Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “design, synthesis and processing”

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.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗
NASA NTRS1986

A general-purpose optimization program for engineering design

A new general-purpose optimization program for engineering design is described. ADS (Automated Design Synthesis) is a FORTRAN program for nonlinear constrained (or unconstrained) function minimization. The optimization process is segmented into three levels: Strategy, Optimizer, and One-dimensional search. At each level, several options are available so that a total of nearly 100 possible combinations can be created. An example of available combinations is the Augmented Lagrange Multiplier method, using the BFGS variable metric unconstrained minimization together with polynomial interpolation for the one-dimensional search.

Vanderplaats, G. N.↗
NASA NTRS2004

Designing Domain-Specific HUMS Architectures: An Automated Approach

The HUMS automation system automates the design of HUMS architectures. The automated design process involves selection of solutions from a large space of designs as well as pure synthesis of designs. Hence the whole objective is to efficiently search for or synthesize designs or parts of designs in the database and to integrate them to form the entire system design. The automation system adopts two approaches in order to produce the designs: (a) Bottom-up approach and (b) Top down approach. Both the approaches are endowed with a Suite of quantitative and quantitative techniques that enable a) the selection of matching component instances, b) the determination of design parameters, c) the evaluation of candidate designs at component-level and at system-level, d) the performance of cost-benefit analyses, e) the performance of trade-off analyses, etc. In short, the automation system attempts to capitalize on the knowledge developed from years of experience in engineering, system design and operation of the HUMS systems in order to economically produce the most optimal and domain-specific designs.

Mukkamala, Ravi↗
NASA NTRS1998

FPGA Based Reconfigurable ATM Switch Test Bed

Various issues associated with "FPGA Based Reconfigurable ATM Switch Test Bed" are presented in viewgraph form. Specific topics include: 1) Network performance evaluation; 2) traditional approaches; 3) software simulation; 4) hardware emulation; 5) test bed highlights; 6) design environment; 7) test bed architecture; 8) abstract sheared-memory switch; 9) detailed switch diagram; 10) traffic generator; 11) data collection circuit and user interface; 12) initial results; and 13) the following conclusions: Advances in FPGA make hardware emulation feasible for performance evaluation, hardware emulation can provide several orders of magnitude speed-up over software simulation; due to the complexity of hardware synthesis process, development in emulation is much more difficult than simulation and requires knowledge in both networks and digital design.

Chu, Pong P.↗
DOE OSTI2026

Facile synthesis of Co(OH)2 nanoneedle arrays grown on stainless steel for industrial electrochemical oxygen evolution reaction

The electrochemical oxygen evolution reaction (OER) is a critical half-reaction in a variety of energy conversion and storage applications, however, OER suffers from sluggish kinetics due to the four proton-electron transfer processes. To remedy this, interfacial engineering proves an effective strategy to design high active OER catalysts. Herein, we report a facile synthesis of Co(OH)2 nanoneedle (Co-NN) arrays grown on stainless steel (SS) via a one-step hydrothermal reaction, and the resultant impressive OER performance. Particularly, the Co-NN/SS needs 267, 307, and 576 mV overpotentials to reach 10, 100, and 1000 mA cm−2 in 1 M KOH & room temperature, and the overpotentials drop to 199, 257, and 283 mV to achieve the same current densities in 30 wt% KOH & 80 °C. Additionally, an alkaline water electrolysis (AWE) cell coupled with Co-NN/SS anode and PtRu/NiMo cathode only requires 1.65 and 1.73 V iR-free cell voltage to reach 1.0 and 2.0 A cm−2, respectively. The sterling OER performance could be attributed to four possible reasons, the Fe in SS surface tailoring the electronic properties of Co(OH)2, the mixed metal oxides on SS surface accelerating surface reconstruction of Co(OH)2 nanoneedles into CoOOH active species, a Ni-rich surface layer formation cooperatively boosting the OER activity, and a local electric field effect concentrating reactants on the tip surface and accelerating the mass transfer. This work provides a facile approach for synthesizing highly efficient OER catalysts for industrial applications by interfacial engineering.

Lyu, Xiang [ORNL] (ORCID:0000000208673248)↗
NASA NTRS1993

Software synthesis using generic architectures

A framework for synthesizing software systems based on abstracting software system designs and the design process is described. The result of such an abstraction process is a generic architecture and the process knowledge for customizing the architecture. The customization process knowledge is used to assist a designer in customizing the architecture as opposed to completely automating the design of systems. Our approach using an implemented example of a generic tracking architecture which was customized in two different domains is illustrated. How the designs produced using KASE compare to the original designs of the two systems, and current work and plans for extending KASE to other application areas are described.

Bhansali, Sanjay↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Life Cycle Analysis of Electrofuels: Fischer–Tropsch Fuel Production from Hydrogen and Corn Ethanol Byproduct CO 2

Electrofuels from renewable H 2 and waste CO 2 streams are of increasing interest because of their CO 2 emissions reduction potentials compared to fossil counterparts. This study evaluated the well-to-wheel (WTW) greenhouse gas (GHG) emissions of Fischer-Tropsch (FT) fuels from various electrolytic H 2 pathways and CO 2 sources, using various process designs (i.e., with and without H 2 recycle) and system boundaries. Two systems with different boundaries were considered: a stand-alone plant (with CO 2 from any source) and an integrated plant with corn ethanol production (supplying CO 2 ). The FT fuel synthesis process was modeled using Aspen Plus, which showed that 45% of the carbon in CO 2 can be fixed in the FT fuel, with a fuel production energy efficiency of 58%. Using nuclear or solar/wind electricity, the stand-alone FT fuel production from various plant designs can reduce WTW GHG emissions by 90-108%, relative to petroleum fuels. When integrating the FT fuel production process with corn ethanol production, the WTW GHG emissions of FT fuels are 57-65% lower compared to petroleum counterparts. This study highlights the sensitivity of the carbon intensity of FT fuels to the system boundary selection (i.e., stand-alone vs integrated), which has different implications under various GHG emission credit frameworks.

08 HYDROGEN↗
NASA NTRS1991

Next generation functional components for space telemetry data processing

The use of new design methodologies to continue the evolution of telemetry data systems to higher levels of integration is discussed. In particular, hardware description languages and automated logic synthesis tools are used to create very dense Application Specific Integrated Circuits (ASICs) that generically implement telemetry processing functions. The use of the ASICs will make it possible to develop a new generation of functional components, each of the components integrating several telemetry processing subsystems into a single card.

Bennett, Toby↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗