Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “efficiency improvement”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Near and long-term perspectives on strategies to decarbonize China's heavy-duty trucks through 2050

China needs to drastically reduce carbon dioxide (CO 2 ) emissions from heavy-duty trucks (HDTs), a key emitter in the growing transport sector, in order to address energy security concerns and meet its climate targets. We address existing research gaps by modeling feasibility, applicability, and energy and emissions impacts of multiple decarbonization strategies at different points in time. China still relies heavily on coal power, so impacts of new HDT technologies depend on the timing of their introduction relative to progress toward non-fossil power. We use a bottom-up model to simulate HDT energy consumption and CO 2 emissions through 2050. Results show that beginning to deploy battery electric and fuel-cell HDTs as early as 2020 and 2035, respectively, could achieve significant and the largest CO 2 emissions reduction by 2050 with a decarbonized power sector. However, viable near-term strategies-improving efficiency and logistics, switching to liquefied natural gas-could halve HDTs' current diesel consumption and CO 2 emissions by 2050. Our results underscore the need for a mix of near- and long-term policy and technology options to decarbonize China's HDTs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing the proton exchange kinetics of BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3− δ ceramic electrolyte by operando diffuse reflectance infrared Fourier transform spectroscopy

Proton exchange kinetics plays an important role in governing the performance of intermediate-temperature protonic ceramic electrolysis cells (PCECs) for hydrogen production. Our understanding of the nature of the surface hydration reaction at the single-cell level, however, remains very limited, hampering further efficiency improvements. Here, in this study, we developed a custom operando diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) platform that operates under high temperature and steam conditions with applied bias. Quantitative investigations of surface H 2 O/D 2 O isotope exchange in a BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3−δ (BZCYYb1711) protonic electrolyte-based single cell were conducted under different applied voltages using this DRIFTS platform, to gain molecular-level insight into hydration kinetics. The findings show that the application of an external voltage significantly enhances the surface proton exchange rate, decreasing the apparent activation energy from 29.1 kJ mol −1 at open-circuit voltage (OCV) to 6.8 kJ mol −1 at 1.3 V. In addition, distinct voltage-induced spectral shifts in O–D vibrations point to dynamic changes in surface hydration. These findings demonstrate a sensitive spectroscopic platform for probing interfacial proton processes and reveal strong electrochemical control over surface proton kinetics, offering new opportunities for probing electrolyte hydration behavior in PCECs.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Linear convergence of accelerated conditional gradient algorithms in spaces of measures

A class of generalized conditional gradient algorithms for the solution of optimization problem in spaces of Radon measures is presented. The method iteratively inserts additional Dirac-delta functions and optimizes the corresponding coefficients. Under general assumptions, a sub-linear [see formula in PDF] rate in the objective functional is obtained, which is sharp in most cases. To improve efficiency, one can fully resolve the finite-dimensional subproblems occurring in each iteration of the method. We provide an analysis for the resulting procedure: under a structural assumption on the optimal solution, a linear [see formula in PDF] convergence rate is obtained locally.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

The ATLAS Workflow Management System Evolution in the LHC Run3 and towards the High-Luminosity LHC era

The ATLAS experiment has 18+ years of experience using workload management systems to deploy and develop workflows to process and to simulate data on the distributed computing infrastructure. Simulation, processing and analysis of LHC experiment data require the coordinated work of heterogeneous computing resources. In particular, the ATLAS experiment utilizes the resources of 250 computing centers worldwide, the power of supercomputing centres, and national, academic and commercial cloud computing resources. In this contribution, we present new techniques for cost-effectively improving efficiency introduced in workflow management system software. The evolution from a mesh framework to new types of computing facilities such as cloud and HPCs is described, as well as new types of production and analysis workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Efficient Langevin dynamics for “noisy” forces

Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Additionally, stochastic approaches provide a gentler system-size dependency at the cost of introducing “noisy” forces, which could limit the efficiency of the sampling. When the forces are deterministic, the first-order Langevin dynamics (FOLD) offers efficient sampling by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator [G. Mazzola and S. Sorella, Phys. Rev. Lett. 118, 015703 (2017)]. However, when forces are noisy, S is set equal to the force-covariance matrix, a procedure that severely limits the efficiency and the stability of the sampling. Here, we develop a new, general, optimal, and stable sampling approach for FOLD under noisy forces. We apply it for silicon nanocrystals treated with stochastic density functional theory and show efficiency improvements by an order-of-magnitude.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Flexible hose interconnect testing for parabolic troughs with nitrate salt

The use of molten salt as a heat transfer fluid in parabolic trough concentrated solar power plants, in lieu of oil-based heat transfer fluids, presents the possibility to increase the plant's operating temperature and corresponding Rankine cycle efficiency, improve dispatchability, and decrease costs. However, several barriers still exist to commercialization of the technology. This paper discusses meaningful progress toward overcoming two specific barriers to commercialization; 1) the lack of a reliable interconnect solution for molten salt service to connect rotating collectors with stationary header pipes and 2) the limitations of publicly available system modeling tools for a plant using molten salt as both the heat transfer fluid and energy storage medium.

14 SOLAR ENERGY↗
DOE OSTI2025

Assessment of minnealloy fabrication via three routes

Transformer cores exhibiting higher operating power and improved efficiency are of great interest to electrical utilities, industry, and the de-carbonization effort. Minnealloy, α″-Fe16(C,N)2, a martensite made only of iron, nitrogen, and carbon, has shown the largest saturation magnetization of any soft ferromagnet, 250 emu/g, and tunable magnetocrystalline anisotropy. Given this represents a significant increase in power transferred per cycle compared to legacy transformer core materials, we investigate three novel, industrially scalable routes for fabricating Minnealloy. The martensite phase content is investigated for each route. Vibrating sample magnetometry is used to investigate the change in saturation magnetization and coercivity with respect to the relative content of the desired phase and other iron, iron-nitride, and iron oxide phase impurities. The relationship between structure and magnetic properties of bulk α″-Fe16(C,N)2 is investigated using LDA, PBE, and PBEsol exchange-correlation functionals within the frameworks of Hubbard-corrected density functional theory (DFT+U).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerating the development of new solar absorbers by photoemission characterization coupled with density functional theory

Abstract The expectation to progress towards Terawatts production by solar technologies requires continuous development of new materials to improve efficiency and lower the cost of devices beyond what is currently available at industrial level. At the same time, the turnaround time to make the investment worthwhile is progressively shrinking. Whereas traditional absorbers have developed in a timeframe spanning decades, there is an expectation that emerging materials will be converted into industrially relevant reality in a much shorter timeframe. Thus, it becomes necessary to develop new approaches and techniques that could accelerate decision-making steps on whether further research on a material is worth pursuing or not. In this review, we will provide an overview of the photoemission characterization methods and theoretical approaches that have been developed in the past decades to accelerate the transfer of emerging solar absorbers into efficient devices.

14 SOLAR ENERGY↗
DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗
DOE OSTI2023

Inverse modeling of circular lattices via orbit response measurements in the presence of degeneracy

The number and location of beam position monitors (BPMs) and steerers with respect to the quadrupoles in a circular lattice can lead to degeneracy in the context of fitting linear optics and extracting lattice information from measured closed orbits. Furthermore, the measurement uncertainties due to the imperfection of BPMs and steerers can be propagated by the fitting process in ways that prohibit the successful extraction of discrepancies between lattice elements in the real machine and their description in the corresponding model. We systematically studied the influence of the placement of BPMs and steerers on the reconstruction of linear optics and corresponding lattice information. The derivative of orbit response coefficients with respect to the quadrupole strengths, the Jacobian, is derived as an analytical formula. This analytical version of the Jacobian is used to further derive the theoretical limitations of fitting linear optics from closed orbits in terms of the placement of BPMs and steerers. It is further demonstrated that when evaluating the Jacobian during the fitting procedure, the analytical version can be used in place of the conventional finite-difference computation. This allows for greatly improved efficiency when computing the Jacobian during each iteration of the fitting procedure. The approach is tested with large-scale simulations and the findings are verified by measurement data taken on SIS18 synchrotron at GSI Helmholtz Centre for Heavy Ion Research. The presented methods are of general nature and can be applied to other accelerator lattices as well. The fitting procedure by using the analytical Jacobian is tested in conjunction with various methods for mitigating quasidegeneracy and the results agree with those obtained by using the conventional Jacobian via finite-difference approximation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-Zr⁢F 4

The local atomic structure of NaF-Zr⁢F 4 (53–47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements which are then used to validate the quality of ab initio and neural network-based molecular dynamics (NNMD) calculations in the temperature range 515–700°⁢C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab initio calculations. Looking at the evolution of the pair distribution function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the coexistence of three different fluorozirconate complexes: [Zr⁢F 6 ] 2– , [Zr⁢F 7 ] 3– , and [Zr⁢F 8 ] 4– , with a shift in the dominant coordination state from the 7-coordinated Zr cation toward a 6-coordinated cation with increasing temperature. The study also highlights the metastability of different local coordination structures, with frequent interconversions between the 6- and 7-coordinate states. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both “edge-sharing” and “corner-sharing” fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest-neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing nonrandom arrangement. Finally, these findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Methodology for interpretable reinforcement learning model for HVAC energy control

Deep reinforcement learning (DRL) approaches have been used in various application areas to improve efficiency, optimization, or automation. However, very little is known about how the DRL algorithms make decisions and what features affect their performance. Using a case study of a DRL based Heating, Ventilation and Air Conditioning (HVAC) optimization methodology, we demonstrate how we can address these challenges by applying interpretability tools and systematically exploring the model inputs for better understanding the DRL behaviour and decision making process. We developed a methodology for interpretable reinforcement learning and evaluated our approach in real-world house located in Knoxville, TN. Our findings explain the reasoning behind DRL-based optimization decisions under different circumstances which has been discussed and confirmed by the experts in the field.

Kotevska, Olivera↗
DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗
DOE OSTI2020

Design of Auxiliary Circuit Elements for Achieving Zero Voltage Switching in a Wireless Power Transfer System

The auxiliary resonant commutated pole (ARCP) is being used to achieve zero voltage switching (ZVS) of power switches for improved efficiency and/or to reduce dv/dt at the switch node for better electromagnetic interference (EMI) performance and overall system reliability. This paper shows how an ARCP circuit can be used in a wireless power transfer system to achieve ZVS. Detailed analysis of ARCP circuit operation is presented to determine the requirements for achieving ZVS in the main switches of a wireless electric vehicle charging system. Based on this analysis, a design algorithm is developed to optimize the design of the ZVS assisting circuit components. The designed ARCP achieves soft switching of the MOSFETs in the primary inverter of an 85 kHz, 10 kW series-series compensated, stationary wireless power transfer system.

Saha, Tarak↗