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DOE OSTI2022

Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

17 WIND ENERGY↗

DOE OSTI2020

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO 2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Polybenzimidazole-derived carbon molecular sieve hollow fiber membranes with tailored oxygen selective transport

Polymer membrane carbonization is a promising strategy to obtain carbon molecular sieve (CMS) membrane materials with tailorable cavity size distributions for high gas selectivities beyond that attained from traditional polymer membrane materials. Despite their already demonstrated exceptional separation performance characteristics for several gas separation applications (e.g. CO 2 /CH 4 and H 2 /CO 2 ), further development of CMS membranes having high O 2 /N 2 perm-selectivity for energy-efficient high-purity O 2 production has proven challenging. Herein, we explore CMS hollow fiber membranes (HFMs) derived from highly rigid and tightly-packed polybenzimidazole (PBI) materials. Nearly defect-free PBI-derived CMS HFMs were fabricated and evaluated for O 2 /N 2 separation performance for the first time. The micro-structural and O 2 selective gas separation characteristics of PBI-CMS HFMs pyrolyzed at different pyrolysis conditions were intensively studied. With rigorous carbonization protocol optimization, we obtained highly O 2 permselective PBI-CMS HFMs having O 2 /N 2 selectivities approaching 14, enabling high purity O 2 production. The PBI-CMS HFM O 2 /N 2 separation performance dependence on operating conditions (e.g. temperature and feed pressure) are reported.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Selection, Evaluation, and Rating of Compact Heat Exchangers v. 1.006

SEARCH determines and optimizes the design of a compact heat exchanger for specified process conditions. The user specifies process boundary conditions including the fluid state and flow rate and SEARCH will determine the optimum flow arrangement, channel geometry, and mechanical design for the unit. Fluids are modeled using NIST Refprop or tabulated values. A variety of thermal-hydraulic correlations are available including user-defined equations to accurately capture the heat transfer and pressure drop behavior of the process flows.

Carlson, Matthew D.↗

DOE OSTI2023

HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures

The goal of influence maximization is to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. The problem has a number of important applications such as viral marketing, information spread, and epidemic control. Although computing optimal seed set is NP-Hard, due to the submodular nature of the problem efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM algorithm over large real-world social networks. We present an adaptive and memory-efficient optimization approach for a state-of-the-art multi-threaded parallel influence maximization algorithm. Our approach,HuffMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding, and queries directly on the compressed data to preserve the memory savings obtained through compression. We also propose an efficient sampling strategy based on the distribution of RR sets, which can further reduce the computation time for typical social networks with long-tail distributions. Considering a NUMA architecture, we scale up our solution on 128-core CPUs and reduce the memory footprint by up to 45.7% with negligible time overhead (or even faster) and without perceivable loss of accuracy.

Chen, Xinyu↗

DOE OSTI2022

Selective Thermal Emission with Radiation and Adsorption in Annuli (STERADIAN): An Advanced Heat Exchanger Concept for Supercritical CO 2 Power Cycles

One of the key limitations affecting ability to achieve the efficiency and cost saving advantages of a supercritical CO 2 (scCO 2 ) power cycle resides with the primary heat exchanger where costs can exceed 50% of the plant capital budget depending on design. This project established the foundational groundwork for a new type of heat exchanger optimized to take advantage of radiative heat transfer to scCO 2 . Selective thermal emission coatings were designed and tested that radiated more light tuned to the 4.2 µm infrared absorption band of CO 2 . Coating design utilized principles of nanophotonic bandgap theory to design a selective emitter that has: 1) low emissivity in the visible band, 2) near 100% effective emissivity in the mid-IR wavelength between 4 and 4.5 µm wavelength, and 3) strong suppression of far-IR light emission that will maximize radiative heat transfer efficiency to scCO 2 . Computational fluid dynamics simulations show that an advanced heat exchanger implementing this selective thermal emitter coating is 25 to as much as 40% smaller than conventional heat exchanger designs.

14 SOLAR ENERGY↗

DOE OSTI2017

Wireless Chemical Sensor Systems (Optimization of Field-Structured Chemiresistors for in situ Quantification of Propellant Stabilizer Concentration)

This project is a Cooperative Research and Development Agreement (CRADA) with International Electronic Machines Corp. (IEM, Troy, NY), who is contracted through the U.S. Naval Air Systems Command (NAVAIR) (under SBIR N111-010) to develop an in situ wireless chemical sensor system called the Stabilizer Level Accurate Measurement System (SLAMS). The SLAMS device is specified to include a miniature, very thin, self-contained chemical-sensor node to detect and track the level of stabilizer present in various PADs and CADs. IEM is focused on the wireless sensor platform, node communication, and electronics; whereas Sandia National Laboratories’ (SNL’s) efforts are focused on the chemical sensor development and integration. The sensor technology being employed is known as field-structured chemiresistors (FSCRs), which was invented by James Martin, et al. (SNL) and refined by Martin and Read. These sensors are small, require very little power, and have tunable sensitivity and selectivity. In the following, we discuss SNL’s efforts to optimize the FSCRs for the demanding chemical and environmental conditions that will be encountered in this application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Effect of fiber reinforcement on the process parameters of injection molding manufacturing process

Use of fillers with the resin is common in injection molding manufacturing technique to enhance the stiffness and strength of the manufactured components. However, the addition of fillers changes the rheological and mechanical properties of the material, required adjustments in the process parameters used for the manufacturing. The present study aims to investigate this impact of fillers on the process parameters of the injection molding manufacturing process. Neat Acrylonitrile Butadiene Styrene (ABS) and ABS reinforced with short glass fibers are used as the materials to perform the comprehensive numerical analysis including complete process sequence, namely filling, packing, cooling and warpage. Parametric studies are conducted to optimize the manufacturing process for minimal warpage (or shrinkage) in the produced part. The selected process parameters for the parametric study include cooling time, packing pressure, and mold temperature, all of which directly impact the quality of the manufactured parts. The optimal values obtained for these parameters are compared to examine the effect fiber reinforcement has on the manufacturing process.

Garg, Nikhil↗

DOE OSTI2015

Additive Manufacturing for Cost Efficient Production of Compact Ceramic Heat Exchangers and Recuperators

An additive manufacture technique known as laminated object manufacturing (LOM) was used to fabricate compact ceramic heat exchanger prototypes. LOM uses precision CO2 laser cutting of ceramic green tapes, which are then precision stacked to build a 3D object with fine internal features. Modeling was used to develop prototype designs and predict the thermal response, stress, and efficiency in the ceramic heat exchangers. Build testing and materials analyses were used to provide feedback for the design selection. During this development process, laminated object manufacturing protocols were established. This included laser optimization, strategies for fine feature integrity, lamination fluid control, green handling, and firing profile. Three full size prototypes were fabricated using two different designs. One prototype was selected for performance testing. During testing, cross talk leakage prevented the application of a high pressure differential, however, the prototype was successful at withstanding the high temperature operating conditions (1300 °F). In addition, analysis showed that the bulk of the part did not have cracks or leakage issues. This led to the development of a module method for next generation LOM heat exchangers. A scale-up cost analysis showed that given a purpose built LOM system, these ceramic heat exchangers would be affordable for the applications.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

MOOSE Optimization Module Overview: Application to Residual Stress Inversion in Nuclear Fuel Plates

Manufacturing methods, material selections and unique plate geometries used in low enriched uranium plate fuels for the High Performance Research Reactor (HPRR) have led to concerns about the residual stresses effect on bond strength of the fuel/cladding interfaces and overall fuel performance under irradiation. The current HPRR plate fuel test program performed residual stress calculations using the contour method with experimental displacement data obtained by incrementally cutting the plate fuel. In this work, we use a gradient based inverse optimization module developed within the MOOSE framework to estimate normal and shear residual stresses within the plate fuels using displacement data taken from a cut placed in the HPRR fuel plate. These results are verified against residual stress calculations obtained from the contour method. The inversion protocol used in this work utilizes two meshes to separately discretize the physics and parameter space. This allows a fine mesh to be used to fully resolve the physics and geometry of the forward problem. A coarser mesh is used to resolve the parameter space with spatially varying mesh density determined from a sensitivity analysis. The adaptive resolution of the parameter space essentially has the effect of regularization as it is designed to achieve the bias-variance tradeoff.

42 ENGINEERING↗

DOE OSTI2021

Influence of Cs Promoter on Ethanol Steam-Reforming Selectivity of Pt/m-ZrO 2 Catalysts at Low Temperature

The decarboxylation pathway in ethanol steam reforming ultimately favors higher selectivity to hydrogen over the decarbonylation mechanism. The addition of an optimized amount of Cs to Pt/m-ZrO 2 catalysts increases the basicity and promotes the decarboxylation route, converting ethanol to mainly H 2 , CO 2 , and CH 4 at low temperature with virtually no decarbonylation being detected. This offers the potential to feed the product stream into a conventional methane steam reformer for the production of hydrogen with higher selectivity. DRIFTS and the temperature-programmed reaction of ethanol steam reforming, as well as fixed bed catalyst testing, revealed that the addition of just 2.9% Cs was able to stave off decarbonylation almost completely by attenuating the metallic function. This occurs with a decrease in ethanol conversion of just 16% relative to the undoped catalyst. In comparison with our previous work with Na, this amount is—on an equivalent atomic basis—just 28% of the amount of Na that is required to achieve the same effect. Thus, Cs is a much more efficient promoter than Na in facilitating decarboxylation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

An updated estimate of the Mu2e experiment sensitivity

The Mu2e experiment at Fermilab will search for the conversion of a negative muon into an electron inside the field of a nucleus. This process does not conserve charged-lepton flavour and is heavily suppressed in the Standard Model (SM), with a branching ratio < 10-50. Any evidence of it would be an undeniable evidence of new physics beyond the SM. The project sets out to achieve a single event sensitivity of $\sim 3 × 10^{-17}$ on the ratio between the probability for a conversion of a negative muon into an electron and the one for a muon capture by the nucleus. Such a sensitivity would represent a 4 orders of magnitude improvement on the previous upper limit for the process, making possible to test predictions of different extensions of the SM. Mu2e uses three superconducting solenoids to produce and measure the muon conversions. In the first solenoid, the Production Solenoid, pions and kaons are produced, together with other secondary products, by 8 GeV kinetic energy proton interactions in a tungsten target. A gradient magnetic field is specifically designed to direct low momentum particles into the Transport Solenoid, an S-shaped magnet that filters out particles with unwanted charge and momentum. Muons, produced by pion and kaon decays, finally reach the aluminum Stopping Target in the Detector Solenoid, where they eventually stop and convert. The result of the conversion process is a monochromatic electron of ~105 MeV/c momentum. The Detector Solenoid also hosts the two main detectors: a straw tube tracker and two CsI calorimeter disks, both providing measurement of event kinematics. A germanium detector and a LaBr crystal are located downstream of the Detector Solenoid to measure the X and gamma rays produced by the muon captures in the Stopping Target. A veto system of scintillators covering the Detector Solenoid and half of the Transport Solenoid is used to identify and reject cosmic rays interactions. With respect to the initial project, the Mu2e running plan has evolved to a staged configuration with 2 years at reduced intensity before the 2025 accelerator shutdown for the neutrino beam upgrade and 2 or 3 years at full intensity after that. This, together with geometry changes and a better knowledge of detector performances obtained by the first slice tests, has required a full revision of the signal over background selection that is the subject of this thesis. In order to achieve a new estimate of Mu2e sensitivity, the simulation of the data corresponding to the first 2 years of data acquisition has been performed. This includes both conversion electrons (CE) and the main sources of background: cosmics, decay-in orbits (DIO), radiative pion captures (RPC) and antiprotons. The characteristics of the signal and of the main backgrounds have been studied to define the best selection variables for CE. A special effort has been devoted to the evaluation of the antiproton background. The lack of experimental data for antiproton production cross section makes the systematic uncertainty on this background significant. A new parameterization of the cross section has been developed to fit the existing data and to provide a more reliable estimate of the systematic uncertainty by comparing the results of the old and the new model. A special effort has been devoted to the optimization of the antiprotons Monte Carlo generator. This study has also revealed that the dominant component within this background is represented by antiprotons produced in the opposite direction with respect to the Transport Solenoid entrance and then redirected to it by back-scattering processes in the Production Target. This ultimately highlights the sensitivity of the background estimate to the G4 handling of antiproton interactions in the 1 to 3 GeV/c momentum range. Finally, the final experimental sensitivity has been studied. The momentum and time selection have been optimized to obtain the 5-sigma discovery reach or, in case of no signal, the upper limit on conversion probability. The results confirm that, in the firs t two years of data taking, Mu2e will be able to improve the current experimental sensitivity for muon-to-electron conversion in an atomic field by more than 3 order of magnitudes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Optimization Simulations for a Gamma-Ray Calibration Standard for a Cyclic Neutron Activation Analysis Pneumatic System at the Penn State Breazeale Reactor

For new experimental setups, the initial testing and calibrations can be expensive and time consuming without prior optimization. However, these issues can be mitigated using realistic modeling and simulation studies of the proposed system a priori. Specifically, an experiment can be performed virtually using realistic simulations and the expected results evaluated to inform adjustments of the experimental setup to achieve optimum results. At the Penn State Breazeale Reactor, a new pneumatic transfer system has been developed for the detection and characterization of short-lived fission fragments to enhance and complement existing nuclear data. The system accomplishes this task by transporting samples cyclically between an assortment of gamma-ray/neutron detectors and the reactor-core vicinity with sub-second transit times. The measured neutron flux paired with simulations of detector response using Geant4 and a custom module for estimating cascade summing effects and corrections were used to select sample-material masses for system characterization. With the optimized sample masses, an experimental counting plan for characterizing the repeatability of the newly developed pneumatic system was developed. Finally, the predictions made with these simulations allowed for optimization of the irradiation sample characteristics and gamma-ray detection system.

cyclic neutron activation, gamma-ray spectroscopy↗

DOE OSTI2022

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Computing the optimal path in stochastic dynamical systems

In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensional system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

SU-F-T-197: Investigating Optimal Oblique-Beam Arrangement for Bilateral Metallic Prosthesis Prostate Cancer in Pencil Beam Scanning Proton Therapy

Purpose: The main purpose of this study is to investigate the optimum oblique-beam arrangement for bilateral metallic prosthesis prostate cancer treatment in pencil beam scanning (PBS) proton therapy. Methods: A computed tomography dataset of bilateral metallic prosthesis prostate cancer case was selected for this retrospective study. A total of four beams (rightanterior- oblique [RAO], left-anterior-oblique [LAO], left-posterior-oblique [LPO], and right-posterior-oblique [RPO]) were selected for treatment planning. PBS plans were generated using multi-field-optimization technique for a total dose of 79.2 Gy[RBE] to be delivered in 44 fractions. Specifically, five different PBS plans were generated based on 2.5% ± 2 mm range uncertainty using five different beam arrangements (i)LAO+RAO+LPO+RPO, (ii)LAO+RAO, (iii)LPO+RPO, (iv)RAO+LPO, and (v)LAO+RPO. Each PBS plan was optimized by applying identical dose-volume constraints to the PTV, rectum, and bladder. Treatment plans were then compared based on the dose-volume histograms results. Results: The PTV coverage was found to be greater than 99% in all five plans. The homogeneity index (HI) was found to be almost identical (range, 0.03–0.04). The PTV mean dose was found to be comparable (range, 81.0–81.1 Gy[RBE]). For the rectum, the lowest mean dose (8.0 Gy[RBE]) and highest mean dose (31.1 Gy[RBE]) were found in RAO+LAO plan and LPO+RPO plan, respectively. LAO+RAO plan produced the most favorable dosimetric results of the rectum in the medium-dose region (V50) and high-dose region (V70). For the bladder, the lowest (5.0 Gy[RBE]) and highest mean dose (10.3 Gy[RBE]) were found in LPO+RPO plan and RAO+LAO plan, respectively. Other dosimetric results (V50 and V70) of the bladder were slightly better in LPO+RPO plan than in other plans. Conclusion: Dosimetric findings from this study suggest that two anterior-oblique proton beams arrangement (LAO+RAO) is a more favorable option with the possibility of reducing rectal dose significantly while maintaining comparable target coverage and acceptable bladder dose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

The Target-Matrix Optimization Paradigm for High-Order Meshes

Here, we describe a framework for controlling and improving the quality of high-order finite element meshes based on extensions of the Target-Matrix Optimization Paradigm (TMOP) of [P. Knupp, Eng. Comput., 28 (2012), pp. 419--429]. This approach allows high-order applications to have a very precise control over local mesh quality, while still improving the mesh globally. We address the adaption of various TMOP components to the settings of general isoparametric element mappings, including the mesh quality metric in 2D and 3D, the selection of sample points and the solution of the resulting mesh optimization problem. We also investigate additional practical concerns, such as tangential relaxation and restricting the deviation from the original mesh. The benefits of the new high-order TMOP algorithms are illustrated on a number of test problems and examples from a high-order arbitrary Lagrangian--Eulerian (ALE) application [BLAST: High-order curvilinear finite elements for shock hydrodynamics, http://www.llnl.gov/CASC/blast]. Our implementation is freely available in an open-source library form [MFEM: Modular parallel finite element methods library, http://mfem.org].

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Genomic insights into local adaptation and migration success in reintroduced Coho Salmon of the Wenatchee River basin

ABSTRACT Objective Reintroduction of salmonids into regions where they have been extirpated is a common conservation strategy that is often implemented through natural recolonization, translocation of natural populations, or hatchery-based programs. Locally adapting to specific environmental conditions is critical for long-term population viability, particularly for species like Coho Salmon Oncorhynchus kisutch, which face diverse selective pressures during their migration. This study focused on the mid-Columbia River Coho Salmon reintroduction program managed by Yakama Nation Fisheries, which has successfully reintroduced Coho Salmon into the Wenatchee and Methow River basins, Washington. Notably, these populations have adapted to the longer migration route than those in the founding stock, with selection favoring individuals with an earlier arrival time and that can navigate a 15-km, high-gradient canyon to reach optimal spawning grounds. The objectives of this study were to investigate whether specific genomic regions are under selection for traits associated with return location and timing in Coho Salmon. Methods Low-coverage whole-genome resequencing data were used to screen for genomic regions associated with the phenotypes of interest. Results A weak polygenic signal in female Coho Salmon was found to be associated with return group, with a subset of candidate adaptive regions occurring across eight chromosomes. Conclusions These findings provide insights into the genomic mechanisms underlying local adaptation in reintroduced salmon populations and inform broodstock selection strategies aimed at promoting natural production and long-term population sustainability.

Horn, Rebekah L.↗