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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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Space exploration programs and space science - lunar and planetary-interplanetary programs, deep space network, and supporting activities
The Mars Exploration Program and constituent Mars Technology Program are described. Current ongoing and future NASA-led missions are presented, including discussions of scientific accomplishments and objectives as well as technology validations accomplished and technological enablers for future missions.
This presentation covers the Nuclear Criticality Safety Program (NCSP) training & education program. The presentation details the training courses offered by the NCSP and the NCSP training pipelines. Additionally, the presentation shows the Nuclear Criticality Safety (NCS) Education Training (NCSET) modules along with new capabilities and new courses. This lecture finishes up with some concluding remarks.
Space exploration programs and science - Surveyor project, Mariner program, Deep Space Instrumentation Facility, space flight operation facility, environment test facility, and physics
This case study from DOE’s Home Upgrade Program Accelerator highlights process improvements undertaken by Build It Green and Pacific Gas & Electric to streamline California's Home Energy Program. These improvements are intended to result in fewer errors and more reliable predicted energy savings.
Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a function of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain
Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projects also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.
The India National Gas Hydrate Program Expedition 02 (NGHP-02) discovered gas hydrate at high saturation in sand reservoirs at several sites in the deepwater Bay of Bengal. To assess the potential response of those deposits to scientific depressurization experiments, comprehensive geologic models were constructed to enable numerical simulation for two sites. Both sites (NGHP-02-09 and NGHP-02-16) feature thick sequences of thinly-interbedded reservoir and non-reservoir facies at sub-seafloor depths less than 300m and sub-sea depths of 2400m or more. These settings pose significant challenges to current modeling capabilities. First, the thinly-interbedded reservoir architecture complicates the determination of basic reservoir parameters from both log and core data due to measurement resolution issues. Secondly, the fine scale variation in sediment properties imparts great contrasts in key parameters over very short distances, creating high gradients at multiple scales and varying orientations that necessitate careful design of high-definition simulation grids. Thirdly, the deposits include internal sources of water, as well as a range of complex boundary conditions, including variable permeability within the overlying mud-rich “seals,” that complicate reservoir depressurization. Lastly, because of the unique combination of great water depth and relatively shallow sub-seafloor depth: models designed to maximize the dissociation rate impose large pressure drawdowns on relatively low-strength sediments. This condition renders the proper evaluation and integration of the geomechanical response to hydrate dissociation critical. In this report, we review the history of gas hydrate reservoir simulation, discuss methods for creating geologic input models, and summarize the key findings and implications of the collaborative NGHP-02 numerical simulation effort. Together, the studies confirm the viability of the modeled accumulations for scientific testing and identify key challenges related to the selection of specific test sites and the design of test wells.
The Majorana collaboration is constructing the Majorana Demonstrator at the Sanford Underground Research Facility at the Homestake gold mine, in Lead, SD. The apparatus will use Ge detectors, enriched in isotope 76 Ge, to demonstrate the feasibility of a large-scale Ge detector experiment to search for neutrinoless double beta decay. The long half-life of this postulated process requires that the apparatus be extremely low in radioactive isotopes whose decays may produce backgrounds to the search. The radioassay program conducted by the collaboration to ensure that the materials comprising the apparatus are sufficiently pure is described. The resulting measurements from gamma-ray counting, neutron activation and mass spectroscopy of the radioactive-isotope contamination for the materials studied for use in the detector are reported. In conclusion, we interpret these numbers in the context of the expected background for the experiment.
The nucleon spin structure has been studied at Jefferson Lab’s Hall C in experiments RSS (E01-006) and SANE (E07-003), which measured double spin asymmetries using the U. of Virginia solid polarized target and CEBAF’s 6 GeV polarized electrons. The proton longitudinal spin structure g{sub 1} and transverse structure g{sub 2} have been investigated at kinematics extending from the elastic point to DIS, for four-momenta squared ranging from 0.8 to 5 GeV{sup 2}. The neutron structures have been measured in the region of the nucleon resonances at 1.3 GeV{sup 2} on a deuteron target. Results of both experiments will be highlighted. A brief survey of approved experiments for the 12 GeV program will also be presented.
We present BVRI and unfiltered (Clear) light curves of 70 stripped-envelope supernovae (SESNe), observed between 2003 and 2020, from the Lick Observatory Supernova Search follow-up program. Our SESN sample consists of 19 spectroscopically normal SNe Ib, 2 peculiar SNe Ib, six SNe Ibn, 14 normal SNe Ic, 1 peculiar SN Ic, 10 SNe Ic-BL, 15 SNe IIb, 1 ambiguous SN IIb/Ib/c, and 2 superluminous SNe. Our follow-up photometry has (on a per-SN basis) a mean coverage of 81 photometric points (median of 58 points) and a mean cadence of 3.6 d (median of 1.2 d). From our full sample, a subset of 38 SNe have pre-maximum coverage in at least one passband, allowing for the peak brightness of each SN in this subset to be quantitatively determined. We describe our data collection and processing techniques, with emphasis toward our automated photometry pipeline, from which we derive publicly available data products to enable and encourage further study by the community. Using these data products, we derive host-galaxy extinction values through the empirical colour evolution relationship and, for the first time, produce accurate rise-time measurements for a large sample of SESNe in both optical and infrared passbands. By modelling multiband light curves, we find that SNe Ic tend to have lower ejecta masses and lower ejecta velocities than SNe Ib and IIb, but higher 56Ni masses.
The National Council on Radiation Protection and Measurements (NCRP) Program Area Committee (PAC) 3 covers the broad subject of nuclear and radiological security and safety and provides guidance and recommendations for response to nuclear and radiological incidents of both an accidental and deliberate nature. In 2017, PAC 3 Scientific Committee 3-1 completed the development of Guidance for Emergency Responder Dosimetry, and began development of a companion commentary on operational aspects of that guidance. PAC 3 members also organized the technical program for the 2017 Annual Meeting of the NCRP on “Assessment of National Efforts in Emergency Preparedness for Nuclear Terrorism: Is There a Need for Realignment to Close Remaining Gaps.” Based on discussions and presentations at the annual meeting, PAC 3 is working to develop a commentary on the subject that could serve as a roadmap for focusing our national efforts on the most pressing needs for preparing the nation for nuclear and radiological emergencies. PAC 3 is also engaged in active discussions, exploring the landscape of priority issues for its future activities. Lastly, an important consideration in this discussion is the extent of NCRP’s present and potential future resources to support the work of its scientific committees.
Security constrained unit commitment (SCUC) is solved in the day-ahead electricity market to determine electricity generation schedule for the following date. The associated mixed integer programming (MIP) problem is known computationally hard. Heuristics like rounding can be computationally efficient for solving large MIP problems however often produces lower quality solutions due to discrepancy between MIP solution and linear programming relaxation (LPR) solution, i.e., variables taking different discrete values between MIP and LPR solutions. In this work, we applied the machine learning-based algorithms—Classification and Regression Tree (CART) model and random forest (RF) model—to obtain an LPR-based approximation that close to an MIP solution without actually solving the MIP problem. By computing feature importance derived from the CART and RF models, we assessed the link between coefficients in the MIP model, the LPR solution, and the optimal solution of the MIP problem. We applied the CART and RF approaches to solve SCUC problems using day-ahead energy market cases from Midcontinent Independent System Operator. The results show that our method is computationally efficient and could achieve an approximation sufficiently close to the SCUC solution with <0.15% error and ~78% reduction in the discrepancies between LPR and MIP solutions.
The purpose of this code is to automatically create random C programs that are guaranteed to have bugs of a particular "interestingness". We are using this to train learning machines to fuzz programs better. Other people might want to use it for training humans to examine software.
Nuclear wastes generated from reprocessing of used nuclear fuel tend to contain a large fraction of rare earth (RE, e.g., Nd3+), transition (TM, e.g., Mo6+, Zr4+), alkali (A, e.g., Cs+), and alkaline earth cations (AE, e.g., Ba2+, Sr2+). Various strategies have been considered for immobilizing such waste streams, varying from nominally crystal-free glass to glass-ceramic to multi-phase ceramic waste forms. For glass and glass-ceramic waste forms, the added glass-forming system is generally alkali-alkaline earth-aluminoborosilicate (i.e., Na-Ca-Al-B-Si oxide). In a US-UK collaborative project, summarized here, we investigated the glass structure and crystallization dependence on compositional changes in simulated nuclear waste glasses and glass-ceramics. Compositions ranged in complexity from five – to – eight oxides. Specifically, the roles of Mo and rare earths are investigated, since a proposed glass-ceramic waste form contains crystalline phases such as powellite [(AE,A,RE)MoO4] and oxyapatite [(RE,AE,A)10Si6O26], and the precipitation of molybdenum phases is known to be affected by the rare earth concentration in the glass. Additionally, the effects of other chemical additions have been systematically investigated, including Zr, Ru, P, and Ti. A series of studies were also undertaken to ascertain the effect of the RE size on glass structure and on partitioning to crystal phases, investigating similarities and differences in glasses containing single RE oxides of Sc, Y, La, Ce, Nd, Sm, Er, Yb, or Lu. Finally, the effect of charge compensation was investigated by considering not only the commonly assessed peralkaline glass but also metaluminous and peraluminous compositions. Glass structure and crystallization studies were conducted by spectroscopic methods (i.e., Raman, X-ray absorption, nuclear magnetic resonance (NMR), optical absorption, photoluminescence, photoluminescence excitation, X-ray photoelectron spectroscopy), microscopy (i.e., scanning electron microscopy, transmission electron microscopy, electron probe microanalysis), scattering (i.e., X-ray and neutron diffraction, small angle measurements), and physical characterization (i.e., differential thermal analysis, liquidus, viscosity, density). This paper will give an overview of the research program and some example unpublished results on glass-ceramic crystallization kinetics, microstructure, and Raman spectra, as well as some examples of the effects of rare earths on the absorption, luminescence, and NMR spectra of starting glasses. The formal collaboration described here has resulted in the generation of a large number of results, some of which are still in the process of being published as separate studies.
Estimates of the total opportunity for investment in cost-effective energy efficiency in the United States are typically in the range of several hundred billion dollars (Choi Granade, et al., 2009 and Fulton & Brandenburg, 2012).1,2 To access this potential, many state policymakers and utility regulators have established aggressive energy efficiency savings targets. Current levels of taxpayer and utility bill-payer funding for energy efficiency is only a small fraction of the total investment needed to meet these targets (SEE Action Financing Solutions Working Group, 2013). Given this challenge, some energy efficiency program administrators are working to access private capital sources with the aim of amplifying the funds available for investment. In this context, efficient access to secondary market capital has been advanced as one important enabler of the energy efficiency industry “at scale.”3 The question of what role secondary markets can play in bringing energy efficiency to scale is largely untested despite extensive attention from media, technical publications, advocates, and others. Only a handful of transactions of energy efficiency loan products have been executed to date, and it is too soon to draw robust conclusions from these deals. At the same time, energy efficiency program administrators and policymakers face very real decisions regarding whether and how to access secondary markets as part of their energy efficiency deployment strategy.
Electric utility customers spent about $5.8 billion on energy efficiency programs in 2016 to cost-effectively offset a portion of growth in U.S. power needs. That, in turn, affects the need for investment in new electricity infrastructure, across generation, transmission and distribution systems. A new study by Berkeley Lab provides a bottom-up assessment of the potential impact of existing and likely state policies and market conditions to promote or constrain future spending and savings for electricity efficiency programs funded by utility customers in all U.S. states.
Avoided energy and capacity costs are the primary yardstick utilities use to determine which energy efficiency programs are cost-effective for their customers. But sometimes "non-energy impacts" — not commonly recognized as directly associated with energy generation, transmission and distribution — represent substantial benefits, such as improving comfort, air quality and public health. Considering whether and how to include non-energy impacts is an important part of cost-benefit analyses for these programs. This report offers practical considerations for deciding which non-energy impacts to include and how to apply values or methods from other jurisdictions. Researchers reviewed studies quantifying non-energy impacts used in 30 states and applied a five-point system to indicate transferability of a value or method from each study for 16 categories of non-energy impacts: Water resource costs and benefits; Other fuels costs and benefits; Avoided environmental compliance costs; Environmental impacts; Productivity; Health and safety; Asset value; Energy and/or capacity price suppression effects; Avoided costs of compliance with Renewable Portfolio Standard requirements; Avoided credit and collection costs; Avoided ancillary services; Comfort; Economic development and job impacts; Public health impacts; Energy security impacts; Increased reliability. The U.S. Department of Energy’s Building Technologies Office supported this work.