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SimCCS2.0 is an open-source software package for industry, researchers, and government to design carbon dioxide (CO2) capture, transport, and storage (CCS) infrastructure that optimally links CO2 sources (e.g., power plants) with CO2 sinks (e.g., saline aquifers, depleted oil fields) to reduce industry carbon footprints and increase energy production.
Management of spent nuclear fuel and high-level radioactive waste consists of three main phases – storage, transportation, and disposal – commonly referred to as the back end of the nuclear fuel cycle. Current practice for commercial spent nuclear fuel management in the United States (US) includes temporary storage of spent fuel in both pools and dry storage systems at operating or shutdown nuclear power plants. Storage pools are filling to their operational capacity, and management of the approximately 2,200 metric tons of spent fuel newly discharged each year requires transferring older and cooler spent fuel from pools into dry storage. Unless a repository becomes available that can accept spent fuel for permanent disposal, projections indicate that the US will have approximately 136,000 metric tons of spent fuel in dry storage systems by mid-century, when the last plants in the current reactor fleet are decommissioned. Current designs for dry storage systems rely on large multi-assembly canisters, the most common of which are so-called “dual-purpose canisters” (DPCs). DPCs are certified for both storage and transportation, but are not designed or licensed for permanent disposal. The large capacity (greater number of spent fuel assemblies) of these canisters can lead to higher canister temperatures, which can delay transportation and/or complicate disposal. This current management practice, in which the utilities continue loading an ever-increasing inventory of larger DPCs, does not emphasize integration among storage, transportation, and disposal. This lack of integration does not cause safety issues, but it does lead to a suboptimal system that increases costs, complicates storage and transportation operations, and limits options for permanent disposal. This paper describes strategies for improving integration of management practices in the US across the entire back end of the nuclear fuel cycle. The complex interactions between storage, transportation, and disposal make a single optimal solution unlikely. However, efforts to integrate various phases of nuclear waste management can have the greatest impact if they begin promptly and continue to evolve throughout the remaining life of the current fuel cycle. A key factor that influences the path forward for integration of nuclear waste management practices is the identification of the timing and location for a repository. The most cost-effective path forward would be to open a repository by mid-century with the capability to directly dispose of DPCs without repackaging the spent fuel into disposalready canisters. Options that involve repackaging of spent fuel from DPCs into disposalready canisters or that delay the repository opening significantly beyond mid-century could add 10s of billions of dollars to the total system life cycle cost.
The main objective of this early-phase research was to evaluate the techno-economic feasibility and risk associated with combined brine and CO 2 storage in SWD wells using brine dissolution in the North Dakota portion of the Williston Basin. Three simulation studies were conducted to investigate: (1) CO 2 phases at different conditions, (2) wellbore compatibility, and (3) long-term storage fate in reservoir. (1) A simple reservoir model and injection simulations were created using data to represent the BEST (brine extraction and storage test) site, an operational SWD facility located near Watford City, North Dakota. The pressure evolution caused by CO 2 comingled in produced water injectate in a layer cake reservoir was then modeled while tracking aqueous CO 2 throughout the project. The salinity of the injection water, the salinity of the reservoir brine, and the amount of dissolved CO 2 comingled in the injection water were varied. (2) A wellbore corrosion model was performed using the CO 2 concentrations selected based on the reservoir modeling to examine the carbonated produced water impact on wellbore. (3) Reactive transport modeling was conducted with the optimal CO 2 concentration for this injection site to study the rock-fluid interactions and CO 2 fate in the reservoir. Results suggest that CO 2 dissolved in produced water can be injected without appreciably increasing subsurface pressure or leakage risks. Pressure buildup was found to vary with salinity but not with CO 2 mass fraction. Simulation results show that lower CO 2 percent mass fraction leads to a higher amount of CO 2 that can be dissolved at a higher injection salinity. Furthermore, the long-term goal of dissolution trapping in a traditional carbon storage project is accomplished from the start, mitigating risks associated with potential migration of buoyant CO 2 , so long as the reservoir pressure and temperature are used to determine the maximum mass fraction of the dissolved CO 2 .
Together with Realta Fusion, we set up several simulation scenarios and ran simulations using the WarpX code which is developed by LBNL and especially by members of the LBNL team. We investigated beam transport and beam neutralization and ran optimization and benchmark simulations. To achieve this, several new features were added to the warpX code. These code additions will also be beneficial to other users of warpX including several fusion companies and researchers at National Laboratories. Furthermore, we supported Realta Fusion in setting up their own instances of WarpX. We presented our results at the American Physical Society Division of Plasma Physics meeting and are in the process of writing a publication that will be submitted to a peer-reviewed journal to share our results with the broader community.
This project seeks to develop device design, materials composition, and processing tools and parameters to fabricate semi-transparent perovskite solar cells and modules for application in stand-alone products or added to other solar cells in a mechanically stacked tandem configuration. This technology presents significant advanced manufacturing challenges and opportunities in getting to scale, including development of perovskite inks, scalable perovskite and heterojunction deposition and annealing processes, heterojunction composition, transparent electrode composition and deposition process, anti-reflection layer composition and deposition process, and cell to module integration processes. Modification 5: The proposed project seeks to develop device design, materials composition, and processing tools and parameters to fabricate semi-transparent perovskite solar cells and modules for application in stand-alone products or added to other solar cells in a mechanically-stacked tandem configuration. This technology presents significant advanced manufacturing challenges and opportunities in getting to scale, including development of perovskite inks, scalable perovskite and heterojunction deposition and annealing processes, heterojunction composition, transparent electrode composition and deposition process, passivation layers including in module scribes, anti-reflection layer composition and deposition process, and cell to module integration processes. Advanced metrology and characterization will be performed on perovskite films, cells and module. Furthermore, we will examine module or materials recycling for circular economy considerations. Modifcation 6: Gigahertz frequency microwave pump-probe spectroscopies are highly sensitive to thin film semiconductor photoconductivity of individual and stacks of layers that comprise perovskite solar cells. As such, these techniques will be used to qualify reproducibility and quality correlations during the manufacturing process. Modification 7: Mechanical adhesion of top contacts within perovskite modules significantly impacts the durability of the module when exposed to accelerated degradation testing. The adhesion between the perovskite/transport layer interface and the transport layer/top contact interface are both very sensitive small changes in processing. ALD processing conditions of the transport layer will be tuned to optimize the mechanical adhesion within the perovskite module stack.
Co-Optimization of Fuels & Engines (Co-Optima) explores how simultaneous innovations in fuels and engines can boost fuel economy and vehicle performance while reducing emissions. The Analysis of Sustainability, Scale, Economics, Risk and Trade (ASSERT) team supports Co-Optima goals by evaluating environmental and economic drivers and the potential of bioblendstock candidates. The team assesses potential benefits or drawbacks of deploying co-optimized fuels and engines in the transportation sector. This presentation provides guidance into which Co-Optima bioblendstocks may be most economically and environmentally viable when scaled up.
Magnetic topological defects are energetically stable spin configurations characterized by symmetry breaking. Vortices and skyrmions are two well-known examples of 2D spin textures that have been actively studied for both fundamental interest and practical applications. However, experimental evidence of the 3D spin textures has been largely indirect or qualitative to date, due to the difficulty of quantitively characterizing them within nanoscale volumes. Here, we develop soft x-ray vector ptychography to quantitatively image the 3D magnetization vector field in a frustrated superlattice with 10 nm spatial resolution. By applying homotopy theory to the experimental data, we quantify the topological charge of hedgehogs and anti-hedgehogs as emergent magnetic monopoles and probe their interactions inside the frustrated superlattice. We also directly observe virtual hedgehogs and anti-hedgehogs created by magnetically inert voids. We expect that this new quantitative imaging method will open the door to study 3D topological spin textures in a broad class of magnetic materials. Our work also demonstrates that magnetically frustrated superlattices could be used as a new platform to investigate hedgehog interactions and dynamics and to exploit optimized geometries for information storage and transport applications.
Idaho National Laboratory (INL) is developing a first-of-a-kind leadout instrumented capsule experiment design to enable in-situ measurement capabilities in the Advanced Test Reactor (ATR) core. The Ceramic Advanced Thermal Evolution Research (CRATER) experiment supports the aLEU program objective to accelerate fuel performance irradiation testing for identifying alternative high-assay, low enriched uranium (HALEU) fuel systems. CRATER is a fueled, instrumented capsule experiment to measure in-situ temperature and thermal conductivity of ceramic fuels. Two ceramic fuel types will be used, uranium mono-nitride (UN) and uranium mono-carbide (UC), with a third metallic fuel used for comparison (UMo). The three fuel specimens will use a stainless-steel cladding. Programmatic objectives include linear heat generation rates (LHGR) of 210 ± 25 Watts per cm. and an inner clad temperature of 300-450 °C. The evolution of fuel thermal conductivity during irradiation has never been successfully measured in-situ for these systems and this experiment is designed to use advances in measurement sciences to characterize how thermal transport properties evolve while in reactor. Neutronic simulations of the experiment and its surrounding reactor environment were conducted using the Monte Carlo N-Particle Transport code (MCNP) and result in optimized fuel enrichment to meet target linear heat generation rates (LHGRs) influencing fuel temperatures, and fuel burnup requirements. Fabrication research and development (R&D) efforts are underway to produce annular right cylinder UC and UN pellets using carbothermic reduction and nitridation (or hydride-dehydride-nitride) synthesis methods, followed double-action die cold isostatic pressing.
Fundamental understanding of ionic transport plays a pivotal role in designing and optimizing fast ionic conductors. Here, through a systematic neutron scattering and theoretical investigation, we discovered new insights about how anion sublattice affects Li + distribution and transport in Li-argyrodite. We found that the promotion of Li + conductivities is strongly correlated with a previously overlooked Li + interstitial site (16e), which is critical for realizing intercage Li + migration. More isotropic Li + migration pathways with higher Li + occupancies on the interstitial 16e site are found to be the underlying reason for the much higher Li + conductivity in Li 6 PS 5 Cl relative to the Br- and I-based analogues. We further confirm that they are also the universal driving force for the ultrahigh Li + conductivities in both anion-substituted Li-poor (Li 6–a PS 5–a X a, X = Cl and Br) and cation-substituted Li-rich argyrodite (e.g., Li 6+a GeaP 1–a S 5 I and Li 6+a SiaSb 1–a S 5 I). In conclusion, it is expected this strategy can be generally adopted to improve the ionic conductivity of the broad family of Li-rich argyrodite and beyond.
The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.
The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.
We report alpha particle confinement is one of the most demanding issues for stellarators. It now seems clear that it is possible to design optimized stellarators that confine the background plasma at near tokamak radial transport levels. Moreover, adequate collisionless alpha particle confinement is possible in the core of a highly optimized stellarator. Here, the collisional confinement of barely trapped alphas in an optimized stellarator is considered by accounting for the resonance due to the reversal in direction of the drift within a flux surface and investigating the sensitive role of magnetic shear in keeping this resonance close to the passing boundary in some nearly quasisymmetric stellarator configurations. The treatment relies on a narrow collisional boundary layer formulation that combines the responses of both these resonant pitch angle alphas and the remaining barely trapped alphas. A novel merged regime treatment leads to explicit expressions for the energy diffusivity for both superbanana plateau (or resonant plateau) and $\sqrt{v}$ transport in the large aspect ratio limit for a slowing down tail alpha distribution function, where v is the effective pitch angle scattering collision frequency of the trapped alphas off the background ions. Depending on the details of the optimization scheme and the sign of the magnetic shear, modest magnetic shear can be used to reduce superbanana (or resonant) plateau transport to below the $\sqrt{v}$ transport level. In addition, a quasilinear equation retaining spatial diffusion is derived for a general alpha distribution function that allows the radial alpha transport to modify the distribution so it is no longer isotropic in velocity space.
Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.
Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.
Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.
The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.
When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.
Thermoelectric properties of conducting polymers typically suffer from molecular chain disordering, as charge transport is predominantly controlled by morphology. This is especially more problematic when counterions are introduced to tune the carrier concentration for optimal thermoelectric performance, which disturbs the morphology further. Here, in this work, we introduce a new avenue for enhancing thermoelectric properties without needing to regulate the morphology, namely, by controlling the coulombic interaction between polarons and counterions. We perform in situ de-doping thermoelectric experiments over 3 orders of magnitude change in electrical conductivity of three distinct thermoelectric polymers, namely, poly(3-hexylthiophene-2,5-diyl) (P3HT), poly[2,5-bis(3-dodecylthiophen-2-yl)thieno[3,2-b]thiophene] (PBTTT-C 12 ), and poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno[3,2-b]thiophene)] (OD-PDPP2T-TT) conjugated polymers, followed by grazing-incidence wide-angle X-ray scattering (GIWAXS) to study their respective morphologies. We demonstrate a 9-fold enhancement in the thermoelectric power factor in OD-PDPP2T-TT compared to PBTTT-C 12 and link it to the coulombic screening of charge carriers, including in the optimally doped regime. We support this hypothesis using Boltzmann transport equations and show that, in both P3HT and PBTTT-C 12 , as the polymer is doped, impurity scattering remains the dominant scattering mechanism, while in OD-PDPP2T-TT, the scattering mechanism changes from impurity to acoustic phonon limited, resulting in more effective screening of ionized counterions. Our results provide an additional knob to enhance the fundamental understanding of thermoelectric physics of conducting polymers and provide a pathway to achieve higher performance in the field of organic thermoelectrics.