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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Microalgae-Based Platform for the Beneficial Re-use of Carbon Dioxide Emissions from Power Plants

This project sought to address the technical and economic barriers to carbon dioxide capture and utilization using microalgae. Operating data were collected in 2016 and 2017 during cultivation of Scenedesmus acutus at Duke Energy’s East Bend Station – a coal-fired power plant located in northern Kentucky – using flue gas as the CO 2 source. Algae were grown in a 1200 L “cyclic flow” photobioreactor (PBR) designed by the University of Kentucky. A key finding was that the harvested algae contained only very low concentrations of heavy metals (As, Cd, Hg, Se), consistent with heavy metals incorporation from the supplied nutrients. This indicates that algal biomass produced from coal-derived flue gas would be suitable for a variety of applications, including the production of bioplastics, use as fertilizer, etc. A lifecycle assessment showed that the UK-designed PBR employed in this work qualifies as a net CO 2 capture technology. Indeed, over a 30-year period, net CO 2 capture would equate to 43% of the targeted amount, i.e., the amount captured from the supplied flue gas. A techno-economic analysis indicated that the minimum production cost of Scenedesmus acutus biomass in the US is in the order of 875 USD/ton, excluding the cost of capital. While this figure is not too dissimilar to values reported for open raceway ponds in similar scenarios, it emphasizes that for current cultivation technology any pathway to economic viability will require applications for which algal boimass can be sold at prices in excess of 1,000 USD/ton. Currently, such applications represent relatively small markets, such as pigments (e.g., astaxanthin) and nutraceuticals (ω-3 unsaturated fatty acids), as well as nutritional supplements (whole algae) for human consumption and for use in pet food. Consequently, the commercialization of large-scale algae-based CO 2 capture and utilization will require the development of new technologies to reduce the cost of algae production and/or the development of new, high-value applications for algal biomass. One of the more promising applications for algal biomass is in the production of bioplastics. In this work, the potential for the algae grown in the East Bend PBR for the production of bioplastic with adequate mechanical properties was clearly shown. Positive features of the produced biomass included a high protein content and a composition that was generally more homogeneous than biomass grown in open ponds (in which many species may be present). The best candidate for further review, after incorporation into ethylene-vinyl acetate, was a lipid and sugar extracted material, which showed the highest extension values with comparable load values to other UK-derived samples. It also demonstrated extension benefits against the Algix Bloom product (currently offered commercially), even though it contained agglomerates which generally exert a negative effect on mechanical properties. This leads to speculation that with enhanced milling that exists on the commercial scale, the sugar and fat extracted product may be even more competitive. This, in turn, points to the need for additional work in order to assess the properties of such optimized algae-based plastics and the price point they can command.

09 BIOMASS FUELS↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process – 23280

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from spent fuel for recycling. The extraction of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. Beartooth will allow researchers to refine extraction processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry that uses centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on sensor data to determine signal features with the goal of providing a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

General Description of Fission Observables: GEF Model Code

The GEF (“GEneral description of Fission observables”) model code is documented. It describes the observables for spontaneous fission, neutron-induced fission and, more generally, for fission of a compound nucleus from any other entrance channel, with given excitation energy and angular momentum. The GEF model is applicable for a wide range of isotopes from Z = 80 to Z = 112 and beyond, up to excitation energies of about 100 MeV. The results of the GEF model are compared with fission barriers, fission probabilities, fission-fragment mass- and nuclide distributions, isomeric ratios, total kinetic energies, and prompt-neutron and prompt-gamma yields and energy spectra from neutron-induced and spontaneous fission. Derived properties of delayed neutrons and decay heat are also considered. The GEF model is based on a general approach to nuclear fission that explains a great part of the complex appearance of fission observables on the basis of fundamental laws of physics and general properties of microscopic systems and mathematical objects. The topographic theorem is used to estimate the fission-barrier heights from theoretical macroscopic saddle-point and ground-state masses and experimental ground-state masses. Motivated by the theoretically predicted early localisation of nucleonic wave functions in a necked-in shape, the properties of the relevant fragment shells are extracted. These are used to determine the depths and the widths of the fission valleys corresponding to the different fission channels and to describe the fission-fragment distributions and deformations at scission by a statistical approach. A modified composite nuclear-level-density formula is proposed. It respects some features in the superfluid regime that are in accordance with new experimental findings and with theoretical expectations. These are a constant-temperature behaviour that is consistent with a considerably increased heat capacity and an increased pairing condensation energy that is consistent with the collective enhancement of the level density. The exchange of excitation energy and nucleons between the nascent fragments on the way from saddle to scission is estimated according to statistical mechanics. As a result, excitation energy and unpaired nucleons are predominantly transferred to the heavy fragment in the superfluid regime. This description reproduces some rather peculiar observed features of the prompt-neutron multiplicities and of the even-odd effect in fission-fragment Z distributions. For completeness, some conventional descriptions are used for calculating pre-equilibrium emission, fission probabilities and statistical emission of neutrons and gamma radiation from the excited fragments. Preference is given to simple models that can also be applied to exotic nuclei compared to more sophisticated models that need precise empirical input of nuclear properties, e.g. spectroscopic information. The approach reveals a high degree of regularity and provides a considerable insight into the physics of the fission process. Fission observables can be calculated with a precision that complies with the needs for applications in nuclear technology without specific adjustments to measured data of individual systems. The GEF executable runs out of the box with no need for entering any empirical data. This unique feature is of valuable importance, because the number of systems and energies of potential significance for fundamental and applied science will never be possible to be measured. The relevance of the approach for examining the consistency of experimental results and for evaluating nuclear data is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Quantitative analysis of magnetic spin and orbital moments from an oxidized iron (1 1 0) surface using electron magnetic circular dichroism

Understanding the ramifications of reduced crystalline symmetry on magnetic behavior is a critical step in improving our understanding of nanoscale and interfacial magnetism. However, investigations of such effects are often controversial largely due to the challenges inherent in directly correlating nanoscale stoichiometry and structure to magnetic behavior. Here, we describe how to use Transmission Electron Microscope (TEM) to obtain Electron Magnetic Circular Dichroism (EMCD) signals as a function of scattering angle to locally probe the magnetic behavior of thin oxide layers grown on an Fe (1 1 0) surface. Experiments and simulations both reveal a strong dependence of the magnetic orbital to spin ratio on its scattering vector in reciprocal space. We exploit this variation to extract the magnetic properties of the oxide cladding layer, showing that it locally may exhibit an enhanced orbital to spin moment ratio. This finding is supported here by both spatially and angularly resolved EMCD measurements, opening up the way for compelling investigations into how magnetic properties are affected by nanoscale features.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗
DOE OSTI2020

Targeting tRNA-synthetase interactions towards novel therapeutic discovery against eukaryotic pathogens

The development of chemotherapies against eukaryotic pathogens is especially challenging because of both the evolutionary conservation of drug targets between host and parasite, and the evolution of strain-dependent drug resistance. There is a strong need for new nontoxic drugs with broad-spectrum activity against trypanosome parasites such as Leishmania and Trypanosoma. A relatively untested approach is to target macromolecular interactions in parasites rather than small molecular interactions, under the hypothesis that the features specifying macromolecular interactions diverge more rapidly through coevolution. We computed tRNA Class-Informative Features in humans and independently in eight distinct clades of trypanosomes, identifying parasite-specific informative features, including base pairs and base mis-pairs, that are broadly conserved over approximately 250 million years of trypanosome evolution. Validating these observations, we demonstrated biochemically that tRNA:aminoacyl-tRNA synthetase (aaRS) interactions are a promising target for anti-trypanosomal drug discovery. From a marine natural products extract library, we identified several fractions with inhibitory activity toward Leishmania major alanyl-tRNA synthetase (AlaRS) but no activity against the human homolog. These marine natural products extracts showed cross-reactivity towards Trypanosoma cruzi AlaRS indicating the broad-spectrum potential of our network predictions. We also identified Leishmania major threonyl-tRNA synthetase (ThrRS) inhibitors from the same library. We discuss why chemotherapies targeting multiple aaRSs should be less prone to the evolution of resistance than monotherapeutic or synergistic combination chemotherapies targeting only one aaRS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Exploring Causes of Beam Loss at CEBAF

At Jefferson Lab, the Continuous Electron Beam Accelerator (CEBAF) features a unique design with two linear accelerators and two arc sections allowing for multiple turns of the electron beam, as well as four experimental end stations. This topology leads to increased beam losses, especially in the spreader and recombiner regions connecting the arcs to the linacs and in the extraction regions connecting the experimental end stations to the accelerator. These losses result in equipment activation and operational interruptions. Recent upgrades to the facility’s diagnostic systems, including the addition of xenon ion chambers, have provided higher-resolution data regarding these loss events. Building on this improved observational capability, we are developing a simulation framework using optics codes and the Geant4-based BDSIM to model beam extinction and halo formation in these regions. This work aims to correlate simulation results with experimental data to isolate the causes of beam loss and inform future machine tuning strategies. We present a summary of conclusions drawn from recent operational studies and outline a plan to model the beam loss and validate the simulations.

Matthews, C. [Old Dominion Univ., Norfolk, VA (Uni↗
DOE OSTI2021

Bayesian Spatially Varying Multi-Regularization Image Deblurring

Many scientific experiments such as those found in astronomy, geology, microbiology, and X-ray radiography require the use of high-energy instruments to capture images. Since blur and noise are inevitably present in any imaging system, the images must be \deblurred" to extract the full information content. Mathematically, image deblurring is an ill-posed inverse problem that requires regularization. The regularization, in turn, has a large effect on the deblurred image: different regularization strengths, and types, lead to drastically different reconstructions. Moreover, many images contain a mixture of smooth and sharp features which suggests the use of multi-regularization, i.e., varying the type of regularization (e.g. Tikhonov or total variation) across the image. We address these issues by formulating the image deblurring problem within a hierarchical Bayesian framework in which we spatially adapt the strength of the regularization and also vary the regularization type across the image. In this way, the image itself, along with corresponding regularization strength at each pixel, are described jointly by a posterior distribution which we can sample by Markov chain Monte Carlo (MCMC) methods. We illustrate our techniques on simplified test problems and apply them to high-energy X-ray images taken at the Nevada National Security Site. Numerical tests show that our new method is robustly applicable and increases the quality of the image reconstruction when compared to other (Bayesian) methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Noncentrosymmetric Triangular Magnet CaMnTeO 6 : Strong Quantum Fluctuations and Role of s 0 versus s 2 Electronic States in Competing Exchange Interactions

Abstract Noncentrosymmetric triangular magnets offer a unique platform for realizing strong quantum fluctuations. However, designing these quantum materials remains an open challenge attributable to a knowledge gap in the tunability of competing exchange interactions at the atomic level. Here, a new noncentrosymmetric triangular S = 3/2 magnet CaMnTeO 6 is created based on careful chemical and physical considerations. The model material displays competing magnetic interactions and features nonlinear optical responses with the capability of generating coherent photons. The incommensurate magnetic ground state of CaMnTeO 6 with an unusually large spin rotation angle of 127°(1) indicates that the anisotropic interlayer exchange is strong and competing with the isotropic interlayer Heisenberg interaction. The moment of 1.39(1) µB , extracted from low‐temperature heat capacity and neutron diffraction measurements, is only 46% of the expected value of the static moment 3 µB . This reduction indicates the presence of strong quantum fluctuations in the half‐integer spin S = 3/2 CaMnTeO 6 magnet, which is rare. By comparing the spin‐polarized band structure, chemical bonding, and physical properties of AMnTeO 6 ( A = Ca, Sr, Pb), how quantum‐chemical interpretation can illuminate insights into the fundamentals of magnetic exchange interactions, providing a powerful tool for modulating spin dynamics with atomically precise control is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A greedy algorithm for computing eigenvalues of a symmetric matrix with localized eigenvectors

Here, we present a greedy algorithm for computing selected eigenpairs of a large sparse matrix $H$ that can exploit localization features of the eigenvector. When the eigenvector to be computed is localized, meaning only a small number of its components have large magnitudes, the proposed algorithm identifies the location of these components in a greedy manner, and obtains approximations to the desired eigenpairs of $H$ by computing eigenpairs of a submatrix extracted from the corresponding rows and columns of $H$. Even when the eigenvector is not completely localized, the approximate eigenvectors obtained by the greedy algorithm can be used as good starting guesses to accelerate the convergence of an iterative eigensolver applied to $H$. We discuss a few possibilities for selecting important rows and columns of $H$ and techniques for constructing good initial guesses for an iterative eigensolver using the approximate eigenvectors returned from the greedy algorithm. We demonstrate the effectiveness of this approach with examples from nuclear quantum many-body calculations and many-body localization studies of quantum spin chains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data

Our study aims to explore the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain-hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain-hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain-hillslope transect at 0.50-m resolution and extracted LiDAR-derived metrics to model the rapid change in microtopography. Furthermore, we investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long-term monitoring to capture community-specific physiological responses to perturbations, offering the possibility of bridging local plot-scale observations with large landscape monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Accurate determination of the valence band edge in hard x-ray photoemission spectra using GW theory

We introduce a new method for determining accurate values of the valence-band maximum in x-ray photoemission spectra. Specifically, we align the sharpest peak in the valence-band region of the experimental spectrum with the corresponding feature of a theoretical valence-band density of states curve from ab initio GW theory calculations. This method is particularly useful for soft and hard x-ray photoemission studies of materials with a mixture of valence-band characters, where strong matrix element effects can render standard methods for extracting the valence-band maximum unreliable. We apply our method to hydrogen-terminated boron-doped diamond, which is a promising substrate material for novel solar cell devices. By carrying out photoemission experiments with variable light polarizations, we verify the accuracy of our analysis and the general validity of the method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Endogenous and asymmetric magnetic reconnection with associated processes of relevance to fusion burning plasmas

We report an endogenous magnetic reconnection process is characterized by a driving factor that lays within the layer where a drastic change of magnetic field topology occurs. This kind of process is shown to take place in the presence of an electron temperature gradient in a well-confined plasma where, referring to quasi-collisionless regimes, the resulting electron temperature fluctuations can be anisotropic. Then a class of (radially) localized reconnecting modes is identified. These involve a transverse generated field $\tilde{B}_x$ of odd parity (as a function of the radial variable), and have finite (phase) velocities of propagation contrary to commonly considered reconnecting modes. The widths of the relevant reconnection layers remain significant even when large macroscopic distances are considered. Given that there are plasmas in the Universe with considerable electron thermal energy contents, these features can be relied upon in order to produce magnetic field generation, or conversion of magnetic energy into particle energy when the coupling of the localized odd modes to extended even modes can be significant. In any case, the resulting magnetic islands are not symmetric. With their excitation these modes can extract momentum from the main body of the plasma column which should recoil in the opposite direction. The excitation of antisymmetric endogenous modes is shown to be relevant to the electron temperature heating due to the reaction products in a fusion burning plasma as, in this case, the longitudinal thermal conductivity on selected rational magnetic surfaces can be decreased, relative to its collisional value, by the effects of reconnection. This kind of steepening is proposed to have a role in enhancing the growth rate of the instability involved in disruption events of the plasma column. The best agreement between theory and experiments concerning the onset of magnetic reconnection is (probably) represented by the theory of the internal kink mode. The observed accelerated reconnection rate following the onset is suggested as being explained by the formation of a relatively large magnetic island with a local steepening of the electron temperature gradient. A new kind of odd 'thermonuclear heating' mode associated with symmetric reconnection is identified.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Compact, Low-Cost, Light-Weight, Superconducting, Ironless Cyclotrons for Hadron Radiotherapy

Superconducting cyclotrons are increasingly employed for proton beam radiotherapy treatment (PBRT). The use of superconductivity in a cyclotron design can reduce its mass by an order of magnitude and size by a factor of 3-4 over conventional resistive magnet technology, yielding significant reduction in overall cost of the device, the accelerator vault, and its infrastructure, as well as reduced operating costs. At MIT, previous work was focused on developing a very high field (9 T at the pole face) superconducting synchrocyclotron that resulted in a highly compact device that is about an order of magnitude lighter, and much smaller in diameter than a conventional, resistive cyclotron. The results of the study reported here were focused on a conceptual design for a compact superconducting synchrocyclotron to demonstrate the possibility to further reduce its weight by almost another order of magnitude by eliminating all iron from the device. In the absence of magnetic iron poles, the magnetic field profile in the beam gap is achieved through a set of main superconducting split pair coils energized in series with a set of distributed field-shaping superconducting coils. External magnetic field shielding is achieved through a set of outer, superconducting ring coils, also connected in series with the other coils, to cancel the stray magnetic field. These shielding coils replace the heavy iron yoke which is the conventional method to return the magnetic flux. It is noted that the 10 Gauss surface is located at a radius of about 3.5 m comparable in both ironless and conventional devices, even in the absence of iron in the ironless device. An important result from eliminating all magnetic iron in the flux circuit is the resulting linear relationship between the operating current and the magnetic field intensity. In the case with iron, the saturation of the magnetic field forces operation at one value of magnetic field. This feature design then enables continuous beam energy variation without the use of an energy degrader, thus eliminating secondary radiation during the in-depth beam scanning, increasing the ion current delivered to the patient and improving the beam quality. The beam energy is determined by the magnetic field strength at the extraction radius, and changing the field enables selection of the final beam energy. The magnetic field can be adjusted while maintaining the needed radial field profile.

Minervini, Joseph V.↗
DOE OSTI2016

2D and 3D characterization of pore defects in die cast AM60

The widespread application of die castings can be hampered due to the potential of large scale porosity to act as nucleation sites for fracture and fatigue. It is therefore important to develop robust approaches to the characterization of porosity providing parameters that can be linked to the material's mechanical properties. We have tackled this problem in a study of the AM60 die cast Mg alloy, using samples extracted from a prototype shock tower. A quantitative characterization of porosity has been undertaken, analyzing porosity in both 2D (using classical metallographic methods) and in 3D (using X-ray computed tomography (XCT)). Metallographic characterization results show that shrinkage pores and small gas pores can be distinguished based on their distinct geometrical features. Shrinkage pores are irregular with multiple arms, resulting in a form factor less than 0.4. In contrast, gas pores are generally more circular in shape yielding form factors larger than 0.6. XCT provides deeper insight into the shape of pores, although this understanding is limited by the resolution obtainable by laboratory based XCT. It also shows how 2D sectioning can produce artefacts as single complex pores are sectioned into multiple small pores. - Highlights: • Mg (e.g. AM60) die castings may contain large scale porosity that act as nucleation sites for fracture and fatigue • Quantitative characterization of porosity metallography (2D) and X-ray tomography (3D) is used • Shrinkage pores and small gas pores can be distinguished based on their distinct geometrical features. • Shrinkage pores are irregular giving a form factor < 0.4; gas pores are rounder with form factors > 0.6 • XCT enables pore visualization, although limited by the resolution obtainable by laboratory based XCT.

36 MATERIALS SCIENCE↗