SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Appendix 4 of the Study of One- and Two-Dimensional Filtering and Deconvolution Algorithms for a Streaming Array Computer discusses coordinate axes, location of origin, and redundancy for the one- and two-dimensional Fourier transform for complex and real data.
It is well known that all linear time-invariant controllable systems can be transformed to Brunovsky canonical form by a transformation consisting only of coordinate changes and linear feedback. However, the actual procedures for doing this have tended to be overly complex. The technique introduced here is envisioned as an on-line procedure and is inspired by George Meyer's tangent model for nonlinear systems. The process utilizes Meyer's block triangular form as an intermedicate step in going to Brunovsky form. The method also involves orthogonal matrices, thus eliminating the need for the computation of matrix inverses. In addition, the Kronecker indices can be computed as a by-product of this transformation so it is necessary to know them in advance.
A numerical simulation of the incompressible viscous flow through a prosthetic tilting disk heart valve is presented in order to demonstrate the current capability to model unsteady flows with moving boundaries. Both steady and unsteady flow calculations are performed by solving the incompressible Navier-Stokes equations in three-dimensional generalized curvilinear coordinates. In order to handle the moving boundary problems, the chimera grid embedding scheme which decomposes a complex computational domain into several simple subdomains is used. An algebraic turbulence model for internal flows is incorporated to reach the physiological values of Reynolds number. Good agreement is obtained between the numerical results and experimental measurements. It is found that the tilting disk valve causes large regions of separated flow, and regions of high shear.
A numerical simulation of the incompressible viscous flow through a prosthetic tilting disk heart valve is presented in order to demonstrate the current capability to model unsteady flows with moving boundaries. Both steady state and unsteady flow calculations are done by solving the incompressible Navier-Stokes equations in 3-D generalized curvilinear coordinates. In order to handle the moving boundary problems, the chimera grid embedding scheme which decomposes a complex computational domain into several simple subdomains is used. An algebraic turbulence model for internal flows is incorporated to reach the physiological values of Reynolds number. Good agreement is obtained between the numerical results and experimental measurements. It is found that the tilting disk valve causes large regions of separated flow, and regions of high shear.
The regulatory process for licensing mobile satellite systems is complex and can require many years to complete. This process involves frequency allocations, national licensing, and frequency coordination. The regulatory process that resulted in the establishment of the radiodetermination satellite service (RDSS) between 1983 and 1987 is described. In contrast, each of these steps in the licensing of the mobile satellite service (MSS) is taking a significantly longer period of time to complete.
This report is concerned with agents that act to gain information. In previous work, we developed agent models combining qualitative modeling with real-time control. That work, however, focused primarily on actions that affect physical states of the environment. The current study extends that work by explicitly considering problems of active information-gathering and by exploring specialized aspects of information-gathering in computational perception, learning, and language. In our theoretical investigations, we analyzed agents into their perceptual and action components and identified these with elements of a state-machine model of control. The mathematical properties of each was developed in isolation and interactions were then studied. We considered the complexity dimension and the uncertainty dimension and related these to intelligent-agent design issues. We also explored active information gathering in visual processing. Working within the active vision paradigm, we developed a concept of 'minimal meaningful measurements' suitable for demand-driven vision. We then developed and tested an architecture for ongoing recognition and interpretation of visual information. In the area of information gathering through learning, we explored techniques for coping with combinatorial complexity. We also explored information gathering through explicit linguistic action by considering the nature of conversational rules, coordination, and situated communication behavior.
Remote monitoring of crew, spacecraft, and environmental health has always been an integral part of the National Aeronautics and Space Administration's (NASA's) operations. Crew safety and mission success face a number of challenges in outerspace, including physiological adaptations to microgravity, radiation exposure, extreme temperatures and vacuum, and psychosocial reactions to space flight. The NASA effort to monitor and maintain crew health, system performance, and environmental integrity in space flight is a sophisticated and coordinated program of telemedicine combining cutting-edge engineering with medical expertise. As missions have increased in complexity, NASA telemedicine capabilities have grown apace, underlying its role in the field. At the same time, the terrestrial validation of telemedicine technologies to bring healthcare to remote locations provides feedback, improvement, and enhancement of the space program. As NASA progresses in its space exploration program, astronauts will join missions lasting months, even years, that take them millions of miles from home. These long-duration missions necessitate further technological breakthroughs in tele-operations and autonomous technology. Earth-based monitoring will no longer be real-time, requiring telemedicine capabilities to advance with future explorers as they travel deeper into space. The International Space Station will serve as a testbed for the telemedicine technologies to enable future missions as well as improve the quality of healthcare delivery on Earth.
The Airspace Operations Laboratory at NASA Ames conducts research to provide a better understanding of roles, responsibilities, and requirements for human operators and automation in future air traffic management (ATM) systems. The research encompasses developing, evaluating, and integrating operational concepts and technologies for near-, mid-, and far-term air traffic operations. Current research threads include efficient arrival operations, function allocation in separation assurance and efficient airspace and trajectory management. The AOL has developed powerful air traffic simulation capabilities, most notably the Multi Aircraft Control System (MACS) that is used for many air traffic control simulations at NASA and its partners in government, academia and industry. Several additional NASA technologies have been integrated with the AOL's primary simulation capabilities where appropriate. Using this environment, large and small-scale system-level evaluations can be conducted to help make near-term improvements and transition NASA technologies to the FAA, such as the technologies developed under NASA's Air Traffic Management Demonstration-1 (ATD-1). The AOL's rapid prototyping and flexible simulation capabilities have proven a highly effective environment to progress the initiation of trajectory-based operations and support the mid-term implementation of NextGen. Fundamental questions about accuracy requirements have been investigated as well as realworld problems on how to improve operations in some of the most complex airspaces in the US. This includes using advanced trajectory-based operations and prototype tools for coordinating arrivals to converging runways at Newark airport and coordinating departures and arrivals in the San Francisco and the New York metro areas. Looking beyond NextGen, the AOL has started exploring hybrid human/automation control strategies as well as highly autonomous operations in the air traffic control domain. Initial results indicate improved capacity, low operator workload, good situation awareness and acceptability for controllers teaming with autonomous air traffic systems. While much research and development needs to be conducted to make such concepts a reality, these approaches have the potential to truly transform the airspace system towards increased mobility, safe and efficient growth in global operations and enabling many of the new vehicles and operations that are expected over the next decades. This paper describes how the AOL currently contributes to the ongoing air transportation transformation.
A key goal of the Solar Orbiter mission is to connect elemental abundance measurements of the solar wind enveloping the spacecraft with extreme-UV (EUV) spectroscopic observations of their solar sources, but this is not an easy exercise. Observations from previous missions have revealed a highly complex picture of spatial and temporal variations of elemental abundances in the solar corona. We have used coordinated observations from Hinode and Solar Orbiter to attempt new abundance measurements with the Spectral Imaging of the Coronal Environment (SPICE) instrument, and benchmark them against standard analyses from the EUV Imaging Spectrometer (EIS). We use observations of several solar features in active region (AR) 12781 taken from an Earth-facing view by EIS on 2020 November 10, and SPICE data obtained one week later on 2020 November 17, when the AR had rotated into the Solar Orbiter field of view. We identify a range of spectral lines that are useful for determining the transition region and low-coronal-temperature structure with SPICE, and demonstrate that SPICE measurements are able to differentiate between photospheric and coronal magnesium/neon abundances. The combination of SPICE and EIS is able to establish the atmospheric composition structure of a fan loop/outflow area at the AR edge. We also discuss the problem of resolving the degree of elemental fractionation with SPICE, which is more challenging without further constraints on the temperature structure, and comment on what that can tell us about the sources of the solar wind and solar energetic particles.
Increasing need for distribution circuit reliability and resilience prompt a clearer understanding of community microgrid interconnection and operational requirements. While customer-owned, behind-the-meter microgrid implementation is not always simple, increasing demand for reliability has commercialized many installation approaches. This rapid commercialization has broadened industry understanding of single-customer microgrid integration. Moreover, off-grid behaviors of such microgrids are not required to meet utility standard interconnection requirements. However, multi-customer community microgrids (utility- or third-party operated), are less well explored and introduce more complex design and operational challenges. The control of DER along with distribution automation devices, unmanned operation, protection coordination, and other aspects makes community microgrids challenging to implement and operate in both on-grid and off-grid modes. This whitepaper is a technical primer for understanding community microgrids. It provides a foundation for understanding the components, configurations, design, and operational considerations for community microgrids.
Iron redox flow batteries (Fe-RFBs) hold significant promise for achieving cost-effectiveness and utilizing abundant materials for stationary energy storage applications. Here, a design of a novel Fe complex utilizing a nitrogenous phosphonate/carboxylate mixed ligand, N,N-Bis(phosphonomethyl)glycine (BMPG), is presented to achieve high performance Fe anolyte. Compared to its all-phosphonate form, nitrilotri(methylphosphonic acid) (NTMPA), the new complex Fe(BPMG) 2 demonstrates a negatively shifted redox potential, resulting in ≈0.07 V (≈10%) increase in battery output voltage. Full battery testing paired with ferrocyanide catholyte demonstrates stable cycling (capacity degradation <0.0001%/cycle) over 730 consecutive charge/discharge cycles with Coulombic Efficiency of 100% at a current density of 20 mA cm -2 under near neutral pH (≈8). Of particular interest, density functional theory (DFT) studies and operando Raman measurements provide strong evidence supporting a molecular structure in BPMG, which reveals the mixed phosphonate/carboxylate groups in BPMG maintain the octahedral coordination of the Fe ion center with phosphonates exclusively, while leaving the carboxylate unbound for both Fe(II) and Fe(III) complexes. This structural similarity between BPMG-based Fe(II) and Fe(III) complexes effectively mitigates the slow redox reaction kinetics observed in Fe(NTMPA) 2 anolyte, where significant ligand reorientation occurs between Fe(II) and Fe(III) complexes.
Here uranium complexes ( Mes DAE) 2 U(THF) ( 1-DAE ) and Cp 2 U( Mes DAE) ( 2-DAE ) ( Mes DAE = [ArN-CH 2 CH 2 -NAr]; Ar = 2,4,6-trimethylphenyl (Mes)), bearing redox-innocent diamide ligands, have been synthesized and characterized for a full comparison with previously published, redox-active diimine complexes, ( Mes DAB Me ) 2 U(THF) ( 1-DAB ) and Cp 2 U( Mes DAB Me ) ( 2-DAB ) ( Mes DAB Me = [ArN=C(Me)C(Me)=NAr]; Ar = Mes). These redox-innocent analogues maintain an analogous steric environment to their redox-active ligand counterparts to facilitate a study aimed at determining the differing electronic behavior around the uranium center. Structural analysis by X-ray crystallography showed 1-DAE and 2-DAE have a structural environment very similar to 1-DAB and 2-DAB , respectively. The main difference occurs with coordination of the ene-backbone to the uranium center in the latter species. Electronic absorption spectroscopy reveals these new DAE complexes are nearly identical to each other. X-ray absorption spectroscopy suggests all four species contain +4 uranium ions. The data also indicates that there is an electronic difference between the bis(diamide)-THF uranium complexes as opposed to those that only contain one diamide and two cyclopentadienyl rings. Finally, magnetic measurements reveal that all complexes display temperature-dependent behavior consistent with uranium(IV) ions that do not include ligand radicals. Overall, this study determines that there is no significant bonding difference between the redox-innocent and redox-active ligand frameworks on uranium. Furthermore, there are no data to suggest covalent bonding character using the latter ligand framework on uranium, despite what is known for transition metals.
A suitable grid system for complex three dimensional configurations such as wing/body/nacelle shape for the solution of nonlinear transonic flow problems was constructed. Two approaches were explored based on Thompson's body fitted coordinate concept. The most general approach was to divide the computational domain into multiple rectangular blocks where the configuration itself was also represented by a set of blocks whose structure follows the natural lines of the configuration. The block structured grid system was adaptable to complex configurations and gives good grid quality near physical corners. However, it introduced algorithm issues for the flow solution concerning the treatment of nonanalytic grid block boundaries and nonstandard grid cells. These issues were explored in relation to the grid generation. A more limited approach treats a wing/body configuration with only a single rectangular block in computational space. The issues involving nonstandard cells were avoided, but other limitations on grid resolution appear. Both a linear and a nonlinear system of grid generation equations were developed including methods of grid control.
Block copolymer electrolytes (BCE) such as polystyrene-block-poly(ethylene oxide) (SEO) blended with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and composed of mechanically robust insulating and rubbery conducting nanodomains are promising solid-state electrolytes for Li batteries. Here, we compare ionic solvation, association, distribution, and conductivity in SEO-LiTFSI BCEs and their homopolymer PEO-LiTFSI analogs toward a fundamental understanding of the maximum in conductivity and transport mechanisms as a function of salt concentration. Ionic conductivity measurements reveal that SEO-LiTFSI and PEO-LiTFSI exhibit similar behaviors up to a Li/EO ratio of 1/12, where roughly half of the available solvation sites in the system are filled, and conductivity is maximized. As the Li/EO ratios increase to 1/5 the conductivity, of the PEO-LiTFSI drops nearly 3-fold, while the conductivity of SEO-LiTFSI remains constant. FTIR spectroscopy reveals that additional Li cations in the homopolymer electrolyte are complexed by additional EO units when the Li/EO ratio exceeds 1/12, while in the BCE, the proportion of complexed and uncomplexed EO units remains constant; Raman spectroscopy data at the same concentrations show that Li cations in the SEO-LiTFSI samples tend to coordinate more to their counteranions. Atomistic-scale molecular dynamics simulations corroborate these results and further show that associated ions tend to segregate to the SEO-LiTFSI domain interfaces. The opportunity for "excess" salt to be sequestered at BCE interfaces results in the retention of an optimum ratio of uncompleted and complexed PEO solvation sites in the middle of the conductive nanodomains of the BCE and maximized conductivity over a broad range of salt concentrations.
Here, we provide an experimental confirmation of the suggestion, based on thermodynamic simulations and extrapolations (Zhong et al. 2015), that Zn is transported in the form of chloride complexes in most shallow hydrothermal systems, while bisulfide complexes become increasingly important in deep hydrothermal systems. We used in situ X-ray absorption spectroscopy (XAS) diamond anvil cell experiments to determine Zn(II) speciation in a 1 m NaHS + 0.2 m HCl solution in contact with sphalerite. XANES data clearly show that Zn coordinates to chloride and/or oxygen ligands from room temperature up to and including 200 C, and then at higher temperatures (≥ 300 C) and pressures (> 2 kbar) changes to complexing with sulfur. Our data confirm that bisulfide complexes become increasingly important at high pressure and temperature, due to an increase in sulfur solubility and to favorable entropy contributions for bisulfide versus chloride complexes.
Here, three new uranium species (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(=S), (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(η 2 -S 2 ), and (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(S=PMe 3 ) were synthesized and fully characterized by a combination of NMR, IR, and UV/vis–NIR spectroscopies, elemental analysis, and cyclic voltammetry. The solid state structures of (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(=S) and (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(η 2 -S 2 ) were also determined. The compound (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(=S) is the first neutral uranium complex with a terminal sulphido ligand, and (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(S=PMe 3 ) is the first uranium compound with a coordinated phosphine sulphide ligand. The phosphine sulphide adduct, (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(S=PMe 3 ), can be synthesized either by reaction of the uranium(IV) complex (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(thf) with S=PMe 3 or by the reaction of the uranium(VI) terminal sulphido complex (C 5 Me 5 ) 2 U(=N-2,6- i Pr 2 -C 6 H 3 )(=S) with PMe 3 .
The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.
Abstract Whole‐plant hydraulic performance depends on the integrated function of trait complexes, such as embolism resistance, stomatal closure mechanisms, and root properties. The diversity of such traits produces a wide range of response strategies to both short‐term variation of environmental conditions and long‐term changes to climate and hydrological cycles. This study aims to assess the role of different emergent hydraulic trait combinations in trees' vulnerability to drought using a quantitative modelling framework. This modelling framework may be helpful in studying the influence of plant hydraulic traits independently. It will also be used to assess how different groups of traits interact to form viable hydraulic strategies in response to different environmental conditions. We use the advanced plant hydrodynamic model, finite‐difference ecosystem‐scale tree crown hydrodynamics model version 2, to resolve plant functional traits of roots, stems, and leaves and simulate the transpiration. We define a multidimensional hydraulic “strategy space” by considering a continuum of these traits to test the outcomes of different hypothetical strategies in response to environmental conditions as observed in Northern Michigan, USA. We evaluate the degree to which simulated trees suffer hydraulic failure due to cavitation or carbon deficiency in response to reduced stomatal conductance. Our results demonstrate how the relationship between plant hydraulic strategy and hydraulic safety margin emerges from trait combinations along different tissue levels. Our findings suggest that hydrodynamic models present an exciting new possibility to define and study plant traits and hydrodynamics.