Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Software frameworks”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimizing the hit finding algorithm for liquid argon TPC neutrino detectors using parallel architectures

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speedup factors of two times from vectorization and 30–100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, more speedups comparable to the standalone program are achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

ALICE upgrades during the LHC Long Shutdown 2

A Large Ion Collider Experiment (ALICE) has been conceived and constructed as a heavy-ion experiment at the LHC. During LHC Runs 1 and 2, it has produced a wide range of physics results using all collision systems available at the LHC. In order to best exploit new physics opportunities opening up with the upgraded LHC and new detector technologies, the experiment has undergone a major upgrade during the LHC Long Shutdown 2 (2019–2022). This comprises the move to continuous readout, the complete overhaul of core detectors, as well as a new online event processing farm with a redesigned online-offline software framework. These improvements will allow to record Pb-Pb collisions at rates up to 50 kHz, while ensuring sensitivity for signals without a triggerable signature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Improving ICARUS track reconstruction algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino program at Fermilab. Its primary objective is to explore the possible existence of sterile neutrinos in the O(1 eV) mass range and to clarify the anomalies observed in the Liquid Scintillator Neutrino Detector and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber, capable of producing high-resolution 3D images and precise calorimetric measurements of ionizing particles. This technology allows for a detailed study of neutrino interactions across a broad energy range, from a few keV to several hundred GeV. The track reconstruction is achieved through a software framework that applies a series of pattern recognition algorithms, transforming raw detector signals into fully reconstructed event topologies. This process involves identifying interaction vertices, particle tracks, and electromagnetic showers within the TPC. However, in certain cases, these algorithms may mistakenly break a single particle track into several shorter segments, interpreting each as a distinct particle. Since track length is used to estimate the particle's energy, such fragmentation can result in an energy underestimation of several hundred MeV. Furthermore, when a track is split into multiple segments, the particle identification (which relies on analyzing the energy loss as a function of the residual range) may fail, potentially leading to the loss of the entire event. To mitigate this problem, we have developed a dedicated algorithm designed to identify and reconnect (“stitch”) the tracks that were erroneously divided into multiple segments.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗
DOE OSTI2026

Active learning emulators for nuclear two-body scattering in momentum space

In this work we extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering t-matrix as opposed to the radial Schrödinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google's JAX library. We present results for emulating scattering phase shifts in coupled and uncoupled channels and cross sections, and assess the accuracy of the developed ROMs and their computational speedup factors. We also develop emulator error estimation for both the t-matrix and the total cross section. The software framework for reproducing and extending our results is publicly available. Together with our recent advances in developing active-learning emulators for three-body scattering, these emulator frameworks set the stage for full Bayesian calibrations of chiral nuclear interactions and optical models against scattering data with quantified emulator errors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A general Bayesian algorithm for the autonomous alignment of beamlines

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.

Lebakula, Viswadeep↗
DOE OSTI2022

ROAM: A Remotely Operated Accelerator Monitor

Monitoring accelerators in operation is a well known challenge due to the radiation environment. However, there are significant benefits in being able to deploy particular sensors in specific locations of accelerator enclosures for monitoring or troubleshooting purposes. Learning from experience at other labs, we used Commercial Off The Shelf (COTS) components and an open source robot control software framework (ROS) to build a remotely controlled robot platform including a standard suite of instruments such as cameras, LIDAR, and ultrasound, with the ability to incorporate other ad-hoc sensors for specific measurements, such as a Gamma radiation monitor. Special attention was given to the robot's ability to maintain safety in the high risk environment of an accelerator, with multiple failure contingencies in place to ensure collision free operation. Testing was performed to prove the platform's viability and showcase its capability for accelerator monitoring.

Thayer, Thomas C.↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2022

Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application-specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. Here, we present the software-defined accelerator (SODA) synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multilevel representations to progressively lower and optimize the input code. Our approach does not require the application developer to write any register-transfer level code, and it is able to reach up to 364 giga floating point operations per second (GFLOPS)/W efficiency (32-bit precision) on typical convolutional neural network operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Extending XACC for Quantum Optimal Control

Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C++ and Python. We demonstrate this extension with familiar gradient-based methods like gradient ascent pulse engineering (GRAPE), gradient optimization of analytic controls (GOAT), and Krotov's method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.

Nguyen, Thien↗
DOE OSTI2021

Pulsar Based Timing for Grid Synchronization

Existing synchronization systems in the power grid, such as the global positioning system, are susceptible to temporary or permanent failures due to various unpredictable and uncontrollable factors such as cyber-attack and electromagnetic interferences, thus affecting the accuracy and reliability of generated timing signal. In this article, a pulsar astronomy-based timing system is proposed to provide an alternative synchronization signal. Further, this clock will offer significant security improvements to power grid applications, such as a wide-area monitoring system, which depends on a precise timing signal. The hardware and software frameworks are described in detail. First, a high-speed sampling hardware platform is designed to collect signals from radio telescopes. Then a periodic pulse extraction method with three steps is proposed to process the pulsar signal, including polyphase filterbanks, incoherent de-dispersion, and sliding window folding. Lastly, three experiments are conducted to verify the effectiveness of the frameworks. The generated pulsar timing pulse is presented, and the factors affecting its accuracy are also discussed. The analysis results demonstrate that the pulsar signals can provide high-accurate timing pulses for grid synchronization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Reliable edge machine learning hardware for scientific applications

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.

Baldi, Tommaso↗
DOE OSTI2024

Smart Adaptive Structures for an Ocean Wave Energy Converter

Ocean wave energy converters face significant challenges including cost-effectiveness, minimizing maintenance requirements, and withstanding extreme conditions. However, by utilizing smart materials, these converters could overcome these challenges. Such energy harvesters could use dielectric elastomer generators to convert ocean wave energy into electricity through their dynamic straining. Conversely, by applying electricity to these generators, they become actuators - dielectric elastomer actuators - thereby enabling them to alter their stiffness and adapt to the ever-changing ocean energy environments. Such active adaptation could enhance the converter's ability to: reach resonance with ocean waves and protect itself from dangerous waves. This study utilizes numerical analyses through the COMSOL software framework to evaluate the potential energy that could be harvested by a conceptual ocean wave energy converter based upon dielectric elastomer generators/actuators. The converter is composed of an external hull (that is a hollow cylinder), an inertial mass (that is a hollow cylinder and concentric with the hull), and 'spokes' - made of dielectric elastomer generators/actuators - that connect the hull to the inertial mass. Results of the numerical analyses include those outcomes arising from the conceptual converter being simulated via a sinusoidal motion analogous to ocean waves. That motion, therefore, causes relative motion between the converter's hull and inertial mass thereby dynamically stretching the corresponding connecting elastomers. The stretching of the elastomers enables them to 'gain elastic strain energy' and is, therefore, considered to be the theoretical limit of possible electrical energy conversion for the dielectric elastomer generator/actuator spokes. Additionally, the elastomer material properties of the spokes were altered to simulate the actuation of those same elastomers; with overall strain energy being subsequently investigated. Ultimately this is a preliminary study exploring the ability of such smart materials - electricity-generating and self-actuating elastomers - to actively adapt an ocean wave energy converter's structure to address and overcome the aforementioned challenges.

active materials↗
DOE OSTI2026

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector

The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023-2024) conditions, the jet energy resolution improves by 10-20% for jets with transverse momentum between 30-100 GeV. Inference time is evaluated using simulated multijet events, with a median of $20\,\hbox {ms}$ per event on an Nvidia L4 GPU, compared to approximately $110\,\hbox {ms}$ for the standard CMS PF reconstruction.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2022

mesoflow [SWR-22-56]

Mesoflow is a continuum scale simulation tool developed specifically for modeling transport and chemistry at the mesoscale. Our solver utilizes Cartesian block-structured adaptive mesh refinement to resolve complex surface morphologies (of catalysts/biomass particles among others) directly obtained from X-ray tomography data. An immersed boundary based formulation enables rapid representation of complex geometries prevalent in most mesoporous interfaces. The solver is developed on top of open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Our flexible software framework enables integration of complex chemical mechanisms at heterogenous interfaces and time-split algorithms for circumventing highly disparate reaction and flow time-scales. Our current studies indicate a ten-fold performance gain by using graphics-processing-units (GPU) compared to a single processor for representative problem sizes (2 million cell mesh).

Sitaraman, Hariswaran↗
DOE OSTI2022

Fugu v.0.1

SAND2021-15052 O Fugu provides a common software framework for designing and prototyping algorithms for spiking neuromorphic hardware and compiling to multiple hardware platforms. 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.

Aimone, James↗