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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Role of spin-transfer torques on synchronization and resonance phenomena in stochastic magnetic oscillators

A theoretical study on how synchronization and resonance-like phenomena in superparamagnetic tunnel junctions can be driven by spin-transfer torques is presented. We examine the magnetization of a superparamagnetic free layer that reverses randomly between two well-defined orientations due to thermal fluctuations, acting as a stochastic oscillator. When subject to an external ac forcing, this system can present stochastic resonance and noise-enhanced synchronization. We focus on the roles of the mutually perpendicular damping-like and field-like torques, showing that the response of the system is very different at low and high frequencies. We also demonstrate that the field-like torque can increase the efficiency of the current-driven forcing, especially at sub-threshold electric currents. These results can be useful for possible low-power, more energy efficient applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine

In the past, precision medicine involves classifying patients to identify subpopulations that respond favorably to specific therapeutics. We discuss precision medicine as a dynamic feedback control problem, where treatment administered to a patient is guided by measurements taken during the course of treatment. We consider sepsis, a life-threatening condition in which dysregulation of the immune system causes tissue damage. We leverage an existing simulation of the innate immune response to infection and apply deep reinforcement learning (DRL) to discover an adaptive personalized treatment policy that specifies effective multicytokine therapy to simulated sepsis patients based on systemic measurements. The learned policy achieves a dramatic reduction in mortality rate over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are threefold: (1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, (2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and (3) optimized treatments respond to a patient's individual disease progression over time, therefore, capturing both differences across patients and the inherent randomness of disease progression within a single patient. We believe that this work motivates both considering adaptive personalized multicytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dynamical resilience to disorder: The dilute Hubbard model on the Lieb lattice

In itinerant systems, electron-electron interactions may lead to the formation of local magnetic moments and their effective exchange coupling, which in turn gives rise to long-range magnetic order. Therefore, when moment formation is weakened, such as in the single-band Hubbard model on a square lattice with the on-site repulsion being randomly switched off on a fraction $\textit{x}$ of sites, magnetic order is suppressed beyond some critical $x_c$, which was found to lie below the classical percolation threshold $x^{\text{(perc,sq)}}_c$. Here we study dilute magnetism in flat band systems, namely, in the Hubbard model on a “Lieb” lattice. Interestingly, we show that magnetic order persists to x almost twice as large as the classical percolation threshold for the lattice, thus emphasizing the central role of electron itinerancy to the magnetic response. Further, the analysis of the orbital-resolved order parameters reveals that the contribution of the fourfold coordinated $\textit{“d”}$ sites to magnetism is dramatically affected by dilution, while the localized $\textit{“p”}$ states of the flat band provide the dominant contribution to long-range correlations. We also examine the transport properties, which suggest the existence of an insulator-to-metal transition in the same range of the critical magnetic dilution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Freezing of a Disorder Induced Spin Liquid with Strong Quantum Fluctuations

We report Sr2CuTe 0.5 W 0.5 O 6 is a square-lattice magnet with superexchange between S =$\frac{1}{2}$Cu 2+ spins mediated by randomly distributed Te and W ions. Here, using sub-K temperature and 20 μeV energy resolution neutron scattering experiments we show that this system transits from a gapless disorder-induced spin liquid to a new quantum state below T f =1.7(1) K, exhibiting a weak frozen moment of $\langle$S$\rangle$/S~0.1 and low energy dynamic susceptibility, χ"(ℏω), linear in energy which is surprising for such a weak freezing in this highly fluctuating quantum regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

TU-FG-BRB-08: Challenges, Limitations and Future Outlook Towards Clinical Translation of Proton Acoustic Range Verification

Purpose: To report our investigations of proton acoustic imaging, including computer simulations and preliminary experimental studies at clinical facilities. The ultimate achievable accuracy, sensitivity and clinical translation challenges are discussed. Methods: The acoustic pulse due to pressure rise was estimated using finite element model. Since the ionoacoustic pulse is highly dependent on the proton pulse width and energy, multiple pulse widths were studied. Based on the received signal spectrum at piezoelectric ultrasound transducer with consideration of random thermal noise, maximum spatial resolution of the proton-acoustic imaging modality was calculated. The simulation studies defined the design specifications of the system to detect proton acoustic signal from Hitachi and Mevion clinical machines. A 500 KHz hydrophone with 100 dB amplification was set up in a water tank placed in front of the proton nozzle A 40 MHz data acquisition was synchronized by a trigger signal provided by the machine. Results: Given 30–800 mGy dose per pulse at the Bragg peak, the minimum number of protons detectable by the proton acoustic technique was on the order of 10×10^6 per pulse. The broader pulse widths produce signal with lower acoustic frequencies, with 10 µs pulses producing signals with frequency less than 100 kHz. As the proton beam pulse width increases, a higher dose rate is required to measure the acoustic signal. Conclusion: We have established the minimal detection limit for protonacoustic range validation for a variety of pulse parameters. Our study indicated practical proton-acoustic range verification can be feasible with a pulse shorter than 10 µs, 5×10^6 protons/pulse, 50 nA beam current and a highly sensitive ultrasonic transducer. The translational challenges into current clinical machines include proper magnetic shielding of the measurement equipment, providing a clean trigger signal from the proton machine, providing a shorter proton beam pulse and higher dose per pulse.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Flocking ferromagnetic colloids

Assemblages of microscopic colloidal particles exhibit fascinating collective motion when energized by electric or magnetic fields. The behaviors range from coherent vortical motion to phase separation and dynamic self-assembly. While colloidal systems are relatively simple, understanding their collective response, especially in out of equilibrium conditions, remains elusive. Here, we report on the emergence of flocking and global rotation in the system of rolling ferromagnetic microparticles energized by a vertical alternating magnetic field. By combing experiments and discrete particle simulations, we have identified primary physical mechanisms leading to the emergence of largescale collective motion: spontaneous symmetry breaking of the clock / counterclockwise particle rotation, collisional alignment of particle velocities, and random particle re-orientations due to shape imperfections. We have also shown that hydrodynamic interactions between the particles do not have a qualitative effect on the collective dynamics. Lastly, our findings shed light on the onset of spatial and temporal coherence in a large class of active systems, both synthetic (colloids, swarms of robots, biopolymers) and living (suspensions of bacteria, cell colonies, bird flocks).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Light transport with weak angular dependence in fog

Random scattering and absorption of light by tiny particles in aerosols, like fog, reduce situational awareness and cause unacceptable down-time for critical systems or operations. Computationally efficient light transport models are desired for computational imaging to improve remote sensing capabilities in degraded optical environments. To this end, we have developed a model based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes, thereby covering the applicability domain of both the small angle and diffusion approximations. An analytic solution was derived and validated using experimental data acquired at the Sandia National Laboratory Fog Chamber facility. The evolution of the fog particle density and size distribution were measured and used to determine macroscopic absorption and scattering properties using Mie theory. A three-band (0.532, 1.55, and 9.68 µ m) transmissometer with lock-in amplifiers enabled changes in fog density of over an order of magnitude to be measured due to the increased transmission at higher wavelengths, covering both the moderate and highly scattering regimes. The meteorological optical range parameter is shown to be about 0.6 times the transport mean free path length, suggesting an improved physical interpretation of this parameter.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Inferring transmission heterogeneity using virus genealogies: estimation and targeted prevention

Spread of HIV typically involves uneven transmission patterns where some individuals spread to a large number of individuals while others to only a few or none. Such transmission heterogeneity can impact how fast and how much an epidemic spreads. Further, more efficient interventions may be achieved by taking such transmission heterogeneity into account. To address these issues, we developed two phylogenetic methods based on virus sequence data: 1) to generally detect if significant transmission heterogeneity is present, and 2) to pinpoint where in a phylogeny high-level spread is occurring. We derive inference procedures to estimate model parameters, including the amount of transmission heterogeneity, in a sampled epidemic. We show that it is possible to detect transmission heterogeneity under a wide range of simulated situations, including incomplete sampling, varying levels of heterogeneity, and including within-host genetic diversity. When evaluating real HIV-1 data from different epidemic scenarios, we found a lower level of transmission heterogeneity in slowly spreading situations and a higher level of heterogeneity in data that included a rapid outbreak, while R0 and Sackin’s index (overall tree shape statistic) were similar in the two scenarios, suggesting that our new method is able to detect transmission heterogeneity in real data. We then show by simulations that targeted prevention, where we pinpoint high-level spread using a coalescence measurement, is efficient when sequence data are collected in an ongoing surveillance system. Such phylogeny-guided prevention is efficient under both single-step contact tracing as well as iterative contact tracing as compared to random intervention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Learning Interactions in Complex Biological Systems

In this project, we developed novel methods for integrating simulation and data-driven methods to accelerate biomedical discovery. In particular, we exploit agent-based models of disease processes and deep reinforcement learning to identify potential novel multi-drug, patient-specific, and adaptive therapeutic strategies. We use the Innate Immune Response agent-based model (IIRABM) to model sepsis as a demonstration of our approach. We first calibrate the agent-based model using novel stochastic optimization algorithms develop in this project, then use the calibrated model to identify optimal cytokine mediation strategies using deep reinforcement learning (DRL). The learned policy achieves a dramatic reduction in mortality over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are three-fold: 1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, 2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and 3) optimized treatments respond to a patient’s individual disease progression over time, therefore capturing both differences across patients and the inherent randomness of disease progression within a single patient. We hope that this work motivates both considering adaptive, personalized multi-cytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗
DOE OSTI2025

Frontal Slice Approaches for Tensor Linear Systems

Inspired by the row and column action methods for solving large-scale linear systems, in this work, we explore the use of frontal slices for solving tensor linear systems. In particular, this paper presents a novel approach for using frontal slices of a tensor $\mathcal{A}$ to solve tensor linear systems $\mathcal{A} ∗\mathcal{X} = \mathcal{B}$ where ∗ denotes the $t$-product. In addition, we consider variations of this method, including cyclic, block, and randomized approaches, each designed to optimize performance in different operational contexts. Our primary contribution lies in the development and convergence analysis of these methods. Experimental results on synthetically generated and real-world data, including applications such as image and video deblurring, demonstrate the efficacy of our proposed approaches and validate our theoretical findings.

Luo, Hengrui↗
DOE OSTI2017

High Pressure Hydrogen Pressure Relief Devices: Accelerated Life Testing and Application Best Practices

Pressure relief devices (PRDs ) are used to protect high pressure systems from burst failure caused by overpressurization. Codes and standards require the use of PRDs for the safe design of many pressurized systems. These systems require high reliability due to the risks associated with a burst failure. Hydrogen service can increase the risk of PRD failure due to material property degradation caused by hydrogen attack. The National Renewable Energy Laboratory (NREL) has conducted an accelerated life test on a conventional spring loaded PRD. Based on previous failures in the field, the nozzles specific to these PRDs are of particular interest. A nozzle in a PRD is a small part that directs the flow of fluid toward the sealing surface to maintain the open state of the valve once the spring force is overcome. The nozzle in this specific PRD is subjected to the full tensile force of the fluid pressure. These nozzles are made from 440C material, which is a type of hardened steel that is commonly chosen for high pressure applications because of its high strength properties. In a hydrogen environment, however, 440C is considered a worst case material since hydrogen attack results in a loss of almost all ductility and thus 440C is prone to fatigue and material failure. Accordingly, 440C is not recommended for hydrogen service. Conducting an accelerated life test on a PRD with 440C material provides information on necessary and sufficient conditions required to produce crack initiation and failure. The accelerated life test also provides information on other PRD failure modes that are somewhat statistically random in nature.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Wildfire smoke exposure and early childhood respiratory health: a study of prescription claims data

Abstract Wildfire smoke is associated with short-term respiratory outcomes including asthma exacerbation in children. As investigations into developmental wildfire smoke exposure on children’s longer-term respiratory health are sparse, we investigated associations between developmental wildfire smoke exposure and first use of respiratory medications. Prescription claims from IBM MarketScan Commercial Claims and Encounters database were linked with wildfire smoke plume data from NASA satellites based on Metropolitan Statistical Area (MSA). A retrospective cohort of live infants (2010–2016) born into MSAs in six western states (U.S.A.), having prescription insurance, and whose birthdate was estimable from claims data was constructed ( N = 184,703); of these, gestational age was estimated for 113,154 infants. The residential MSA, gestational age, and birthdate were used to estimate average weekly smoke exposure days ( smoke-day ) for each developmental period: three trimesters, and two sequential 12-week periods post-birth. Medications treating respiratory tract inflammation were classified using active ingredient and mode of administration into three categories:: 'upper respiratory', 'lower respiratory', 'systemic anti-inflammatory'. To evaluate associations between wildfire smoke exposure and medication usage, Cox models associating smoke-days with first observed prescription of each medication category were adjusted for infant sex, birth-season, and birthyear with a random intercept for MSA. Smoke exposure during postnatal periods was associated with earlier first use of upper respiratory medications (1–12 weeks: hazard ratio (HR) = 1.094 per 1-day increase in average weekly smoke-day, 95%CI: (1.005,1.191); 13–24 weeks: HR = 1.108, 95%CI: (1.016,1.209)). Protective associations were observed during gestational windows for both lower respiratory and systemic anti-inflammatory medications; it is possible that these associations may be a consequence of live-birth bias. These findings suggest wildfire smoke exposure during early postnatal developmental periods impact subsequent early life respiratory health.

Dhingra, Radhika↗
DOE OSTI2018

Optimal Black Start Allocation for Power System Restoration

Equipment failures, operator errors, natural disasters and cyber-attacks can and have caused extended blackouts of the electric grid. Even though such events are rare, preparedness for them is critical because extended power outages endanger human lives, compromise national security, or result in economic losses of billions of dollars. Since most of the generating units cannot restart without connecting to an energized grid, the system operator relies on a few units with the ability to start autonomously, called Black Start (BS) units, to restore the power system. Allocating and maintaining these units is costly and can severely impact the restoration security and time. We formulate an optimization problem to optimally allocate BS units in the grid, while simultaneously optimizing over the restoration sequence. We extend existing optimal allocation models by including grid considerations such as active power nodal balance, transmission switching, nodal reactive power support and voltage limits. In order to aid the branch and bound tree that solves the resulting large scale mixed integer program, we propose a randomized heuristic that is executed multiple times in parallel on a high-performance computing environment to find feasible solutions. Here, we proceed to solve the IEEE-39, the IEEE-118, and a simplified WECC system with 225 nodes and 136 generators to near optimality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Seasonal Cost-Benefit Analysis of Automated Distribution Feeder Upgrades with Advanced Mitigation Technologies

The increasing deployment of distributed solar photovoltaics (DPV) to meet clean energy goals can trigger adverse grid operation issues, such as voltage excursions and the violation of thermal loading constraints of the power delivery elements (e.g., lines and transformers) on the evolving electricity infrastructure. Such integration issues would require distribution upgrades with associated costs to mitigate them and to maintain reliable and resilient grid operating conditions. Traditional distribution network upgrade approaches use a specific single snapshot analysis that is overly conservative. This study considers a multi-time point analysis to capture both moderate (probable bounds) and extreme grid operating conditions using time points such as minimum load with minimum photovoltaics (PV), maximum load with maximum PV, maximum load with minimum PV, and minimum load with maximum PV. Further, this study investigates seasonal variation impacts and associated distribution upgrade costs for a spring season case (March, representing a low load and high PV scenario) and a summer case (July, representing a high load and high PV scenario). Such seasonal analysis will allow system operators to characterize upgrade requirements and associated costs across various periods. Because the spatial distribution of DPV can impact upgrade and associated costs, this study investigates three common DPV deployment scenarios - randomly deployed, close to the substation, and far from the substation - at different penetration levels. Apart from spatial distribution impacts, this project evaluates the techno-economic impacts of the nodal photovoltaic penetration factor (NPPF) for generating the various DPV deployment scenarios at increasing penetration levels. This project investigates the impact of varying nodal PV-to-load ratios using conservative and extreme NPPF values of 3 and 10, respectively. This study investigates the deployment of traditional infrastructure upgrade strategies, such as installing new voltage regulating equipment, transformers and lines replacements, and the activation of advanced inverter functionality (e.g., autonomous volt/VAR) in expanding PV hosting capacity. Existing DPV systems are assumed to operate with the legacy unity power factor, and we considered the possibility of retrofitting such systems with the activation of volt/VAR control as integration standards and regulations continue to evolve to allow such functions. The cost-benefit analysis metrics used in study include distribution upgrade costs, average cost per watt of the upgrade cost, average marginal cost per watt of the upgrade cost, and power losses.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data

Commercial buildings account for a significant amount of total energy produced in the US, and the Heating Ventilation and Cooling (HVAC) systems are one of the most significant components of their overall consumption. In this study, we proposed a new data-driven approach to evaluate HVAC cooling systems in commercial buildings and identify savings opportunities. The focus is an investigation of the impact of thermostat setpoint setback but using only whole building, electricity data taken at 15-min intervals for the analysis. We conducted a comparative study of setpoint setback characteristics on 432 commercial buildings with 5 building usage types across the United States. To accomplish this, both piecewise and Random Forest regression algorithms were employed using electricity and exterior temperature datasets to identify operational characteristics and the effective setpoints in the building to determine the corresponding savings opportunities. Both occupied and unoccupied time periods were studied across cooling degree days (CDD), when air conditioning is typically operational. Here the results show that in commercial buildings, on average, cooling systems account for 9.5% of total consumption. When a one degree setback during the cooling season is applied, an average of approximately 1.1% of annual consumption is achieved; retail and office buildings demonstrate the highest potential for savings. Additionally, we identified that the number of cooling degree days and base to peak ratio (BPR) are the most important variables for predicting the magnitude of the consumption of cooling systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Direct Numerical Simulation of Hypersonic Turbulent Boundary Layer Flow using SPARC: Initial Evaluation

This report documents the initial testing of the Sandia Parallel Aerodynamics and Reentry Code (SPARC) to directly simulate hypersonic, turbulent boundary layer flow over a sharp 7- degree half-angle cone. This type of computation involves a tremendously large range of scales both in time and space, requiring a large number of grid cells and the efficient utilization of a large pool of resources. The goal of the simulation is to mimic and verify a wind tunnel experiment that seeks to measure the turbulent surface pressure fluctuations. These data are necessary for building a model to predict random vibration loading in the reentry flight environment. A low-dissipation flux scheme in SPARC is used on a 2.7 billion cell mesh to capture the turbulent fluctuations in the boundary layer flow. The grid is divided into 115200 partitions and simulated using the Knight's Landings (KNL) partition of the Trinity system. The parallel performance of SPARC is explored on the Trinity system, as well as some of the other new architectures. Extracting data from the simulation shows good agreement with the experiment as well as a colleague's simulation. The data provide a guide for which a new model can be built for better prediction of the reentry random vibration loads.

42 ENGINEERING↗