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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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With this work, we aim to speed up simulation and reduce computational complexity of Converter Dominated Power System (CDPS) within an acceptable accuracy.
A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.
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Transdisciplinary modeling of the cerebellum across histology, physiology, and network engineering provides preliminary results at three organization levels: input/output links to central nervous system networks; links between the six neuron populations in the cerebellum; and computation among the neurons of the populations. Older models probably underestimated the importance and role of climbing fiber input which seems to supply write as well as read signals, not just to Purkinje but also to basket and stellate neurons. The well-known mossy fiber-granule cell-Golgi cell system should also respond to inputs originating from climbing fibers. Corticonuclear microcomplexing might be aided by stellate and basket computation and associate processing. Technological and scientific implications of the proposed cerebellum model are discussed.
Mechanical loading helps define the architecture of weight-bearing bone via the tightly regulated process of skeletal turnover. Turnover occurs by the concerted activity of osteoblasts, responsible for bone formation. and osteoclasts, responsible for bone resorption. Osteoclasts are specialized megakaryon macrophages, which differentiate from monocytes in response to resorption stimuli, such as reduced weight-bearing. Habitation in space dramatically alters musculoskeletal loading, which modulates both cell function and bone structure. Our long-term objective is to define the molecular and cellular mechanisms that mediate skeletal adaptations to altered gravity environments. Our experimental approach is to apply hypergravity loads by centrifugation to rodents and cultured cells. As a first step, we examined the influence of centrifugation on the structure of cancellous bone in rats to test the ability of hypergravity to change skeletal architecture. Since cancellous bone undergoes rapid turnover we expected the most dramatic structural changes to occur in the shape of trabeculae of weight-bearing, cancellous bone. To define the cellular responses to hypergravity loads, we exposed cultured osteoblasts and macrophages to centrifugation. The intraosseous and intramedullary pressures within long bones in vivo reportedly range from 12-40 mm Hg, which would correspond to 18-59 gravity (g) in our cultures. We assumed that hydrostatic pressure from the medium above the cell layer is at least one major component of the mechanical load generated by centrifuging cultured cells. and therefore we exposed the cells to 10-50g. In osteoblasts, we examined the structure of their actin and microtubule networks, production of prostaglandin E2 (PGE2), and cell survival. Analysis of the shape of the cytoskeletal networks provides evidence for the ability of centrifugation to affect cell structure, while the production of PGE2 serves as a convenient marker for mechanical stimulation. We examined cell survival, reasoning that osteoblasts might mold skeletal structure in a hypergravity environment in part by regulating apoptosis and thus the duration of osteoblast productivity. Finally, we tested the influence of centrifugation on microbial activation of a macrophage cell line (RAW264.7). In response to the appropriate hormonal stimulation, this cell line is reportedly capable of undergoing differentiation to express osteoclast markers. In addition, a component of the cell wall of gram-negative bacteria, lipopolysaccaride (LPS), stimulates the formation of osteoclasts in vivo. Thus we tested the influence on centrifugation on RAW264.7 cells stimulated with LPS to provide an index of the function of osteoclast precursors.
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BL Lac objects, extracted from the Einstein Extended Medium Sensitivity Survey (EMSS), are examined. X-ray selection proved to be a powerful tool to find new BL Lacs, and allows the creation of complete, well defined and sizable samples. X-ray selected BL Lac objects (XBL) have in general more starlight in the optical spectra than radio selected BL Lacs. Redshifts for a significant fraction of objects in samples of XBL can be determined. It is thus possible to study the cosmological properties of BL Lac objects. Different models of luminosity functions of BL Lacs, including relativistic beaming, are considered and integrated over luminosity and redshift. The results are compared with the observed number-counts. The observed redshift distribution and the models' predictions are analyzed.
Low-dimensional spin-1/2 transition metal oxides and oxyhalides continue to be at the forefront of research investigating nonclassical phases such as quantum spin liquids. In this work, we examine the magnetic properties of the oxyhalide Cu 2 (OH) 3 Br in the botallackite structure using first-principles density functional theory, linear spin-wave theory, and exact diagonalization calculations. This quasi-two-dimensional system consists of Cu 2+ S = 1/2 moments arranged on a distorted triangular lattice. Our exact diagonalization calculations, which rely on a first-principles-based magnetic model, generate spectral functions consistent with inelastic neutron scattering data. By performing computational experiments to disentangle the chemical and steric effects of the halide ions, we find that the dominant effect of the halogen ions is steric in the Cu 2 (OH) 3 X series of compounds.
A thermodynamic framework (UNIQUAC model with temperature dependent parameters) is applied to model the salt-induced protein crystallization equilibrium, i.e., protein solubility. The framework introduces a term for the solubility product describing protein transfer between the liquid and solid phase and a term for the solution behavior describing deviation from ideal solution. Protein solubility is modeled as a function of salt concentration and temperature for a four-component system consisting of a protein, pseudo solvent (water and buffer), cation, and anion (salt). Two different systems, lysozyme with sodium chloride and concanavalin A with ammonium sulfate, are investigated. Comparison of the modeled and experimental protein solubility data results in an average root mean square deviation of 5.8%, demonstrating that the model closely follows the experimental behavior. Model calculations and model parameters are reviewed to examine the model and protein crystallization process. Copyright 1999 John Wiley & Sons, Inc.
In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.
Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.
This is an exciting time for aviation. New vehicle and airspace technologies promise large increases in the number of aircraft in operation. One critical technology for these emerging markets is the increased use of automated systems to reduce pilot skill, training, and proficiency requirements. While the use of these systems promises to reduce or eliminate pilot functions in the long-term, the technology development for the required functions will necessitate a phased transition. The transition to, and adoption of automated systems will generate new safety challenges. This paper is a first look at a model to help frame flight crew functions for evaluation of future operational requirements. The model is intended to provide required flight crew functions regardless of whether the functions are performed by human or artificial agent. It is hoped that the model will be useful in identifying safety challenges and enabling a safe transition for the new aviation markets. The paper presents some background for a model for framing the flight crew function model and some thoughts about next steps.