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Abstract Combining new constraints on future socio‐economic trajectories and the climate system's response to emissions can substantially reduce the projection uncertainty currently clouding regional climate adaptation decisions—more than either constraint individually.
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Stabilizing the global climate will require large-scale global deployment of low-carbon technologies. Even in the presence of aggressive climate policies, however, the diffusion of such technologies may be limited by several, institutional, behavioral, and social factors. In this paper, we review the literature on the sources of such diffusion constraints, and explore the potential implications of such non-economic constraints based on the GCAM integrated assessment model. Our analysis highlights that non-economic factors that limit technology deployment may have sizeable impacts on the feasibility and mitigation costs of achieving stringent stabilization targets. And such impacts are greatly amplified with major delays in serious climate policies. The results generally indicate that constraints on the expansions of CCS and renewables are more costly than those on nuclear or bioenergy, and jointly constraining these technologies leaves some scenarios infeasible.
Brassica carinata or Ethiopian mustard, a non-edible oilseed brassica, is a low carbon, purpose-grown, and none-to-low indirect land-use change bioenergy feedstock for the production of drop-in sustainable aviation fuel, biodiesel, renewable diesel, and a suite of value-added coproducts. Carinata oil converted to drop-in fuel using an American Society for Testing and Materials approved Catalytic Hydrothermolysis process has been successfully tested in commercial and military aviation. Carinata meal, the residue after oil extraction, is a high-protein feed supplement for livestock, poultry, and swine, and can also yield specialty products. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a public–private partnership formed with a twofold mission: (1) Removing physical, environmental, social, and economic constraints that prevent regional intensification of carinata production as a low-carbon feedstock for renewable fuel and coproducts and (2) demonstrating enhanced value across the entire value chain by mitigating risk to farmers and other stakeholders. The partnership's goal is to energize the US bioeconomy through sustainable agriculture and thus contribute to energy security and economic diversification. SPARC relies on a combination of cutting-edge multidisciplinary research and active industry engagement to facilitate adoption of the crop. This involves informing stakeholders along the entire supply chain, from producers to end-users, policymakers, influencers, and the public, about the opportunities and best practices related to carinata. This article provides context and background concerning carinata commercialization as a winter cash crop in the Southeast US for renewable fuels and bioproducts. The advances made to date in the areas of feedstock development, fuel and coproduct development, meal valorization, supply chain logistics, and stakeholder engagement are outlined.
The Biomass Scenario Model (BSM) is a unique, carefully validated, state-of-the-art fourth-generation model of the domestic bioenergy supply chain which explicitly focuses on policy issues and their potential side effects. It integrates resource availability, behavior, policy, and physical, technological, and economic constraints. The BSM uses system-dynamics simulation to model dynamic interactions across the supply chain; it tracks the deployment of biofuels given technological development and the reaction of the investment community to those technologies in the context of land availability, the competing oil market, consumer demand for biofuels, and government policies over time. It places a strong emphasis on the behavior and decision-making of various economic agents. The model treats the major infrastructure-compatible fuels. Scenario analysis based on the BSM shows that the biofuels industry tends not to rapidly thrive without significant external actions in the early years of its evolution. An initial focus for jumpstarting the industry typically has strongest results in the BSM in areas where effects of intervention have been identified to be multiplicative. In general, we find that policies which are coordinated across the whole supply chain have significant impact in fostering the growth of the biofuels industry and that the production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.
The role that automation, robotics, and artificial intelligence will play in Space Station operations is now beginning to take shape. Although there is only limited data on the precise nature of the payoffs that these technologies are likely to afford there is a general consensus that, at a minimum, the following benefits will be realized: increased responsiveness to innovation, lower operating costs, and reduction of exposure to hazards. Nevertheless, the question arises as to how much automation can be justified with the technical and economic constraints of the program? The purpose of this paper is to present a methodology which can be used to evaluate and rank different approaches to automating the functions and tasks planned for the Space Station. Special attention is given to the impact of advanced automation on human productivity. The methodology employed is based on the Analytic Hierarchy Process. This permits the introduction of individual judgements to resolve the confict that normally arises when incomparable criteria underly the selection process. Because of the large number of factors involved in the model, the overall problem is decomposed into four subproblems individually focusing on human productivity, economics, design, and operations, respectively. The results from each are then combined to yield the final rankings. To demonstrate the methodology, an example is developed based on the selection of an on-orbit assembly system. Five alternatives for performing this task are identified, ranging from an astronaut working in space, to a dexterous manipulator with sensory feedback. Computational results are presented along with their implications. A final parametric analysis shows that the outcome is locally insensitive to all but complete reversals in preference.
The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).
Current economic constraints indicate the need for incorporating the satellite servicing philosophy of commonality within the design of spacecraft subsytems. This philosophy is essential for conserving resources including hardware/software development and implementation costs, on-orbit and ground-based manpower, crew training/testing time, and documentation. In addition, spacecraft subsystem commonality may be coupled with standardization of operation procedures, and test and verification techniques for spacecraft design. Several spacecraft have adopted this practice, including Hubble Space Telescope, Space Station Freedom, and the Explorer Platform. As these and other programs continue and if effective crew interfaces and procedures are clearly and consistently defined, crew retraining for similar spacecraft subsystems will lessen, and procurement efforts will diminish. A relatively high fidelity zero-gravity simulation using water immersion is available to establish crew interfaces economically. The flexibility and utility of this space simulation medium for planning and assisting on-orbit operations was exemplified by astronaut evaluations of potential EVA electrical connectors. The testing was conducted at a NASA underwater neutral buoyancy training facility.
Abstract Accurate knowledge of the mechanical loads of wind turbine gearboxes has become essential in modern, highly loaded gearbox designs, as maintaining or even improving gearbox reliability with increasing torque density demands is proving to be challenging. Unfortunately, the traditional method of measuring dynamic mechanical torque using strain gauges placed on the outer surface of a rotating shaft and transmitting the resulting signal is unsuitable for serial deployment due to technical and economic constraints. An alternative method based on fiber-optic strain sensors placed on the stationary outer surface of the gearbox ring gear has been proposed. Like shaft torsion, the radial deformation of the ring gear is proportionate to the rotor torque. Placing the sensors on a stationary component is a cost-effective alternative for serial implementation because the need for complex and expensive data transfer via wireless transmission or a slip ring is eliminated. In this paper, we present the results of an extensive field experiment conducted to evaluate the torque measurement accuracy of this novel sensing solution installed on the gearbox of a Gamesa G97 2-MW wind turbine at the National Renewable Energy Laboratory’s Flatirons Campus. Torque measurements derived from fiber-optic strain sensors placed on the ring gear of the planetary stage are compared to conventional torque measurements from strain gauges placed on the main shaft. Two different torque estimation data processing methods were evaluated, with the method based on operational deflection shapes providing the most accurate results with an average normalized root mean square error below 0.7% for a load revolution distribution analysis. The effect of operating conditions on the torque estimate was also investigated, and the third planet-passing operational deflection shape was found to be the least sensitive to nontorque load-related effects. The fiber-optic strain sensors’ successful operation during the complete test campaign has demonstrated a robust and accurate solution for fleet-wide enhanced gearbox remaining useful life estimation.
Carbon dioxide (CO 2 ) generated by fossil fuel combustion can have serious consequences for humans and environments. Carbon capture, utilization and geological storage (CCUS) is a key approach to reduce the emissions of the anthropogenic CO 2 into the atmosphere. Of those, sequestering the CO 2 into depleted hydrocarbon reservoirs with associated enhanced oil recovery is the most achievable approach under current economic constraints since it increases recovery of existing oil reserves, and bridges the gap between nonprofit geological CO 2 sequestration and profit CO 2 -EOR. The Upper Devonian fluvial sandstone reservoir in Jacksonburg-Stringtown oil field in West Virginia, which has produced over 22 million barrels of oil since 1895, is an ideal candidate for CO 2 sequestration coupled with EOR. This research illustrates an example of CCUS, in which CO 2 is sequestered into underground meanwhile enhanced oil recovery from the depleted hydrocarbon reservoir by water alternating gas (WAG) strategy. Three CO 2 storage mechanisms including structural/stratigraphic trapping, dissolution trapping, and residual trapping are considered. A 3D geological model is constructed based on existing geological data from the Jacksonburg-Stringtown oil field. A 3D composition fluid model with 0.4 pore volume (PV) of water injection before WAG process is considered as a benchmark of this study. Several factors including WAG cycle period, injection time ratio, CO 2 injection rate are evaluated. After injection wells are completely shut-in, stored CO 2 is monitored for additional 200 years as a function of various storage mechanisms. The result shows that over 6 years of WAG injection, total 12 - 40 MMlb-mol CO 2 is sequestrated and oil recovery factor can achieve 28%. As a conclusion, this research constructs a basic workflow for CO 2 storage coupled with CO 2 -EOR which can be applied to other super-mature oil fields.
Alaska is an expansive region known for its abundant natural resources, including thousands of miles of streams and rivers. These rivers represent potential opportunities for future hydropower development that could provide reliable energy supply for local communities. There is limited long-term high temporal resolution streamflow data available for the region, making data-driven estimates of potential hydropower and its variability across the state challenging. This study provides a novel data-driven approach for hydropower capacity estimation across Alaska. We use supervised machine learning to develop a relationship between the daily and peak flow duration curves in order to augment the size of our dataset from 44 sites to 67 sites. We perform a stochastic hydropower estimation across the 67 sites and identify approximately 1000 MW of total potential hydropower capacity distributed across these sites. Our study provides the first step towards more comprehensive hydropower estimation for this critical region, highlighting the need for future work integrating high-resolution spatial data, community needs, and economic constraints in estimates of potential hydropower development in Alaska.
Iron electrocoagulation (Fe-EC) has been shown to effectively remove arsenic from contaminated groundwater at low cost and has the potential to improve access to safe drinking water for millions of people. Understanding how operating conditions, such as the Fe dosage rate and the O 2 recharge rate, affect arsenic removal at different pH values is crucial to maximize the performance of Fe-EC under economic constraints. In this work, we improved upon an existing computational model to investigate the combined effects of pH, Fe dosage rate, and O 2 recharge rate on arsenic removal in Fe-EC. We showed that the impact of the Fe dosage rate strongly depends on pH and on the O 2 recharge rate, which has important practical implications. We identified the process limiting arsenic removal (As(III) oxidation versus As(V) adsorption) at different pH values, which allowed us to interpret the effect of operating conditions on Fe-EC performance. Finally, we assessed the robustness of the trends predicted by the model, which assumes a constant pH, against lab experiments reproducing more realistic conditions where pH is allowed to drift during treatment as a result of equilibration with atmospheric CO 2 . Furthermore, our results provide a nuanced understanding of how operating conditions impact arsenic removal by Fe-EC and can inform decisions regarding the operation of this technology in a range of groundwaters.
A transducer based on a virtual flow meter is proposed for monitoring helium distribution and consumption in cryogenic systems for particle accelerators. The virtual flow meter allows technical and economical constraints, preventing installation of physical instruments in all the needed measurement points, to be overcome. Virtual flow meter performance for the alternative models of Samson [ http://www.samson.de (2015)] and Sereg-Schlumberger [ http://www.slb.com/ (2015)] is compared with the standard IEC 60534-2-1 [Industrial-process control valves—Part 2-1: Flow capacity—sizing equations for fluid flow under installed conditions (2011), https://webstore.iec.ch/publication/2461], for a large temperature range, for both gaseous and liquid helium phases, and for different pressure drops. Then, the calibration function of the transducer is derived. Finally, the experimental validation for the helium gaseous state on the test station for superconducting magnets in the laboratory SM18 [Pirotte et al., AIP Conf. Proc. 1573, 187 (2014)] at CERN is reported.
Wind energy is among the most mature renewable energy technologies, accounting for 11% of the current US electricity generation in 2024, with the lowest average levelized cost. While it is known that substantial opportunity exists for further development, a key question has been where wind energy is best suited compared to other technologies. This study leverages an immense dataset of parcel-resolved technoeconomic potential for the contiguous United States, focusing on distributed wind (DW) energy—a configuration where one or more turbines, typically 30–60 m in height are used to satisfy nearby energy needs. The analysis is conducted at multiple spatial scales and considers land use, crop land, census, and incentive program data to determine the most opportune areas for market development. The results show that rural, agricultural and residential areas are most suited to DW. Connection type (in front of, or behind the meter) and regulations determine the best application, while siting constraints, economics, demand and the wind resource determines the optimal size of turbine.
The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.