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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Using Qualitative Hazard Analysis to Guide Quantitative Safety Analysis

Quantitative methods can be beneficial in many types of safety investigations. However, there are many difficulties in using quantitative m ethods. Far example, there may be little relevant data available. This paper proposes a framework for using quantitative hazard analysis to prioritize hazard scenarios most suitable for quantitative mziysis. The framework first categorizes hazard scenarios by severity and likelihood. We then propose another metric "modeling difficulty" that desc ribes the complexity in modeling a given hazard scenario quantitatively. The combined metrics of severity, likelihood, and modeling difficu lty help to prioritize hazard scenarios for which quantitative analys is should be applied. We have applied this methodology to proposed concepts of operations for reduced wake separation for airplane operatio ns at closely spaced parallel runways.

Shortle, J. F.↗
DOE OSTI2023

Performance portable ice-sheet modeling with MALI

High-resolution simulations of polar ice sheets play a crucial role in the ongoing effort to develop more accurate and reliable Earth system models for probabilistic sea-level projections. These simulations often require a massive amount of memory and computation from large supercomputing clusters to provide sufficient accuracy and resolution; therefore, it has become essential to ensure performance on these platforms. Many of today’s supercomputers contain a diverse set of computing architectures and require specific programming interfaces in order to obtain optimal efficiency. In an effort to avoid architecture-specific programming and maintain productivity across platforms, the ice-sheet modeling code known as MPAS-Albany Land Ice (MALI) uses high-level abstractions to integrate Trilinos libraries and the Kokkos programming model for performance portable code across a variety of different architectures. In this article, we analyze the performance portable features of MALI via a performance analysis on current CPU-based and GPU-based supercomputers. The analysis highlights not only the performance portable improvements made in finite element assembly and multigrid preconditioning within MALI with speedups between 1.26 and 1.82x across CPU and GPU architectures but also identifies the need to further improve performance in software coupling and preconditioning on GPUs. We perform a weak scalability study and show that simulations on GPU-based machines perform 1.24–1.92x faster when utilizing the GPUs. The best performance is found in finite element assembly, which achieved a speedup of up to 8.65x and a weak scaling efficiency of 82.6% with GPUs. We additionally describe an automated performance testing framework developed for this code base using a changepoint detection method. The framework is used to make actionable decisions about performance within MALI. We provide several concrete examples of scenarios in which the framework has identified performance regressions, improvements, and algorithm differences over the course of 2 years of development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗
DOE OSTI2022

Exploring the Effects of Geopolitical Shifts on Global Wildlife Trade

Abstract International wildlife trade is a major driver of species extinction and biological invasions. Anticipating environmental risks requires inferences about trade patterns, which are shaped by geopolitics. Although the future cannot be predicted, scenarios can help deal with the uncertainty of future geopolitical dynamics. We propose a framework for generating and analyzing scenarios based on four geopolitical storylines, distinguished by combinations of international trade barrier strength and domestic law enforcement degree across countries supplying and demanding wildlife. We then use historical data on bird trade to classify countries into geopolitical profiles and confirm that trade barriers and law enforcement allow predicting bird trade patterns, supporting our scenarios’ plausibility and enabling projections for future global bird trade. Our framework can be used to examine the consequences of geopolitical changes for wildlife trade and to advise policy and legislation. Reducing demand for wildlife and ameliorating global inequality are key for curbing trade related risks.

Ribeiro, Joana (ORCID:0000000237555785)↗
DOE OSTI2024

Scenario storyline discovery for complex multi-actor human-natural systems

This poster was presented at the AGU Fall Meeting 2024. Abstract:Scenario analysis is a useful tool for assessing the impacts of future conditions or alternative strategies. However, the common practice of focusing on a small number of predetermined scenarios can limit our understanding of key uncertainties, and fail to represent diverse stakeholder impacts. Exploratory modeling approaches have been developed to address these issues by simulating a wide range of possible futures and system perspectives. A challenge with these approaches is that they often involve large ensemble experiments which limit interpretability and usability. We recently introduced the FRamework for Narrative Storylines and Impact Classification (FRNSIC; pronounced ``forensic''), a scenario discovery framework that helps users identify scenario storylines that capture key system dynamics and as well as important outcomes. In this poster presentation, we present training materials to support the generalizable application of the framework to other multi-actor systems with complex dynamics. Specifically, we will present a step-by-step methodological typology of tools and methods that can be used to generate and classify plausible states of the world on key metrics and consequential dynamics. The typology will also discuss potential implications of these choices and their applicability to different systems.

Colorado River↗
DOE OSTI2022

Induced seismicity potential based on probabilistic geomechanics for the San Juan Basin CarbonSAFE project

Although geologic carbon sequestration projects have yet to induce — or may never induce — a damaging earthquake, experiences from other deep injection industries such as hydraulic fracturing, enhanced geothermal systems, and saltwater disposal suggest that effective quantitative seismic risk assessment is necessary for deep saline carbon capture and sequestration (CCS) projects. One such imminent CCS project is the San Juan Basin CarbonSAFE Phase III program. We use Monte Carlo probabilistic geomechanical analyses combined with observations of the geologic and operational parameters of the San Juan Basin site and suggest that this project is of low-induced seismic risk. The primary analysis is split into four sections. First, we assess the literature for faults and past seismicity, and at least five faulting scenarios are directly relevant. Second, we develop and calibrate an integrated earth model for the project site. Third, we perform Monte Carlo simulations that consider reasonable uncertainties of the geomechanical parameters. Of five tested faulting scenarios, only one presents high Coulomb failure functions, but fourth, we determine the risk to be low based on the combined lack of historical seismicity, the geologic framework of the faulting scenario, and the presence of saltwater injection at the same depth as the proposed supercritical carbon dioxide injection. The most sensitive parameters in the geomechanical calculations are the fault dip and the coefficient of friction. The least sensitive are the fault strike and the orientation of the maximum horizontal principal stress.

58 GEOSCIENCES↗
DOE OSTI2025

Optimal Mitigation Planning For Adversarial Scenarios

We propose a generalized framework which performs an optimal partitioning of a limited budget into various organizational sectors in order to improve the cybersecurity of a smart device or component in the Cyber Physical Energy System (CPS). The framework identifies the adversarial threats and possible attack sequences which can be performed to exploit cyber vulnerabilities of the component. Thereafter, we formulate an Mixed Integer Linear Programming (MILP) optimization problem which aims to evaluate the optimal budget partitions in order to minimize the number of highly likely attack sequences. Though we provide results for using the framework in CPES, the proposed methodology can be extended for multiple domains with a set of known adversarial and mitigation actions.

Purohit, Sumit [Pacific Northwest National Laborat↗
NASA NTRS2004

Virtual Mission Operation Framework (VMOF)

The Virtual Mission Operation Framework (VMOF) is one of the project lifecycle engineering process improvement efforts initiated by the institutional technology infrastructure program at JPL. The VMOF is composed of three frameworks: a model integration framework, a simulation framework, and a visualization framework. The model integration framework interfaces with spacecraft system design, mission design, and structure design. The simulation framework interfaces with the operation scenario design, environmental phenomena science, and science payload system design. The visualization framework interfaces with the flight system testbed, the ground system, and the science analysis. The three frameworks of the VMOF collaborate to create a comprehensive virtual mission operation that enables a "validation-in-the-loop" system design process and lifecycle-continuous science-return validation.This paper discusses the technical approaches for each framework implementation, challenges and approaches involved in streamlining mission information access, and on-going activities toward enabling Model-Based Engineering Design in a collaborative distributed environment.

virtual mission operations↗
DOE OSTI2026

Decentralised Reinforcement Learning for Dynamic Cyberattack Response in Microgrid Networks

Microgrids rely on communication networks for reliable operation, which makes them inherently vulnerable to cyberattacks. Such attacks can destabilise system dynamics and drive states away from their nominal operating trajectories. Although several physics-informed and machine learning-based strategies have been developed to counter these threats, the rapidly evolving cyber landscape enables adversaries to bypass static defences or rules-based mitigation approaches. This paper proposes a dynamic, online-trained and fully decentralised reinforcement learning (RL)-based cyberattack response framework to protect microgrids from evolving cyberattacks. The proposed framework deploys multiple deep Q-networks (DQNs), each associated with a distributed energy resource (DER), to enable localised and adaptive attack mitigation. In this framework, each DQN processes local voltage and frequency measurements—combined with intrusion detection system (IDS) alerts—as observations and rewards to guide decision-making. Extensive simulation studies demonstrate the robustness of the proposed framework under diverse attack scenarios and varying IDS-induced detection delays. Comparative analysis highlights its superiority over existing static or preexisting rules-based mitigation approaches. Finally, we present an analysis that shows the framework's scalability to real-life microgrids with more interacting agents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production

A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Signature Analysis Utilizing a Dynamic Molten Salt Reactor Model for MC&A

Moving from traditional fixed fuel nuclear reactor systems to a mobile, dynamic fuel system that has dissolved special nuclear material in a molten salt is a paradigm shift in several respects. One major consideration is how to develop effective nuclear material controls and accounting for these novel reactor systems. The Molten Salt Reactor Experiment is one example of a critical molten salt system and provided a significant reference library based on the documented effort. But it was low thermal power that was not intended to reflect a commercial scale electricity production design. Therefore, to facilitate and assist vendors with domestic licensing considerations, research is underway to identify methods that could be used for domestic safeguards approaches for these novel reactor systems. This research presents the results of a signature analysis of data generated from three simulated scenarios using the molten salt demonstration reactor model defined in the Transient Simulation Framework of Reconfigurable Modules. Each scenario provides 1 hour isotope inventories over a 180 day period. The scenarios investigated provide test cases to examine if direct gamma-ray spectroscopy of the fuel can be used to identify changes comparing a base case (no reactivity control and fixed fission contribution) to an insertion of reactivity (10 pcm no change in fission composition) and a change in fission composition. The analysis demonstrates that monitoring the total count rate in a highly collimated high-resolution photon energy spectrum is sensitive to perturbations imposed into the reactor model. The total photon count rate changes ≈2% for the fission composition change and ≈4.5% for the reactivity insertion compared to the base case. However, both scenarios show an increase in the total photon count rate. The total photon count rate can be used to identify changes due to power (number of fissions) but not due to a change in the material undergoing fission. To distinguish between the two cases of increased power, the photon spectrum would require an intensive analysis technique. A photon peak count rate ratio analysis could be used to identify changes in the fissile material fission generation in the core through identification of a static peak that shows little variation to the source of fission and a highly varying peak. The photon peak strength will ultimately be determined by the isotope’s fission yield. A preliminary analysis investigating the coupling of the isotope’s fission yield and its concentration in the fuel salt derived from the modeling has been performed. A ratio analysis of the photon peak count rates of 140 La to 99 Mo demonstrated that the reactivity insertion creates a distinct difference in the ratio compared to the fission composition change scenario.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗
DOE OSTI2026

Techno-Economic Analysis of Recycling Strategies for Catalyst and Acid During Catalytic Graphitization

With the aim of meeting the urgent demand for active anode materials (AAM) in energy storage systems, bio-based graphite (biographite) emerges as an affordable solution to de-risk the turbulent supply chain of critical minerals. Anode grade biographite requires high crystallinity and purity, which can be achieved by catalytic graphitization with iron, followed by acid washing. Therefore, a well-conceived process integration that recycles catalyst can be the starting point to commercialization. This study evaluates closed-loop catalyst recovery, and byproducts valorization scenarios through a technoeconomic framework to help understand the scale-up potential of biographite. For the acid washing, three reactors in series meet the required biographite purity at 99.95%. Iron and acid recovery can reduce material consumption and waste generation by ~95%, albeit at the expense of ~80% increase in capital costs. Recovery scenarios present similar capital and operational expenses, yielding minimum selling prices (MSP) near $6 kg-1 of biographite. Monte Carlo methodology reveals that feedstock price accounts for ~60% of MSP variance, followed by plant capacity ~20%. The likelihood of reaching a competitive profit margin of 30% in the U.S. AAM market sits at 85% average for recovery scenarios, and 103% when iron oxide is sold as byproduct. Additionally, an IRR >= 15% can be achieved for half of Monte Carlo simulations, representing promising early-stage results. Biographite production offers a strategic pathway to stabilize the anode market beyond China by integrating established technologies for a scalable, economically viable, and sustainable process. The role of catalyst recovery and byproducts utilization is critical for advancing the biomaterials industry.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Implementation of an Open-Scenario, Long-Term Space Debris Simulation Approach

This paper provides a status update on the implementation of a flexible, long-term space debris simulation approach. The motivation is to build a tool that can assess the long-term impact of various options for debris-remediation, including the LightForce space debris collision avoidance concept that diverts objects using photon pressure [9]. State-of-the-art simulation approaches that assess the long-term development of the debris environment use either completely statistical approaches, or they rely on large time steps on the order of several days if they simulate the positions of single objects over time. They cannot be easily adapted to investigate the impact of specific collision avoidance schemes or de-orbit schemes, because the efficiency of a collision avoidance maneuver can depend on various input parameters, including ground station positions and orbital and physical parameters of the objects involved in close encounters (conjunctions). Furthermore, maneuvers take place on timescales much smaller than days. For example, LightForce only changes the orbit of a certain object (aiming to reduce the probability of collision), but it does not remove entire objects or groups of objects. In the same sense, it is also not straightforward to compare specific de-orbit methods in regard to potential collision risks during a de-orbit maneuver. To gain flexibility in assessing interactions with objects, we implement a simulation that includes every tracked space object in Low Earth Orbit (LEO) and propagates all objects with high precision and variable time-steps as small as one second. It allows the assessment of the (potential) impact of physical or orbital changes to any object. The final goal is to employ a Monte Carlo approach to assess the debris evolution during the simulation time-frame of 100 years and to compare a baseline scenario to debris remediation scenarios or other scenarios of interest. To populate the initial simulation, we use the entire space-track object catalog in LEO. We then use a high precision propagator to propagate all objects over the entire simulation duration. If collisions are detected, the appropriate number of debris objects are created and inserted into the simulation framework. Depending on the scenario, further objects, e.g. due to new launches, can be added. At the end of the simulation, the total number of objects above a cut-off size and the number of detected collisions provide benchmark parameters for the comparison between scenarios. The simulation approach is computationally intensive as it involves tens of thousands of objects; hence we use a highly parallel approach employing up to a thousand cores on the NASA Pleiades supercomputer for a single run. This paper describes our simulation approach, the status of its implementation, the approach to developing scenarios and examples of first test runs.

laser↗
DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗
NASA NTRS2016

Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

Real-time Safety Monitoring↗
NASA NTRS2013

Star-jet Interactions and Gamma-ray Outbursts from 3C454.3

We propose a model to explain the ultra-bright GeV gamma-ray flares observed from the blazar 3C454.3. The model is based on the concept of a relativistic jet interacting with compact gas condensations produced when a star (a red giant) crosses the jet close to the central black hole. The study includes an analytical treatment of the evolution of the envelope lost by the star within the jet, and calculations of the related high-energy radiation. The model readily explains the day-long that varies on timescales of hours, GeV gamma-ray flare from 3C454.3, observed during 2010 November on top of a plateau lasting weeks. In the proposed scenario, the plateau state is caused by a strong wind generated by the heating of the stellar atmosphere due to nonthermal particles accelerated at the jet-star interaction region. The flare itself could be produced by a few clouds of matter lost by the red giant after the initial impact of the jet. In the framework of the proposed scenario, the observations constrain the key model parameters of the source, including the mass of the central black hole: Blackhole Mass is approx. equal to 10(exp 9) Solar Mass, the total jet power: L(j) is approx. equal to 10(exp 48) erg s(exp −1), and the Doppler factor of the gamma-ray emitting clouds: Delta is approx. equal to 20. Whereas we do not specify the particle acceleration mechanisms, the potential gamma-ray production processes are discussed and compared in the context of the proposed model.We argue that synchrotron radiation of protons has certain advantages compared to other radiation channels of directlyaccelerated electrons. An injected proton distribution varies as E(exp −1) or harder below the relevant energies would be favored to alleviate the tight energetic constraints and to avoid the violation of the observational low-energy constraints.

radiation mechanisms↗
DOE OSTI2025

Evaluating the carbon capture potential of industrial waste as a feedstock for enhanced weathering

Abstract Limiting anthropogenic global climate warming since the start of the industrial period to less than 2 °C will very likely require both deep and rapid reductions in anthropogenic greenhouse gas emissions and a range of approaches toward carbon dioxide removal (CDR). One prominent CDR approach is enhanced weathering (EW), in which crushed silicate rock is applied on land or in the open ocean to accelerate natural weathering processes that absorb carbon dioxide from Earth’s ocean–atmosphere system. However, in addition to a range of potential environmental, socioeconomic, and ethical issues associated with this pathway, bottlenecks in feedstock sourcing represent a key barrier for deployment of EW at scale. Here, we evaluate the potential of silicate wastes produced from industrial processes—such as steel slag and cement waste—as feedstocks for the EW process. An empirical model that links industrial alkaline waste production to gross domestic product at purchase power parity is developed to forecast waste production in the alternative futures described by the shared socioeconomic pathway (SSP) framework. By incorporating these results into an intermediate-complexity Earth system model, we also explore the impacts of EW using industrial waste on changes to global temperature, ocean pH, and ocean aragonite saturation state, while also quantifying overall CDR efficiency through the end of the century. We estimate a maximum cumulative end-of-century capture potential of ∼400 GtCO 2 for industrial waste, which could represent a significant fraction of the projected CDR requirement of many mitigation scenarios in the SSP framework. However, feedstock-dependent environmental impacts and the technoeconomics of feedstock redistribution may ultimately limit deployment scope.

Xu, Pengxiao (ORCID:0009000633724293)↗