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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Intrinsic two-dimensional features as textons

We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.

Discrimination (Psychology)/physiology↗
NASA NTRS2006

New Optical Constants for Amorphous and Crystalline H2O-ice and H2O-mixtures.

We will present the products of new laboratory measurements of ices relevant to Trans-Neptunian Objects. We have calculated the real and imaginary indices of refraction for amorphous and crystalline H2O-ice and also H2O-rich ices containing other molecular species. We create ice samples by condensing gases onto a cold substrate. We measure the thickness of the sample by reflecting a He-Ne laser off of the sample and counting interference fringes as it grows. We then collect transmission spectra of the samples in the wavelength range from 0.7-22 micrometers. Using the thickness and the transmission spectra of the ice we calculate the imaginary part of the index of refraction. We then use a Kramers-Kronig calculation to calculate the real part of the index of refraction (Berland et al. 1994; Hudgins et al. 1993). These optical constants can then be used to create model spectra for comparison to spectra from Solar System objects, including TNOs. We will summarize the difference between the amorphous and crystalline H2O-ice spectra. These changes include weakening of features and shifting of features to shorter wavelength. One important result is that the 2 pm feature is stronger in amorphous H2O ice than it is in crystalline H2O-ice. We will also discuss the changes seen when H2O is mixed with other components, including CO2, CH4, HCN, and NH3 (Bernstein et al. 2005; Bernstein et al. 2006).

Mastrapa, Rachel↗
NASA NTRS2014

Spectroscopic Measurement Techniques for Aerospace Flows

The conditions that characterize aerospace flows are so varied, that a single diagnostic technique is not sufficient for its measurement. Fluid dynamists use knowledge of similarity to help categorize and focus on different flow conditions. For example, the Reynolds number represents the ratio of inertial to viscous forces in a flow. When the velocity scales, length scales, and gas density are large and the magnitude of the molecular viscosity is low, the Reynolds number becomes large. This corresponds to large scale vehicles (e.g Airbus A380), fast moving objects (e.g. artillery projectiles), vehicles in dense fluids (e.g. submarine in water), or flows with low dynamic viscosity (e.g. skydiver in air). In each of these cases, the inertial forces dominate viscous forces, and unsteady turbulent fluctuations in the flow variables are observed. In contrast, flows with small length scales (e.g. dispersion of micro-particles in a solid rocket nozzle), slow moving objects (e.g. micro aerial vehicles), flows with low density gases (e.g. atmospheric re-entry), or fluids with a large magnitude of viscosity (e.g. engine coolant flow), all have low Reynolds numbers. In these cases, viscous forces become very important and often the flows can be steady and laminar. The Mach number, which is the ratio of the velocity to the speed of sound in the medium, also helps to differentiate types of flows. At very low Mach numbers, acoustic waves travel much faster than the object, and the flow can be assumed to be incompressible (e.g. Cessna 172 aircraft). As the object speed approaches the speed of sound, the gas density can become variable (e.g. flow over wing of Learjet 85). When the object speed is higher than the speed of sound (Ma > 1), the presences of shock waves and other gas dynamic features can become important to the vehicle performance (e.g. SR-71 Blackbird). In the hypersonic flow regime (Ma > 5), large changes in temperature begin to affect flow properties, causing real-gas effects to occur (e.g. X-43 Scramjet). At even higher Mach numbers, chemistry and nonequilibrium effects come into play (e.g. Startdust re-entry capsule), further complicating the measurement. These limits can be predicted by calculating the ratio of chemical and thermal relaxation time to the flow time scales. Other non-dimensional numbers can be used to further differentiate types of aerospace flows.

Danehy, Paul M.↗
NASA NTRS1993

Morphology and models for the evolution of eastern Hecate Chasma, Venus

Hecate Chasma is a deep trough characterized by a chain-like concentration of coronae and corona-like features trending approximately southwest-northeast between approximately 200 and 260 degrees east longitude (terminating at Beta Regio). The section of Hecate in which we have concentrated our study is centered at 15N, 249, where the trough is especially well-defined. Nearby, a smaller chain of eight coronae lies along a minor trough parallel to the general trend of the greater chain. The trough itself is unusual in this area because it has a highly asymmetric profile. Using Magellan radar and topography data, we have examined the morphology of this area in order to assess the tectonic and volcanic history of the area. After examining the most important types of features (linear, arcuate and circular) in eastern Hecate, we present two possible models of origin. A companion abstract presents an overview of the Hecate and Parga linear deformation zones.

Hamilton, Victoria E.↗
NASA NTRS1993

Nonlinear analysis of generalized cross-field current instability

Analysis of the generalized cross-field current instability is carried out in which cross-field drift of both the ions and electrons and their temperatures are permitted to vary in time. The unstable mode under consideration is the electromagnetic generalization of the classical modified-two-stream instability. The generalized instability is made of the modified-two-stream and ion-Weibel modes. The relative importance of the features associated with the ion-Weibel mode and those of the modified-two-stream mode is assessed. Specific applications are made to the Earth's neutral sheet prior to substorm onset and to the Earth's bow shock. The numerical solution indicates that the ion-Weibel mode dominates in the Earth's neutral sheet environment. In contrast, the situation for the bow shock is dominated by the modified-two-stream mode. Notable differences are found between the present calculation and previous results on ion-Weibel mode which restrict the analysis to only parallel propagating waves. However, in the case of Earth's bow shock for which the ion-Weibel mode plays no important role, the inclusion of the electromagnetic ion response is found to differ little from the previous results which treats ions responding only to the electrostatic component of the excited waves.

Yoon, Peter H.↗
DOE OSTI2022

Highly tunable band inversion in AB 2 X 4 (A=Ge, Sn, Pb; B=As, Sb, Bi; X=Se, Te) compounds

Topological materials have been discovered so far largely by searching for existing compounds in crystallographic databases, but there are potentially new topological materials with desirable features that have not been synthesized. One of the desirable features is high tunability resulting from the band inversion with a very small direct band gap, which can be tuned by changes in pressure or strain to induce a topological phase transition. Here, using density-functional theory (DFT) calculations, we have studied the septuple layered AB 2 X 4 series compounds, where A=(Ge, Sn and Pb), B=(As, Sb and Bi), and X=(Se and Te). With the DFT thermodynamic stability validated by the already-reported compounds in these series, we predict stable Se compounds, which are not found in crystallographic database. Among them, we find that GeBi 2 Se 4 and GeSb 2 Se 4 having a small direct band gap at the Z point are very close to a strong topological insulator, which can be tuned by a moderate pressure to induce the band inversion. Importantly, the topological features with the small direct band gap are well isolated in both momentum and energy windows, which offers high tunability for studying the topological phase transition.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Understanding and utilization of Thematic Mapper and other remotely sensed data for vegetation monitoring

The TM Tasseled Cap transformation, which provides both a 50% reduction in data volume with little or no loss of important information and spectral features with direct physical association, is presented and discussed. Using both simulated and actual TM data, some important characteristics of vegetation and soils in this feature space are described, as are the effects of solar elevation angle and atmospheric haze. A preliminary spectral haze diagnostic feature, based on only simulated data, is also examined. The characteristics of the TM thermal band are discussed, as is a demonstration of the use of TM data in energy balance studies. Some characteristics of AVHRR data are described, as are the sensitivities to scene content of several LANDSAT-MSS preprocessing techniques.

Crist, E. P.↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2003

Phase Sensitive X-Ray Diffraction Imaging Study of Protein Crystals

The study of defects and growth of protein crystals is of importance in providing a fundamental understanding of this important category of systems and the rationale for crystallization of better ordered crystals for structural determination and drug design. Yet, as a result of the extremely weak scattering power of x-rays in protein and other biological macromolecular crystals, the extinction lengths for those crystals are extremely large and, roughly speaking, of the order of millimeters on average compared to the scale of micrometers for most small molecular crystals. This has significant implication for x-ray diffraction and imaging study of protein crystals, and presents an interesting challenge to currently available x-ray analytical techniques. We proposed that coherence-based phase sensitive x-ray diffraction imaging could provide a way to augment defect contrast in x-ray diffraction images of weakly diffracting biological macromolecular crystals. I shall examine the principles and ideas behind this approach and compare it to other available x-ray topography and diffraction methods. I shall then present some recent experimental results in two model protein systems-cubic apofemtin and tetragonal lysozyme crystals to demonstrate the capability of the coherence-based imaging method in mapping point defects, dislocations, and the degree of perfection of biological macromolecular crystals with extreme sensitivity. While further work is under way, it is intended to show that the observed new features have yielded important information on protein crystal perfection and nucleation and growth mechanism otherwise unobtainable.

Hu, Z. W.↗
DOE OSTI2023

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

Importance of Sea Ice for Validating Global Climate Models

Reproduction of current day large-scale physical features and processes is a critical test of global climate model performance. Without this benchmark, prognoses of future climate conditions are at best speculation. A fundamental question relevant to this issue is, which processes and observations are both robust and sensitive enough to be used for model validation and furthermore are they also indicators of the problem at hand? In the case of global climate, one of the problems at hand is to distinguish between anthropogenic and naturally occuring climate responses. The polar regions provide an excellent testing ground to examine this problem because few humans make their livelihood there, such that anthropogenic influences in the polar regions usually spawn from global redistribution of a source originating elsewhere. Concomitantly, polar regions are one of the few places where responses to climate are non-anthropogenic. Thus, if an anthropogenic effect has reached the polar regions (e.g. the case of upper atmospheric ozone sensitivity to CFCs), it has most likely had an impact globally but is more difficult to sort out from local effects in areas where anthropogenic activity is high. Within this context, sea ice has served as both a monitoring platform and sensitivity parameter of polar climate response since the time of Fridtjof Nansen. Sea ice resides in the polar regions at the air-sea interface such that changes in either the global atmospheric or oceanic circulation set up complex non-linear responses in sea ice which are uniquely determined. Sea ice currently covers a maximum of about 7% of the earth's surface but was completely absent during the Jurassic Period and far more extensive during the various ice ages. It is also geophysically very thin (typically <10 m in Arctic, <3 m in Antarctic) compared to the troposphere (roughly 10 km) and deep ocean (roughly 3 to 4 km). Because of these unique conditions, polar researchers regard sea ice as one of the more important features to monitor in terms of heat, mass, and momentum transfer between the air and sea and furthermore, the impact of such responses to global climate.

Geiger, Cathleen A.↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2020

Quantum Fourier transform revisited

Summary The fast Fourier transform (FFT) is one of the most successful numerical algorithms of the 20th century and has found numerous applications in many branches of computational science and engineering. The FFT algorithm can be derived from a particular matrix decomposition of the discrete Fourier transform (DFT) matrix. In this paper, we show that the quantum Fourier transform (QFT) can be derived by further decomposing the diagonal factors of the FFT matrix decomposition into products of matrices with Kronecker product structure. We analyze the implication of this Kronecker product structure on the discrete Fourier transform of rank‐1 tensors on a classical computer. We also explain why such a structure can take advantage of an important quantum computer feature that enables the QFT algorithm to attain an exponential speedup on a quantum computer over the FFT algorithm on a classical computer. Further, the connection between the matrix decomposition of the DFT matrix and a quantum circuit is made. We also discuss a natural extension of a radix‐2 QFT decomposition to a radix‐ d QFT decomposition. No prior knowledge of quantum computing is required to understand what is presented in this paper. Yet, we believe this paper may help readers to gain some rudimentary understanding of the nature of quantum computing from a matrix computation point of view.

Camps, Daan↗
DOE OSTI2021

Real-time gravitational replicas: formalism and a variational principle

This work is the first step in a two-part investigation of real-time replica wormholes. Here we study the associated real-time gravitational path integral and construct the variational principle that will define its saddle-points. We also describe the general structure of the resulting real-time replica wormhole saddles, setting the stage for construction of explicit examples. These saddles necessarily involve complex metrics, and thus are accessed by deforming the original real contour of integration. However, the construction of these saddles need not rely on analytic continuation, and our formulation can be used even in the presence of non-analytic boundary-sources. Furthermore, at least for replica- and CPT-symmetric saddles we show that the metrics may be taken to be real in regions spacelike separated from a so-called ‘splitting surface’. This feature is an important hallmark of unitarity in a field theory dual.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Multi-plane moment-of-fluid interface reconstruction in 3D

Moment-of-fluid (MOF) methods for interface reconstruction approximate the region occupied by material in each mesh element only through reference to its geometric moments. Here, we present a 3D MOF method that represents the material (POM) in each cell as the convex intersection of the cell and multiple half-spaces, each selected to minimize the least-squares error between computed moments of the approximated material and provided reference moments. This optimization problem is highly non-linear and non-convex, making the numerical result very sensitive to the initial guess. To create an effective initial guess in each cell, we construct an ellipsoid from 0th–2nd order reference moments such that its shape corresponds with that of the POM. Within this ellipsoid we inscribe a polyhedron, and initialize the minimization problem with the half-spaces defined by each of its faces. The inscribed polyhedron has minimally 4 faces, and using up to 3rd order moments permits optimization over up to 20 unknown values. We therefore define MOF methods that utilize 4, 5, or 6 half-spaces, correspondingly initialized with the faces of a single inscribed tetrahedron, triangular prism, or hexahedron. Stability of the non-linear optimization is further improved with a prepossessing step that normalizes the reference moments according to the axes of the reference ellipsoid. Using this approach, the non-linear least-squares solver reliably converges to a near-global minimum from a single initial guess. We demonstrate accuracy and robustness using single-cell and multi-cell examples over a wide spectrum of geometry. In particular, we demonstrate our ability to exactly reproduce several important and complex features defined by up to four half-spaces, such as corners, filaments, filament tips, and embedded material in the cell.

3D interface reconstruction↗
DOE OSTI2022

Effect of tool design and pass strategy on defect elimination and uniform, enhanced tensile properties of friction stir processed high-pressure die-cast A380 alloy

This work reports implementation of friction stir processing (FSP) to locally modify microstructure and create a defect-free processing zone with the aid of different tool designs and altering the passing strategy for two different process parameter combinations. Here, FSP is applied on high pressure die-cast (HPDC) A380 alloy, a widely used Al-Si alloy fabricated in the die casting sector for automotive and aerospace applications. However, the presence of gas and shrinkage porosity, brittle needle-shaped Fe-containing ß-FeSiAl 5 intermetallic compounds, Al-dendrites, coarse and acicular silicon particles, and large second-phase particulates have a detrimental influence on the uniformity of tensile properties across the thickness direction of an HPDC plate. FSP is able to eliminate porosity, refine microstructure and improve tensile properties. An FSP tool design and a passing strategy are identified for the best microstructure consolidation and uniform enhanced tensile properties. This study noted the importance of tool features such as scroll design and flats on tool pin and multiple pass orientation strategy to find best combination of defect-free refined microstructure and uniform tensile strength and ductility across the processing zone of HPDC Al alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Phase-based design of CO 2 capture, transport, and storage infrastructure via SimCCS 3.0

The design of optimal infrastructure is essential for the deployment of commercial and large-scale carbon capture and storage (CCS) technology. During the design process, it is important to consider CO 2 capture and storage locations and CO 2 transportation pipelines to minimize the total project cost. SimCCS, first introduced in 2009, is an integrated open-source tool to optimize CCS infrastructure. The core CCS infrastructure design problem in SimCCS is structured as a mixed-integer linear programming problem by selecting the optimal pipeline routes, searching CO 2 source capture and storage locations, and determining the corresponding CO 2 amounts to meet desired capture targets. Multiple important and practical features have been developed to the latest version of SimCCS, SimCCS 3.0 . One of these features is phase-based modeling which enables users to dynamically design the CCS infrastructure. We demonstrate the phased-based modeling capability using two CCS infrastructure optimization case studies. The results from these case studies reveal that the phase-based modeling capability in SimCCS is particularly useful to optimize the dynamic deployment of CCS projects.

54 ENVIRONMENTAL SCIENCES↗