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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Numerical Optimization of Spray-Guided Spark Assistance for Cold Idle Operation in a Heavy-Duty Gasoline Compression Ignition Engine

This article describes the results of a response surface model (RSM)-based numerical optimization campaign for spray-guided spark assistance at cold operations in a heavy-duty gasoline compression ignition (GCI) engine. On the basis of an earlier work on spark-assisted GCI cold combustion, a space-filling design of experiments (DoE) method was first undertaken to investigate a multitude of hardware design variables and engine operating parameters. The main design variables included the number of injector nozzles, fuel split quantities and injection timings, and spark timing. The objective variables were engine combustion efficiency (ŋ c ), maximum pressure rise rate (MPRR), and engine-out nitrogen oxide (NO x ) emissions. A total of 150 design candidates were automatically generated using the Sobol sequence method provided by the commercial software package, CAESES. Then, closed-cycle computational fluid dynamic (CFD) spark-assisted GCI simulations under cold idling operations were performed. The outcomes from the CFD-DoE design campaign were utilized to construct high-fidelity RSMs that allowed for further design optimization of the spark plug- and fuel injector-related design variables, along with fuel injection strategy parameters. A merit function with respect to objective variables was formulated with an appropriate weight assignment on each objective variable. Finally, the best design candidate was identified from the RSM-based optimization process and further validated in the CFD analysis. The best design candidate showed the potential to significantly improve combustion efficiency (ŋ c > 90%) over the baseline at cold idle while satisfying MPRR and NOx emissions constraints (MPRR < 5 bar/CAD and NO x < 4.5 g/kWh).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗
DOE OSTI2023

Ground vehicle lane-keeping assistance system via differential flatness output feedback control and algebraic derivative estimation

Vehicle run-off-road is one of the most frequent and fatal traffic accidents in the United States. Various lane-keeping assistance (LKA) systems have been developed in the last decade to help drivers stay on the road. Most of them are built upon linear driver–vehicle–road (DVR) models and treat road curvature as a disturbance. Albeit effective, their control performance would degrade if road curvature varies rapidly. Here, this paper proposes a novel nonlinear DVR model by integrating a driver steering model into vehicle–road kinematics, which explicitly considers road curvature. Particularly, this nonlinear DVR system has been proven to be differentially flat, and a flatness-based LKA system is designed. Additionally, Model-Free Control is introduced to compensate for system modeling errors. Hardware-in-the-loop simulations and diver-in-the-loop experiments validate the proposed control framework and demonstrate the performance enhancement with respect to a representative linear robust LKA system.

42 ENGINEERING↗
DOE OSTI2023

Scoping Study for Fast Flux Testing in the Advanced Test Reactor

The value of fast spectrum reactors remains prominent in the nuclear technology portfolio. The performance of these reactors can be maximized with advancements in nuclear fuel technologies, but development of these technologies is currently held back by lack of fast spectrum test reactors available to the United States. Spectral modification of experiment positions in the thermal spectrum Advanced Test Reactor (ATR) has long been used to support fast reactor fuel development, but these methods have not been progressed to their full potential. This study investigated the use of concentric rings of aluminum-clad fuel plates in ATR flux traps and thermal neutron absorbing filters to increase fast neutron flux on test specimens. This concept was termed the Boosted Energy Advanced Spectrum Test (BEAST). This approach will enable irradiation of advanced fuel designs in prototypic-length fuel pins and representative flux environment to support post irradiation exams, enable transient testing, and produce the type of data that will permit lead test assembly irradiations in true Sodium Fast Reactors (SFRs) when they become available. Neutronic predictions were performed to investigate BEAST design options and thermal hydraulic models were produced to ensure feasibility of BEAST. Two versions were considered based on the geometric limitations of ATR’s small and large flux traps. The small version was found to be preferable due to slightly higher fast flux and fast-to-thermal neutron ratio. Perhaps more influentially, the small flux trap option was also preferred to avoid conflict with ongoing very high temperature reactor fuel irradiation programs in ATR’s large northeast flux trap. The small flux trap option provided less than half the test volume of the large version, but still had adequate volume for seven SFR pins in cross section which could be stacked two-high in ATR’s 1.2m long core to accommodate up to 14 EBR-II size pins. The preference for the small flux trap configuration should be revisited if additional collaborative test programs emerge with the need to irradiate a significant volume of additional specimens. Calculations were performed regarding a lithium deuteride ring to convert thermal neutrons into 14 MeV fusion neutrons. At the time this report was written these calculations were partially complete and it remains to be seen whether the concept would be worth including in BEAST. Given the preference for the small flux trap option, which does not afford enough room for the 14 MeV ring, it was concluded to defer future work on the lithium deuteride ring. This decision could be revisited if fusion material research programs emerge for collaborative testing in BEAST. A cadmium-lined specimen holder design was found to be adequate in filtering thermal neutrons and preferred over other neutron absorbers based on past experience with cadmium baskets. It was acknowledged that cadmium-bearing hardware would become depleted and need to be replaced occasionally, which appeared feasible from a mechanical design perspective. Neutronic studies investigated different enrichment levels in the booster fuel using uranium-molybdenum alloy dispersion fuel which has performed well in past ATR irradiations. Both options were able to drive fuel pins to SFR-like fission heating rates. The high enriched booster fuel option outperformed the low enriched option by ~20% on key metrics including fast flux and fast-to-thermal ratio, but the low enriched option was favored in order to broaden options for potential fuel suppliers. The preferred BEAST design options including cadmium filter with low enriched booster fuel in the small flux trap configuration was predicted to achieve 6.2E14 n/cm2sec fast flux (>0.1 MeV) with a fast-to-thermal ratio of 44.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Prototype Design of Global Common Module for ATLAS Experiment’s Phase-II Upgrade

A new Global Trigger subsystem will be installed in the Level-0 Trigger as part of HL-LHC Upgrade of ATLAS during the upcoming Long-Shutdown 3. It will feature new and improved trigger hardware and algorithms, and an increased maximum output rate of 1 MHz. The Global Trigger will run offline-like trigger algorithms on full-granularity data, gathered from several sub-detectors and trigger-processing subsystems. A single Global Common Module (GCM) hardware is implemented across the Global Trigger system to be used as Multiplexer Processor, Global Event Processor and CTP Interface (gCTPi). This common hardware platform method will minimize the complexity of the firmware and simplify the system design and long-term maintenance. The GCM prototype is an ATCA front form factor board with two Xilinx Virtex UltraScale+ FPGA VU13P and one ZYNQ UltraScale+ FPGA ZU19EG and seventeen 25.78125 Gb/s FireFly duplex optical modules on it. The total power consumption of this board must be less than 350 W, and the temperature of the optical modules should be less than 70 °C in the worst case. The VU13Ps serve as algorithms processor nodes such as MUX, GEP and gCTPi, and the ZU19EG with Peta Linux OS running on it, is used as Command/Control/Readout Unit to configure and monitor the board and communicate with the ATLAS Detector Control System (DCS). The development of an ATCA blade with three large FPGAs and about 200 optical links running at 25Gb/s is a very challenging task, and the successful test results have demonstrated this GCM prototype as an advancement of state-of-the-art electronics module design in HEP experiments. This paper presents the hardware design considerations, functionalities, and performance test results of this GCM prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Nuclear two point correlation functions on a quantum computer

The calculation of dynamic response functions is expected to be an early application benefiting from rapidly developing quantum hardware resources. The ability to calculate real-time quantities of strongly correlated quantum systems is one of the most exciting applications that can easily reach beyond the capabilities of traditional classical hardware. Response functions of fermionic systems at moderate momenta and energies corresponding roughly to the Fermi energy of the system are a potential early application because the relevant operators are nearly local, and the energies can be resolved in moderately short real time, reducing the spatial resolution and gate depth required. This is particularly the case in quasielastic electron and neutrino scattering from nuclei, a topic of great interest in the nuclear and particle physics communities and directly related to experiments designed to probe neutrino properties. In this paper we use current quantum hardware and error mitigation protocols to calculate response functions for a highly simplified nuclear model through calculations of a 2-point real time correlation function for a modified Fermi-Hubbard model in two dimensions with three distinguishable nucleons on four lattice sites.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

ROOT’s RNTuple I/O Subsystem: The Path to Production

The RNTuple I/O subsystem is ROOT’s future event data file format and access API. It is driven by the expected data volume increase at upcoming HEP experiments, e.g. at the HL-LHC, and recent opportunities in the storage hardware and software landscape such as NVMe drives and distributed object stores. RNTuple is a redesign of the TTree binary format and API and has shown to deliver substantially faster data throughput and better data compression both compared to TTree and to industry standard formats. In order to let HENP computing workflows benefit from RNTuple’s superior performance, however, the I/O stack needs to connect efficiently to the rest of the ecosystem, from grid storage to (distributed) analysis frameworks to (multithreaded) experiment frameworks for reconstruction and ntuple derivation. With the RNTuple binary format soon arriving at its first production release, we present RNTuple’s feature set, integration efforts, and its performance impact on the time-to-solution. We show the latest performance figures of RDataFrame analysis code of realistic complexity, comparing RNTuple and TTree as data sources. We discuss RNTuple’s approach to functionality critical to the HENP I/O (such as multithreaded writes, fast data merging, schema evolution) and we provide an outlook on the road to its use in production.

Blomer, Jakob↗
DOE OSTI2020

Measuring and Simulating T1 and T2 for Qubits

Quantum computers perform computations exploiting quantum mechanics to a possible advantage, allowing us to prepare and manipulate states that do not have a classical equivalent. In particular, phenomena like superposition and entanglement may enable quantum computers to outperform their classical counterparts in certain applications. Implementing these useful quantum algorithms is contingent upon building accurate quantum hardware that is not affected by noise. Environmental noise decreases coherence time of qubits, meaning that qubits do not stay in a desired state long enough to carry out a complex computation. To that end, harnessing the full power of quantum computers necessitate characterization of noise sources and how they impact a given quantum system. Often times, T1and T2 are used to quantify noise. In this project, we provide an approach as to how T1 and T2 values are calculated and simulated for quantum systems. In addition, we compare simulated values of T1 and T2 with those of real quantum computer’s measurements. IBMQ Experience, an open source software allowing users to simulate and use real quantum hardware, is used. QuTip, a Python-based toolbox offering quantum simulation tools for open quantum systems, is also used.

Youssef, Rahaf↗
DOE OSTI2026

AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation

The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs approaches expert-authored reference procedures on the benchmark (F1-score > 0.89) on challenging multi-plate syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine Learning-based False Data Injection Attack Detection and Localization in Power Grids

Cyberattacks on critical infrastructures can be catastrophic and bring nations to their knees. Therefore, detecting these attacks is crucial and challenging. This paper presents a novel approach for detecting and locating cyberattacks affecting an electrical power system. The adversary employs a man-in-the-middle technique to inject false data into the communication between distributed energy resources (DER) and Microgrid Controller (MGC) with the goal of disrupting power delivery. The approach for detection and localization is based on integrating multiple machine learning-based anomaly detection models that combine network traffic data and grid measurements. Experiments are performed to assess the method's performance using a hardware-in-the-loop real-time simulation testbed which includes Modbus TCP/IP communication. Power system topology and operating conditions are based on actual topology and real-world data provided by the Holy Cross Energy utility network. Results confirm that the method can be successfully employed for detecting and localizing cyberattacks.

Leao, Bruno P.↗
DOE OSTI2021

Orchestrated trios: compiling for efficient communication in Quantum programs with 3-Qubit gates

Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-qubit gates and only two-qubit gates between physically connected pairs of qubits. Typical compilers first decompose operations, then route data to connected qubits. We propose a new compiler structure, Orchestrated Trios, that first decomposes to the three-qubit Toffoli, routes the inputs of the higher-level Toffoli operations to groups of nearby qubits, then finishes decomposition to hardware-supported gates. This significantly reduces communication overhead by giving the routing pass access to the higher-level structure of the circuit instead of discarding it. A second benefit is the ability to now select an architecture-tuned Toffoli decomposition such as the 8-CNOT Toffoli for the specific hardware qubits now known after the routing pass. Furthermore, we perform real experiments on IBM Johannesburg showing an average 35% decrease in two-qubit gate count and 23% increase in success rate of a single Toffoli over Qiskit. We additionally compile many near-term benchmark algorithms showing an average 344% increase in (or 4.44x) simulated success rate on the Johannesburg architecture and compare with other architecture types.

compiler↗
DOE OSTI2025

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.

FOS: Computer and information sciences↗
DOE OSTI2023

Exploring Benchmarks for Self-Driving Labs using Color Matching

Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying quantities of supplied colored pigments that match a target color, the color matching problem, provides a simple and flexible SDL test case, as it requires experiment proposal, sample creation, and sample analysis, three common components in autonomous discovery applications. We present a robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol. Our solution leverages the WEI science factory platform to enable portability across different robotic hardware, the use of alternative optimization methods for continuous refinement, and automated publication of results for experiment tracking and post-hoc analysis.

Artificial intelligence↗
DOE OSTI2020

DAQExpert the service to increase CMS data-taking efficiency

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly in the event of hardware or software failure of the subsystems, an expert system, the DAQ Expert, has been developed. It aims at improving the data taking efficiency, reducing the human error in the operations and minimising the on-call expert demand. Introduced in the beginning of 2017, it assists the shift crew and the system experts in recovering from operational faults, streamlining the post mortem analysis and, at the end of Run 2, triggering fully automatic recovery without human intervention. DAQ Expert analyses the real-time monitoring data originating from the DAQ components and the high-level trigger updated every few seconds. It pinpoints data flow problems, and recovers them automatically or after given operator approval. We analyse the CMS downtime in the 2018 run focusing on what was improved with the introduction of automated recovery; present challenges and design of encoding the expert knowledge into automated recovery jobs. Furthermore, we demonstrate the web-based, ReactJS interfaces that ensure an effective cooperation between the human operators in the control room and the automated recovery system. We report on the operational experience with automated recovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Quantitative concentration measurements in a turbulent helium jet using rainbow schlieren deflectometry

Rainbow schlieren deflectometry (RSD) is applied to acquire quantitative concentration measurements in a turbulent environment for the first time. RSD methodology is developed and validated using Rayleigh scattering data available in the literature. Experiments were performed in an initially laminar, momentum-driven helium jet injected into ambient air. Full-field measurements are acquired that span the laminar, transition, and fully turbulent regions of the jet. Furthermore, the RSD results are shown to provide accurate measurements in each region and also confirm the self-similar nature of the fully turbulent jet. Analysis shows that the transition from laminar to turbulent flow occurs across an axial span that decreases with increasing Reynolds number. Experiments with a long exposure time were conducted to evaluate the limitations of hardware averaging of the RSD signal in comparison with software averaging.

02 PETROLEUM↗
DOE OSTI2025

Direct I/O for RNTuple Columnar Data

RNTuple is the new columnar data format designed as the successor to ROOT’s TTree format. It allows to make use of modern hardware capabilities and is expected to be used in production by the LHC experiments during the HL-LHC. In this paper, we discuss the usage of Direct I/O to fully exploit modern SSDs, especially in the context of the recent addition of parallel RNTuple writing. We describe the alignment requirements imposed by Direct I/O and approaches to meet them for columnar data formats. Finally, we discuss performance results for both writing and reading, in synthetic benchmarks as well as real-world applications.

Hahnfeld, Jonas [CERN; Goethe U., Frankfurt (main)↗
DOE OSTI2021

Experimental quantum learning of a spectral decomposition

Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine to compress the depth of dynamical quantum simulations. One can view our implementation as a demonstration of entanglement-enhanced machine learning, as only a single (entangled) training data pair is required to learn a 4 × 4 unitary matrix.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Transforming Science Through Software: Improving While Delivering 100×

The U.S. Department of Energy (DOE) Exascale Computing Project (ECP) funded the development of new (and the transformation of important existing) applications, libraries, and tools that realized improvement in performance and capabilities of often 100 times or more on emerging exascale computers. This exceptional gain inspired the title of this special issue: Transforming Science through Software: Improving while delivering 100X. The term 100X refers to advancing capabilities in modeling, simulation, and analysis by a factor of 100 or more using some combination of new algorithms, optimization techniques, software libraries, and programming models, coupled with the next generation of hardware for high-performance computing (HPC). The papers in this issue share experiences with the practice and science of scientific software development, with an emphasis on developing a coherent, portable, and sustainable HPC software ecosystem for next-generation computational science. Finally, we hope to foster expanded community efforts related to the fundamental role of sustainable scientific software ecosystems in advancing the computing sciences.

97 MATHEMATICS AND COMPUTING↗