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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Effect of fluid ingestion on orthostatic responses following acute exercise

Orthostatic tolerance is impaired following an acute bout of exercise. This study examined the effect of fluid ingestion following treadmill exercise in restoring the cardiovascular responses to an orthostatic stress. Five men (age, 29.6 +/- 3.4 yrs) were exposed to a graded lower body negative (LBNP) pressure protocol (0 to -50 mmHg) during euhydration without exercise (C), 20 minutes after exercise dehydration (D), 20 minutes after exercise and fluid ingestion (FI20), and 60 minutes after exercise and fluid ingestion (FI60). Fluid ingestion (mean +/- SE) consisted of water-ingestion equivalent to 50% of the body weight lost during exercise (520 +/- 15 ml). Exercise dehydration resulted in significantly higher heart rates (119 +/- 8 vs 82 +/- 7 bpm), lower systolic blood pressures (95 +/- 1.7 vs 108 +/- 2.3 mmHg), a smaller increase in leg circumference (3.7 +/- 4 vs 6.9 +/- 1.0 mm), and an attenuated increase in total peripheral resistance (2.58 +/- 1.2 vs 4.28 +/- 0.9 mmHg/L/min) at -50 mmHg LBNP compared to the C condition. Fluid ingestion (both 20 and 60), partially restored the heart rate, systolic blood pressure, and total peripheral resistance responses to LBNP, but did not influence the change in leg circumference during LBNP (4 +/- 0.3 for R20 and 2.8 +/- 0.4 mm for R60). These data illustrate the effectiveness of fluid ingestion on improving orthostatic responses following exercise, and suggest that dehydration is a contributing factor to orthostatic intolerance following exercise.

NASA Center JSC↗
NASA NTRS2018

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

fluid lensing↗
NASA NTRSDate not supplied

Fluid System Stability Analysis Techniques

Fluid systems, or networks, consist of multiple components that work together to achieve some desired thermofluid state. For any generic application, this fluid state can be a combination of the fluid pressure, flow rate, enthalpy, or species concentration. Fluid system components, such as pumps and valves, are often governed by nonlinear differential equations, resulting in complex component-to-component interactions. System-level fluid network stability occurs when the flow through the system can maintain a steady-state solution in the presence of small perturbations, which depends on these component interactions. System instability, however, can go undetected until issues arise during integrated system testing. This presentation explores a method for system designers to think of the fluid network as an assembly of components, each with their own thermofluid surfaces of partial stability, called nullclines. The intersections of all nullclines yields system-level solutions, called equilibrium points. When designers define operating points, they are tuning system parameters so that these equilibrium points move to the desired location in the thermofluid state plane. However, linearization theory shows us that the dynamic behavior around these points can be unstable. The local stability of these equilibrium points can be assessed analytically with eigen-analysis, or numerically by propagating state-plane samples to construct a phase portrait. Investigating a phase portrait can help designers gain a qualitative understanding of a system’s dynamic performance. This understanding can then help inform requirement definitions, component selection, and operational procedures. This presentation includes an example of the phase portrait technique on a system featuring a centrifugal pump and a back-pressure regulator (BPR). Numerical modeling of this system suggests that equilibrium points on the left-hand side of the pump curve are dynamically unstable.

Fluid System↗
DOE OSTI2016

A Study of Physics-Informed Deep Learning for System Fluid Dynamics Closures

The sub-grid-scale (SGS) physics models, or so-called closure relations (CR), are essential in thermal-fluid modeling and simulation codes, ranging from large-eddy simulation to system-level thermo-fluid dynamic problems. While details vary, all thermo-fluid codes solve coupled mass-momentum-energy conservation partial differential equations (PDE). Traditionally, the analytical form of closure models has to be known, and then we calibrate the model using data from a set of applicable experiments. Characteristically, it takes long effort to gain insights and mechanistic understanding needed for developing closure models. Such a model is a compact form of data. The long time required for new model development limits the pace of model application while dealing with new geometries, new coolants or system conditions, particularly in newly designed systems. Alternative to mechanistic and semi-analytical models, it is possible to discover the pattern of closure relations by applying the modern machine learning methods to the body of relevant experimental data. Deep learning (DL) is a relatively new branch in the machine learning family, and a major breakthrough has been achieved since Hinton introduces a fast algorithm to efficiently train a feed-forward neural network (NN) with multilayer perceptrons. Generally, any NN with more than two layers is considered as deep learning, and it contains a large number of hyper-parameters to achieve pattern recognition or regression for complex datasets. Hornik shows that multilayer NN is compatible with universal approximation theorem. There is no constraint for a multilayer NN to capture the properties of any measurable information. This capability makes DL attractive for closure models in a system thermal-hydraulic code. As system geometry and/or operating conditions change, new physical experiments (increasingly sophisticated) are conducted to support evaluation and revision of closure models. Such a so-called data-driven modeling relaxes the structural form of system codes to directly assimilate data instead of hard-wiring the model to pre-calibrated correlations. The objective of this research is to enable cost-effective development of a DL-based closure that reflects the data. The previous work on applying machine learning techniques to fluid dynamics focused on statistical learning with basic NN from direct numerical simulations (DNS) dataset. In the present work, we show that DL-based closure models can cause the fluid calculation to diverge if multilayer NN is used. To address the issue, we investigate conditions by which the DL-based closure models work compatibly, stably, and effectively with PDE-constrained forward prediction problems. Optimally meeting the compatibility conditions constitutes a physics-informed deep learning (PIDL) strategy. We refer to this process as PDE-constrained machine learning or PDE-ML. To develop the technical basis for PDE-ML using DL-based fluid dynamics closure, numerical experiments are performed for manufactured problems of increasing complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Tabulated Fluid Properties Research Report

The Multiphysics Object-Oriented Simulation Environment (MOOSE) enables a wide range of advanced nuclear reactor simulations.[6] Under the guidance of MOOSE’s Thermal Hydraulics Team,I worked to expand the capabilities of Tabulated Fluid Properties (TFP) in the fluid properties module. The fluid properties module allows the user to determine a variety of fluid properties by interpolating points between tabulated data. I implemented the ability to use bilinear interpolation instead of bicubic interpolation for interpolating tabulated data. I also changed the method of variable set inversions to use a 2-dimensional Newton’s Method utility that I created. Variable set inversions are often done from (v,e) to (p,T), where v is specific volume, e is specific internal energy, p is pressure and T is temperature. New routines have also been added into TFP such that it can be used with more applications, such as the Navier Stokes and Thermal Hydraulics modules in MOOSE for Pronghorn[5] and RELAP-7[1] respectively. This work was spurred by interest from NASA in testing a Nuclear Thermal Propulsion (NTP) engine system. NTP engines have drastically different fluid properties throughout the engine and Tabulated Fluid Properties provides the flexibility needed to properly simulate and test these engines.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

An adaptive lattice Boltzmann scheme for modeling two-fluid-phase flow in porous medium systems

In this paper, we formulate a multiple-relaxation-time (MRT) lattice-Boltzmann method (LBM) to simulate two-fluid-phase flow in porous medium systems. The MRT LBM is applied to simulate the displacement of a wetting fluid by a nonwetting fluid in a system corresponding to a microfluidic cell. Analysis of the simulation shows widely varying time scales for the dynamics of fluid pressures, fluid saturations, and interfacial curvatures that are typical characteristics of such systems. Displacement phenomena include Haines jumps, which are relatively short duration isolated events of rapid fluid displacement driven by capillary instability. An adaptive algorithm is advanced using a level-set method to locate interfaces and estimate their rate of advancement. Because the displacement dynamics are confined to the interfacial regions for a majority of the relaxation time, the computational effort is focused on these regions. The proposed algorithm is shown to reduce computational effort by an order of magnitude, while yielding essentially identical solutions to a conventional fully coupled approach. Finally, the challenges posed by Haines jumps are also resolved by the adaptive algorithm. Possible extensions to the advanced method are discussed.

42 ENGINEERING↗
DOE OSTI2015

Impact of drilling fluid rheology and wellbore pressure on rock cuttings removal performance: numerical investigation

Drilling fluids exhibit non-Newtonian behavior which is generally described in the literature by two rheological models: Power Law and Herschel-Bulkley models. We have used Eulerian- Eulerian multiphase flow model to evaluate the drilling fluid flow patterns and filter cake formation patterns for both models for ultra-deep drilling conditions. Simulated drilling fluid flow patterns clearly show that the Herschel - Bulkley model exhibits fewer vortices around the drilling pipe, indicating better rock cuttings removal performance. In addition to mud rheology, we have analyzed the pressure drop variation with the filter cake formation on the wellbore during ultradeep drilling process. We have shown that filter cake thickness at the wall linearly increases with differential pressure between the well and formation. This implies that the differential pressure needs to be optimized. Very thin filter cake may lead to excessive fluid seepage into the permeable subterranean vertical zone resulting in a loss of fluid circulation and hence in a loss of rock cuttings removal. Yet, very thick filter cake reduces the effective diameter of the hole which may lead to a poor rock cuttings removal performance and stuck pipe. The mathematical model presented here needs to be experimentally validated when filter cake thickness data in deep drilling conditions become available. The model could be useful to provide drilling engineers insight on the filter cake formation, drilling fluid flow pattern, and give guide-line to optimize the filter cake as field experimental data are scarce.

02 PETROLEUM↗
DOE OSTI2019

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed here two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R 2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

58 GEOSCIENCES↗
DOE OSTI2020

Assessment of the Utility of the Oral Fluid and Plasma Proteome for Hydrocodone Usage

Non-medical use and abuse of prescription opioids is a growing problem in both the civilian and military communities, with minimal technologies for detecting hydrocodone use. This study explored the proteomic changes that occur in the oral fluid and blood plasma following controlled hydrocodone administration in twenty subjects. The global proteomic profile was determined for samples taken at four time points per subject of pre-exposure and 4, 6, or 168 hours post-exposure. The oral fluid samples analyzed herein provided greater differentiation between “baseline” and “response” time points than was observed with blood plasma, at least partially due to significant person-to-person variability in the plasma proteome. A total of 399 proteins were identified from oral fluid samples, and the abundance of 102 of those proteins was determined to be significantly different upon metabolism of hydrocodone (4 and 6 hour time points) as compared to baseline levels in the oral fluid (pre-dose and 168 hour). This is the first, to our knowledge, assessment of the oral fluid and blood plasma proteome following hydrocodone administration, which demonstrates the potential of oral fluid as a non-invasive sample that may reveal features of hydrocodone or other opioid use and/or misuse.

Kaiser, Brooke LD↗
DOE OSTI2024

Mineral and fluid transformation of hydraulically fractured shale: case study of Caney Shale in Southern Oklahoma

This study explores the geochemical reactions that can cause permeability loss in hydraulically fractured reservoirs. The experiments involved the reaction of powdered-rock samples with produced brines in batch reactor system at temperature of 95 °C and atmospheric pressure for 7-days and 30-days respectively. Results show changes in mineralogy and chemistry of rock and fluid samples respectively, therefore confirming chemical reactions between the two during the experiments. The mineralogical changes of the rock included decreases of pyrite and feldspar content, whilst carbonate and illite content showed an initial stability and increase respectively before decreasing. Results from analyses of post-reaction fluids generally corroborate the results obtained from mineralogical analyses. Integrating the results obtained from both rocks and fluids reveal a complex trend of reactions between rock and fluid samples which is summarized as follows. Dissolution of pyrite by oxygenated fluid causes transient and localized acidity which triggers the dissolution of feldspar, carbonates, and other minerals susceptible to dissolution under acidic conditions. The dissolution of minerals releases high concentrations of ions, some of which subsequently precipitate secondary minerals. On the field scale, the formation of secondary minerals in the pores and flow paths of hydrocarbons can cause significant reduction in the permeability of the reservoir, which will culminate in rapid productivity decline. This study provides an understanding of the geochemical rock–fluid reactions that impact long term permeability of shale reservoirs.

58 GEOSCIENCES↗
DOE OSTI2018

Corrigendum to "Environmentally friendly, rheoreversible, hydraulic fracturing fluids for enhanced geothermal systems" [Geothermics 58 (2015) 22–31]

Cost-effective creation of high-permeability reservoirs inside deep crystalline bedrock is the primary challenge for the feasibility of enhanced geothermal systems (EGS). Current reservoir stimulation entails adverse environmental impacts and substantial economic costs due to the utilization of large volumes of water “doped” with chemicals including rheology modifiers, scale and corrosion inhibitors, biocides, friction reducers among others where, typically, little or no information of composition and toxicity is disclosed. An environmentally benign, CO2-activated, rheoreversible fracturing fluid has recently been developed that significantly enhances rock permeability at effective stress significantly lower than current technology. We evaluate the potential of this novel fracturing fluid for application on geothermal sites under different chemical and geomechanical conditions, by performing laboratory-scale fracturing experiments with different rock sources under different confining pressures, temperatures, and pH environments. The results demonstrate that CO2-reactive aqueous solutions of environmentally amenable Polyallylamine (PAA) represent a highly versatile fracturing fluid technology. This fracturing fluid creates/propagates fracture networks through highly impermeable crystalline rock at significantly lower effective stress as compared to control experiments where no PAA was present, and permeability enhancement was significantly increased for PAA compared to conventional hydraulic fracturing controls. This was evident in all experiments, including variable rock source/type, operation pressure and temperature (over the entire range for EGS applications), as well as over a wide range of formation-water pH values. This versatile novel fracturing fluid technology represents a great alternative to industrially available fracturing fluids for cost-effective and competitive geothermal energy production.

Shao, Hongbo↗
DOE OSTI2015

A novel SPH method for sedimentation in a turbulent fluid

A novel method for simulating sedimentation is described and applied to the sedimentation of dust in a turbulent fluid. We assume the dust grains are sufficiently numerous that they may be treated as a fluid and modelled by SPH particles. A different set of SPH particles describes the fluid. The equations of motion are therefore similar to those of Monaghan and Kocharyan [14] with the exception that the sedimentation of dust onto a solid surface is treated as if the surface mimics a sink for the dust fluid. The continuity equation for the dust then contains a sink term that can be modelled in the SPH formulation by allowing the mass of each SPH dust particle to decrease when it is sufficiently close to the boundary. We apply this method both to sedimentation in a nearly static fluid, and to sedimentation in a turbulent fluid. In the latter case we produce the turbulence by both a mechanical stirrer and by a stochastic algorithm. Our results agree very closely with the experiments of Martin and Nokes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

A general peridynamics model for multiphase transport of non-Newtonian compressible fluids in porous media

Herein a general state-based peridynamics model is developed to simulate transport of fluids in an arbitrary heterogeneous porous medium. The generality encompasses modeling of multiphase, multi-component flow of non-Newtonian and compressible fluids, which is often encountered in but not limited to subsurface reservoirs. Peridynamic model is especially useful for solving non-local problems, such as crack propagation, since it does not assume spatial continuity of field variables. Thus, the formulation presented here, combined with peridynamics-based damage model, can be used to simulate hydraulic fracturing with complex fluids. To demonstrate its capability to simulate multi-phase flow in porous media, the derived model is verified against the analytical Buckley-Leverett solution for immiscible Newtonian two-phase flow. Further, the non-Newtonian two-phase fluid flow in porous media is verified by simulating the polymer flood process involving immiscible displacement of a Newtonian fluid by a non-Newtonian fluid against a generalized solution obtained by Wu et al. The non-local solutions are shown to be consistent with the corresponding local solutions in limiting cases. Moreover, mass conservation of all the phases is satisfied, irrespective of discretization and extent of non-locality.

42 ENGINEERING↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Phase diagram to demarcate supercritical, transcritical, and continuous phase regimes for binary fluid equilibrium mixing relevant to combustion applications

Here, a robust methodology to develop phase diagrams of binary fluid mixtures at fixed thermo dynamic conditions (pressure, temperature, and mole fraction) as well as of two initially separated fluids undergoing mixing near critical conditions are presented for fluids and con ditions relevant to rockets, gas turbines, and diesel engine applications. Phase equilibria of mixtures is first examined to provide insight into the continuous-phase mixing behavior (including but not limited to supercritical behavior), and to develop a broadly applicable phase-diagram for binary fluid mixtures at fixed conditions. Next, adiabatic mixing theory and reduced Helmholtz equations of state are used to predict the thermodynamic conditions required to attain continuous-phase binary fluid mixing near critical conditions. Then, a 3D surface diagram (P,T fuel ,T amb ) separating single and two-phase regions is constructed by varying the ambient pressure and the initial temperatures of the two fluids. Polynomial fits of the 3D surfaces for 10 different binary mixtures are tabulated for nitrogen-alkane and methane-oxygen blends relevant to air-breathing and propellant based engines, respectively.

42 ENGINEERING↗
DOE OSTI2021

Anomalous normal fluid response in a chiral superconductor UTe2

Abstract Chiral superconductors have been proposed as one pathway to realize Majorana normal fluid at its boundary. However, the long-sought 2D and 3D chiral superconductors with edge and surface Majorana normal fluid are yet to be conclusively found. Here, we report evidence for a chiral spin-triplet pairing state of UTe 2 with surface normal fluid response. The microwave surface impedance of the UTe 2 crystal was measured and converted to complex conductivity, which is sensitive to both normal and superfluid responses. The anomalous residual normal fluid conductivity supports the presence of a significant normal fluid response. The superfluid conductivity follows the temperature behavior predicted for an axial spin-triplet state, which is further narrowed down to a chiral spin-triplet state with evidence of broken time-reversal symmetry. Further analysis excludes trivial origins for the observed normal fluid response. Our findings suggest that UTe 2 can be a new platform to study exotic topological excitations in higher dimension.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Dynamic compression of dense oxide (Gd 3 Ga 5 O 12 ) from 0.4 to 2.6 TPa: Universal Hugoniot of fluid metals

Materials at high pressures and temperatures are of great current interest for warm dense matter physics, planetary sciences, and inertial fusion energy research. Shock-compression equation-of-state data and optical reflectivities of the fluid dense oxide, Gd 3 Ga 5 O 12 (GGG), were measured at extremely high pressures up to 2.6 TPa (26 Mbar) generated by high-power laser irradiation and magnetically-driven hypervelocity impacts. Above 0.75 TPa, the GGG Hugoniot data approach/reach a universal linear line of fluid metals, and the optical reflectivity most likely reaches a constant value indicating that GGG undergoes a crossover from fluid semiconductor to poor metal with minimum metallic conductivity (MMC). These results suggest that most fluid compounds, e.g., strong planetary oxides, reach a common state on the universal Hugoniot of fluid metals (UHFM) with MMC at sufficiently extreme pressures and temperatures. Lastly, the systematic behaviors of warm dense fluid would be useful benchmarks for developing theoretical equation-of-state and transport models in the warm dense matter regime in determining computational predictions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗