SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The National Technology Transfer and Advancement Act (NTTAA) of 1995 mandates that all federal agencies use technical standards developed and adopted by voluntary consensus standards bodies, as opposed to developing unique government standards. But why use a voluntary consensus standard? Why use ASME/NQA-1?
A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.
Disclosed herein are processes for fabricating organic photosensitive optoelectronic devices with a vertical compositionally graded organic active layer. The processes use either a single-stamp or double-stamp printing technique to transfer the vertical compositionally graded organic active layer from a starting substrate to a device layer.
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.
During the Energy Commercialization Center’s (ECC) three years in operation, the only thing constant was change. The world of commercialization and cleantech evolved significantly during the time the ECC was formed and operating, including: the availability of cleantech funding lessoned, the growth of incubators and accelerators skyrocketed, the State of Utah created an office dedicated to energy development, the University of Utah was both praised and criticized for its success in commercialization, and the Federal government temporarily shut down. During the three-year grant there were three principle investigators on the grant, as well as three directors for the University’s Commercialization Office. Change can be hard for an organization,but as we instruct the companies we support, “Fail fast and fail often, because it is the fastest path to success.” Although there were some unanticipated challenges along the way, the local ecosystem is stronger because of the ECC’s efforts. Perhaps the greatest lesson learned was the importance of aligned incentives between key stakeholders in the commercialization process and the need for resources at the company and individual entrepreneur levels. The universities have systems and incentives to commercialize technologies, but creating value and companies generally rest with the individuals and entrepreneurs. Unfortunately the ECC was unable to create a viable mechanism to transfer the commercialization process that successfully aligned incentives and achieve a more effective ecosystem within the Rocky Mountain West. However, the ECC was successful in adding value to the individual ecosystems, and connecting national resources to regional and local needs. Regarding the ECC’s effectiveness in developing a cleantech commercialization ecosystem, initial inroads and relationships were established with key stakeholders. However, incentives, perceived or real competition, differences in commercialization processes, and culture all played a role in inhibiting the development and distribution of a regional ecosystem and commercialization process. Had the University and the ECC been able to develop a software platform, some of these challenges may have been overcome, but without the final development and release of the Western Innovation Network, the ECC realistically could not scale and distribute a commercialization platform. Further, cleantech startups need to engage in a more intensive customer validation process, and establish strong community connections if they are to succeed in commercializing their products. The university system incentivizes research and access to research funding and risk capital is competitive, so by nature collaboration on commercialization was difficult. Each of the local ecosystems within the Rocky Mountain West was unique. Utah did not, and does not, have a system outside of the universities to support entrepreneurs and cleantech commercialization. Through the ECC’s efforts developing a regional ecosystem, it became clear that successful ecosystems had a community and associated mechanisms that supported local entrepreneurs and startups. Most importantly the ECC aided in the creation of Utah’s cleantech ecosystem, one that supports entrepreneurs and startup companies that need help and support in their efforts to commercialize clean technologies. The absence of support for clean tech from state government and local organizations was a significant impediment to cleantech commercialization. To overcome this challenge, the ECC has formed Sustainable Startups. Sustainable Startups is a new non-profit organization designed to build a culture and community in Utah that supports and understands the importance of cleantech and sustainable development. While the ECC generated mixed success in building a regional commercialization ecosystem for cleantech, the organization did provide tremendous benefit to startups and the broader public. Over 60 companies were given direct business development support by the ECC, many of whom then generated direct economic development impacts. In addition, the ECC served an important role as community convener, educator and relationship builder through hosting numerous public and private events including: Energize 2013; Millennial Train whistle stop; business plan competition supporter; Clean Tech Open Accelerator organizer; Sustainable Startups Series developer, and much more. While the ECC did not fully apply, develop, and transmit the University of Utah’s TCO commercialization model to cleantech, it nevertheless assisted numerous inventors, entrepreneurs and institutions in furthering the growth of clean energy and energy efficiency technologies. The TCO’s commercialization model was not applied to regional clean tech initiatives for several main reasons. First, flaws with the commercialization model were realized after the ECC’s formation. Second, leadership changes within the TCO and ECC hampered early organizational development and implementation initiatives. Third, misaligned incentives between the ECC, regional universities, institutions, and the State of Utah resulted in a lack of collaboration and knowledge transfer regarding commercialization. In principle, everyone was aligned and willing to collaborate, but reality was much different and challenging.
Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.
The large repABC plasmids of the order Rhizobiales with Class I quorum-regulated conjugative transfer systems often define the nature of the bacterium that harbors them. These otherwise diverse plasmids contain a core of highly conserved genes for replication and conjugation raising the question of their evolutionary relationships. In an analysis of 18 such plasmids these elements fall into two organizational classes, Group I and Group II, based on the sites at which cargo DNA is located. Cladograms constructed from proteins of the transfer and quorum-sensing components indicated that those of the Group I plasmids, while coevolving, have diverged from those coevolving proteins of the Group II plasmids. Moreover, within these groups the phylogenies of the proteins usually occupy similar, if not identical, tree topologies. Remarkably, such relationships were not seen among proteins of the replication system; although RepA and RepB coevolve, RepC does not. Nor do the replication proteins coevolve with the proteins of the transfer and quorum-sensing systems. Functional analysis was mostly consistent with phylogenies. TraR activated promoters from plasmids within its group, but not between groups and dimerized with TraR proteins from within but not between groups. However, oriT sequences, which are highly conserved, were processed by the transfer system of plasmids regardless of group. Here, we conclude that these plasmids diverged into two classes based on the locations at which cargo DNA is inserted, that the quorum-sensing and transfer functions are coevolving within but not between the two groups, and that this divergent evolution extends to function.
When the interaction between a molecular system and confined light modes in an optical or plasmonic cavity is strong enough to overcome the dissipative process, hybrid light-matter states (polaritons) become the fundamental excitations in the system. The mixing between the light and matter characters modifies the photophysical and photochemical properties. Notably, it was reported that these polaritons can be employed to control photochemical reactions, charge and energy transfer, and other processes. In addition, according to recent studies, vibrational strong coupling can be employed to resonantly enhance the thermally-activated chemical reactions. In this work, a theoretical model and an efficient numerical method for studying the dynamics of molecules strongly interacting with quantum light are developed based on nonadiabatic excited-state molecular dynamics. The methodology was employed to study the cis-trans photoisomerization of a realistic molecule in a cavity. Numerical simulations demonstrate that the photochemical reactions can be controlled by tuning the properties of the cavity. In the calculated example, the isomerization is suppressed when polaritonic states develop a local minimum on the lower polaritonic state. Moreover, the observed reduction of isomerization is tunable via the photon energy and light-molecule coupling strength. However, the fluctuation in the transition dipole screens the effect of light-matter, which makes it harder to tune the photochemical properties via the coupling strength. These insights gained from this study suggest quantum control of photochemical reactions is possible by specially designed photonic or plasmonic cavities.
This study explored the feasibility and potential benefits provided by the addition of through-the-thickness reinforcement to sandwich structures. Through-the-thickness stitching is proposed to increase the interlaminar strength and damage tolerance of composite sandwich structures. A low-cost, out-of-autoclave processing method was developed to produce composite sandwich panels with carbon fiber face sheets, a closed-cell foam core, and through-the-thickness Kevlar stitching. The sandwich panels were stitched in a dry preform state, vacuum bagged, and infiltrated using Vacuum Assisted Resin Transfer Molding (VARTM) processing. For comparison purposes, unstitched sandwich panels were produced using the same materials and manufacturing methodology. Test panels were produced initially at the University of Utah and later at NASA Langley Research Center. Four types of mechanical tests were performed: flexural testing, flatwise tensile testing, core shear testing, and edgewise compression testing. Drop-weight impact testing followed by specimen sectioning was performed to characterize the damage resistance of stitched sandwich panels. Compression after impact (CAI) testing was performed to evaluate the damage tolerance of the sandwich panels. Results show significant increases in the flexural stiffness and strength, out-of-plane tensile strength, core shear strength, edgewise compression strength, and compression-after-impact strength of stitched sandwich structures.
Uranyl peroxide capsules are a recent addition to polyoxometalate (POM) chemistry. Ten years of development has ensued only in water, while transition metal POMs are commonly exploited in aqueous and organic media, controlled by counterions or ligation to render the clusters hydrophilic or hydrophobic. Here, new uranyl POM behavior is recognized in organic media, including (1) stabilization and immobilization of encapsulated hydrophilic countercations, identified by Li nuclear magnetic resonance (NMR) spectroscopy, (2) formation of new cluster species upon phase transfer, (3) extraction of uranyl clusters from different starting materials including simulated spent nuclear fuel, (4) selective phase transfer of one cluster type from a mixture, and (5) phase transfer of clusters from both acidic and alkaline media. The capsule morphology of the uranyl POMs renders accurate characterization by X‐ray scattering, including the distinction of geometrically similar clusters. Compositional analysis of the aqueous phase post‐extraction provided a quantitative determination of the ion exchange process that enables transfer of the clusters into the organic phase. Preferential partitioning of uranyl POMs into organic media presents new frontiers in metal ion behavior and chemical reactions in the confined space of the cluster capsules in hydrophobic media, as well as the reactivity of clusters at the organic/aqueous interface.
Here, using an OpenMP Application Programming Interface, the resolution-of-the-identity second-order Møller–Plesset perturbation (RI-MP2) method has been off-loaded onto graphical processing units (GPUs), both as a standalone method in the GAMESS electronic structure program and as an electron correlation energy component in the effective fragment molecular orbital (EFMO) framework. First, a new scheme has been proposed to maximize data digestion on GPUs that subsequently linearizes data transfer from central processing units (CPUs) to GPUs. Second, the GAMESS Fortran code has been interfaced with GPU numerical libraries (e.g., NVIDIA cuBLAS and cuSOLVER) for efficient matrix operations (e.g., matrix multiplication, matrix decomposition, and matrix inversion). The standalone GPU RI-MP2 code shows an increasing speedup of up to 7.5× using one NVIDIA V100 GPU with one IBM 42-core P9 CPU for calculations on fullerenes of increasing size from 40 to 260 carbon atoms using the 6-31G(d)/cc-pVDZ-RI basis sets. A single Summit node with six V100s can compute the RI-MP2 correlation energy of a cluster of 175 water molecules using the correlation consistent basis sets cc-pVDZ/cc-pVDZ-RI containing 4375 atomic orbitals and 14 700 auxiliary basis functions in ~0.85 h. In the EFMO framework, the GPU RI-MP2 component shows near linear scaling for a large number of V100s when computing the energy of an 1800-atom mesoporous silica nanoparticle in a bath of 4000 water molecules. The parallel efficiencies of the GPU RI-MP2 component with 2304 and 4608 V100s are 98.0% and 96.1%, respectively.
Heat loss through wall boundaries play a dominant role in the overall performance and efficiency of internal combustion engines. Typical engine simulations use constant temperature wall boundary conditions. These boundary conditions cannot be estimated accurately from experiments due to the complexities involved with engine combustion. As a result they introduce a large uncertainty in engine simulations and serve as a tuning parameter. Modeling the process of heat transfer through the solid walls in an unsteady engine computational fluid dynamics (CFD) simulation can lead to the development of higher fidelity engine calculations. These models can be used to study the impact of heat loss on engine efficiency and explore new design methodologies that can reduce heat losses. In this work, a single cylinder diesel engine is modeled along with the solid piston coupled to the fluid domain. Conjugate heat transfer (CHT) modeling techniques were implemented to model heat losses for a full cycle of a Navistar diesel engine. This CFD model is then validated against experimental data available from thermocouples embedded inside the piston surface. The overall predictions from the model match closely with the experimental observations. The validated model is further used to explore the benefits of thermal barrier coatings (TBC) on piston bowls. The effect of TBC coatings were modeled as a thermal resistance in the heat transfer models. Full cycle 3D engine simulations provide quantitative insights into heat loss and thus calculate the efficiency gain by the use of TBC coatings. The work establishes a validated modeling framework for CHT modeling in reciprocating engine simulations.
The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders of magnitude the search sensitivity reached so far. Mu2e's Trigger and Data Acquisition System (TDAQ) uses otsdaq as its solution. Developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under the-hood, for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability, while providing a multi-user, web-based, interface accessible through the Chrome or Firefox web browser. The detector Read Out Controller (ROC), from the tracker and calorimeter, stream out zero-suppressed data continuously to the Data Transfer Controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes froma Cosmic Ray Ve to System (CRV). A Detector Control System (DCS) for monitoring, controlling, alarming, and archiving has been developed using the Experimental Physics and Industrial Control System (EPICS) Open Source Platform. The DCS System has also been itegrated into otsdaq. The installation of the TDAQ and the DCS systems in the Mu2e building is planned for 2021-2022, and a prototype has been built at Fermilab's Feynman Computing Center. We report here on the developments and achievements of the integration of Mu2e's DCS system into the online otsdaq software.
The effect of elastic strains produced in epitaxial Zn{sub x}Cd{sub 1-x}Te alloy layers during deposition on the CdTe substrate on the parameters of growth is studied experimentally and theoretically. Phase equilibria between the elastically strained alloy layer, the crystal lattice of which is continuously (coherently) matched to the crystal lattice of the bulk substrate, are described by a coherent state diagram. The processes of mass transfer during the formation of heterostructures on the basis of the above-mentioned alloys and the substrate formed of the initial binary compounds are described in the context of the model of diffusion-limited crystallization. It is shown that such thermodynamic concepts can be used to describe experimental data on the parameters of the process of crystallization of elastically strained alloy layers during the formation of heterojunctions in the system involving the Zn-Cd-Te alloy and the substrate based on the initial binary compound.
A review of research on the effects of gravity on combustion processes is presented, with an emphasis on a discussion of the ways in which reduced-gravity experiments and modeling has led to new understanding. Comparison of time scales shows that the removal of buoyancy-induced convection leads to manifestations of other transport mechanisms, notably radiative heat transfer and diffusional processes such as Lewis number effects. Examples from premixed-gas combustion, non-premixed gas-jet flames, droplet combustion, flame spread over solid and liquid fuels, and other fields are presented. Promising directions for new research are outlined, the most important of which is suggested to be radiative reabsorption effects in weakly burning flames.
The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10 000 over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable with direct searches at either present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system exploits otsdaq as its online Data Acquisition System (DAQ) solution. Furthermore, developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The read out controllers (ROCs) stream out zero-suppressed data continuously from the detector subsystems to the data transfer controllers (DTCs). The data stream is then read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events which are combined with the data flux coming from a cosmic-ray veto (CRV) system. The detector control system (DCS) has been developed using the experimental physics and industrial control system (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS has been integrated into otsdaq. A prototype of the TDAQ system and the DCS has been built at Fermilab's Feynman Computing Center. In this article, we report on the progress of the integration of this prototype in the online otsdaq software.
STS-113 Mission Specialist 2 John Herrington is seen during a prelaunch interview. He answers questions about his inspiration to become an astronaut and his career path, as well as his thoughts on becoming the first Native American in space. He gives details on the mission's goals and significance, which include the transfer of the International Space Station's (ISS) Expedition 6 crew for the Expedition 5 crew, as well as the installation of the ISS's P-1 integrated truss structure. Herrington, who will participate in three EVAs (extravehicular activity), provides details on the installation of the truss structure. He also describes the process of crew transfer, which also involves the transfer of soft goods and scientific experiments, such as the MEMS (microelectromechanical systems)-based Picosatellite Inspector (MEPSI) which will be ejected from the shuttle shortly after it undocks from the ISS.
With the current trend in parallel computer architectures towards clusters of shared memory symmetric multi-processors, parallel programming techniques have evolved that support parallelism beyond a single level. When comparing the performance of applications based on different programming paradigms, it is important to differentiate between the influence of the programming model itself and other factors, such as implementation specific behavior of the operating system (OS) or architectural issues. Rewriting-a large scientific application in order to employ a new programming paradigms is usually a time consuming and error prone task. Before embarking on such an endeavor it is important to determine that there is really a gain that would not be possible with the current implementation. A detailed performance analysis is crucial to clarify these issues. The multilevel programming paradigms considered in this study are hybrid MPI/OpenMP, MLP, and nested OpenMP. The hybrid MPI/OpenMP approach is based on using MPI [7] for the coarse grained parallelization and OpenMP [9] for fine grained loop level parallelism. The MPI programming paradigm assumes a private address space for each process. Data is transferred by explicitly exchanging messages via calls to the MPI library. This model was originally designed for distributed memory architectures but is also suitable for shared memory systems. The second paradigm under consideration is MLP which was developed by Taft. The approach is similar to MPi/OpenMP, using a mix of coarse grain process level parallelization and loop level OpenMP parallelization. As it is the case with MPI, a private address space is assumed for each process. The MLP approach was developed for ccNUMA architectures and explicitly takes advantage of the availability of shared memory. A shared memory arena which is accessible by all processes is required. Communication is done by reading from and writing to the shared memory.