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571 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Adding GPU Support to the Markov Chain Monte Carlo Code Catmip

In geophysics, we are confronted with many under-determined inverse problems. For example, all of our observations of earthquakes are made at the Earth’s surface. So, when we try to infer how slip during an earthquake evolves in space and time, we find that there are many potential slip histories that are consistent with our limited observations and our understanding of earthquake physics. One way to approach these problems is with Bayesian analysis which allows us to infer the ensemble of all potential slip models that satisfy the observations and our prior knowledge of earthquake physics. In Bayesian analysis, our prior knowledge is known as the prior probability density function or prior PDF, the fit to the data is known as the data likelihood, and the target PDF that satisfies both the prior PDF and data likelihood is known as the posterior PDF. However, simulating the posterior PDF typically requires using Markov Chain Monte Carlo (MCMC) to draw tens of billions of random realizations of earthquake slip models, which may not be computationally feasible. To make this and similar geophysical inversions computationally tractable, we developed the Cascading Adaptive Transitional Metropolis In Parallel (CATMIP) algorithm. CATMIP is an efficient parallel Markov Chain Monte Carlo (MCMC) sampler that is used for model fitting and uncertainty quantification in geophysics. Example use cases are earthquake rupture modeling, determining mineral composition on Mars, reconstructing the history of ocean salinity, and historical earthquake relocation. CATMIP employs many parallel instances of the Metropolis algorithm for sampling in a transitioning framework. Transitioning is a process in which a set of random samples at equilibrium with a known probability density function (PDF) are used as seeds for the Markov chains to sample successive target PDFs that incrementally move the distribution from the starting seeds to the final desired PDF that describes the relative plausibility of potential values for the model parameters. The algorithm is implemented as a Master-Worker model employing MPI for communication. The worker processes are loosely coupled with global parameters periodically optimized by the master process. This provides a very high amount of parallelism with little communication between updates. During the presentation we will discuss the history of the algorithm and elaborate the earthquake rupture modeling use case for the CATMIP package. Our first step toward GPU optimization was to optimize the code for the CPU. CPU profiling revealed that most of the compute time is spent in calls to level 2 BLAS routines and calls to GSL random number generators. We revised the algorithm to employ level 3 BLAS routines instead. In our presentation we will describe how this was accomplished. Adding GPU support to CATMIP consisted mostly of replacing the calls to GSL with calls to GPU vendor-provided library routines. A small number of loops were directly implemented in CUDA. In the presentation will provide implementation details. Finally, we will discuss methods for profiling and opportunities for further optimizing GPU execution. By creating a code with the flexibility to run on either a CPU or GPU architecture, CATMIP can be used on systems ranging from large CPU-based HPC environments to single servers with GPU acceleration and everything in between.

HECC↗
DOE OSTI2015

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Calibration of ITER Instant Power Neutron Monitors: Recommended Scenario of Experiments at the Reactor

Instant power is a key parameter of the ITER. Its monitoring with an accuracy of a few percent is an urgent and challenging aspect of neutron diagnostics. In a series of works published in Problems of Atomic Science and Technology, Series: Thermonuclear Fusion under a common title, the step-by-step neutronics analysis was given to substantiate a calibration technique for the DT and DD modes of the ITER. A Gauss quadrature scheme, optimal for processing “expensive” experiments, is used for numerical integration of {sup 235}U and {sup 238}U detector responses to the point sources of 14-MeV neutrons. This approach allows controlling the integration accuracy in relation to the number of coordinate mesh points and thus minimizing the number of irradiations at the given uncertainty of the full monitor response. In the previous works, responses of the divertor and blanket monitors to the isotropic point sources of DT and DD neutrons in the plasma profile and to the models of real sources were calculated within the ITER model using the MCNP code. The neutronics analyses have allowed formulating the basic principles of calibration that are optimal for having the maximum accuracy at the minimum duration of in situ experiments at the reactor. In this work, scenarios of the preliminary and basic experimental ITER runs are suggested on the basis of those principles. It is proposed to calibrate the monitors only with DT neutrons and use correction factors to the DT mode calibration for the DD mode. It is reasonable to perform full calibration only with {sup 235}U chambers and calibrate {sup 238}U chambers by responses of the {sup 235}U chambers during reactor operation (cross-calibration). The divertor monitor can be calibrated using both direct measurement of responses at the Gauss positions of a point source and simplified techniques based on the concepts of equivalent ring sources and inverse response distributions, which will considerably reduce the amount of measurements. It is shown that the monitor based on the average responses of the horizontal and vertical neutron chambers remains spatially stable as the source moves and can be used in addition to the staff monitor at neutron fluxes in the detectors four orders of magnitude lower than on the first wall, where staff detectors are located. Owing to low background, detectors of neutron chambers do not need calibration in the reactor because it is actually determination of the absolute detector efficiency for 14-MeV neutrons, which is a routine out-of-reactor procedure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2010

TIME EVOLUTION OF CORONAL MAGNETIC HELICITY IN THE FLARING ACTIVE REGION NOAA 10930

To study the three-dimensional (3D) magnetic field topology and its long-term evolution associated with the X3.4 flare of 2006 December 13, we investigate the coronal relative magnetic helicity in the flaring active region (AR) NOAA 10930 during the time period of December 8-14. The coronal helicity is calculated based on the 3D nonlinear force-free magnetic fields reconstructed by the weighted optimization method of Wiegelmann, and is compared with the amount of helicity injected through the photospheric surface of the AR. The helicity injection is determined from the magnetic helicity flux density proposed by Pariat et al. using Solar and Heliospheric Observatory/Michelson Doppler Imager magnetograms. The major findings of this study are the following. (1) The time profile of the coronal helicity shows a good correlation with that of the helicity accumulation by injection through the surface. (2) The coronal helicity of the AR is estimated to be -4.3 x 10{sup 43} Mx{sup 2} just before the X3.4 flare. (3) This flare is preceded not only by a large increase of negative helicity, -3.2 x 10{sup 43} Mx{sup 2}, in the corona over {approx}1.5 days but also by noticeable injections of positive helicity through the photospheric surface around the flaring magnetic polarity inversion line during the time period of the channel structure development. We conjecture that the occurrence of the X3.4 flare is involved with the positive helicity injection into an existing system of negative helicity.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Recent Developments of Thermal Protection Materials to Enable Lower Cost Space Missions

Introduction: Starting with the Commercial Crew Program, a new paradigm has emerged at NASA. Rather than designing rockets and spacecrafts for every mission optimized to achieve science, NASA has begun to use a service-based model and utilizing public-private partnership in developing the vehicles that can bring broader benefits as well as lower the cost for NASA missions. Commercial companies own and operate those vehicles. This allows NASA to not design missions from the bottom up, and has cost, risk, and schedule savings implications. On the other side, the constraints require meeting the requirements in terms of mass, volume, power, etc. By leveraging NASA developed technologies, commercial companies can quickly demonstrate the commercial mission concept, and, through technology transfer, adopt needed technology to address supply chain problems. A downside is that the technology has to be sufficiently mature to be transferred by NASA, which means that it requires significant investment, expertise, and time to develop. Space entities are focused on rapid development with an emphasis on manufacturing and integration innovation with reduced cost and schedule and quick entrance into the market. Thermal Protection Systems (TPS) are mission critical, but their development takes years, and involve access to arc jets or unique test facilities. Therefore, their development is both risky and investment heavy. NASA ARC developed several new TPS materials over the last decade (C-PICA, HEEET, 3MDCP, 3DMAT, ADEPT woven TPS) and brought them to high TPS maturity, making them enablers for commercials space missions from LEO, Lunar Sample Return, Mars, and Venus missions. LEO missions are relevant to future Mars missions due to the comparable entry conditions. External Partners' Missions: C-PICA is a recent improvement on NASA’s heritage PICA lightweight TPS ablator. C-PICA is now considered an enabling technology for New Frontiers and other NASA missions. C-PICA was infused into several missions from external partners. Varda Space Industries’ Winnebago-1 spacecraft successfully returned to Earth from LEO on Feb. 21st, 2024, using a C-PICA heatshield. Inversion Space’s Ray vehicle will test both ARC’s C-PICA and SIRCA TPS materials on a LEO return mission later in 2024. The Kentucky Re-Entry Probe Experiment (KREPE) is another example of a low-cost flight experiment to demonstrate the use of small entry capsules to gather data with three instrumented Kentucky Re-entry and Universal Payload System (KRUPS) capsules. NASA Arc provided C-PICA and Soft-PICA for two of the next KRUPS capsules scheduled to re-enter Earth from the ISS later in 2024. Finally, Rocket Lab’s low-cost mission to Venus, scheduled to launch in December 2024, will search for habitable conditions in Venus’ cloud layer, making use of NASA ARC provided HEEET insulation layer heat shield, and SIRCA backshell TPS materials. Future NASA Missions: NASA’s ability to help commercial missions can lead to future low-cost missions for several reasons: Competition encourages lower cost; technology maturation is now done at an integrated system level; and a common design architecture between commercial and scientific applications requires no specialized engineering design. From an engineering perspective, both of these commercial LEO capsules aforementioned are capable of a Mars entry; the commercial payload mut be replaced with as science payload. Finally, several NASA mission concepts, that could be candidates for future SIMPLEx program calls, such as VATMOS-SR and Nephele, both proposing to target the Venusian atmosphere, would make use of the HEEET insulation layer TPS for part of their heat shield.

TPS materials↗
NASA NTRSDate not supplied

Recent Developments of Thermal Protection Materials to Enable Lower Cost Space Missions

Introduction: Starting with the Commercial Crew Program, a new paradigm has emerged at NASA. Rather than designing rockets and spacecrafts for every mission optimized to achieve science, NASA has begun to use a service-based model and utilizing public-private partnership in developing the vehicles that can bring broader benefits as well as lower the cost for NASA missions. Commercial companies own and operate those vehicles. This allows NASA to not design missions from the bottom up, and has cost, risk, and schedule savings implications. On the other side, the constraints require meeting the requirements in terms of mass, volume, power, etc. By leveraging NASA developed technologies, commercial companies can quickly demonstrate the commercial mission concept, and, through technology transfer, adopt needed technology to address supply chain problems. A downside is that the technology has to be sufficiently mature to be transferred by NASA, which means that it requires significant investment, expertise, and time to develop. Space entities are focused on rapid development with an emphasis on manufacturing and integration innovation with reduced cost and schedule and quick entrance into the market. Thermal Protection Systems (TPS) are mission critical, but their development takes years, and involve access to arc jets or unique test facilities. Therefore, their development is both risky and investment heavy. NASA ARC developed several new TPS materials over the last decade (C-PICA, HEEET, 3MDCP, 3DMAT, ADEPT woven TPS) and brought them to high TPS maturity, making them enablers for commercials space missions from LEO, Lunar Sample Return, Mars, and Venus missions. LEO missions are relevant to future Mars missions due to the comparable entry conditions. External Partners' Missions: C-PICA is a recent improvement on NASA’s heritage PICA lightweight TPS ablator. C-PICA is now considered an enabling technology for New Frontiers and other NASA missions. C-PICA was infused into several missions from external partners. Varda Space Industries’ Winnebago-1 spacecraft successfully returned to Earth from LEO on Feb. 21st, 2024, using a C-PICA heatshield. Inversion Space’s Ray vehicle will test both ARC’s C-PICA and SIRCA TPS materials on a LEO return mission later in 2024. The Kentucky Re-Entry Probe Experiment (KREPE) is another example of a low-cost flight experiment to demonstrate the use of small entry capsules to gather data with three instrumented Kentucky Re-entry and Universal Payload System (KRUPS) capsules. NASA Arc provided C-PICA and Soft-PICA for two of the next KRUPS capsules scheduled to re-enter Earth from the ISS later in 2024. Finally, Rocket Lab’s low-cost mission to Venus, scheduled to launch in December 2024, will search for habitable conditions in Venus’ cloud layer, making use of NASA ARC provided HEEET insulation layer heat shield, and SIRCA backshell TPS materials. Future NASA Missions: NASA’s ability to help commercial missions can lead to future low-cost missions for several reasons: Competition encourages lower cost; technology maturation is now done at an integrated system level; and a common design architecture between commercial and scientific applications requires no specialized engineering design. From an engineering perspective, both of these commercial LEO capsules aforementioned are capable of a Mars entry; the commercial payload mut be replaced with as science payload. Finally, several NASA mission concepts, that could be candidates for future SIMPLEx program calls, such as VATMOS-SR and Nephele, both proposing to target the Venusian atmosphere, would make use of the HEEET insulation layer TPS for part of their heat shield.

TPS materials↗
DOE OSTI2025

Diagnostics: Chapter 8 of the special issue: on the path to tokamak burning plasma operation

This chapter presents the activity conducted by the ITPA topical group (TG) on Diagnostics over about the last 15 years. Following a general introduction of the ITER Diagnostics led by their measurement roles, the document is organized in several subchapters detailing the design support, research and development activity conducted by each of the specialist working groups (WGs) of the TG. Please note that the magnetic diagnostics were supported at the TG without a specific WG. Their status is included in the general introduction. In the following some highlights of the subchapter’s contents are provided. Recent advances in ITER first wall (FW) diagnostics for the measurements of plasma-metallic wall interaction in support of the ITER research plan are reported. An InfraRed imaging Video Bolometer for ITER has been developed and tested on several tokamaks to measure the radiated power loss. A laser-induced breakdown spectroscopy (LIBS) technique which utilizes a pulsed laser beam to ablate locally by forming a crater, will measure local tritium inventory in the FW material. Real-time Residual Gas Analyzers will measure the neutral gas composition in a divertor port and an equatorial port during plasma operation. Due to the full metallic FW environment, the plasma-wall interaction in ITER will face several challenges such as the compromised radiated power and divertor heat flux measurements by reflection. Ray tracing and analysis codes have been developed to eliminate and correct the effects of reflection in the measurements. The characteristics of the reflecting surfaces depending on the roughness and angle of the incidence have been measured by dedicated experiments, and the results were applied to the reflection elimination. For the measurement of the metallic impurity radiation induced by eroded metallic atoms, a vacuum ultraviolet spectrometer has been developed and tested. An extensive thermonuclear diagnostic suite will be required to support the operation of ITER and the planned experimental program for future burning plasma experiments. Due to the harsh environmental conditions, the implementation of diagnostic systems in ITER is a major challenge. These conditions include high levels of neutron and gamma fluxes, neutron heating, particle bombardment. Therefore, the selection and design of diagnostic systems must take into account a number of phenomena previously unseen in diagnostic design. For this reason, the measurement of neutrons and confined or lost fast ions, with particular emphasis on alpha particles, is critical to ITER. The diagnostics associated with these measurements will be important for future plasma-burning experiments at ITER. The high neutron emission and very large plasma size in ITER make neutron diagnostics the main diagnostic method used to measure plasma parameters such as fusion power, fusion power density, ion temperature, energy of fast ions and their spatial distributions in the plasma core. Active spectroscopy techniques are methods where a neutral particle beam is injected into the plasma and information on plasma parameters is extracted from the measurement of line emission resulting from the beam-plasma interaction, either by plasma ions or by beam atoms. Spatial localization is achieved by crossing the beamline and multiple observation lines. The ITER plasma will be a high temperature, moderately dense, fully ionized collisional plasma. The plasma facing surfaces are principally metallic being fashioned from beryllium or tungsten but many other elements, arising from either structural or from operational needs, may enter this plasma. The energy range of the emitted photons range from meV (infra-red) to multi keV (x-rays) and originate from all areas of the plasma volume. The primary role of passive emission diagnostics is to identify what is in the plasma from spectral signatures. Extracting quantitative information from these measurements such as impurity content, ion temperature, rotation, degree of detachment and radiated power depends on calibrated instruments, a physics model of the atomic and molecular processes and plasma transport and an analysis workflow that takes into account environmental effects such as reflections. The particular needs for ITER have prompted a multi-machine, many-year effort to address all these aspects and this chapter reviews the work on diagnostic design, experiments and new analysis techniques. An overview of the laser diagnostics to be implemented on ITER is also provided in this paper. This includes descriptions of the Thomson scattering in the core, edge and divertor regions, polarimetry and interferometry diagnostics used for measuring plasma density and also measurements of helium density in the divertor using Laser Induced Flourescence. Techniques which can allow improvements on current measurements are also addressed in particular expanding poloidal polarimetry measurements to measure field fluctuations and proposed use of dispersion interferometery which has a number of advantages over existing methods. This paper identifies particular areas where further research and testing on existing tokamaks is useful even at this advanced stage to inform the design of diagnostics for ITER. Outstanding areas of concern for the implementation of laser diagnostics, in particular with a view to reliable operation are identified. An overview of the latest developments of microwave diagnostic systems and techniques is given. The primary focus is the contributions for ITER—the next step burning plasma experiment—which is supplemented by describing recent progress of techniques applicable for fusion experiments beyond ITER. The contributions are intentionally kept concise, and are being supplemented by a rich list of references for further studies. Radiation induced effects are receiving continuous and well-deserved attention of the ITER diagnostic community and they are in many cases one of the primary design drivers of the ITER diagnostic systems. The paper summarizes recent progress in this area focusing primarily on the ITER diagnostics but in some cases provides also outlook for the possible solutions for even more demanding radiation environment of fusion reactors beyond ITER. Despite advancements in the area of modeling and simulation of various radiation induced effects, experimental testing in a nuclear environment as close as possible to the target one is still seen as unavoidable for proper qualification of particular diagnostic functional elements. Recent advancement within three diagnostic areas: optical diagnostics, magnetics and bolometers is covered. Encouraging results on qualification of silica glass vacuum window assemblies are presented. In the area of magnetic sensors, progress of irradiation tests performed on ITER in-vessel LTCC inductive sensors is presented with outlook for novel technological approaches to inductive sensors utilizing thick printing and photolithography technologies being highlighted. Summary of advancements in the area of steady state magnetic field sensors based on Hall effect is given. New results of neutron irradiation test of the ITER borosilicate glass inserts for vacuum electrical feedthroughs are summarized finding negligible swelling at target level of neutron fluence. Off-line irradiation tests of fiber optic current sensors for plasma current measurement demonstrated that both for gamma doses up to 5 MGy and a total neutron fluence up to 10 15 cm −2 , radiation induced changes are still compatible with required measurement accuracy on ITER. The ITER bolometers are given as an example how considering radiation effects may influence the diagnostic design. Finally, outlook for future main R&D directions is outlined. All optical and laser-based diagnostics in ITER will be using mirrors to guide plasma radiation toward detectors, cameras and sensors. In the hostile plasma, radiation and particle environment the optical characteristics of diagnostic mirrors will degrade directly affecting the entire performance of involved diagnostic systems. An assessment of factors affecting mirror performance is provided. Among the prime adverse factors are deposition of plasma impurities, sputtering of mirror surface and steam ingress in the vicinity of mirrors. Within the International Tokamak Physics Activity with active support by ITER central team and domestic agencies, the structured research and development (R&D) program on mitigation of risks for diagnostic mirrors is underway. Within this program the mirror material development, the passive mitigation of mirror degradation by using diagnostic ducts and shutters along with an active mirror recovery program comprising the in-situ mirror cleaning and calibration is underway. Recent developments in diagnostic mirror R&D are described in this Chapter along with an example of their implementation of R&D solutions in ITER Infrared Thermography diagnostic. An assessment of still open engineering and physics questions, considerations on mirror risks during an early phase of ITER operation are given along with an overview of diagnostic mirror evolution in the late ITER operation stage toward the demonstration fusion power plant. Several crucial areas of diagnostic R&D outlined in ITER Research Plan are addressed. The basic control groups in a fusion reactor can be broken-down in five categories: (1) plasma position, magnetic configuration, and plasma current control, (2) profile control and confinement optimization, (3) MHD control and suppression, (4) edge dissipation control, radiation and plasma exhaust control and (5) break-down optimization. These categories are coupled via the physics (a control action in one domain will affect the other domains) and via shared actuators (e.g. ECRH for impurity accumulation avoidance, current density distribution control and MHD suppression). Consequently, a supervisory control system should determine the priority of the various control tasks, their couplings, and the interfaces with the safety and interlock system. For the systematic development of the various controllers taking the complexity of the plasma and the control system into account, a model-based approach is required. A short historical overview is given of the developments in systems and control theory and control engineering with special emphasis on those developments that are most relevant for Nuclear Fusion research and operation. An overview is given of the state of the field of fusion plasma control for the control categories. It will be shown how synthetic diagnostics are being developed in ITER and how they are used in diagnostic design and design validation and how they can be in model-based controller synthesis using relatively simple models. In modern control methods, multiple diagnostics are used to constrain relatively simple models. The constrained models provide an estimate for the state. This opens the route to state controllers, such as model predictive control. A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modeling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g. to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modeling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of integrated data analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

ITER↗
DOE OSTI2013

Atmospheric retrieval analysis of the directly imaged exoplanet HR 8799b

Directly imaged exoplanets are unexplored laboratories for the application of the spectral and temperature retrieval method, where the chemistry and composition of their atmospheres are inferred from inverse modeling of the available data. As a pilot study, we focus on the extrasolar gas giant HR 8799b, for which more than 50 data points are available. We upgrade our non-linear optimal estimation retrieval method to include a phenomenological model of clouds that requires the cloud optical depth and monodisperse particle size to be specified. Previous studies have focused on forward models with assumed values of the exoplanetary properties; there is no consensus on the best-fit values of the radius, mass, surface gravity, and effective temperature of HR 8799b. We show that cloud-free models produce reasonable fits to the data if the atmosphere is of super-solar metallicity and non-solar elemental abundances. Intermediate cloudy models with moderate values of the cloud optical depth and micron-sized particles provide an equally reasonable fit to the data and require a lower mean molecular weight. We report our best-fit values for the radius, mass, surface gravity, and effective temperature of HR 8799b. The mean molecular weight is about 3.8, while the carbon-to-oxygen ratio is about unity due to the prevalence of carbon monoxide. Our study emphasizes the need for robust claims about the nature of an exoplanetary atmosphere to be based on analyses involving both photometry and spectroscopy and inferred from beyond a few photometric data points, such as are typically reported for hot Jupiters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Impact of K-Optimization on Trap Concentration in (Ag,Cu)(In,Ga)Se 2 Solar Cells

Recent progress has been made in improving efficiency in Cu(In,Ga)Se 2 (CIGS) absorbers with steady increases in peak efficiency. One way this has been achieved is by adding Ag to CIGS (ACIGS) to achieve higher V OC . However, these efficiencies are still well below the Shockley-Queisser efficiency limit, and traps acting as recombination centers and compensating centers are one thing limiting the solar efficiency. Past research in CIGS has shown that alkali treatment can improve the efficiency and metastability problems in CIGS, but less is understood about the role of alkali treatments in ACIGS. Here, we investigate the impact of potassium in ACIGS through its impact on trap incorporation and minority carrier lifetime. The samples in this study were grown in a roll-to-roll coater with Mo, ACIGS, CdS, and ZnO structure. Both cells were grown identically except the second (K-optimized) cell was grown with increased K-doping of the Mo contact and was more Cu-poor near the surface to optimize the K profile. This resulted in an efficiency increase from 16.8% to 18.7% for the K-optimized cell, which was mostly due to a 65 mV V OC increase. The doping profile showed a 2X increase in acceptor concentration of the K-optimized cell (2.5×10 15 cm -3 ) which suggests that the K was better incorporated into the ACIGS after these optimizations. To characterize the traps in these cells, deep level transient and optical spectroscopy were used. The DLOS measurements showed an EV+0.98 eV trap whose concentration was reduced 10X in the K-optimized cell (4×10 14 cm -3 ). This is consistent with previous results from the alkali treatment in CIGS, which also showed a decrease in near-conduction band trap concentration [1], but the reduction was larger than previous studies possibly indicating the combination of K and Cu-poor surface may both have aided in reducing the EV+0.98 eV trap concentration. The other primary trap is the mid-gap trap (EV+0.58 eV), which was quantified using DLTS. Its concentration decreased from 9×10 13 cm -3 to 4×10 13 cm -3 in the K-optimized sample, which is a 2X reduction. Previous studies have shown this trap is likely an efficient recombination center [2], which makes sense because it is near mid-gap. This trap has also been associated with V OC instability and reduction, so reducing this trap concentration is essential to achieve high V OC and cell efficiency. To help confirm this association, time-resolved photoluminescence measurements have shown in this sample set and a previous study that the minority carrier lifetime (19 ns baseline to 56 ns K-optimized) and this EV+0.56 eV trap concentration are inversely proportional, which would be expected for a recombination-limited carrier lifetime. The K optimization of ACIGS results in significantly improved cell efficiency and reduced trap concentrations that correlate with the improved material and electrical properties. We will report on this and the impact of the K optimization on the cell metastability.

14 SOLAR ENERGY↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2012

Validation of the BERT Point Source Inversion Scheme Using the Joint Urban 2003 Tracer Experiment Dataset (Final Report)

A terrorist attack in a U.S. city utilizing biological weapons could have severe consequences. A biological agent could be aerosolized and emitted into the air in the middle of a city, invisibly traveling with the winds, and dosing an unknowing populace. The magnitude of the problem would only be revealed as sick people started arriving several days later at hospitals with symptoms, many already too ill to be saved. A national program has deployed a network of biological agent collectors in U.S. cities to provide early detection of a bio-weapon attack, thereby hastening medical intervention and potentially saving many thousands of lives. In fact, the most effective treatment takes place prior to infection or in its early stages and early warning might reduce the disease progression and, consequently, the possibility of an outbreak. If a biological attack were to occur in a city, one or more collectors may register hits with specific dosages and the city would be alerted that an attack had taken place. This piece of information alone, however, would not be enough to determine how serious the attack was, i.e., how much biological agent was released into the air and where the bio-plume traveled. The first responders and public health communities will want to know what regions were impacted, how many persons might get sick, which people most need medical supplies, and where to clean up. The law enforcement community will want to look for forensic evidence at the release location. The Bio-Agent Event Reconstruction Tool (BERT) has been developed in order to recreate what might have happened during an airborne biological agent attack based on biological agent collector measurements and wind collectors mounted around a city. The tool can be used to estimate possible release areas while eliminating other areas, and can estimate bounds on the amount of material released. The tool can then be used to project forward from the possible source areas to estimate potential hazard zones. Due to a unique source inversion technique - called the upwind collector footprint approach - the tool runs fast and the source regions can be determined in a few minutes. In this report, we provide an overview of the BERT framework, followed by a description of the source inversion technique. The Joint URBAN 2003 field experiment held in Oklahoma City that was used to validate BERT is then described. Subsequent sections describe the metrics used for evaluation, the comparison of the experimental data and BERT output, and under what conditions the BERT tool succeeds and performs poorly. Results are aggregated in different ways (e.g., daytime vs. nighttime releases, 1 vs. 2 vs. 3 hit collectors) to determine if BERT shows any systematic errors. Finally, recommendations are given for how to improve the code and procedures for optimizing performance in operational mode.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. In this work, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. Finally, the NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

58 GEOSCIENCES↗