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NASA NTRS2017

Simulation Propulsion System and Trajectory Optimization

A number of new aircraft concepts have recently been proposed which tightly couple the propulsion system design and operation with the overall vehicle design and performance characteristics. These concepts include propulsion technology such as boundary layer ingestion, hybrid electric propulsion systems, distributed propulsion systems and variable cycle engines. Initial studies examining these concepts have typically used a traditional decoupled approach to aircraft design where the aerodynamics and propulsion designs are done a-priori and tabular data is used to provide inexpensive look ups to the trajectory ana-ysis. However the cost of generating the tabular data begins to grow exponentially when newer aircraft concepts require consideration of additional operational parameters such as multiple throttle settings, angle-of-attack effects on the propulsion system, or propulsion throttle setting effects on aerodynamics. This paper proposes a new modeling approach that eliminated the need to generate tabular data, instead allowing an expensive propulsion or aerodynamic analysis to be directly integrated into the trajectory analysis model and the entire design problem optimized in a fully coupled manner. The new method is demonstrated by implementing a canonical optimal control problem, the F-4 minimum time-to-climb trajectory optimization using three relatively new analysis tools: Open M-DAO, PyCycle and Pointer. Pycycle and Pointer both provide analytic derivatives and Open MDAO enables the two tools to be combined into a coupled model that can be run in an efficient parallel manner that helps to cost the increased cost of the more expensive propulsion analysis. Results generated with this model serve as a validation of the tightly coupled design method and guide future studies to examine aircraft concepts with more complex operational dependencies for the aerodynamic and propulsion models.

Optimization↗

NASA NTRS2019

European Service Module - Structural Test Article (E-STA) Building Block Test Approach and Model Correlation Observations

The Orion European Service Module - Structural Test Article (E-STA) underwent sine vibration testing in 2016 using the Mechanical Vibration Facility (MVF) multi-axis shaker system at NASA Glenn Research Center’s (GRC) Plum Brook Station (PBS) Space Power Facility (SPF). The main objective was to verify the structural integrity of the European Service Module (ESM) under sine sweep dynamic qualification vibration testing. A secondary objective was to perform a fixed-base modal survey, while E-STA was still mounted to MVF, in order to achieve a test correlate the finite element model (FEM). To facilitate the E-STA system level correlation effort, a building block test approach was implemented. Modal tests were performed on two major subassemblies, the crew module/launch abort structure (CM/LAS) and the crew module adapter (CMA) mass simulators. These subassembly FEMs were individually correlated and then integrated into the E-STA FEM prior to the start of the E-STA sine vibration test. This paper summarizes the modal testing and model correlation efforts of both of these subassemblies and how the building block approach assisted in the overall correlation of the E-STA FEM. This paper will also cover modeling practices that should be avoided, recommended instrumentation positioning on complex structures, and the importance of the FEM geometrically matching CAD in sufficient detail in order to adequately replicate internal load paths. The goal of this paper is to inform the reader of the hard earned lessons learned and pitfalls to avoid when applying a building block test approach.

Winkel, James P.↗

DOE OSTI2023

Integration of Experimental Hydroprocessing and FCC Data with Process (Aspen Plus) and Refinery Optimization (Aspen PIMS) Models

NREL's Economic, Sustainability, and Market Analysis (ESMA) team develops process models in Aspen Plus to support techno-economic analysis (TEA) and life-cycle assessment (LCA) of conversion pathways from renewable and circular resources to fuels and chemicals. These tools are being applied to assess probable operational constraints or bottlenecks associated with refinery co-processing and repurposing opportunities. The team also utilizes optimizable refinery models to quantify opportunities for biofuel and bioproduct pathways through integration with existing refining infrastructure. The goals of analysis are to model the transition of refineries to renewable and circular feedstocks and quantify the costs production and CO2 abatement through utilization of existing refineries. At the core of the ESMA team's work is the process data derived from conversion experiments on hydroprocessing and fluid catalytic cracking performed at NREL. The objective of this presentation is to highlight new analysis approaches and capabilities in pathway analysis and refinery optimization modeling with specific emphasis on integration of experimental data.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2001

Real-Time Adaptive Control Allocation Applied to a High Performance Aircraft

Abstract This paper presents the development and application of one approach to the control of aircraft with large numbers of control effectors. This approach, referred to as real-time adaptive control allocation, combines a nonlinear method for control allocation with actuator failure detection and isolation. The control allocator maps moment (or angular acceleration) commands into physical control effector commands as functions of individual control effectiveness and availability. The actuator failure detection and isolation algorithm is a model-based approach that uses models of the actuators to predict actuator behavior and an adaptive decision threshold to achieve acceptable false alarm/missed detection rates. This integrated approach provides control reconfiguration when an aircraft is subjected to actuator failure, thereby improving maneuverability and survivability of the degraded aircraft. This method is demonstrated on a next generation military aircraft Lockheed-Martin Innovative Control Effector) simulation that has been modified to include a novel nonlinear fluid flow control control effector based on passive porosity. Desktop and real-time piloted simulation results demonstrate the performance of this integrated adaptive control allocation approach.

Davidson, John B.↗

DOE OSTI2022

Configuration Space Integration for Adsorbate Partition Functions: The Effect of Anharmonicity on the Thermophysical Properties of CO–Pt(111) and CH 3 OH–Cu(111)

A method for computing anharmonic thermophysical properties for adsorbates on metal surfaces has been extended to include libration, or frustrated rotation. Classical phase space integration is used with Monte Carlo sampling of the configuration space to obtain the partition function of CO on Pt(111) and CH 3 OH on Cu(111). A minima-preserving neural network potential energy surrogate is used within the integration routines. Direct state counting using discrete variable representation is used to benchmark the results. We find that the phase space integration approach is in excellent agreement with the direct state counting results. Comparison with standard models such as the harmonic oscillator indicates that anharmonicity contributes significantly to the thermodynamic properties of CH 3 OH on Cu(111). We find that there is also a considerable difference between the harmonic oscillator and phase space integration for CO on Pt(111), although the discrepancy can largely be attributed to the presence of multiple binding sites within the unit cell. We demonstrate that a multisite harmonic oscillator model might be sufficient for CO-Pt(111). A more thorough description of the potential energy surface, which can be achieved with phase space integration, is necessary for weakly bound adsorbates such as CH 3 OH. In conclusion, the thermophysical properties were used to calculate free energies of adsorption on the respective metals, and subsequently the equilibrium constants and Langmuir isotherms in relevant temperature ranges. The results show that the choice of model to obtain partition functions greatly affects the resulting surface coverages in kinetic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2013

New Approaches in Reusable Booster System Life Cycle Cost Modeling

This paper presents the results of a 2012 life cycle cost (LCC) study of hybrid Reusable Booster Systems (RBS) conducted by NASA Kennedy Space Center (KSC) and the Air Force Research Laboratory (AFRL). The work included the creation of a new cost estimating model and an LCC analysis, building on past work where applicable, but emphasizing the integration of new approaches in life cycle cost estimation. Specifically, the inclusion of industry processes/practices and indirect costs were a new and significant part of the analysis. The focus of LCC estimation has traditionally been from the perspective of technology, design characteristics, and related factors such as reliability. Technology has informed the cost related support to decision makers interested in risk and budget insight. This traditional emphasis on technology occurs even though it is well established that complex aerospace systems costs are mostly about indirect costs, with likely only partial influence in these indirect costs being due to the more visible technology products. Organizational considerations, processes/practices, and indirect costs are traditionally derived ("wrapped") only by relationship to tangible product characteristics. This traditional approach works well as long as it is understood that no significant changes, and by relation no significant improvements, are being pursued in the area of either the government acquisition or industry?s indirect costs. In this sense then, most launch systems cost models ignore most costs. The alternative was implemented in this LCC study, whereby the approach considered technology and process/practices in balance, with as much detail for one as the other. This RBS LCC study has avoided point-designs, for now, instead emphasizing exploring the trade-space of potential technology advances joined with potential process/practice advances. Given the range of decisions, and all their combinations, it was necessary to create a model of the original model and use genetic algorithms to explore results. A strong business case occurs when viable paths are identified for an affordable up-front investment, and these paths can credibly achieve affordable, responsive operations, characterized by smaller direct touch labor efforts at the wing level from flight to flight. The results supporting this approach, its potential, and its conclusions are presented here.

Zapata, Edgar↗

NASA NTRS2013

New Approaches in Reuseable Booster System Life Cycle Cost Modeling

Zapata, Edgar↗

DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Feasibility of DEIM for retrieving the initial field via dimensionality reduction

When parameter estimation is solved in a high-dimensional space, the dimensionality reduction strategy becomes the primary consideration for alleviating the tremendous computational cost. Here, the discrete empirical interpolation method (DEIM) is explored to retrieve the initial condition (IC) by combining the polynomial chaos (PC) based ensemble Kalman filter (i.e. PC-EnKF), where a non-intrusive PC expansion is considered as a surrogate model in place of the forward model in the prediction step of the ensemble Kalman filter, resulting in fewer forward model integrations but with a comparable accuracy as Monte Carlo-based approaches. The DEIM acts as a hyper-reduction tool to provide the low-dimensional input for the high-dimensional initial field, which can be reconstructed using the information on the sparse interpolation grid points that is adaptively obtained through PC-EnKF data assimilation method. Thus an innovative framework to reconstruct the IC is developed. The detailed procedure at each assimilation iteration includes: the determination of the spatial interpolation points, the estimation of the initial values on the interpolation locations using the optimal observations, and the reconstruction of IC in the full space. The current study uses the reconstruction field of initial conditions of the Navier-Stokes equations as an example to illustrate the efficacy of our method. The experimental results demonstrate the proposed algorithm achieves a satisfactory reconstruction for the initial field. The proposed method helps to extend the applicable area of DEIM in solving inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Environmental Thermodynamic Effects on Future Changes of Mesoscale Convective Systems and Isolated Deep Convection Over the US: A Case Study

Mesoscale convective systems (MCSs) and isolated deep convection (IDC) significantly influence local weather conditions and the hydrological cycle across the United States. Their future changes under warming is critical to the society. However, the exact impacts of thermodynamic environmental changes on them are difficult to resolve using climate model simulations which integrate both dynamic and thermodynamic factors. This study explores a theoretical modeling approach to isolate environmental thermodynamic effects on their future changes through a case study. For a 50-day period during the 2020 summer, a pair of convection-permitting model simulations indicate contrasting changes in MCSs and IDC between inland and coastal regions under pseudo global warming. Driven by the thermodynamic environments of these simulations, a single-column parcel model indicates a decrease in the frequency of IDC occurrences, along with increases in duration and precipitation amount under warming, attributable to rising most unstable convective available potential energy (MUCAPE), convective inhibition (MUCIN), and precipitable water (PW). A multi-column parcel model reveals contrasting changes in the frequency and mean area of MCSs between inland and coastal regions, underscoring the increase in mean MUCIN over inland regions and increases in mean MUCAPE and PW over coastal regions. The increase in mean MCS area over inland regions is linked to the interplay between accelerated gust fronts and enhanced subsidence strength, which is further traced to the unchanged mean MUCIN but increased mean MUCAPE in large-scale environments. In conclusion, these case study results suggest a major role of thermodynamic environmental changes in controlling characteristics of MCSs and IDC under warming.

Mesoscale convective system↗

DOE OSTI2020

Visualization of the HIV-1 Env glycan shield across scales

The dense array of N-linked glycans on the HIV-1 envelope glycoprotein (Env), known as the “glycan shield,” is a key determinant of immunogenicity, yet intrinsic heterogeneity confounds typical structure–function analysis. Here, we present an integrated approach of single-particle electron cryomicroscopy (cryo-EM), computational modeling, and site-specific mass spectrometry (MS) to probe glycan shield structure and behavior at multiple levels. We found that dynamics lead to an extensive network of interglycan interactions that drive the formation of higher-order structure within the glycan shield. This structure defines diffuse boundaries between buried and exposed protein surface and creates a mapping of potentially immunogenic sites on Env. Analysis of Env expressed in different cell lines revealed how cryo-EM can detect subtle changes in glycan occupancy, composition, and dynamics that impact glycan shield structure and epitope accessibility. Importantly, this identified unforeseen changes in the glycan shield of Env obtained from expression in the same cell line used for vaccine production. Finally, by capturing the enzymatic deglycosylation of Env in a time-resolved manner, we found that highly connected glycan clusters are resistant to digestion and help stabilize the prefusion trimer, suggesting the glycan shield may function beyond immune evasion.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Boron nitride nanotube precursor formation during high-temperature synthesis: kinetic and thermodynamic modelling

In this work, we performed integrated modelling of the chemical pathways of formation for boron nitride nanotube (BNNT) precursors during high-temperature synthesis in a B/N 2 mixture. Integrated modelling includes quantum chemistry, Quantum–classical molecular dynamics, thermodynamic modelling, and kinetic approaches. We demonstrate that BN compounds are formed via the interaction of molecular nitrogen with small boron clusters, rather than through interactions with less reactive liquid boron. (This process can also be described as N 2 molecule fixation.) Liquid boron evaporates to produce these boron clusters (B m with m ≤ 5), which are subsequently converted into B m N n chains. The production of such chains is crucial to the growth of BNNTs because these chains form the building blocks of bigger and longer BN chains and rings, which are in turn the building blocks of fullborenes and BNNTs. Additionally, kinetic modelling revealed that B 4 N 4 and B 5 N 4 species in particular play a major role in the N 2 molecule fixation process. Furthermore, the formation of these species via reactions with B 4 and B 5 clusters is not adequately described under the assumption of thermodynamic equilibrium, as is demonstrated in our kinetic modelling. Thus, the accumulation of both B 4 N 4 and B 5 N 4 depends on the background gas pressure and the gas cooling rate. Long BN chains and rings, which are precursors of the fullborene and BNNT growth, form via self-assembly of components B 4 N 4 and B 5 N 4 . Our modelling results—particularly the increased densities of B 4 N 4 and B 5 N 4 species at higher gas pressures—explain the experimentally observed effect of gas pressure on the yield of high-quality BNNTs. The catalytic role of hydrogen was also studied; it is shown that HBNH molecules can be the main precursor of BNNT synthesis in the presence of hydrogen.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Spin-textured neutron beams with orbital angular momentum

In this work, we present a rigorous theoretical framework underpinning the technique of spin-echo modulated small-angle neutron scattering (SEMSANS), and show how the technique can be extended in order to generate spin-textured neutron beams with orbital angular momentum (OAM) via birefringent neutron spin-polarization devices known as magnetic Wollaston prisms. Neutron OAM beams are mathematically characterized by a “cork-screw” phase singularity e iℓΦ about the propagation axis where ℓ is the OAM quantum number. To understand the precise relationship between the emergent OAM state and the variety of spin textures realized by various setups, we have developed a path-integral approach that in the interferometric limit makes a judicious use of magnetic Snell's law. We show that our proposed technique produces a complex two-dimensional pattern of spin-OAM entangled states which may be useful as a probe of quantum magnetic materials. We compare our path-integral approach to the well-known single-path Larmor precession model and present a pedagogical derivation of magnetic Snell's law of refraction for both massive and massless particles based on Maupertuis's action principle.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.

data-driven model predictive control↗

DOE OSTI2024

Labeling sequential data from noisy annotations

Crowdsourcing algorithms often work under the assumption that the data samples are independent. Recent work has shown that data dependence, such as temporal correlations in sequential data, can be leveraged to improve the label quality. Existing methods that exploit this special structure rely on third-order statistics of the annotator outputs to ensure the identifiability of key latent parameters, which are costly to acquire. This work proposes an approach for integrating crowdsourced annotations under the Dawid-Skene/Hidden Markov Model (DS-HMM) for sequential data based on second-order statistics, which naturally enjoys a lower sample complexity. An effective algorithm is proposed to tackle the challenging optimization problem associated with the proposed estimator. Numerical experiments showcase the effectiveness of the data labeling paradigm.

Marrinan, Timothy P.↗

DOE OSTI2020

Preliminary description of a new creep-fatigue design method that reduces over conservatism and simplifies the high temperature design process

The report provides the initial description of a new creep-fatigue design method for structural components in high temperature nuclear service. The new method is based on an integrated elastic-perfectly plastic (EPP) analysis and Simplified Model Test (SMT) approach that reduces over conservatism, improves the treatment of elastic follow up, and simplifies the design procedure, when compared with the current creep-fatigue design methods in ASME Boiler and Pressure Vessel Code. Developing the design charts for the EPP-SMT design method requires extrapolating SMT test data as a function of hold time and follow up factor. The report develops the preliminary design charts for Alloy 617 at temperatures between 800°C and 950°C by combining two extrapolation approaches developed in a previous work. The report also presents a comparative analysis between the EPP-SMT design method and the current ASME creep-fatigue design methods by evaluating design life of two sample geometries under different loading conditions. Results from the comparative analysis verify the EPP-SMT design charts but suggest the requirement of additional test data in the low strain range regime for improving the extrapolation procedure that will further reduce the over conservatism in the creep-fatigue damage evaluation. The report also concludes that the EPP-SMT design procedure can account for effect of primary load on creep-fatigue life by using a fixed, bounding value of follow up in constructing the design charts. The conclusions to this report describe the future work required to complete this new design method so it can be codified through a nuclear Code Case.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Solute effects upon dislocation motion and recovery in Mg alloys (Final Report)

The objective the research was to develop a firmer understanding of the interactions between substitutional solute atoms and dislocations within Mg alloys. These interactions govern the absolute and relative mobilities of various types of dislocations in Mg (e.g., basal < a>, non-basal < a>, and pyramidal < c+a>). Furthermore, they have an impact on dislocation recovery processes (e.g., cross-glide, climb, rearrangement, and annihilation). Ultimately, solute-dislocation interactions strongly impact a) strain hardening, b) strain rate sensitivity, c) plastic anisotropy of textured polycrystals, d) texture evolution, e) dislocation substructures evolution, and even f) recrystallization behavior. It is broadly known that Mg alloys exhibit poor low temperature formability. Since formability is largely governed by properties (a) – (c) in this list, it is critical to develop a better understanding of solute-dislocation interactions, if one hopes to improve the situation. While the theory of static solute strengthening is well developed, especially for alloys with face centered cubic (FCC) crystal structures, there are outstanding questions related to applications to hexagonal close packed (HCP) crystal structures and dynamic strain aging (DSA) in materials of various structures. DSA has far-reaching implications for metal formability and, in the case of Mg alloys, appears to correlate with the so-called rare-earth (RE) texture which has been shown to benefit formability. The Portevin-Le Chatelier (PLC) effect, and associated negative strain rate sensitivity, occur at higher temperatures (>100℃) in Mg alloys as compared with similar Al alloys, even though they have similar melting points and solute diffusivities. Our preliminary research has shown that modern, physics-based models of DSA can be tuned to describe the behavior of Mg alloys if a rather higher activation enthalpy is assumed for cross-core diffusivity. While this partially explains the delay in DSA to higher temperatures, it is also hypothesized that this delay is due in part to the intrinsically more thermally activated (rate sensitive) nature of non-basal < a> dislocation motion which is required for macroscopic flow of Mg alloys, whereas octahedral slip in many FCC metals like aluminum is essentially athermal at room temperature. It was originally proposed to employ a combination of in-situ diffraction-based experimental characterization to validate existing theory. It was envisioned to perform in-situ transmission electron microscopy (TEM) to assess individual dislocation behavior and in-situ high-energy X-ray diffraction (HEXRD) techniques which were showing great promise for elucidating collective dislocation behavior, 2 including recovery, especially if the contributions to various forms of diffraction peak broadening (𝜂𝜂,𝜔𝜔,and 2𝜃𝜃) can be effectively integrated. Finally, it was envisioned to perform discrete dislocation dynamics (DDD) modeling approaches to aide in the interpretation of both TEM and HEXRD experiments. In the end, mechanical tests were performed on more complex Mg alloys which exhibited evidence of dynamic strain aging, and this led to the establishment of another project. Mechanical test data obtained at McMaster University served as the basis of an assessment of the applicability of Bazinski’s “stress equivalence” theory of solute strengthening to polycrystalline alloys of Mg. Although we did not succeed in applying the approach to Mg alloys, we did develop expertise with the HEXRD approach using a BCC, β-Ti alloy and demonstrated numerous new capabilities that may be applied to any polycrystalline material in collaboration with researchers at CHESS and around the world: (1) assessment of details of the elastoplastic transition (yielding) using a combination of HEXRD and full-field polycrystal plasticity modeling, (2) the first-ever experimental observation of strong stress rotation within the individual grains of a polycrystalline material, and (3) a comprehensive analysis of the grain-level dislocation density evolution based upon diffraction peak broadening along 𝜂𝜂,𝜔𝜔,and 2𝜃𝜃 directions. This final aspect allowed us to confirm that dislocations were gliding on multiple plane types and not restricted to {110} type planes, and it also provided clues as to why some grains were unloading during straining, with surprising implications for our understanding of the effects of geometrically necessary dislocations (GNDs). Finally, graduate student, Mohammed Shabana, developed a MATLAB code which confirmed the conclusions of Prof. Catalin Picu (Rensallear Polytechnic Institute, RPI) regarding the effect of solute-trapped, forest dislocations on the breaking stress of Lomer lock junctions in FCC metal alloys. He applied the same anisotropic line-tension model to a variety of dislocation junction configurations and found an inconsistency in the widely cited results of Dupuy and Fivel regarding the Hirth Lock, and he outlined an approach to extend these finding to HCP Mg alloys that we are still pursuing with discretionary fundings at UVA.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Interactions between molecular-scale biogeochemical processes and hyporheic exchange for understanding coupled Fe-S-C cycling in iron-rich riparian wetlands (Final Technical Report)

Riparian wetlands are dynamic interfaces that exert strong control over water quality, contaminant mobility, and greenhouse gas emissions. These systems are characterized by hyporheic exchange between oxic surface water and anoxic groundwater, which generates steep redox gradients and promotes spatially and temporally variable microbial activity. Despite growing recognition of tightly coupled iron (Fe), sulfur (S), and carbon (C) cycling in these environments, the mechanisms governing these interactions—particularly under low-sulfate freshwater conditions—remain poorly constrained. This project developed a mechanistic understanding of how hydrologic variability and microbial processes interact to control Fe–S–C cycling in iron-rich riparian wetlands. Using a multi-scale and multi-method approach integrating field observations, geochemical and spectroscopic analyses, metagenomics, and reactive transport modeling, we demonstrate that “cryptic” sulfur cycling—rapid sulfur transformations involving intermediate-valence species—plays a dominant and previously underrecognized role in freshwater wetlands. These processes persist despite low sulfate concentrations and significantly influence iron reduction, carbon mineralization, and methane dynamics. The results show that cryptic sulfur cycling enhances dissolved Fe 2+ production, regulates methane concentrations, and is strongly controlled by climate-driven hydrologic fluxes. By linking hydroclimate, subsurface flow, and biogeochemical reactions, this work provides a predictive framework for understanding how wetland systems respond to environmental change, with direct implications for water quality and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗