energy Exascale Earth System Model (ELM) at four urban sites in Knoxville, Tennessee, USA. It includes the model inputs, simulation outputs, and associated scripts for running ELM simulations and analyzing the resulting data. The Model_Inputs folder includes static surface data, satellite-derived phenology (i.e., leaf area index), and atmospheric forcing data used to drive ELM simulations. Detailed descriptions of these datasets are provided in Section 2.3.2 of the associated manuscript. The Model_Outputs folder contains simulation results for the baseline, treatment, and ensemble experiments. Outputs from the baseline and treatment simulations are provided as raw ELM NetCDF files. Because the raw outputs from the 4,000-member ensemble are prohibitively large, the ensemble results are provided as summarized CSV files, which also serve as the source data for Figure 5 of the associated manuscript. The Scripts folder contains three components: E3SM, the core codebase of the Energy Exascale Earth System Model (E3SM); elm-olmt, the Offline Land Model Testbed (OLMT) used to perform the simulations; and knoxville_elm, which contains the analysis scripts used to process model outputs and generate the figures and results presented in the associated manuscript. Additional information is provided in Scripts_readme.txt within the Scripts directory.

Lu, Xiaoman [ORNL] (ORCID:0000000306698780)↗
DOE OSTI2015

Impact of nuclear fission on r-process nucleosynthesis and origin of solar r-process elements

Binary neutron star mergers (NSMs) are expected to be main production sites of r-process elements. Their ejecta are extremely neutron-rich (Y{sub e}<0.1), and the r-process path proceeds along the neutron drip line and enters the region of fissile nuclei. In this situation, although superheavy nuclei may be synthesized and the r-process path may reach the island of stability, those are sensitive to theoretical models of nuclear masses and nuclear fission. In this study, we carry out r-process nucleosynthesis simulations in the NSMs. Our new nuclear reaction network code include new theoretical models of nuclear masses and nuclear fission. Our r-process simulation of a binary NSM shows that the final r-process elemental abundances exhibit flat pattern for A∼110-160, and several fission cycling operate in extremely neutron-rich conditions of the NSM. We find that the combination of the NSMs and the magnetorotational supernovae can reproduce the solar r-process elements. We discuss the validity of this interpretation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Atmosphere expansion and mass loss of close-orbit giant exoplanets heated by stellar XUV. II. Effects of planetary magnetic field; structuring of inner magnetosphere

This is the second paper in a series where we build a self-consistent model to simulate the mass-loss process of a close-orbit magnetized giant exoplanet, so-called hot Jupiter (HJ). In this paper we generalize the hydrodynamic (HD) model of an HJ's expanding hydrogen atmosphere, proposed in the first paper, to include the effects of intrinsic planetary magnetic field. The proposed self-consistent axisymmetric 2D magnetohydrodynamics model incorporates radiative heating and ionization of the atmospheric gas, basic hydrogen chemistry for the appropriate account of major species composing HJ's upper atmosphere and related radiative energy deposition, and H{sub 3}{sup +} and Lyα cooling processes. The model also takes into account a realistic solar-type X-ray/EUV spectrum for calculation of intensity and column density distribution of the radiative energy input, as well as gravitational and rotational forces acting in a tidally locked planet–star system. An interaction between the expanding atmospheric plasma and an intrinsic planetary magnetic dipole field leads to the formation of a current-carrying magnetodisk that plays an important role for topology and scaling of the planetary magnetosphere. A cyclic character of the magnetodisk behavior, composed of consequent phases of the disk formation followed by the magnetic reconnection with the ejection of a ring-type plasmoid, has been discovered and investigated. We found that the mass-loss rate of an HD 209458b analog planet is weakly affected by the equatorial surface field <0.3 G, but is suppressed by an order of magnitude at the field of 1 G.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Coupled Gas-Exchange Model for C4 Leaves Comparing Stomatal Conductance Models

Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball–Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball–Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Resolving Mesoscale Convective Systems: Grid Spacing Sensitivity in the Tropics and Midlatitudes

Abstract Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer‐scale horizontal grid spacings offer notable improvements in simulating deep convection compared to coarser‐resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process‐oriented observational data remain difficult. This study presents an ensemble of MCS simulations with spanning the deep convective gray zone ( from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub‐kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of , suggesting a need for 250 m in this region. Nevertheless, bulk convergence—agreement of storm‐average statistics—is achievable with kilometer‐scale simulations within a 10% error margin with 1 km providing a good balance between accuracy and computational cost.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Conserving Land–Atmosphere Synthesis Suite (CLASS)

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design and Modeling of an 80 bar Oxy-Combustor for Direct Fired Supercritical CO2 Applications

As supercritical CO2 power cycles for fossil energy power generation continue to generate interest, there are significant issues and unanswered questions regarding injector design, flame stabilization, wall heat transfer, CO emissions, combustion dynamics and other combustion phenomenon. For natural gas, direct-fired cycles with carbon capture it is believed that Computational Fluid Dynamics (CFD) modeling will play an essential role in the combustor design process. To accurately model turbulent reacting flows at these unique conditions, experimental data is needed to validate CFD codes and sub-models and is currently lacking at conditions relevant for these cycles. This paper presents the conceptual design and CFD simulations of an experimental 80 bar oxy-combustion facility and test article currently under construction at NETL. The facility is targeted towards the testing of a single injector, direct-fired sCO2 combustor at the 100 kW thermal output level. While these conditions do not reflect the actual Allam cycle operating conditions (300 bar) they are viewed as a stepping stone in the model validation process at supercritical conditions. Reynolds Averaged Navier-Stokes as well as Large Eddy Simulation are used to model the turbulent combustion process and aid in the design of the combustor and injector. Process parameters including oxidizer preheat temperature and combustor flowrate are investigated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Isotherm Modeling and Techno-Economic Analysis of Contactor Technologies for New Tetraamine-Appended MOF for NGCC Applications

Recently, a family of tetraamine-functionalized metal-organic frameworks (MOF) has been reported as promising sorbent materials for capturing CO2 from flue gas conditions relevant to natural gas combined cycle (NGCC) applications. The main advantages of these materials are their two-step cooperative CO2 adsorption, which gives rise to unusual two step-shaped CO2 adsorption profiles and their high thermal stability. This work presents the modelling of the two-transition isotherm of the tetraamine-appended MOF, N,N'-bis(3-aminopropyl)-1,4-diaminobutane (3-4-3)-appended Mg2(dobpdc), and the techno-economic analysis (TEA) of carbon capture processes utilizing this sorbent. Due to the unusual isotherm shapes of the experimental CO2 adsorption data for tetraamine-appended Mg2(dobpdc) and the strong nonlinearity of CO2 loading with respect to temperature and pressure, we tested two different models which use logistic functions for representing the different isotherm behaviors in the different pressure ranges. The first model uses the quadratic isotherm model in the low-pressure region, the Langmuir isotherm model in the middle pressure range, and the dual site Langmuir isotherm model in the high-pressure range. To model the transition between regions we used the arctangent functions independent of temperature and the thermal effect was accounted by using the Clausius-Clapeyron relation]. The second model is an extension of the weighted dual-site Langmuir isotherm model presented by Hughes et al.. In this extended model, the dual-site Langmuir isotherm is employed in the three transition regions, using temperature-dependent logistic functions to activate or deactivate the isotherm model in the low, middle, and high-pressure ranges. Both models fit the experimental data quite well with root mean squared errors (RMSE’s) of 0.41 and 0.17 for model 1 and model 2, respectively. Since model 2 resulted in a lower RMSE, it was leveraged for the development of the gas/solid contactor models used by the TEA. Specifically, two different contactor models, an axial-flow fixed bed and moving bed contactor, were developed as part of this work. These models are dynamic, pressure-driven, and consist of mass, energy, and momentum conservation equations. A kinetic model was also developed by performing parameter estimation using experimental fixed bed breakthrough data. These models are then used to simulate CO2 capture processes from the flue gas generated from a ~600 gross MW NGCC power plant. A cost model was developed which considers the capital cost of the reactors and the significant operating costs such as steam and electricity. Using NETL’s Framework for the Optimization and Quantification of Uncertainty of Uncertainty and Surrogates tool (FOQUS), which has the capability of linking models built using numerous modelling platforms with derivative-free optimization solvers, a techno-economic optimization of the carbon capture processes was performed which minimizes the cost of capture.

Caballero, Daison↗
DOE OSTI2016

Development of a CO 2 Chemical Sensor for Downhole CO 2 Monitoring in Carbon Sequestration

Geologic storage of carbon dioxide (CO 2 ) has been proposed as a viable means for reducing anthropogenic CO 2 emissions. The means for geological sequestration of CO 2 is injection of supercritical CO 2 underground, which requires the CO 2 to remain either supercritical, or in solution in the water/brine present in the underground formation. However, there are aspects of geologic sequestration that need further study, particularly in regards to safety. To date, none of the geologic sequestration locations have been tested for storage integrity under the changing stress conditions that apply to the sequestration of very large amounts of CO 2 . Establishing environmental safety and addressing public concerns require widespread monitoring of the process in the deep subsurface. In addition, studies of subsurface carbon sequestration such as flow simulations, models of underground reactions and transports require a comprehensive monitoring process to accurately characterize and understand the storage process. Real-time information about underground CO 2 movement and concentration change is highly helpful for: (1) better understanding the uncertainties present in CO 2 geologic storage; (2) improvement of simulation models; and (3) evaluation of the feasibility of geologic CO 2 storage. Current methods to monitor underground CO 2 storage include seismic, geoelectric, isotope and tracer methods, and fluid sampling analysis. However, these methods commonly resulted low resolution, high cost, and the inability to monitor continuously over the long time scales of the CO 2 storage process. A preferred way of monitoring in-situ underground CO 2 migration is to continuous measure CO 2 concentration change in brine during the carbon storage process. An approach to obtain the real time information on CO 2 concentration change in formation solution is highly demanded in carbon storage to understand the CO 2 migration subsurface and to answer the public safety problem. The objective of the study is to develop a downhole CO 2 sensor that can in-situ, continuously monitor CO 2 concentration change in deep saline. The sensor is a Severinghaus-type CO 2 sensor with small size, which renders it can be embedded in monitoring well casing or integrated with pressure/temperature transducers, enabling the development of “smart” wells. The studies included: (1) prepare and characterize metal-oxide electrodes. Test the electrodes response to pH change. Investigate different ions and brine concentration effects on the electrode’s performance. Study the stability of the electrode in brine solution; (2) fabricate a downhole CO 2 sensor with the metal-oxide electrodes prepared in the laboratory. Test the performance of the CO 2 sensor in brine solutions. Study high pressure effects on the performance of the sensor; (3) design and conduct CO 2 /brine coreflooding experiments with the CO2 sensor. Monitor CO 2 movement along the core and test the performance of the sensor in coreflooding tests. Develop a data acquisition system that can digitize the sensor’s output voltage. Our completed research has resulted in deep understanding of downhole CO 2 sensor development and CO 2 monitoring in CO 2 storage process. The developed downhole CO 2 sensor included a metal-oxide electrode, a gas-permeable membrane, a porous steel cup, and a bicarbonate-based internal electrolyte solution. Iridium oxide-based electrode was prepared and used for preparation the CO 2 sensor. The prepared iridium oxide-based electrode displayed a linearly response to pH change. Different factors such as different ions and ions concentration, temperature, and pressure effects on the electrode performance on pH response were investigated. The results indicated that the electrode exhibited a good performance even in high salt concentration of produced water. To improve the electrode performance under high pressure, IrO 2 nanoparticles with the particle size in the range of 1-2 nm were prepared and electrodeposited on stainless steel substrate by cyclic voltammetry. It was observed that the thin film of iridium oxide was formed on the substrate surface and such iridium oxide-based electrode displayed excellent performance under high pressure for longer term. A downhole CO 2 sensor with the iridium oxide-based electrode was prepared. The working principle of the CO 2 sensor is based on the measurement of the pH change of the internal electrolyte solution caused by the hydrolysis of CO 2 and then determination of the CO 2 concentration in water. The prepared downhole CO 2 sensor had the size of diameter of 0.7 in. and length of 1.5 in. The sensor was tested under the pressures of 500 psi, 2,000 psi, and 3,000 psi. A linear correlation was observed between the sensor potential change and dissolved CO 2 concentration in water. The response time of the CO 2 sensor was in the range of 60-100 minutes. Further tests indicated that the CO 2 sensor exhibited good reproducibility under high pressure. A CO 2 /brine coreflooding system was constructed to simulate the real-world CO 2 storage process. The prepared downhole CO 2 sensor was loaded in the system to monitor CO 2 movement during CO 2 /brine coreflooding test. The results indicated that the sensor could detect CO 2 movement in the tests. Further studies showed that the sensor could be recovered by brine flooding after CO 2 /brine flushed the core. The results of the coreflooding tests demonstrated that the sensor had potential application for CO 2 monitoring in carbon sequestration. A data acquisition system for the downhoe CO 2 sensor was developed and coded. The system converted the sensor output signal into digital data and transported the data from downhole to wellhead surface. The data acquisition system was tested and evaluated in the laboratory with the prepared sensor for data collection.

36 MATERIALS SCIENCE↗