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Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification
Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.
Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems
Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.
Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method
Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.
Extending Shared Socioeconomic Pathways to Future Water Supply In-frastructure Scenarios: A Case Study of San Antonio, TX
Datasets supporting findings and visualization behind Okoye and McManamay (2025) Extending Shared Socioeconomic Pathways to Future Water Supply Infrastructure Scenarios: A Case Study of San Antonio, TX. Environmental Research Communications, DOI: 10.57931/2563186 These datasets contains the results of a site selection analysis for municipal water supply planning across multiple Shared Socioeconomic Pathways (SSPs 1–5) and hard scenario classification of water systems in San Antonio, TX. It includes data at the resolution of individual surface water supply sources (COMIDs) and integrates a wide range of hydrologic, socioeconomic, infrastructural, and scenario-based planning variables. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions: Dataset_SSP1_SSP4.xlsx - Contains data used for site selection optimization under SSP1 to SSP4. This dataset was generated based on multi-indicator computations (e.g., WAI, WQI, ERI, WTC, WIC), scenario demand projections, and resource and spatial constraints, excluding new reservoir values. Dataset_SSP5.xlsx - Used for site optimization under SSP5. Unlike Dataset_SSP1_SSP4, this dataset includes new reservoir features with updated calculations of WAI, WTC, and WIC to reflect the added infrastructure and supply potential. hard_classification.xlsx - Provides the scenario classification output for each site. Includes both the initial scenario classification based on Euclidean Distance and adjusted classifications based on 30% change reduction BAU.zip - Zipped folder of .shp files showing spatially optimized water supply sites per SSP under the Business-As-Usual (BAU) water demand strategy LowGW.zip - Zipped folder of .shp files showing optimized site selections under the Low Groundwater strategy
High accuracy single-layer free-space diffractive neuromorphic classifiers for spatially incoherent light
Free-space all-optical diffractive systems have shown promise for neuromorphic classification of objects without converting light to the electronic domain. While the factors that govern these systems have been studied for coherent light, the fundamental properties for incoherent light have not been addressed, despite the importance for many applications. Here we use a co-design approach to show that optimized systems for spatially incoherent light can achieve performance on par with the best linear electronic classifiers even with a single layer containing few diffractive features. This performance is limited by the inherent linear nature of incoherent optical detection. We circumvent this limit by using a differential detection scheme that achieves greater than 94% classification accuracy on the MNIST dataset and greater than 85% classification accuracy for Fashion-MNIST, using a single layer metamaterial.
Initial Systems-Level Assessment of a Distributed Direct Air Capture System Concept at the Urban-scale (UrbanDAC)
Direct Air Capture (DAC) systems offer a promising solution for mitigating global carbon emissions by directly removing ambient carbon dioxide (CO 2 ) from the atmosphere. While future DAC facilities are typically envisioned as being large and centralized, small-scale systems present an alternative approach with advantages such as adaptability and lower uptake costs. By harnessing waste heat from the built environment, such small-scale systems become distributed DAC at the urban scale (UrbanDAC) that benefit from existing urban infrastructure, while presenting challenges such as identifying eligible buildings and sustainable transportation and storage of captured CO 2 . Collaborating with engineering experts and developers of a DAC unit that can be co-located with cooling towers of existing commercial buildings, this study explores the systems-level implications of UrbanDAC using a geographically explicit multi-decision criteria analysis (MCDA) framework. By considering various infrastructure and environmental factors, network analysis and geospatial techniques are applied to identify optimal building candidates for distributed DAC units within Knoxville, Tennessee, USA, as a representative mid-size city. The selected outputs of the MCDA are used to explore a scenario that assumes a CO 2 collection and transport route for 20 high-ranking candidate buildings; total carbon emissions, EV energy consumption, and net carbon dioxide removal (CDR) are then calculated. Results suggest that the spatial variation of optimal candidates between thriving commercial areas is an important planning consideration. Examining the feasibility of UrbanDAC at an urban planning level provides valuable insights into the barriers and enabling conditions for CDR in cities, where the vast majority of CO 2 emissions are produced, and supports decision-making processes for the implementation of decarbonization initiatives. Through this initial assessment, this research acts as a pilot study for an emerging technology that highlights the importance of distributed DAC technologies in addressing climate change and emphasizes the need for further research and exploration in this domain.
Optimizing invader suppression to restore native species: Leveraging species traits to overcome collateral damage
Strategies to control ongoing biological invasions are often developed by modelling the invasive species' population and aiming to reduce its abundance. However, if the ultimate objective is to protect and restore native species, focussing solely on the invader may not be optimal because it does not account for (i) species interactions that can cause the invader's impacts to depend nonlinearly on its abundance, (ii) collateral damages to native species incurred due to nonspecific removal methods or (iii) native-invader trait differences. Here, to identify an invader suppression strategy that maximizes average native population size, we applied optimal control theory to a two-species model of a native species threatened by an invasive competitor. We examined trade-offs between iterative physical removals that selectively target invaders and intensifiable chemical control that is nonselective but has higher efficacy. We found that while iterative removals were capable of supporting large native populations when applied continuously, cost could be prohibitively high. In contrast, when favourable native-invader trait differences enabled native species to re-establish more quickly than invaders, intensifiable methods could achieve substantial restoration benefits at lower cost by focussing removal effort into periodic, high-efficacy events. In a metapopulation, removals that rotated among spatial patches were optimal when the native species had higher dispersal, whereas synchronous removals were preferred when native recovery was initiated locally and the invader could disperse. For a case study in Hawaiian streams, we compared how effective two alternative methods of removing invasive live-bearing fishes (poeciliids) might be at restoring the endemic freshwater goby Sicyopterus stimpsoni. We found that rotenone (a piscicidal chemical) offered superior benefits when the control budget was small and efficacy was high, but that electrofishing (use of electricity to manually collect target fish) was better with larger budgets and in many lower-efficacy scenarios. Synthesis and applications. Our findings demonstrate that, by accounting for species interactions and collateral damage, invasive species control strategies can be optimized in light of species traits. Choices about the timing, locations and types of removal events present opportunities to increase the efficiency with which invasive species suppression benefits native species.
Facets of hydro power and future trends in a Nordic Context
Hydropower technologies bolster high penetration of variable renewable energies (VREs) in the net zero emissions scenarios. Nevertheless, there are various challenges to meeting the ambitious goal, such as stability, reliability, resiliency, security, lack of reactive power, voltage support and inertia, large-scale storage deployment and coordination, interconnectedness, demand-side response, higher thermal cycles with increased start/stops, and inadequate Levelized Cost of Energy (LCOE) for system-wise VRE integration and profitability. This survey conducts a bottom-up analysis to unveil the opportunities to utilize hydropower facilities and disentangle the nested problem for intertwining design features, control algorithms, operation, optimization approaches, incentives, services, and market mechanisms using a three-pillar framework perspective: grid owners, power producers, and machine designers. The survey identified emerging trends in real-time and capacity markets, flexible power systems, and enhanced grid capabilities, including advanced voltage support and updated grid codes. These developments present significant opportunities for hydropower, such as achieving super-flexibility through hybridization, expanded reactive power capabilities, and advanced operational modes like a synchronous condenser and power adequator functionalities. These opportunities require novel design philosophies — including new winding, stator, and rotor configurations, optimized ventilation, and active cooling systems — to enhance performance under stressed grid and climate conditions. Finally, integrating climate and energy models for multi-basin optimization with finer spatial and temporal granularity enhances the planning accuracy for water management of hydropower while addressing environmental challenges. The review delivers helpful prospective suggestions and tools that would serve researchers, power engineers, and stakeholders in making decisions about hydropower technologies and services in 2050 and beyond.
Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation
Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.
Energy resolution and gain measurements in Argon-based gas mixtures: Exploring Ar:CF 4 for low energy measurements with TPCs
Time Projection Chambers (TPCs) are among the most advanced charged-particle detectors. Gas-filled TPCs have tracking capabilities that provide 3D-imaging of charged particles with a good energy resolution for spectroscopy. Different gas mixtures have different properties that determine the energy resolution as well as the spatial resolution. Therefore, optimization of operating conditions is required to simultaneously obtain adequate gain, energy resolution, spatial/track resolution, as well as higher drift velocities for high counting rates applications. Ar:CF 4 gas mixture has higher electron drift velocities and lower electron diffusion, which makes it an attractive candidate for TPC filling gas for low energy nuclear physics applications as compared to commonly used Ar:CH 4 and Ar:CO 2 gas mixtures, namely when tracking information is needed. However, other properties, including energy resolution and gain, remain largely unexplored in Ar:CF 4 especially at pressures and other operating conditions relevant for low-energy nuclear physics applications. Here, in this paper we report on gain and energy resolution measurements, using Gas Electron Multipliers (GEMs), in the less explored Ar:CF 4 mixture (Alfonsi et al., 2006), as well as in the more commonly used gas mixtures Ar:CH 4 and Ar:CO 2 . In addition to obtaining energy resolution and gain, we provide results from Garfield++ simulations for gain fluctuations, and their impact on energy resolution is discussed.
A microfluidic investigation of carbon mineralization within a fracture [Slides]
Key Objectives: Use microfluidics as a tool to investigate coupled dissolution-precipitation processes in fractures that flow; Identify key parameters that control optimal carbonation conditions (spatially and quantitatively) that can help guide larger scale field tests.
Utilizing Ontology Structures To Curate the DOE-NETL Carbon Storage Open Database
The specialized ontology for the Carbon Storage Open Database will enable more rapid assignment of appropriate symbology standards for visualization improvements, optimize topical and spatial tagging within keywords, and improve flexibility for utilization in existing data repositories such as EDX. This effort also aims to establish a foundation for utilization of ontologies for organization of other data related to geologic carbon storage in the future.
Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning
In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.
Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask
Abstract Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.
Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.
Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?
Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.