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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2026

Engineering Against Digital Risk in CIP Applications: Cyber-Informed Engineering Use Cases

Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This presentation discusses engineered controls of 7 categories and the CIE database of controls that provides clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Control Selection for the Neutralization Tank in the Aqueous Recovery System at the Savannah River Plutonium Processing Facility

The Aqueous Recovery System (ARS) at the Savannah River Plutonium Processing Facility (SRPPF) recovers and purifies plutonium (Pu) using aqueous chemistry techniques. The neutralization tanks in the ARS collect waste streams with various impurities and greatly reduced concentrations of Pu than are produced during aqueous processing. Criticality safety is primarily achieved in the ARS by using geometrically favorable process vessels. However, due to the geometry of neutralization tanks, each tank is administratively limited to no more than 450 g Pu, thus representing the transition from geometry to mass control. To ensure that this mass limit is not exceeded, an administrative control requires taking two samples of any solution to be sent to a neutralization tank. Normal operations are expected to result in less than 50 g Pu in a neutralization tank filled to its capacity of 250 L. Process upsets may cause an inadvertent transfer of up to 1000 g Pu to a neutralization tank, producing a system that is potentially not subcritical for all possible configurations of the tank. To ensure that a neutralization tank remains safety subcritical, passive engineered controls (e.g. changing tank geometry or adding fixed poisons), active engineered controls (e.g. interlocks), and administrative controls (e.g. soluble poisons and valve isolation) were considered and their viability evaluated. Ultimately, the team chose an administrative, dual-valve isolation strategy. This paper will thoroughly discuss the various control strategy options and why many of the options were not feasible for maintaining criticality safety.

Dressman, Phillip M. [Savannah River Nuclear Solut↗
DOE OSTI2025

Control Selection for the Neutralization Tank in the Aqueous Recovery System at the Savannah River Plutonium Processing Facility

The Aqueous Recovery System (ARS) at the Savannah River Plutonium Processing Facility (SRPPF) recovers and purifies plutonium (Pu) using aqueous chemistry techniques. The neutralization tanks in the ARS collect the waste streams, containing various impurities and greatly reduced concentrations of Pu, that are produced during aqueous processing. Criticality safety is primarily achieved in the ARS by using geometrically favorable process vessels. However, due to the geometry of neutralization tanks, each tank is administratively limited to no more than 450 g Pu, thus representing the transition from geometry to mass control. To ensure that this mass limit is not exceeded, an administrative control requires taking two samples of any solution to be sent to a neutralization tank. Normal operations are expected to result in less than 50 g Pu in a neutralization tank filled to its capacity of 250 L. Process upsets may cause an inadvertent transfer of up to 1000 g Pu to a neutralization tank, producing a system that is potentially not subcritical for all possible configurations of the tank. To ensure that a neutralization tank remains safety subcritical, passive engineered controls (e.g.changing tank geometry or adding fixed poisons), active engineered controls (e.g. interlocks), and administrative controls (e.g. soluble poisons and valve isolation) were considered and their viability evaluated. Ultimately, the team chose an administrative, dual-valve isolation strategy. This paper will thoroughly discuss the various control strategy options and why many of the options were not feasible for maintaining criticality safety.

Dressman, Phillip M. [Savannah River Nuclear Solut↗
DOE OSTI2024

Digital Twin + AI: Control Room of the Future [Slides]

The control room functions as the central brain of the grid, essential for balancing supply and demand and ensuring moment-to-moment grid reliability. Like the human brain, which processes sensory data to make decisions, control room operators analyze operational data from power generation, transmission, and distribution to make informed decisions. Currently, decision-making primarily rests with operators due to hardware and software limitations. However, with technological advancements, Digital Twins and AI are becoming high interest points in the control room's decision-making pilot programs. NREL is developing a comprehensive decision-making platform that integrates Digital Twins, AI, and advanced visualization techniques. As this integration progresses, the role of Digital Twins will evolve from conducting automated simulations to serving as a Trustworthy AI enabler, offering verification and validation of AI-generated response for power systems or providing physics-aware synthetic data of AI pre-training.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2025

Epitaxial Growth of 2D Core‐Crown SnS 2 /SnSe 2 Heterostructure Through Interfacial Modification with Polyvinylpyrrolidone

Abstract Developing generalized strategies for controlled synthesis of 2D heterostructures remains a significant challenge because the existing approaches often suffer from poor reproducibility and scalability. In this study, a solution synthesis approach for epitaxial core‐crown heterostructures with controlled band alignment, that overcomes these challenges is reported. Polyvinylpyrrolidone (PVP) is used as a structure‐directing agent to reduce lattice mismatch between SnS 2 and SnSe 2 (10‐10) surfaces and direct epitaxial growth of SnSe 2 crown on SnS 2 seed. Additionally, PVP adsorption to the basal plane prevents van der Waals stacking and stabilizes 2D heterostructures during synthesis. Driven by interfacial thermodynamics, the formation of the core‐crown heterostructure is highly reproducible and the size of the 2D heterostructure and relative areas of the core and the crown can be precisely controlled in a two‐step process by varying synthesis times for the seed and the crown. The identified growth pathway for 2D heterostructures can be generalized to other combinations of van der Waals materials to provide a platform for synthesizing micron‐size epitaxial heterostructures with a desired electronic structure for catalysis and microelectronics.

Liu, Lili [Physical and Computational Sciences Dir↗
DOE OSTI2026

HRTEM Imaging and Mechanistic Insights Into Carbon Nanotube Nucleation and Growth on Fe Nanocatalysts in a Thermal Plasma

Thermal plasma decomposition of natural gas is a scalable pathway for the production of hydrogen alongside high-value carbon nanotubes (CNTs). Metals evaporate from an electrode and condense to form seed nanoparticles that nucleate and grow CNTs. However, the lack of mechanistic understanding of the CNT nucleation and growth processes in thermal plasma makes control over CNT diameter, chirality, length, and yield difficult. We debundled and separated CNTs from soots produced using iron (Fe) nanocatalysts, and distributed them on monolayer graphene for high-resolution transmission electron microscopy (HRTEM) imaging to gain mechanistic insights. Full graphene encapsulation was found for relatively small Fe nanoparticles that were molten at high temperatures. Zigzag single-wall or double-wall CNTs (SWCNTs or DWCNTs) appeared to have grown out directly from the graphene covering on the conical or cylindrical bodies of small molten Fe nanodroplets with high curvature. Also, SWCNTs likely grew out from H- or O-atom etched single-wall carbon nanocones observed on conical Fe nanoparticles. A SWCNT/DWCNT could also be generated from the cracked opening of the graphene covering on a face-centered cubic (FCC) Fe nanoparticle. A simple, plausible pathway is proposed for the growth of an open, H-passivated, zigzag SWCNT involving reaction of CH 2 and CH radicals at high temperatures.

Fe nanocatalysts↗
DOE OSTI2025

Friction Self-piercing Riveting Process for 3T Configuration of AA7075-AA7075-AA6022

Friction self-piercing riveting (F-SPR), as a solid-state joining process that generates frictional heat during the process, was utilized to join a 3 T configuration of low-ductility AA7075-AA7075-AA6022 sheets. A multi-step F-SPR process was employed to precisely control the mechanical interlocking and solid-state joining, ensuring a strong joint formation. With control of the plunge depth when the transition from rivet penetration to rivet flaring, two joining conditions were developed to assess mechanical joint integrity: one bearing deep penetration with a smaller interlock, and the other yielding a shallow penetration with a larger interlock. Both strategies met the criteria for a sound rivet joint. Customized lap shear tensile configurations were designed to assess the joint strength at interface between first and second layers, as well as between second and third layers. Both conditions demonstrated great joint strength between the first and second layers, while deeper penetration approach showed stronger solid-state joint, leading to improved joint strength between the second and third layers.

Wang, Tianzhao [ORNL] (ORCID:0000000315337602)↗
DOE OSTI2025

Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at s = 13 TeV with the ATLAS detector

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

Aad, G↗
DOE OSTI2024

Alloying Effects on the Transport Properties of Refractory High-entropy Alloys

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. Here we validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil

This study systematically investigates the enhancement of wear resistance in 3D printed surface textures through both experimental and theoretical approaches. Three distinct surface morphologies (Smooth Surface, Surface with uniformly distributed Pits, and Surface with uniformly distributed Bumps) were fabricated using High-Impact Polystyrene, where the meso-scale textures were precisely controlled through the 3D printing process. Wear behavior was evaluated using a 3-body wear tester in an abrasive particle environment, analyzing the influence of surface textures under various operating conditions. Systematic wear tests revealed that optimally designed surface textures achieved a remarkable 77 % reduction in wear compared to the worst-performing sample. The wear mechanisms were comprehensively characterized through weight loss measurements, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) analyses, elucidating the surface morphology changes and their interaction with wear particles. Notably, the study identified how the geometric characteristics of surface textures influence the movement of wear particles and the distribution of contact stresses. Discrete element method simulations corroborated the experimental findings, providing theoretical validation for the enhanced wear resistance of the optimal structure. The high correlation between simulated wear patterns and experimental results validates the reliability of the proposed design methodology. In conclusion, these results demonstrate that 3D printed surface texturing offers a cost-effective and scalable approach to significantly improve wear resistance in engineering applications, presenting a practical alternative to conventional, high-cost surface engineering methods.

3-body abrasion↗
DOE OSTI2024

Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Covalent Functionalization of Silicon with Plasma-Grown “Fuzzy” Graphene: Robust Aqueous Photoelectrodes for CO 2 Reduction by Molecular Catalysts

Carbon electrodes are ideal for electrochemistry with molecular catalysts, exhibiting facile charge transfer and good stability. Yet for solar-driven catalysis with semiconductor light absorbers, stable semiconductor/carbon interfaces can be difficult to achieve, and carbon’s high optical extinction means it can only be used in ultrathin layers. Here, we demonstrate a plasma-enhanced chemical vapor deposition process that achieves well-controlled deposition of out-of-plane “fuzzy” graphene (FG) on thermally oxidized Si substrates. The resulting Si|FG interfaces possess a silicon oxycarbide (SiOC) interfacial layer, implying covalent bonding between Si and the FG film that is consistent with the mechanical robustness observed from the films. The FG layer is uniform and tunable in thickness and optical transparency by deposition time. Using p-type Si|FG substrates, noncovalent immobilization of cobalt phthalocyanine (CoPc) molecular catalysts was employed for the photoelectrochemical reduction of CO 2 in aqueous solution. The Si|FG|CoPc photocathodes exhibited good catalytic activity, yielding a current density of ∼1 mA/cm 2 , Faradaic efficiency for CO of ∼70% (balance H 2 ), and stable photocurrent for at least 30 h at −1.5 V vs Ag/AgCl under 1-sun illumination. Furthermore, the results suggest that plasma-deposited FG is a robust carbon electrode for molecular catalysts and suitable for further development of aqueous-stable Si photocathodes for CO 2 reduction.

CO2 reduction↗
DOE OSTI2026

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

organic↗
DOE OSTI2024

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

58 GEOSCIENCES↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗