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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Iron rescues glucose-mediated photosynthesis repression during lipid accumulation in the green alga Chromochloris zofingiensis

Energy status and nutrients regulate photosynthetic protein expression. The unicellular green alga Chromochloris zofingiensis switches off photosynthesis in the presence of exogenous glucose (+Glc) in a process that depends on hexokinase (HXK1). Here, we show that this response requires that cells lack sufficient iron (–Fe). Cells grown in –Fe+Glc accumulate triacylglycerol (TAG) while losing photosynthesis and thylakoid membranes. However, cells with an iron supplement (+Fe+Glc) maintain photosynthesis and thylakoids while still accumulating TAG. Proteomic analysis shows that known photosynthetic proteins are most depleted in heterotrophy, alongside hundreds of uncharacterized, conserved proteins. Photosynthesis repression is associated with enzyme and transporter regulation that redirects iron resources to (a) respiratory instead of photosynthetic complexes and (b) a ferredoxin-dependent desaturase pathway supporting TAG accumulation rather than thylakoid lipid synthesis. Combining insights from diverse organisms from green algae to vascular plants, we show how iron and trophic constraints on metabolism aid gene discovery for photosynthesis and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhanced Power Grid Maintenance Planning and Quantum-Inspired Combinatorial Prospects

Efficient and reliable scheduling of maintenance for power generation and transmission infrastructure is essential for minimizing operational costs and ensuring grid stability. This paper introduces an integrated optimization framework for coordinated maintenance scheduling of generators and transmission lines under resource and reliability constraints. The model minimizes a composite cost function including maintenance and generation costs, as well as penalties for delayed maintenance, while satisfying N−1 security constraints, operational limits, and crew availability. Case studies on the IEEE 300-bus test system demonstrate the effectiveness of the proposed approach in producing feasible and cost-effective maintenance schedules. To address scalability and combinatorial complexity, the model is mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling exploration of solution approaches based on Quantum Imaginary Time Evolution (QITE). While the QUBO reformulation provides a foundation for future quantum-inspired optimization, this study focuses primarily on the development and demonstration of the classical optimization framework and illustrates the potential applicability of QITE in large-scale maintenance scheduling.

Chen, Yang [ORNL] (ORCID:0000000271693874)↗
DOE OSTI2026

Influence of Powder Characteristics and Processing Methods on Creep Performance of Powder Metallurgy Hot Isostatic Pressed SS316

The U.S. nuclear energy expansion goals are driving the demand for manufacturing routes that can rapidly produce large, complex, near net shape components. Powder metallurgy hot isostatic pressing (PM HIP) is an advanced manufacturing technique that can be economically scaled-up, while alleviating the supply chain challenges that forging and casting face in terms of cost and lead time constraints. This makes PM-HIP a viable technology to aid and accelerate large-scale part manufacturing for nuclear applications. However, large-scale qualification and deployment of this technology require a thorough understanding of the influence of powder feedstock quality, powder handling history, hot isostatic pressing (HIP) parameters, and subsequent heat treatment on microstructural evolution and elevated temperature mechanical performance. The present work focusses on 316 austenitic stainless steel (SS316) which is most commonly used in high temperature environments for nuclear applications Results from this study show that PM HIPed SS316 meets ASME tensile requirements at room temperature and at elevated temperature. However, creep performance of PM-HIPed SS316 remains inferior to its wrought counterpart, demonstrating that tensile performance alone is not a reliable metric for long duration high temperature integrity. Further, this report delineates powder derived microstructural features that govern creep damage, with key evidences pointing to “microstructural inheritance” from gas atomized powder feedstocks. Commercial SS316 powders of varying chemical compositions and recycling histories were studies, and the results showed large differences in elemental segregation, oxide surface layers and secondary phase distributions. Multi-scale characterization revealed segregation of chromium, molybdenum, manganese and silicon at the boundaries and the precipitation of manganese-, silicon-, and molybdenum-oxides. During HIP consolidation, these surface oxides transform into decorated prior particle boundaries (PPBs) and grain boundary inclusions that persist through conventional post-HIP solution annealing treatment. The retained oxides in post-HIP microstructures were found to influence grain growth, precipitation behavior, and ultimately creep cavitation and fracture. Such post-HIP heat treatments are therefore limited by a complex trade-off between grain growth, and oxide coarsening which aggravate creep damage by acting as nucleation sites for cavities. The objective of this work is to establish an integrated processing–structure–property framework for PM-HIP 316 stainless steel by investigating the influence of powder feedstock characteristics in pre- and post-HIP processing as well as to understand the significance of post-HIP heat treatment on microstructural evolution and creep properties. The results from this report emphasize the significance of powder feedstock integrity in improving creep performance of PM-HIPed SS316, by highlight the effect of rapid solidification, elemental segregation, oxide formation and powder recycling on microstructural defect inheritance following HIP consolidation. Rather than considering HIP processing, solution annealing, and mechanical performance independently, this report treats powder production, HIP consolidation, post-HIP thermal processing, and creep deformation as interconnected stages within a continuous metallurgical process. The resulting framework will provide a scientific basis for developing feedstock engineering strategies capable of improving long-term reliability of PM-HIP stainless steels and accelerating their qualification for advanced nuclear applications.

Ajjarapu, Pavan [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Utilizing Distributed Heterogeneous Computing with PanDA in ATLAS

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications’ nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Streaming Analytics for Anomaly Detection in Large-Scale Data

Anomalous behavior poses serious risks to assured performance and reliability of complex, high-consequence systems. For spaceborne assets and their state-of-health (SOH) telemetry, the challenges of high-dimensional data of varying data types are compounded by computational limitations from size, weight, and power (SWaP) constraints as well as data availability. Automated anomaly detection methods tend to perform poorly under these constraints, while current operational approaches can introduce delays in response time due to the manual, retrospective processes for understanding system failures. As a result, presently deployed space systems, and those deployed in the near future, face situations where mission operations might be delayed or only be able to operate under degraded capabilities. Here, we examine a near-term lightweight solution that provides real-time detection capabilities for rare events and assess state-of-the-art anomaly detection techniques against real SOH telemetry from space platforms. This report describes our methodology and research, which could support more automated capabilities for comprehensive space operations as well as for other resource-constrained edge applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On the distribution of stacking faults at dissociated medium-angle grain boundaries: Crystallographic geometry and metastability

Grain boundaries in FCC metals with low stacking-fault energy can form in dissociated configurations of stacking faults. Perhaps the most studied example is 9R stacking at boundaries near Σ3{112}, where the distribution of stacking faults is related to the emission of Shockley partial dislocations. Here, we combine atomic-resolution electron microscopy, atomistic simulations, and dislocation theory to demonstrate that boundaries vicinal to Σ33a support the stabilization of dissociated 9R stacking within a narrow range of inclinations. This boundary is interesting since its misorientation (20.05°) lies in the medium-angle regime, just past the upper misorientation limit for low-angle boundaries, motivating questions for how best to describe it in terms of dislocations. Our HAADF-STEM observations of thin film bicrystals, supported by atomistic modeling, reveal that this inclination dependence arises from specific geometric constraints on the arrangement of Shockley partial dislocations at the interface. Quantification of stacking-fault distributions across multiple boundaries indicates that the density and spacing of faults closely follow the ideal 9R motif, with subtle variations reflecting the complex energy landscape of these boundaries. Through energetic analysis, we establish the presence of competing metastable states enabled by variations in stacking sequences, emphasizing the significant role of crystallographic geometry. We generalize our analysis as a function of misorientation, showing how 9R at the Σ33a boundary is related to previous observations and calculations of HCP at a 29.7° boundary. This study provides a crystallographically grounded framework connecting dislocation structures, stacking-fault distributions, and metastability at grain boundaries in FCC metals.

Atomistic modeling↗
DOE OSTI2024

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗
DOE OSTI2025

Multi-Resolution UAV Path Replanning for Inspection of Tailings Dams

Autonomous inspection of large and complex structures with a commercial unmanned aerial vehicle (UAV) is a challenging problem that has been addressed in recent years. In this paper, we address the global motion planning problem of creating autonomous inspection missions for UAVs considering photogrammetry constraints. We focus on the inspection of large tailings dams, which are dam structures used to store waste byproducts of mining. Our method uses a prior sparse point cloud of the dam to generate a voxel grid, where paths satisfying photogrammetry constraints are tested for collisions. We then apply the A* algorithm as a local planner to avoid obstacles within the global mission. Moreover, we address the problem of changing routes online by using octree-based multi-resolution grids for efficient and fast pathfinding. Our results, obtained using tridimensional maps of an actual coal mine tailings dam, show that using octrees for multi-resolution motion planning is faster than using a fixed voxel grid in online missions while inspecting large structures.

42 ENGINEERING↗
DOE OSTI2025

Spectral Clustering-Based Partitioning of Large-Scale Power Electronics-Based Power Systems for Small-Signal Stability Analysis

The nodal admittance matrix (NAM)-based approach is well-suited for small-signal stability analysis of large-scale power electronics-based power systems (PEPSs), as it preserves the system structure through its admittance matrix. Previous studies have explored partitioning such systems into subareas and interconnections to reduce computational burden; however, they lacked a formal algorithmic procedure for determining feasible partitions. While several grid partitioning methods, such as those based on graph theory or machine learning, exist in the literature, they cannot be directly applied to NAM-based analysis due to differing objectives and constraints. Here, this paper addresses this gap by presenting a systematic, step-by-step procedure for applying a spectral partitioning algorithm that yields a division of the system into subareas suitable for NAM-based analysis. The computational complexity of the proposed method is also derived to demonstrate its efficiency and justify the practicality of the resulting subarea decomposition. The performance of the partitioning method is evaluated by applying the spectral clustering-derived subareas and interconnections to the NAM-based partitioning approach on a 140-bus system. Computational times for the full-system and partitioned NAM analyses are compared using MATLAB. Additionally, PSCAD simulations of the complete system and partitioned subareas are carried out to verify the effectiveness of the proposed method.

Nupur [Univ. of Tennessee, Knoxville, TN (United S↗
DOE OSTI2025

Full-sky Models of Galactic Microwave Emission and Polarization at Subarcminute Scales for the Python Sky Model

Polarized foreground emission from the Galaxy is one of the biggest challenges facing current and upcoming cosmic microwave background (CMB) polarization experiments. We develop new models of polarized Galactic dust and synchrotron emission at CMB frequencies that draw on the latest observational constraints; that employ the “polarization fraction tensor” framework to couple intensity and polarization in a physically motivated way; and that allow for stochastic realizations of small-scale structure at subarcminute angular scales currently unconstrained by full-sky data. We implement these models into the publicly available Python Sky Model (PySM) software and additionally provide PySM interfaces to select models of dust and CO emission from the literature. We characterize the behavior of each model by quantitatively comparing it to observational constraints in both maps and power spectra, demonstrating an overall improvement over previous PySM models. Finally, we synthesize models of the various Galactic foreground components into a coherent suite of three plausible microwave skies that span a range of astrophysical complexity allowed by current data. Author contributions to this paper can be found at the end of this work.

Group, The Pan-Experiment Galactic Science↗
DOE OSTI2026

Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism

Microbial metabolism is a complex, emergent system driven by the coordinated interplay of intricate and dynamic molecular processes. To elucidate cellular behavior and enable biotechnological applications, quantitative models that address the inherent complexity of metabolism have been developed from a resource allocation perspective. Here, we synthesize recent advances in coarse-grained resource allocation frameworks and their applications in understanding microbial physiology and guiding gene circuit design. Here, these frameworks reveal global regulatory constraints and predict cellular adaptation to nutrient and environmental changes. In addition, they enable the quantification of metabolic costs, the dissection of circuit–host interactions, and the development of strategies for burden mitigation. Collectively, these modeling frameworks provide a powerful platform for uncovering quantitative principles of microbial growth and engineering robust synthetic biological systems.

coarse-grained modeling↗
DOE OSTI2024

Orbital-Free Quantum Simulation Methods for Application to Warm Dense Matter (Final Technical Report)

Predictive simulations for prediction of condensed system behavior in state conditions far from ambient is increasingly crucial to DOE priorities. Warm dense matter (WDM) is the paradigm: temperature T > 1-15 eV, pressures P to 1 Mbar or greater. Experiments under such state conditions are difficult and costly. We summarize work driven by the need and opportunity to make free-energy density functional theory (DFT) as powerful a tool for ab initio simulation of matter under such extreme conditions as ground state DFT is for ordinary matter Advancing orbital-free DFT (OF-DFT) to eliminate the Kohn-Sham (KS) scaling bottleneck in such simulations is the other priority. The concurrent challenge for both goals is the intrinsic complexity of WDM. We summarize 15 years of successes and major progress on (1) free energy exchange-correlation functionals; (2) non-interacting free energy functionals (counterpart to T=0 Kohn-Sham kinetic energy density functionals); (3) rigorous results and constraints for free-energy DFT; (4) software for free energy DFT calculations in both conventional Kohn-Sham and OF-DFT form; (5) de-orbitalization of advanced orbital-dependent ground state functionals for use in OF-DFT; (6) demonstration calculations; (7) ancillary achievements (e.g. major review articles, secondary explorations motivated by primary goals).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum Computing in Next-Generation Transportation Optimization

We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time, data-rich environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are presented and new formulations are proposed to demonstrate how emerging quantum algorithms, including quantum annealing and the Quantum Approximation Optimization Algorithm, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss recent trends that indicate when QC may become a viable tool for transportation applications. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Probing exciton diffusion dynamics in photosynthetic supercomplexes via exciton–exciton annihilation

Photosynthesis converts solar energy into chemical energy through coordinated energy transfer between light-harvesting complexes and reaction centers (RCs). Understanding exciton motion, particularly the exciton diffusion length, is essential for optimizing energy efficiency in photosystems. In this work, we combine intensity-cycling transient absorption spectroscopy with kinetic Monte Carlo (kMC) simulation to investigate exciton motion in the C2S2 photosystem II supercomplex of spinach. Using exciton–exciton annihilation, revealed in the fifth-order response, we experimentally estimate an exciton diffusion length of 10.9 nm based on a 3D normal diffusion model, suggesting the ability of excitons to traverse the supercomplex. However, kMC simulations reveal that exciton motion is sub-diffusive because of spatial constraints and the strong RC traps. An anomalous diffusion model analysis of the experimental data yields a diffusion length of 9.7 nm, while the simulated diffusion length is 7.4 nm. The variable exciton residence time across subunits, partly influenced by their connectivity to the trap, indicates inhomogeneous annihilation probability and suggests how plants balance efficient light harvesting with photoprotection. We also explore the influence of specific assumptions in the annihilation simulation, which are challenging to access in more complex environments, such as the thylakoid membrane. Our study provides a framework for studying exciton dynamics using exciton–exciton annihilation, which can be extended to understand the light-harvesting efficiencies of larger, more complex photosynthetic assemblies.

Zhang, Kunyan (ORCID:000000026830409X)↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2024

Atacama Large Aperture Submillimeter Telescope (AtLAST) science: Resolving the hot and ionized Universe through the Sunyaev-Zeldovich effect

An omnipresent feature of the multi-phase “cosmic web” — the large-scale filamentary backbone of the Universe — is that warm/hot (≳ 10 5 K) ionized gas pervades it. This gas constitutes a relevant contribution to the overall universal matter budget across multiple scales, from the several tens of Mpc-scale intergalactic filaments, to the Mpc intracluster medium (ICM), all the way down to the circumgalactic medium (CGM) surrounding individual galaxies, on scales from ~ 1 kpc up to their respective virial radii (~ 100 kpc). The study of the hot baryonic component of cosmic matter density represents a powerful means for constraining the intertwined evolution of galactic populations and large-scale cosmological structures, for tracing the matter assembly in the Universe and its thermal history. To this end, the Sunyaev-Zeldovich (SZ) effect provides the ideal observational tool for measurements out to the beginnings of structure formation. The SZ effect is caused by the scattering of the photons from the cosmic microwave background off the hot electrons embedded within cosmic structures, and provides a redshift-independent perspective on the thermal and kinematic properties of the warm/hot gas. Still, current and next-generation (sub)millimeter facilities have been providing only a partial view of the SZ Universe due to any combination of: limited angular resolution, spectral coverage, field of view, spatial dynamic range, sensitivity, or all of the above. In this paper, we motivate the development of a wide-field, broad-band, multi-chroic continuum instrument for the Atacama Large Aperture Submillimeter Telescope (AtLAST) by identifying the scientific drivers that will deepen our understanding of the complex thermal evolution of cosmic structures. On a technical side, this will necessarily require efficient multi-wavelength mapping of the SZ signal with an unprecedented spatial dynamic range (from arcsecond to degree scales) and we employ detailed theoretical forecasts to determine the key instrumental constraints for achieving our goals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Recent Advances in Immobilizing and Benchmarking Molecular Catalysts for Artificial Photosynthesis

Transition metal complexes have been widely used as catalysts or chromophores in artificial photosynthesis. Traditionally, they are employed in homogeneous settings. Despite their functional versatility and structural tunability, broad industrial applications of these catalysts are impeded by the limitations of homogeneous catalysis such as poor catalyst recyclability, solvent constraints (mostly organic solvents), and catalyst durability. Over the past few decades, researchers have developed various methods for molecular catalyst heterogenization to overcome these limitations. Here, in this review, we summarize recent developments in heterogenization strategies, with a focus on describing methods employed in the heterogenization process and their effects on catalytic performances. Alongside the in-depth discussion of heterogenization strategies, this review aims to provide a concise overview of the key metrics associated with heterogenized systems. We hope this review will aid researchers who are new to this research field in gaining a better understanding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗