Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature design”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rapid Prototyping Techniques for Organic Direct-Bonded-Copper Power Modules

Organic Direct-Bonded-Copper (ODBC) is a novel packaging technology for power modules which allows higher flexibility in layout design. In this work, a set of rapid prototyping techniques are developed for ODBC modules based on a polyimide dielectric material. These techniques enable fast and low-cost fabrication of modules with 3-Dimensional (3D) layout features. An example half-bridge (HB) silicon carbide (SiC) metal oxide semiconductor field effect transistor (MOSFET) module is designed and prototyped using the proposed techniques aiming at ultra-low power loop inductance. Finite element analysis (FEA)-circuit co-simulations results and experimental results validate an approximately 0.71nH power loop inductance for the example module design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning application to single channel design of molten salt reactor

This study proposes a robust approach to quickly design a nuclear reactor core and explores the best performing machine learning (ML) technique for predicting feature parameters of the core. Here we implemented the approach into a hypothetical channel of molten salt reactors to demonstrate the applicability of the method. We prepared a Python tool, named Plankton, which couples to a reactor physics code and an optimization tool, and imports ML methods. The tool performs three consecutive phases: reactor database generation, machine learning application, and design optimization. We identified the extra trees method as the best performing estimator. With the estimator, we found nine optimum designs in total, one for each fuel-salt pair, and estimated all the performance metrics of the designs with a <5% prediction error compared to their actual values. U-Pu-NaCl fuel-salt gave promising results with the highest conversion ratio, the most negative feedback coefficient, and the lowest fast flux.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

A Universal Design of Lithium Anode via Dynamic Stability Strategy for Practical All‐Solid‐State Batteries

Abstract All‐solid‐state Li‐metal battery (ASSLB) chemistry with thin solid‐state electrolyte (SSE) membranes features high energy density and intrinsic safety but suffers from severe dendrite formation and poor interface contact during cycling, which hampers the practical application of rechargeable ASSLB. Here, we propose a universal design of thin Li‐metal anode (LMA) via a dynamic stability strategy to address these issues. The ultra‐thin LMA (20 μm) is in situ constructed with uniform highly Li‐ion conductive solid‐electrolyte interphase and composite‐polymer interphase (CPI) via electroplating process. As a result, the passivation layer with poor Li‐ion conduction on Li anode can be dissolved and small surface resistance can be achieved due to the good compatibility of CPI to SSEs. The cycling of Li symmetric cell with Li 6 PS 5 Cl thin film electrolyte (<100 μm) shows a high critical current density of >2.0 mA cm −2 with excellent cycling stability at 1.0 mA cm −2 . The ASSLBs paring with Ni‐rich LiNi 0.6 Mn 0.2 Co 0.2 O 2 cathode demonstrated the feasibility of engineered LMA design by presenting good rate capability from 0.1 C to 1.0 C at room temperature, as well as long‐term cycling stability (81 % retention after 100 cycles). This work represents a general pathway to make thin dendrite‐free LMA available for high‐energy‐density ASSLBs.

Deng, Tao [Department of Chemical and Biomolecular↗
DOE OSTI2025

A Universal Design of Lithium Anode via Dynamic Stability Strategy for Practical All‐Solid‐State Batteries

Abstract All‐solid‐state Li‐metal battery (ASSLB) chemistry with thin solid‐state electrolyte (SSE) membranes features high energy density and intrinsic safety but suffers from severe dendrite formation and poor interface contact during cycling, which hampers the practical application of rechargeable ASSLB. Here, we propose a universal design of thin Li‐metal anode (LMA) via a dynamic stability strategy to address these issues. The ultra‐thin LMA (20 μm) is in situ constructed with uniform highly Li‐ion conductive solid‐electrolyte interphase and composite‐polymer interphase (CPI) via electroplating process. As a result, the passivation layer with poor Li‐ion conduction on Li anode can be dissolved and small surface resistance can be achieved due to the good compatibility of CPI to SSEs. The cycling of Li symmetric cell with Li 6 PS 5 Cl thin film electrolyte (<100 μm) shows a high critical current density of >2.0 mA cm −2 with excellent cycling stability at 1.0 mA cm −2 . The ASSLBs paring with Ni‐rich LiNi 0.6 Mn 0.2 Co 0.2 O 2 cathode demonstrated the feasibility of engineered LMA design by presenting good rate capability from 0.1 C to 1.0 C at room temperature, as well as long‐term cycling stability (81 % retention after 100 cycles). This work represents a general pathway to make thin dendrite‐free LMA available for high‐energy‐density ASSLBs.

Deng, Tao [Department of Chemical and Biomolecular↗
DOE OSTI2021

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning

We report building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

SAN-Based Block Polymers as a Platform for Manufacturing Strong Isoporous Membranes

Ultrafiltration (UF) membranes are ubiquitous in water purification and bioprocessing. However, co-designing their mechanical and transport properties remains challenging because of the broad pore size distributions at the surface and within the bulk that result from nonsolvent-induced phase separation (NIPS) – their typical manufacturing process. These distributions influence the hydrodynamic resistance to water flow and the stress concentrations around the pores. Developing advanced UF membranes requires innovative molecular designs that offer control over the surface and bulk pores, as well as the mechanical properties of the load-bearing, polymer. Here, we introduce a platform for designing UF membranes by leveraging solution self-assembly of block polymers and chain architectures with pendant polar groups. The block polymers consist of a poly(styrene-co-acrylonitrile) hydrophobic block, which is known for its strength, and a poly(4-vinyl pyridine) hydrophilic block, which drives solution self-assembly. We focus on a series of block polymers with constant molecular weight, M n ≈ 115 kDa, SAN fraction, 75 wt.%, and varying acrylonitrile content, 0 to 40 mol%, to demonstrate that: (i) RAFT dispersion copolymerization of acrylonitrile and styrene provides a facile route to synthesize strong block polymers, (ii) incorporation of acrylonitrile into the hydrophobic block enhances membrane strength by facilitating chain entanglements and dipole-dipole interactions, and (iii) acrylonitrile alters the balance between membrane permeance and rejection, even when the membranes feature similar surface and bulk pores. Overall, our results provide insights into the molecular design of UF membranes with enhanced mechanical and separation properties, contributing to the development of materials for water and energy technologies.

deformation↗
DOE OSTI2021

HTR-1.3 solver: Predicting electrified combustion using the hypersonic task-based research solver

Here this manuscript presents an updated open-source version of the Hypersonics Task-based Research (HTR) solver. The solver, whose main features are presented in Di Renzo et al. (2020) and Di Renzo & Pirozzoli (2021), is designed for direct numerical simulation of reacting flows at high Reynolds numbers. This new version extends the applications of the HTR solver to turbulent combustion in the presence of external electric fields. In particular, a new distributed Poisson solver compatible with heterogeneous architectures has been incorporated in the algorithm to compute the electric potential distribution in bi-periodic configurations. The drift fluxes of the electrically charged species are now included in the transport equations using a targeted essentially non-oscillatory scheme. A verification of these new features of the solver is provided using one-dimensional burner stabilized flames, whereas a three dimensional turbulent flame is utilized to discuss the scalability of the proposed numerical tool.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Piston Bowl Geometry Effects on Gasoline Compression Ignition in a Heavy-Duty Diesel Engine

A design optimization campaign was conducted to search for improved combustion profiles that enhance gasoline compression ignition in a heavy-duty diesel engine with a geometric compression ratio of 17.3. A large-scale design of experiments approach was used for the optimization, employing three-dimensional computational fluid dynamics simulations. The main parameters explored include geometric features, injector specifications, and swirl motion. Both stepped-lip and re-entrant bowls were included in order to assess their respective performance implications. A total of 256 design candidates were prepared using the software package CAESES for automated and simultaneous geometry generation and combustion recipe perturbation. The design optimization was conducted for three engine loads representing light to medium load conditions. The design candidates were evaluated for fuel efficiency, emissions, fuel-air mixing, and global combustion behavior. Simulation results showed that the optimum designs were all stepped-lip bowls, due to improvements in fuel-air mixing, as well as reduced heat loss and emissions formation. Improvements in indicated specific fuel consumption of up to 3.2% were achieved while meeting engine-out NOx emission targets of 1-1.5 g/kW center dot h. Re-entrant bowls performed worse compared to the baseline design, and significant performance variations occurred across the load points. Specifically, the re-entrant bowls were on par with the stepped-lip bowls under light load conditions, but significant deteriorations occurred under higher load conditions. As a final task, selected optimized designs were then evaluated under full-load conditions.

CFD↗
DOE OSTI2022

Design Considerations for Fermilab Multi-MW Proton Facility in the DUNE/LBNF era

Fermilab has submitted two Snowmass whitepapers on a future 2.4~MW upgrade for DUNE/LBNF featuring a 2 GeV extension of the PIP-II linac and the construction of a new rapid-cycling-synchrotron. This paper summarizes the relationship between these two scenarios, emphasizing the commonalities and tracing the differences to their original design questions. In addition to a high-level summary of the two 2.4~MW upgrade scenarios, there is a brief discussion of staging, beamline capabilities, subsequent upgrades, and relevant R&D. We are proposing a vigorous program to address various challenges associated with each scenario and to down-select the concept, most suitable to provide proton beams for years to come.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Toward Rational Design of Supported Vanadia Catalysts of Lignin Conversion to Phenol

In sustainable chemical engineering, catalytic upgrading of lignocellulosic biomass has recently gained attention for producing renewable platform chemicals. To achieve maximal biomass utilization, upgrading the underutilized lignin components is essential. Among various catalysts for lignin upgrading, supported vanadia (V2O5) catalysts are promising because of their cost-effectiveness and tunability of either dopant metals or catalyst supports. Here, computational studies are conducted to derive rational design guidelines of supported V2O5 catalysts for accomplishing the high catalytic activity of lignin upgrading to phenol, a key compound for producing bioplastics and biofuel blendstocks. Guaiacol was used as the model compound since it comprises the highest portion of depolymerized lignin. Computational mechanistic studies for the catalytic guaiacol conversion to phenol were performed for the V2O5 catalysts on Titania (TiO2) and silica (SiO2) to explain higher experimental phenol yields on V2O5/SiO2 than V2O5/TiO2. The hydrogen migration from the methoxy group to the aryl ring was identified as a rate-determining step, and the overall activation energies on the two catalysts were compared. A structural analysis was carried out for the catalysts and rate-determining transition states to gain further insights from mechanistic studies. It was concluded that the tilt angle of the aryl group in the hydrogen migration transition state is a key descriptor determining the catalytic activity of phenol formation. These features correlate well with activation energies and experimental phenol yields, indicating that they provide design guidelines for supported metal catalysts for lignin upgrading before experiments.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2025

High-Resolution Sampling of a River Plume Front with Uncrewed Underwater and Aerial Vehicles

Sampling fast-propagating oceanic features is inherently challenging and demands versatile instrumentation and innovative strategies. This paper introduces a novel sampling strategy designed to capture such phenomena, exemplified by a river plume front. Our method revolves around modifying the preprogrammed pathway of an uncrewed underwater vehicle (UUV) to dynamically track and three-dimensionally sample the evolution of the front. To enable the UUV to follow the feature, we adapt the use of a drifting gateway buoy to be positioned and trapped at the front’s convergence zone, allowing underway navigation relative to the buoy. In our demonstration, we showcase the effectiveness of this strategy by successfully conducting over 30 crossings of a river plume front within a 6-h window. The UUV sensors allowed a comprehensive assessment of key front characteristics, including density, velocity, and turbulence. Supplemental drone footage contributed to the overall picture and facilitated the transformation of the dataset into a front-following reference frame. This article provides an in-depth description of the deployment strategy and required postcollection data processing, including frontal crossing detection, the assessment of the frontal orientation from drone footage, and defining the plume bottom boundaries using backscatter intensity contours.

autonomous observations↗
DOE OSTI2023

CASTLE: Conflict Analysis Strategy Testing Laboratory Environment v.1.0.0

SAND2024-01743O The Conflict Analysis Strategy Testing Laboratory Environment (CASTLE) is a software framework that enables and simplifies building a novel, turn-based strategy game in which it can define its own rules, maps, pieces, and interactions. The software is for novice to experienced programmers with some knowledge of Unity3D, a tool used in game production. CASTLE includes a library of common game mechanics used for strategic wargames and traditional board games, such as cards, tokens, dice, and grid maps. It follows design principles popularized by the video game industry and uses singletons for managing portions of the code. CASTLE builds on Unity's component-based design and can respond to engine events during execution. Among the numerous user-friendly features: Build games quickly and cost-effectively Network in real-time and apply data to new games developed on the framework Host multiple participants online Connect rule- or machine learning-based agents to a CASTLE game to serve as opponents or to simulate games Collect data collection from players and in-game behaviors Create a survey to gather demographics or opinions from players Store data locally or save it to an external database through Representational State Transfer (REST) functions CASTLE, which was prototyped using Microsoft Azure, is also designed for easily distributing online games using popular cloud services. The multiplayer functionality includes an agent interface, allowing developers to construct AI players that can substitute for humans in any of the games. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fabian, Nathan↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗
DOE OSTI2025

ROOT RNTuple and EOS: The Next Generation of Event Data I/O

For several years, the ROOT team is developing the new RNTuple I/O subsystem in preparation of the next generation of collider experiments. Both HL-LHC and DUNE are expected to start data taking by the end of this decade. They pose unprecedented challenges to event data I/O in terms of data rates, event sizes, and event complexity. At the same time, the I/O landscape is becoming more diverse. HPC cluster file systems and object stores, NVMe disk cache layers in analysis facilities, and S3 storage on cloud resources are mixing with traditional XRootD-managed spinning disk pools.The ROOT team will finalize a first production version of the RNTuple binary format by the end of 2024. After this point, ROOT will provide backward compatibility for RNTuple data. This contribution provides an overview of the RNTuple feature set, the related R&D activities and the long-term vision for RNTuple. We report on performance, interface design, tooling, robustness, integration with experiment frameworks, and validation results, as well as recent R&D on parallel reading and writing and exploitation of modern hardware and storage systems. We will give an outlook on possible future features after a first production release.Collaboratively, the IT and EP departments at CERN have launched a formal project within the Research and Computing sector to evaluate the novel data format for physics analysis data utilized in LHC experiments and other fields. This part of the project focuses on validating the scalability of the EOS storage backend during the transition from the over 25 years old TTree production format to the newly developed RNTuple format, using both replicated and erasure-coded storage profiles.

Blomer, Jakob [CERN]↗
DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DNA Reaction–Diffusion Attractor Patterns

Abstract Living systems can form and recover complex chemical patterns with precisely sized features in the ranges of tens or hundreds of microns. We show how designed reaction–diffusion processes can likewise produce precise patterns, termed attractor patterns, that reform their precise shape after being perturbed. We use oligonucleotide reaction networks, photolithography, and microfluidic delivery to form precisely controlled attractor patterns and study the responses of these patterns to different localized perturbations. Linear and “hill”‐shaped patterns formed and stabilized into shapes and at time scales consistent with reaction–diffusion models. When patterns were perturbed in particular locations with UV light, they reliably reformed their steady‐state profiles. Recovery also occurred after repeated perturbations. By designing the far‐from‐equilibrium dynamics of a chemical system, this study shows how it is possible to design spatial patterns of molecules that are sustained and regenerated by continually evolving towards a specific steady state configuration.

Dorsey, Phillip James↗