Unsolicited Proposals Overview
This short fact sheet outlines the basics of understanding and submitting unsolicited proposals to the U.S. Department of Energy
unsolicited, proposal↗
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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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Writing Competitive Proposals [Slides]
A proposal is a written response to an opportunity offered by a funding organization. Proposals must meet a SOLICITOR’S needs, not YOUR needs. A solicited proposal is a written response to a specific solicitation issued by a sponsor. These solicitations are typically called RFPs, or Requests for Proposals. RFPs are specific in their requirements regarding format and technical content. Scientists at Los Alamos often deal with this type of proposal — Laboratory-Directed Research and Development (LDRD) proposals are one example. An unsolicited proposal is one submitted to a sponsor who has not issued a specific solicitation. It usually comes about during interactions at meetings or conferences. In some instances, a sponsor may be visiting for another reason when the possibility for an unsolicited proposal becomes available. A pre-proposal is one in which a sponsor wishes to gage competition before having participants prepare a formal, full-length proposal. These types of scenarios imply that the actual proposal could be highly competitive and will usually be by invitation only.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Department of Energy WindSentinel Loan Program Description
The U.S. Department of Energy (DOE) currently owns two AXYS WindSentinel buoys that collect a comprehensive set of meteorological and oceanographic data to support resource characterization for wind energy offshore. The two buoys were delivered to DOE’s Pacific Northwest National Laboratory (PNNL) in September, 2014. After acceptance testing and initial performance testing and evaluation at PNNL’s Marine Sciences Laboratory in Sequim, Washington, the buoys have been deployed off the U.S. East Coast. One buoy was deployed approximately 42 km east of Virginia Beach, Virginia from December, 2014 through June, 2016. The second buoy was deployed approximately 5 km off Atlantic City, New Jersey in November, 2015. Data from the buoys are available to the public. Interested parties can create an account and log in to http://offshoreweb.pnnl.gov. In response to a number of inquiries and unsolicited proposals, DOE’s Wind Energy Technologies Office is implementing a program, to be managed by PNNL, to lend the buoys to qualified parties for the purpose of acquiring wind resource characterization data in areas of interest for offshore wind energy development. This document describes the buoys, the scope of the loans, the process of how borrowers will be selected, and the schedule for implementation of this program, including completing current deployments.
17 WIND ENERGY↗
Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel
In this LDRD “Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel” a peer-reviewed lab-scale hypersonic wind tunnel (WT) was planned to be assembled in-house, along with time and space resolved and related flow and materials diagnostics to derive high quality measurements and the science that is able to inform the development of our modeling and simulation codes for the hypersonic regime. The baseline capability to achieve Mach 3 flow was scheduled to be completed in Q4 2020, in the first year of the LDRD. However due to the COVID-19 delays and travel restrictions starting in March, 2020 this task could not be completed to carry out the materials response studies in-house, or those planned with external partners that were engaged during this LDRD (NASA Langley, NASA Ames, Texas A&M university, and Virginia Tech). Still, using our hypersonic computational fluid simulation codes, many of the hypersonic components have been designed (for Mach 3, 5, and 7) and procured (or in procurement phase), and a new dedicated hypersonic laboratory was facilitized, 2 WT test sections have been manufactured, and a Mach 3 nozzle was 3D printed for prototyping. In parallel, unique materials diagnostics have been tested under static conditions and fully specified and awaiting validation in our partner facilities. Due to the new hypersonic initiative announced in January, 2020 we proposed a related scope increase in hypersonics research requiring energy interactions and new diagnostics well beyond what could be supported by an LDRD ER. A decision was made to end the current LDRD ER and formally propose a new LDRD SI that leverages the current effort, which will be starting in FY21. As a result of the uniqueness of this LDRD work, our early efforts have been encouraged and well received in the hypersonic research community, and is now part of multiple unsolicited internal and external project proposals expected to be funded in the coming years, which is encouraging to make LLNL a leader in experimentally probing extreme physics of local energetics critical to hypersonics.
36 MATERIALS SCIENCE↗
Clustering VoIP caller for SPIT identification
Abstract The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.
Azad, Muhammad Ajmal↗