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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evaluation of ASCE 4-16 and AISC 43-18 (Draft) for use in the Risk-Informed Performance-Based Seismic Design of Nuclear Power Plant Structures, Systems, and Components

This report describes the assessment of ASCE Standards 4-16 and 43-18 (Draft) for use in the Risk-Informed Performance-Based (RIPB) seismic design of structures, systems, and components (SSCs) at nuclear power plants. This work was performed for the U.S. Nuclear Regulatory Commission (NRC) Office of Regulatory Research (RES), to support potential endorsement of these industry standards for the design of nuclear power plants based on the RIPB approach. Currently, the NRC endorses a deterministic design approach for demonstrating the design adequacy of SSCs based on the Standard Review Plan (NUREG-0800) and NRC Regulatory Guides. In the RIPB approach, the design criteria are developed to achieve a target performance goal, which is defined by the annual frequency of occurrence of the design basis earthquake (i.e., Seismic Design Category) and the acceptable level of structural performance (i.e., Limit State) for the SSCs. ASCE 4-16 provides methods for performing a seismic analysis of structures to obtain the seismic response of these structures (e.g., building displacements, accelerations, in-structure response spectra) which are used in the design of the SSCs. It also provides methods for performing seismic analysis of SSCs to determine the seismic demands (e.g. member forces and displacements) needed to design individual SSCs. ASCE 43-18 (Draft) provides the criteria for the seismic design of SSCs using the seismic demands developed in ASCE 4-16. The use of ASCE Standard 43-18 (Draft), along with ASCE 4-16, provides the criteria for the seismic design of the SSCs. ASCE 43-18 (DRAFT) relies on other consensus codes and standards such as ACI 349 for reinforced concrete, AISC/N690 for steel structures, ASME Section III for pressure-retaining mechanical components and Containments, and IEEE-344 for Class 1E equipment. The goal of this technical review was to assess the adequacy of the provisions in these standards for use by the NRC in developing regulatory guidance for design of SSCs in nuclear power plants, based on the RIPB approach. The research reported herein describes the basis for acceptance of the new standards and identifies areas where additional staff guidance is needed for the seismic design of SSCs at nuclear power plants. This technical review has determined that ASCE 4-16 and ASCE 43-18 (Draft) provide an appropriate framework for the seismic design of SSCs at nuclear power plants using a Risk-Informed Performance-Based approach. However, some of the criteria therein warrant exceptions, qualifications, and/or clarifications.

42 ENGINEERING↗
DOE OSTI2025

Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2024

Testing of Helium-Cooled Metal Molybdenum Disk Target

NorthStar Medical Radioisotopes LLC is planning to produce an important medical radioisotope, molybdenum-99 (Mo-99), through photonuclear reaction on molybdenum-100 (Mo-100). In this approach, molybdenum metal will be bombarded with a 40-MeV electron beam. Because enriched Mo-100 is expensive, it is desired to use as much beam power as possible to achieve maximum production yield and minimize target mass. This objective leads to very high beam power density (heat deposition in the target), which sets challenging requirements for cooling. Together with scientists at Los Alamos National Laboratory, a team at Argonne National Laboratory has developed and demonstrated a cooling approach using pressurized helium, which allows for efficient heat removal. One of the main challenges in this approach is the management of the heat load on the target window. The target window separates the high-pressure helium inside the target from the vacuum in the beamline, so it is constantly under stress from differential pressure. Also, the window is cooled only by the helium gas flowing on one side, making the window cooling even more challenging. High heat deposition in the target disks also imposes a strict requirement on performance of the helium cooling system and thickness of the target disks. The target disks are produced from metal powder via a press-and-sinter process. The resulting disks do not possess tensile strength as high as solid molybdenum and might not survive the vibration from the high-velocity helium coolant and high thermal stress from beam heating. An Argonne team of scientists performed a series of tests at Argonne’s Low Energy Accelerator Facility (LEAF) [3-7]. This report describes two series of tests for scale down production target designs that utilize full-scale 29 millimeters diameter, 0.75 mm thick press-and-sintered disks. We performed two thermal tests with different beam parameters and configurations of the disk laminations. We compared the results of the window temperature measurements and cooling system parameters obtained in the experiments with those predicted by analytical calculations and Computation Flow Dynamic (CFD) simulations. Results of the experiments and calculations are presented below.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Precise transcript targeting by CRISPR-Csm complexes

Robust and precise transcript targeting in mammalian cells remains a difficult challenge using existing approaches due to inefficiency, imprecision and subcellular compartmentalization. Here we show that the clustered regularly interspaced short palindromic repeats (CRISPR)-Csm complex, a multiprotein effector from type III CRISPR immune systems in prokaryotes, provides surgical RNA ablation of both nuclear and cytoplasmic transcripts. As part of the most widely occurring CRISPR adaptive immune pathway, CRISPR-Csm uses a programmable RNA-guided mechanism to find and degrade target RNA molecules without inducing indiscriminate trans-cleavage of cellular RNAs, giving it an important advantage over the CRISPR-Cas13 family of enzymes. Using single-vector delivery of the Streptococcus thermophilus Csm complex, we observe high-efficiency RNA knockdown (90–99%) and minimal off-target effects in human cells, outperforming existing technologies including short hairpin RNA- and Cas13-mediated knockdown. We also find that catalytically inactivated Csm achieves specific and durable RNA binding, a property we harness for live-cell RNA imaging. These results establish the feasibility and efficacy of multiprotein CRISPR-Cas effector complexes as RNA-targeting tools in eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advanced Permanent Magnet Generator Topologies Using Multimaterial Shape Optimization and 3D Printing: Preprint

A vast majority of utility-scale wind turbine generators in the United States are dependent on foreign- sourced rare-earth permanent magnets that are vulnerable to supply chain uncertainties. Many small wind original equipment manufacturers are motivated to pursue continuous improvements to the generator design to lower the material and production costs and improve performance by lowering cogging torque and increasing the efficiency. Traditional design and manufacturing offer limited opportunities. In this work, we demonstrate advanced design approaches for a 15-kW baseline wind turbine generator by making use of recent progress in three-dimensional (3D) printing of polymer- bonded magnets and, electrical and structural steel. We explore three methods of magnet parametrization using Bezier curves resulting in symmetric, asymmetric and multimaterial magnet designs. We employ a multiphysics approach combining parametric computer-aided design modeling, finite- element analysis and targeted sampling to identify novel designs with more opportunities for reducing rare-earth material, improving efficiency and minimizing cogging torque. The results show that asymmetric-pole design and multimaterial-pole designs offer a greater opportunity to minimize rare-earth magnet materials by up to 35% with similar performance as the baseline generator, suggesting newer opportunities with design freedom beyond traditional limits of symmetry and as allowed by 3D printing.

Bezier curves↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗
DOE OSTI2023

A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Three-dimensional simulations of magneto-inertial Magnetic-Direct-Drive targets

For the cylindrically symmetric targets that are normally fielded on the Z machine, two dimensional axisymmetric MHD simulations provide the backbone of our target design capability. These simulations capture the essential operation of the target and allow for a wide range of physics to be addressed at a substantially lower computational cost than 3D simulations. This approach, however, makes some approximations that may impact its ability to accurately provide insight into target operation. As an example, in 2D simulations, targets are able to stagnate directly to the axis in a way that is not entirely physical, leading to uncertainty in the impact of the dynamical instabilities that are an important source of degradation for ICF concepts. In this report, we have performed a series of 3D calculations in order to assess the importance of this higher fidelity treatment on MagLIF target performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A multi-MeV alpha particle source via proton-boron fusion driven by a 10-GW tabletop laser

Nuclear fusion between protons and boron-11 nuclei has undergone a revival of interest thanks to the rapid progress in pulsed laser technology. Potential applications of such reaction range from controlled nuclear fusion to radiobiology and cancer therapy. A laser-driven fusion approach consists in the interaction of high-power, high-intensity pulses with H- and B-rich targets. We report on an experiment exploiting proton-boron fusion in CN-BN targets to obtain high-energy alpha particle beams (up to 5 MeV) using a very compact approach and a tabletop laser system with a peak power of ~10 GW, which can operate at high-repetition rate (up to 1 kHz). The secondary resonance in the cross section of proton-boron fusion (~150 keV in the center-of-mass frame) is exploited using a laser-based approach. The generated alpha particles are characterized in terms of energy, flux, and angular distribution using solid-state nuclear-track detectors, demonstrating a flux of ~10 5 particles per second at 10 Hz, and ~10 6 per second at 1 kHz. Hydrodynamic and particle-in-cell numerical simulations support our experimental findings. Potential impact of our approach on future spread of ultra-compact, multi-MeV alpha particle sources driven by moderate intensity (10 16 -10 17 W/cm 2 ) laser pulses is anticipated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

3D Hybrid Nanofiber Aerogels Combining with Nanoparticles Made of a Biocleavable and Targeting Polycation and MiR‐26a for Bone Repair

Abstract The healing of large bone defects represents a clinical challenge, often requiring some form of grafting. 3D nanofiber aerogels could be a promising bone graft due to their biomimetic morphology and controlled porous structures and composition. miR‐26a has been reported to induce the differentiation of bone marrow‐derived mesenchymal stem cells (BMSCs) and facilitate bone formation. Introducing miR‐26a with a suitable polymeric vector targeting BMSCs could improve and enhance the functions of 3D nanofiber aerogels for bone regeneration. Herein, the comb‐shaped polycation (HA–SS–PGEA) is first developed, carrying a targeting component, biocleavable groups, and short ethanolamine (EA)‐decorated poly(glycidyl methacrylate) (PGMA) (abbreviated as PGEA) arms as miR‐26a delivery vector. Thereafter, the cytotoxicity and transfection efficiency of this polycation and cellular response to miR‐26a‐incorporated nanoparticles (NPs) are assessed in vitro. HA–SS–PGEA exhibits a stronger ability to transport miR‐26a and exert its functions than the gold standard polyethyleneimine (PEI) and low‐molecular‐weight linear PGEA. The efficacy of HA–SS–PGEA/miR‐26a NPs loaded 3D hybrid nanofiber aerogels showing a positive effect on the cranial bone defect healing is finally examined. Together, the combination of 3D nanofiber aerogels and functional NPs consisting of a biodegradable and targeting polycation and therapeutic miRNA could be a promising approach for bone regeneration.

Li, Ruiquan↗
DOE OSTI2020

Sn-Based Alloys Synthesized in an Ionic Liquid at Room Temperature: Cu6Sn5 as a Case Study

Sn-based alloys are increasingly investigated owing to possible electronic/structural modulations of interest for electrocatalysis and energy storage applications. Here, we report on the use of a chemical system consisting of an ionic liquid (1-ethyl-3- methylimidazolium bis(trifluoromethanesulfonyl)imide: [EMIm(+)][TFSI-]) and Sn-based precursor Sn(TFSI)(2) both featuring similar anionic groups. This strategy increases the solubility of the cationic precursor in the IL and avoids the formation of side-products during the precipitation of Sn-nanoparticles formed upon reaction with a reducing agent (NaBH4). Using NMR relaxometry, we further established that these nanoparticles are stabilized by specific interactions with the cationic group of the IL. Targeting the composition Cu6Sn5, we further demonstrated that this approach can be used to prepare Sn-based alloys which could not be prepared using conventional chloride-based precursors.

cyclic voltammetry↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2021

Search for new physics in top quark production with additional leptons in proton-proton collisions at $\sqrt{s} = $ 13 TeV using effective field theory

Events containing one or more top quarks produced with additional prompt leptons are used to search for new physics within the framework of an effective field theory (EFT). The data correspond to an integrated luminosity of 41.5 fb$^{−1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV at the LHC, collected by the CMS experiment in 2017. The selected events are required to have either two leptons with the same charge or more than two leptons; jets, including identified bottom quark jets, are also required, and the selected events are divided into categories based on the multiplicities of these objects. Sixteen dimension-six operators that can affect processes involving top quarks produced with additional charged leptons are considered in this analysis. Constructed to target EFT effects directly, the analysis applies a novel approach in which the observed yields are parameterized in terms of the Wilson coefficients (WCs) of the EFT operators. A simultaneous fit of the 16 WCs to the data is performed and two standard deviation confidence intervals for the WCs are extracted; the standard model expectations for the WC values are within these intervals for all of the WCs probed.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Implicit Multirate GARK Methods

This article considers multirate generalized-structure additively partitioned Runge–Kutta methods for solving stiff systems of ordinary differential equations with multiple time scales. These methods treat different partitions of the system with different timesteps for a more targeted and efficient solution compared to monolithic single rate approaches. With implicit methods used across all partitions, methods must find a balance between stability and the cost of solving nonlinear equations for the stages. In order to characterize this important trade-off, we explore multirate coupling strategies, problems for assessing linear stability, and techniques to efficiently implement Newton iterations for stage equations. Unlike much of the existing multirate stability analysis which is limited in scope to particular methods, we present general statements on stability and describe fundamental limitations for certain types of multirate schemes. New implicit multirate methods up to fourth order are derived, and their accuracy and efficiency properties are verified with numerical tests.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enhanced carbon-transfer and -utilization efficiencies achieved using membrane carbonation with gas sources having a range of CO 2 concentrations

The economic viability of microalgal biofuels relies on increasing productivity in a cost-effective manner. As microalgal biomass contains > 50% carbon, a high rate of CO 2 delivery is required for high productivity, and inefficient CO 2 delivery amplifies operating costs. Membrane carbonation using non-porous hollow fiber membranes can ideally deliver CO 2 without bubble formation and high carbon transfer efficiency. Because CO 2 streams from industrial resources are not 100% CO 2 , the buildup of inert gasses can significantly lower the CO 2 delivery rate when the distal end of the membrane is closed. To overcome the buildup of inert gases, we managed the distal end of the membranes with three different approaches: fully open end, restricted bleed valve, and restricted bleed valve with pH-actuated venting. For all approaches, CO 2 was delivered to membranes ondemand based on a pH set point. Evaluating a wide range of CO 2 concentrations (10% to 100%), we found that all approaches eliminated the buildup of inert gases, could maintain target pH values and gave the same biomass productivities and carbon distributions. However, carbon transfer efficiency depended on the operation of the distal end. Fully open-end operation gave a poor carbon transfer efficiency because of excessive loss of CO 2 from the distal end. However, restricting the exit flow rate to ≤4 cm 3 /min mitigated the problems of excessive CO 2 loss, but without incurring a large loss of CO 2 -delivery flux. For the continuous cultivation, combining a restricted bleed valve with pH-actuated venting improved the carbon-transfer efficiency and -utilization efficiencies up to 85% and 67%, respectively, with a sufficient CO 2 delivery flux.

42 ENGINEERING↗
DOE OSTI2023

Probabilistic feasibility assessment of sequestration reliance for climate targets

Countries worldwide are transforming their energy systems to achieve Carbon-Neutrality. Investing in renewable resources-based technologies and implementing Carbon Capture and Storage (CCS) are common strategies to achieve higher sequestration levels. Negative emissions through Bioenergy with CCS are expected to play an essential role in the transition to full decarbonization. On top of that, biomass is a limited resource that depends on environmental factors, which create uncertainties related to the amount that can be sustainably provided to the energy system. Here this study emphasizes the relevance of variability in carbon sequestration for achieving climate targets by 2050. Here this paper proposes a probabilistic approach that integrates the Global Change Analysis Model for Latin America (GCAM-LA) with a chance constraint approach. GCAM-LA is used to assess the impact on the energy sector of different limits of sustainable biomass and carbon budget scenarios. The risk associated with exceeding the sequestration capacity of a given region is modeled via Chance Constraint. Results show that electrification is an appropriate long-term decarbonization strategy. It smoothes the effects of uncertainty in sequestration capacity and responds to end-user demands. For this case study, higher levels of electrification are obtained at likelihood levels >66% for end-use sectors.

54 ENVIRONMENTAL SCIENCES↗