Manufacturing High Entropy Alloys: Pathway to Industrial Competitiveness

High entropy alloys (HEAs) provide a transformative opportunity to design materials that are custom tailored to the distinct needs of a given application, thereby shifting the paradigm from “apply the material you have” to “engineer the material you need.” HEAs will enable high-performance manufactured goods that are competitive in the international marketplace through extraordinary material properties and unique property combinations. HEAs deliver new choices to manufacturers to create alternatives to materials that are rare, hazardous, expensive, or subject to international restrictions or conflict. The potential benefits of HEAs span diverse fields and applications, and show promise to not only accelerate economic growth and domestic competitive advantage, but also address pressing societal challenges. These include solid state cooling, liquefied natural gas handling, nuclear degradation- resistant materials, corrosion-resistant heat exchangers, and efficiency gains from high temperature performance that advance national energy goals; high-performance aerospace materials and ultra- hardness ballistics that support national security; and strong, corrosion-resistant medical devices and advances in magnetic resonance imaging that are essential to national health priorities. Research advances are setting the stage to realize each of these vital areas. However, research advances made to-date to produce lab-scale prototypes do not lend themselves to manufacturing at scale. For Americans to fully benefit from HEAs, the emerging technologies must be translated into products manufactured at scale in the United States. However, manufacturers and HEA experts who are working to bridge this gap are encountering cross-cutting barriers in manufacturing processes, testing, data, and access to the necessary resources. Through strategic public- and private- sector research and investment, these barriers can be overcome. The United States has invested in both HEA research and advanced materials resources, such as material sample creation at the Ames Laboratory Materials Preparation Center, material characterization at Oak Ridge National Laboratory’s Neutron User Facilities, and modeling and analysis through the National Institute of Standards and Technology’s Material Genome Initiative. A vast array of research and expertise has been fostered at federal laboratories and universities, yielding promising alloys, manufacturing processes, and analysis methods.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/146747/1/Manufacturing-HEAs.pdf-1Description of Manufacturing-HEAs.pdf : Main articl

Metamaterials Manufacturing: Pathway to Industrial Competitiveness

Metamaterials are artificially structured materials with the promise to remove performance constraints associated with conventional materials, redefining the boundaries of materials science and offering a wealth of new opportunities for innovation and economic growth. The prospects are powerful. National security applications of metamaterials range from enhanced stealth technology to improved military communication to higher-quality reconnaissance imaging to next-generation body armor. Health implications range from greatly improved medical imaging and research tools to superior injury protection products. Metamaterials also have promising energy applications in transportation light-weighting, as well as energy generation and storage technologies. By 2025, it is estimated that metamaterials manufacturing will be a multi-billion-dollar market. The United States has invested heavily in the potential of metamaterials, and U.S. experts and research facilities lead the world in publications, citations, and intellectual property related to this emerging field. Realizing the true benefits of these emerging technologies—and return on federal investments—will require advancing metamaterials from prototypes to products manufactured at scale. Manufacturing of these materials at the volume and quality needed for practical applications requires process innovation and establishment of a strong supporting ecosystem. This report examines the challenges and opportunities facing metamaterials manufacturing and presents a set of actionable recommendations for realizing the promised impact.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/145155/1/MetamaterialsManufacturingReport-May21_digital-reduced.pd

Design of Generalized Fiber-reinforced Elasto-fluidic Systems.

From nature to engineered solutions, the metrics of mechanical systems are often strength, power density, resilience, adaptability, safety, scalability, and the ability to generate the necessary forces, motions, and forms. The use of fluidic structures with fiber reinforcement to realize these metrics is seen throughout nature; however, these structures are rarely used by engineers, in part due to the absence of a generalized understanding of their kinematics and forces. Fiber-reinforced elasto-fluidic systems use fluid pressure to actuate an envelope with tuned compliance to provide desired motion, forces, flexibility, and transmission of energy. These structures combine the high strain energy utilization and flexibility of fibers, the versatility and compressive load abilities of fluids, and the continuum nature of soft materials, exploiting the best features of each. This dissertation discovered a vast array of previously unknown fiber-reinforced elasto-fluidic systems, models their mechanical behavior, experimentally verifies the models, creates methods for easy design synthesis, and applies this knowledge to multiple practical applications. Only a small subset of elasto-fluidic systems, popularly known as McKibben actuators, has been thoroughly investigated. Therefore, a vast design space of possible structures with multiple sets of fibers and different orientations yielding a rich array of functionality were yet to be investigated and applied to a wealth of applications. This dissertation develops the mechanics of generalized fiber-reinforced elasto-fluidic systems by first modeling the relationship of volume change and fiber orientation to motion kinematics and force generation. The kinematics of motions including translation, rotation, screw, bending, and helical were all modeled. Fiber configurations spanning the design space were tested to experimentally verify the predicted forces and motion. The force and kinematics were combined to form a design synthesis tool that maps the desired motions, freedoms, and constraints to fiber configurations. Synthesis methods were created for parallel combination of fiber-reinforced structures using discretized force and freedom directions. Lastly, novel applications were created using these fiber-reinforced elasto-fluidic structures, including an orthosis device for arm rotation contractures, a soft hexapod robot with an actuated flexible spine, and a structure for anchoring within pipes.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107202/1/joshbm_1.pd

A comparison of random forests, boosting and support vector machines for genomic selection

Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs

Statistical Modeling of SAR Images: A Survey

Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It aims to describe SAR images through statistical methods and reveal the characteristics of these images. Moreover, statistical modeling can provide a technical support for a comprehensive understanding of terrain scattering mechanism, which helps to develop algorithms for effective image interpretation and creditable image simulation. Numerous statistical models have been developed to describe SAR image data, and the purpose of this paper is to categorize and evaluate these models. We first summarize the development history and the current researching state of statistical modeling, then different SAR image models developed from the product model are mainly discussed in detail. Relevant issues are also discussed. Several promising directions for future research are concluded at last

Time separation as a hidden variable to the Copenhagen school of quantum mechanics

The Bohr radius is a space-like separation between the proton and electron in the hydrogen atom. According to the Copenhagen school of quantum mechanics, the proton is sitting in the absolute Lorentz frame. If this hydrogen atom is observed from a different Lorentz frame, there is a time-like separation linearly mixed with the Bohr radius. Indeed, the time-separation is one of the essential variables in high-energy hadronic physics where the hadron is a bound state of the quarks, while thoroughly hidden in the present form of quantum mechanics. It will be concluded that this variable is hidden in Feynman's rest of the universe. It is noted first that Feynman's Lorentz-invariant differential equation for the bound-state quarks has a set of solutions which describe all essential features of hadronic physics. These solutions explicitly depend on the time separation between the quarks. This set also forms the mathematical basis for two-mode squeezed states in quantum optics, where both photons are observable, but one of them can be treated a variable hidden in the rest of the universe. The physics of this two-mode state can then be translated into the time-separation variable in the quark model. As in the case of the un-observed photon, the hidden time-separation variable manifests itself as an increase in entropy and uncertainty.Comment: LaTex 10 pages with 5 figure. Invited paper presented at the Conference on Advances in Quantum Theory (Vaxjo, Sweden, June 2010), to be published in one of the AIP Conference Proceedings serie

Climate change and visual imagery

© 2013 John Wiley & Sons, Ltd.O'Neill, S. J. and Smith, N. (2014), Climate change and visual imagery. WIREs Clim Change, 5: 73–87. doi: 10.1002/wcc.249 The definitive version is available at http://onlinelibrary.wiley.com/doi/10.1002/wcc.249/abstractMany actors—including scientists, journalists, artists, and campaigning organizations—create visualizations of climate change. In doing so, they evoke climate change in particular ways, and make the issue meaningful in everyday discourse. While a diversity of climate change imagery exists, particular types of climate imagery appear to have gained dominance, promoting particular ways of knowing about climate change (and marginalizing others). This imagery, and public engagement with this imagery, helps to shape the cultural politics of climate change in important ways. This article critically reviews the nascent research area of the visual representations of climate change, and public engagement with visual imagery. It synthesizes a diverse body of research to explore visual representations and engagement across the news media, NGO communications, advertising, and marketing, climate science, art, and virtual reality systems. The discussion brings together three themes which occur throughout the review: time, truth, and power. The article concludes by suggesting fruitful directions for future research in the visual communication of climate change.ESR

The Moderating Effect of Self-Reported State and Trait Anxiety on the Late Positive Potential to Emotional Faces in 6–11-Year-Old Children

Introduction: The emergence of anxiety during childhood is accompanied by the development of attentional biases to threat. However, the neural mechanisms underlying these biases are poorly understood. In addition, previous research has not examined whether state and trait anxiety are independently associated with threat-related biases. Methods: We compared ERP waveforms during the processing of emotional faces in a population sample of 58 6–11-year-olds who completed self-reported measures of trait and state anxiety and depression. Results: The results showed that the P1 was larger to angry than neutral faces in the left hemisphere, though early components (P1, N170) were not strongly associated with child anxiety or depression. In contrast, Late Positive Potential (LPP) amplitudes to angry (vs. neutral) faces were significantly and positively associated with symptoms of anxiety/depression. In addition, the difference between LPPs for angry (vs. neutral) faces was independently associated with state and trait anxiety symptoms. Discussion: The results showed that neural responses to facial emotion in children with elevated symptoms of anxiety and depression were most evident at later processing stages characterized as evaluative and effortful. The findings support cognitive models of threat perception in anxiety and indicate that trait elements of anxiety and more transitory fluctuations in anxious affect are important in understanding individual variation in the neural response to threat in late childhood