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Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.
The continuous emanation of radon due to trace amounts of uranium and thorium in detector materials introduces radon to the active detection volume of low-background rare event search detectors. 222 Rn produces a particularly problematic background in the physics region of interest by the "naked" beta decay of its 214 Pb daughter nucleus. While charcoal-based adsorption traps are expected to be effective for radon reduction in auxiliary circulation loops that service the warm components of current ton-scale detectors at slow flow rates (0.5-2 SLPM), radon reduction in the entire circulation loop at high flow rates $\mathcal{O}$(100s SLPM) is necessary to reach high sensitivity in future generation experiments. In this article we explore radon dynamics with a charcoal-based radon reduction system in the main circulation loop of time projection chamber detectors. Here, we find that even for perfect radon traps, circulation speeds of 2,000 SLPM are needed to reduce radon concentration in a 10 ton detector by 90%. This is faster by a factor of four than the highest circulation speeds currently achieved in dark matter detectors. We further find that the effectiveness of vacuum swing adsorption systems, which have been employed very successfully at reducing atmospheric radon levels in clean-rooms, is limited by the intrinsic radon activity of the charcoal adsorbent in ultra-low radon environments. Adsorbents with significantly lower intrinsic radon activity than in currently available activated charcoals would be necessary to build effective vacuum swing adsorption systems operated at room temperature for rare event search experiments. If such VSA systems are cooled to about 190 K, this requirement relaxes drastically.
Recent experiments on vanadium-based nonmagnetic kagomé metals AV 3 Sb 5 (A=K,Rb,Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, in this work, we calculate the susceptibility of the real and imaginary bond orders on the kagomé lattice near van Hove filling, and reveal the importance of next-nearest-neighbor Coulomb repulsion V 2 in triggering the instability toward imaginary bond ordered CDW. The concrete effective single-orbital t–V 1 –V 2 model on the kagomé lattice is then studied, where t and V 1 are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram in the plane spanned by V 1 and V 2 at van Hove filling. The region dominated by V 1 is occupied by a 2a 0 ×2a 0 real CDW insulator with the inverse of Star-of-David (ISD) bond configuration. Increasing V 2 indeed drives a first-order transition from ISD to stabilized loop-current insulators that exhibit four possible current patterns of different topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove filling and show that electron doping helps the stabilization of loop currents, and gives rise to doped orbital Chern insulators with emergent Chern Fermi pockets carrying large Berry curvature and orbital magnetic moment. Our findings provide a concrete model realization of the loop-current Chern metal at the mean-field level for the TRS breaking normal state of the kagomé superconductors.
CMB-S4—the next-generation ground-based cosmic microwave background (CMB) experiment—is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the universe. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semianalytic projection tool, targeted explicitly toward optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2–3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments, given a desired scientific goal. To form a closed-loop process, we couple this semianalytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r > 0.003 at greater than 5σ, or in the absence of a detection, of reaching an upper limit of r < 0.001 at 95% CL.
KSTAR has recently undergone an upgrade to use a new tungsten divertor to run experiments in ITER-relevant scenarios. Even with a high melting point of tungsten, it is important to control the heat flux impinging on tungsten divertor targets to minimize sputtering and contamination of the core plasma. Heat flux on the divertor is often controlled by increasing the degree of detachment of scrape-off layer plasma from the target plates. In this work, we have demonstrated successful divertor detachment and heat exhaust dissipation control experiments using two different methods. The first method uses attachment fraction as a control variable which is estimated using ion saturation current measurements from embedded Langmuir probes in the divertor. The second method uses a novel machine-learning-based surrogate model of 2D UEDGE simulation database, DivControlNN. We demonstrated running inference operation of DivControlNN in realtime to estimate heat flux at the divertor and use it as the control variable in a feedback loop with impurity gas flow. We present interesting insights from these experiments including a systematic approach to tuning controllers and discuss future improvements in the control infrastructure and control variables for future burning plasma experiments.
Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.
Regulations to establish operational and performance requirements for unmanned aircraft systems (UAS) are being developed by a consortium of government, industry and academic institutions (RTCA, 2013). Those requirements will apply to the new detect-and-avoid (DAA) systems and other equipment necessary to integrate UAS with the United States (U.S) National Airspace System (NAS) and will be determined according to their contribution to the overall safety case. That safety case requires demonstration that DAA-equipped UAS collectively operating in the NAS meet an airspace safety threshold (AST). Several key gaps must be closed in order to link equipment requirements to an airspace safety case. Foremost among these is calculation of the systems risk ratio, the degree to which a particular system mitigates violation of an aircraft separation standard (FAA, 2013). The risk ratio of a DAA system, in combination with risk ratios of other collision mitigation mechanisms, will determine the overall safety of the airspace measured in terms of the number of collisions per flight hour. It is not known what the effectiveness is of a pilot-in-the-loop DAA system or even what parameters of the DAA system most improve the pilots ability to maintain separation. The relationship between the DAA system design and the overall effectiveness of the DAA system that includes the pilot, expressed as a risk ratio, must be determined before DAA operational and performance requirements can be finalized. Much research has been devoted to integrating UAS into non-segregated airspace (Dalamagkidis, 2009, Ostwald, 2007, Gillian, 2012, Hesselink, 2011, Santiago, 2015, Rorie 2015 and 2016). Several traffic displays intended for use as part of a DAA system have gone through human-in-the-loop simulation and flight-testing. Most of these evaluations were part of development programs to produce a deployable system, so it is unclear how to generalize particular aspects of those designs to general requirements for future traffic displays (Calhoun, 2014). Other displays have undergone testing to collect data that may generalize to new displays, but have not been evaluated in the context of the development of an overall safety case for UAS equipped with DAA systems in the NAS (Bell, 2012). Other research efforts focus on DAA surveillance performance and separation standards. Together with this work, they are expected to facilitate validation of the airspace safety case (Park, 2014 and Johnson, 2015). The contribution of the present work is to quantify the effectiveness of the pilot-automation system to remain well clear as a function of display features and surveillance sensor error. This quantification will help enable selection of a minimum set of DAA design features that meets the AST, a set that may not be unique for all UAS platforms. A second objective is to collect and analyze pilot performance parameters that will improve the modeling of overall DAA system performance in non-human-in-the-loop simulations. Simulating the DAA-equipped UAS in such batch experiments will allow investigation of a much larger number of encounters than is possible in human simulations. This capability is necessary to demonstrate that a particular set of DAA requirements meets the AST under all foreseeable operational conditions.
A microscale heater array was used to study boiling in earth gravity and microgravity. The heater array consisted of 96 serpentine heaters on a quartz substrate. Each heater was 0.27 square millimeters. Electronic feedback loops kept each heater's temperature at a specified value. The University of Maryland constructed an experiment for the Terrier-Improved Orion sounding rocket that was delivered to NASA Wallops and flown. About 200 s of high quality microgravity and heat transfer data were obtained. The VCR malfunctioned, and no video was acquired. Subsequently, the test package was redesigned to fly on the KC-135 to obtain both data and video. The pressure was held at atmospheric pressure and the bulk temperature was about 20 C. The wall temperature was varied from 85 to 65 C. Results show that gravity has little effect on boiling heat transfer at wall superheats below 25 C, despite vast differences in bubble behavior between gravity levels. In microgravity, a large primary bubble was surrounded by smaller bubbles, which eventually merged with the primary bubble. This bubble was formed by smaller bubbles coalescing, but had a constant size for a given superheat, indicating a balance between evaporation at the base and condensation on the cap. Most of the heaters under the bubble indicated low heat transfer, suggesting dryout at those heaters. High heat transfer occurred at the contact line surrounding the primary bubble. Marangoni convection formed a "jet" of fluid into the bulk fluid that forced the bubble onto the heater.
A development of the in plane open loop rotational equations of motion for the proposed Spacecraft Control Laboratory Experiment (SCOLE) in orbit configuration is presented based on an Eulerian formulation. The mast is considered to be a flexible beam connected to the (rigid) shuttle and the reflector. Frequencies and mode shapes are obtained for the mast vibrational appendage modes (assumed to be decoupled) for different boundary conditions based on continuum approaches and also preliminary results are obtained using a finite element representation of the mast reflector system. The linearized rotational in plane equation is characterized by periodic coefficients and open loop system stability can be examined with an application of the Floquet theorem. Numerical results are presented to illustrate the potential instability associated with actuator time delays even for delays which represent only a small fraction of the natural period of oscillation of the modes contained in the open loop model of the system. When plant and measurement noise effects are added to the previously designed deterministic model of the hoop column system, it is seen that both the system transient and steady state performance are degraded. Mission requirements can be satisfied by appropriate assignment of cost function weighting elements and changes in the ratio of plant noise to measurement noise.
Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.
Turbulent viscoseal with gas-liquid interface, using recirculation loop for gas ingestion reduction
NASA Marshall Space Flight Center has developed a facility in which closed loop control of Large Space Structures (LSS) can be demonstrated and verified. The main objective of the facility is to verify LSS control system techniques so that on-orbit performance can be ensured. The facility consists of an LSS test article or payload which is connected to a 3-axis angular pointing mount assembly that provides control torque commands. The angular pointing mount assembly is attached to a base excitation system which will simulate disturbances most likely to occur for Orbiter and DOD payloads. The control computer contains the calibration software, the reference systems, the alignment procedures, the telemetry software, and the control algorithms. The total system is suspended in such a fashion that the LSS test article has the characteristics common to all LSS.
There are a number of different applications that could use heat pipes or loop heat pipes (LHPs) in the intermediate temperature range of 450 to 725 K (170 to 450 C), including space nuclear power system radiators, fuel cells, and high temperature electronics cooling. Historically, water has been used in heat pipes at temperatures up to about 425 K (150 C). Recent life tests, updated below, demonstrate that titanium/water and Monel/water heat pipes can be used at temperatures up to 550 K (277 C), due to water's favorable transport properties. At temperatures above roughly 570 K (300 C), water is no longer a suitable fluid, due to high vapor pressure and low surface tension as the critical point is approached. At higher temperatures, another working fluid/envelope combination is required, either an organic or halide working fluid. An electromotive force method was used to predict the compatibility of halide working fluids with envelope materials. This procedure was used to reject aluminum and aluminum alloys as envelope materials, due to their high decomposition potential. Titanium and three corrosion resistant superalloys were chosen as envelope materials. Life tests were conducted with these envelopes and six different working fluids: AlBr3, GaCl3, SnCl4, TiCl4, TiBr4, and eutectic diphenyl/diphenyl oxide (Therminol VP-1/Dowtherm A). All of the life tests except for the GaCl3 are ongoing; the GaCl3 was incompatible. As the temperature approaches 725 K (450 C), cesium is a potential heat pipe working fluid. Life tests results are also presented for cesium/Monel 400 and cesium/70-30 copper/nickel heat pipes operating near 750 K (477 C). These materials are not suitable for long term operation, due to copper transport from the condenser to the evaporator.
Fermilab Magnet Systems is building three High-frequency AC-Dipole Magnets for the Mu2e experiment at Fermilab. These magnets are composed of three single-loop one-meter-long ferrite loaded segments. The excitation consists of a copper tube, which is also used as a means for its cooling with the inherited challenges of Voltage and Frequency uncoupling. These magnets are designed to operate either at 300 kHz or 4.4 MHz via resonance tuning. Following the completion of the design, prototyping, and analysis phases, all ferrites and other components were procured, and the magnets were fabricated to meet vacuum compatibility requirements using strict procedures. Each magnet undergoes thorough baking and testing before being installed in the experiment. This paper discusses the magnets' role in proton background suppression as it represents a unique and essential device for the Mu2e experiment at Fermilab. We present the magnet design choices, modeling approach, and the challenges encountered during fabrication. Additionally, we outline the power supply driving mechanism, the testing that was performed, and our results.
An atomistically-informed mean field cluster dynamics model has been developed to investigate the nucleation and growth of prismatic loops in irradiated a-Uranium. TEM analysis of neutron irradiated a-Uranium shows the evolution of self-interstitial atom and vacancy loops on (010) and (100) crystallographic planes, respectively, resulting in an anisotropic lattice swelling of its face-centered orthorhombic crystal. To provide model parameters, the crystallography of loops and the binding energy of point defects to these loops were studied using an angular dependent EAM potential and classical molecular dynamics (MD) simulations. Furthermore, using the bond-boost hyperdynamics method, the anisotropic diffusion of interstitials and vacancies in a-Uranium was investigated. Here, the mechanisms of point defect diffusion and the associated migration energies were reported and compared with previous DFT studies. The energetics and kinetic quantities mentioned above were used in the cluster dynamics model to predict the cluster density as a function of dose rate, dose and temperature and the results were compared to the reported neutron irradiation experiments. The model predictions reveal an accumulation of small sized vacancy loops along with a population of large and growing self-interstitial loops, which closely corresponds to the TEM observations.
Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.
Variation of the microwave intensity and spectrum due to synchrotron radiation from semirelativistic particles injected at the top of a closed magnetic loop has been described. Using the recent high spatial resolution X-ray observations from the HXIS experiment of the SMM and observations by the VLA, it is shown that the high microwave brightness observed at the top of the flare loop can come about if (1) the magnetic field from top to footpoints of the loop does not increase very rapidly, and (2) the accelerated particles injected in the loop have a nearly isotropic pitch angle distribution. The limits on the rate of increase of the magnetic field or the average pitch angle depend on the geometry and location of the loop on the solar disk.