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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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At least 181 records · Page 10

Secure Wireless Communication Using Distributed Coherent Transmission and Spatial Signal Decomposition

We present a new approach to secure wireless communications using coherent distributed transmission of signals that are spatially decomposed between a two-element distributed antenna array. High-accuracy distributed coordination of microwave wireless systems supports the ability to transmit different parts of a signal from separate transmitters such that they combine coherently at a designated destination. In this article, we explore this concept using a two-element coherent distributed phased array where each of the two transmitters sends a separate component of a communication signal, where each symbol is decomposed into a sum of two pseudorandom signal vectors, the coherent summation of which yields the intended symbol. By directing the transmission to an intended receiver using distributed beamforming, the summation of the two vector components is largely confined to a spatial region at the destination receiver. We implement the technique in a $50−λ$ array operating at 3 GHz. We evaluate the symbol error rate (SER) in 2-D space through simulation and measurement, showing that the approach yields a spatially confined secure region where the information is recoverable (i.e., the received signal has low SER), and outside of which the information is unrecoverable (high SER). The proposed system is also compared against a traditional beamforming system where each node sends the same data. We validate experimentally that our approach achieves a low SER of 0.0082 at broadside and an SER above 0.25 at all other locations compared to a traditional beamforming approach that achieves a SER of 0 at all locations measured.

Engineering - Electronic and electrical engineerin↗

Progressive Hedging Decomposition for Solutions of Large-Scale Process Family Design Problems

In previous work, we have introduced a mathematical model for solving a discretized version of the process family design problem. This involves two sets of decision variables. One set selects which unit module designs are included in the process platform out of a candidate set of options; the other set determines which of these unit module designs are assigned to each variant. In this work, we exploit a parallelized Progressive Hedging (PH) algorithm to solve even larger scale design problems. PH is a well-known algorithm traditionally used to solve stochastic programming problems. While our problem is not a two-stage stochastic programming problem, the structure is similar, and it can be directly mapped to the PH approach, which we employ here to solve this deterministic optimization problem. We decompose our problem by process variant. We treat the platform unit module design variables as first-stage and the assignment of unit module designs to variants as second-stage, solving the problem using mpi-sppy. We demonstrate this approach on case studies of CC, water desalination, and refrigeration.

Stinchfield, Georgia↗

Decomposition and Algorithmic Approaches for Solving Large-Scale Process Family Design Problems

Our most recent work expands the water desalination case study from 76 variants to 10,897 variants using the equation-oriented model built in Pyomo as part of the PARETO project. Using the discretization formulation presented in Stinchfield (2024a), rather than solving for all 10,897 variants simultaneously, we decompose the formulation into subproblems containing subsets of variants from the process family. We solve the overall problem with Progressive Hedging (PH) deployed in parallel on a distributed HPC cluster using the open-source Python package mpi-sppy (Knueven et al., 2023). This approach allowed us to solve this process family design problem to ~1.5% relative optimality gap in about 5 hours; in comparison, Gurobi reached ~50% relative optimality gap in about 6 hours (Stinchfield et al., 2024b). However, this approach still requires discretization of the common unit module design ranges; additionally, PH acts as a heuristic for MILP’s with gap-closing capabilities. Ideally, we would not have to use ML surrogates or discretization to solve this problem, instead solving the process family design problem with the equation-oriented model directly to achieve the most accurate results. However, recall that we did not consider solving the MINLP directly due to complexity and size. In this work, we aim to decompose and solve this large-scale MINLP using a Structured Nonlinear Global Optimization algorithm presented by Cao and Zavala (2019).

Stinchfield, Georgia↗

Ammonia Decomposition Catalyst Development for Palladium Membrane Reactor

Conclusions and Questions • Tritiated ammonia is a persistent problem in tritium operations. • Tritiated ammonia can be completely converted to nitrogen and tritium using a permeation membrane reactor. • Ruthenium trimetallic catalysts are highly active at PdAg temperatures. • Catalysts from nitrate precursors are more active than from chloride. • Nickel-based catalysts are potential replacements for ruthenium catalysts.

Guin, Tyler [Savannah River National Laboratory (S↗

Probabilistic Error Bounds for Low-Rank Tensor Decompositions Used in Large-Scale Data Analysis Applications (LDRD Final Report)

This report documents a research project on analyzing low-rank tensor models for data analysis that took place at Sandia National Laboratories from October 2023–September 2025. The focus of this work was to extend theoretical frameworks from statistics and probability theory for use with models for scalar, vector, and matrix data to models with tensor, or general multi-dimensional array, data. Through this work, we have provided a new set of tools for bounding errors on low-rank tensor models of both complete and sampled data. The remainder of this report is organized as follows. In Section 1, we describe the proposed work at the start of the project. Section 2 describes the research advances made as part of the project. Other research contributions in the form of conference presentations and software development is provided in Section 3. Workforce development at Sandia and Florida Atlantic University (via a subcontract on this project) is provided in Section 4.

97 MATHEMATICS AND COMPUTING↗