RNET TECHNOLOGIES INC — National Aeronautics and Space Administration SBIR Phase I: S17

RNET TECHNOLOGIES INC — SBIR Phase I award from National Aeronautics and Space Administration.

Phase I SBIR feasibility signal

  • Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from National Aeronautics and Space Administration in a technical approach.
  • Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
  • Obligated amount $156,492. Cross-check similar awards in the same agency and technology tags for going-rate context.
  • Topic code S17 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.

Informational capture context from public federal data — not legal or bid advice.

Amount
$156,492
Agency
National Aeronautics and Space Administration
Program / Phase
SBIR · Phase I
Topic
S17
Solicitation
SBIR_23_P1
NAICS
Place of performance
OH
Period
2023-07-25 → 2024-02-02

Description

NASA#39;s collection of observational and experimental data has undergone a revolutionary change in recent years. The ever-expanding breadth and fidelity of these scientific datasets have allowed NASA scientists to tackle real-world physical problems in unprecedented ways. While the high-fidelity data generated at NASA is a valuable resource for research and scientific exploration, it also presents a number of challenges, the most significant of which is the sheer volume and complexity of the data, which includes information from sources such as satellites, telescopes, spacecraft, and numerical simulations.nbsp;In this proposal, we introduce a cutting-edge data analytics platform that uses a novel dimensional reduction algorithm to promote nbsp;optimal use of NASA#39;s growing collection of scientific data. Preliminary results show that when applied to a 2.2TB turbulent flow dataset our platform#39;s novel algorithms yield a 60-1000x reduction in download and storage costs and a 10-1000x reduction in computational costs, nbsp;depending on the desired accuracy in the solution.nbsp; nbsp;