Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Power Performance”

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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Incorporating Valve Switching Losses Into a Static Optimal Control Algorithm for the Hybrid Hydraulic-Electric Architecture (HHEA)

The Hybrid Hydraulic-Electric Architecture (HHEA) has, in recent years, been proposed as an energy efficient alternative to conventional load-sensing architectures in mobile machines such as excavators and wheel-loaders. HHEA leverages the advantages of hydraulic power and electric power to eliminate throttling valves while also improving the energy and control performance of the system. The architecture utilizes a set of common pressure rails to provide a majority of power and and a small electric motor driven pump to modulate this power to meet the exact demand. Previous work has developed a computationally efficient Lagrange Multiplier approach for determining the optimal pressure rail selections that minimizes the energy losses in the system. The static model used considers only the energy use for each pressure rail selection but not the losses associated with the valves during the transition. This paper presents an approach to include the switching losses in the model and in the optimization procedure. To capture the switching losses, switching events between different rails and at various input and output flow rates were simulated with consideration of valve spool dynamics. A parameterized model that summarizes the losses is then obtained, allowing switching losses to be added to the previous energy analysis. The performance of the switching loss model was compared with reference data obtained from a high-fidelity simulation model. To incorporate the switching losses into optimal control algorithm, an efficient dynamic programming approach that prevents frequent switching is adopted in place of the Lagrange multiplier approach. The overall effect of switching losses on the energy consumption and optimal control decisions is presented. In general, switching losses contribute to about 9–10% of input energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Fermi Level Engineering of Passivation and Electron Transport Materials for p-Type CuBi 2 O 4 Employing a High-Throughput Methodology

Metal oxide semiconductors are promising for solar photochemistry if the issues of excessive charge carrier recombination and material degradation can be resolved, which are both influenced by surface quality and interface chemistry. Coating the semiconductor with an overlayer to passivate surface states is a common remedial strategy but is less desirable than application of a functional coating that can improve carrier extraction and reduce recombination while mitigating corrosion. Here, a data-driven materials science approach utilizing high-throughput methodologies, including inkjet printing and scanning droplet electrochemical cell measurements, is used to create and evaluate multi-element coating libraries to discover new classes of candidate passivation and electron-selective contact materials for p-type CuBi 2 O 4 . The optimized overlayer (Cu 1.5 TiO z ) improves the onset potential by 110 mV, the photocurrent by 2.8×, and the absorbed photon-to-current efficiency by 15.5% compared to non-coated photoelectrodes. It is shown that these enhancements are related to reduced surface recombination through passivation of surface defect states as well as improved carrier extraction efficiency through Fermi level engineering. This work presents a generalizable, high-throughput method to design and optimize passivation materials for a variety of semiconductors, providing a powerful platform for development of high-performance photoelectrodes for incorporation into solar-fuel generation systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Material selection and manufacturing for high‐temperature heat exchangers: Review of state‐of‐the‐art development, opportunities, and challenges

Abstract Many energy systems demand heat transfer at high temperatures to keep up with high demand for power, so high‐temperature material that can perform and last under these harsh conditions is needed for heat exchangers. The engineering requirements for these high‐temperature heat exchanger material call for high thermal conductivity, high resistance to fracture, high resistance to creep deformation, environmental stability in environments associated with the application, and high modulus of elasticity while maintaining low cost to make and maintain. Naturally, ceramics are a good solution for this endeavor. In the past, high‐temperature heat exchangers made from ceramics have been used. We provide examples of ceramics in relevant heat exchange applications and provide motivation where additive manufacturing (AM) can improve efficiency. AM for the relevant material is under development, and we provide insight on the AM of ceramic materials and examples of AM heat exchangers keeping cost in mind. The motivation of the review paper is to provide a framework for material and manufacturing selection for high‐temperature heat exchangers for AM to keep up with the demand for better efficiency, better material, better manufacturing, and cost moving forward with AM technology in high‐temperature ceramic heat exchangers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlating conductivity and Seebeck coefficient to doping within crystalline and amorphous domains in poly(3-(methoxyethoxyethoxy)thiophene)

Molecular doping of conjugated polymers (CPs) plays a vital role in optimizing organic electronic and energy applications. For the case of organic thermoelectrics, it is commonly believed that doping CPs with a strong dopant could result in higher conductivity (σ) and thus better power factor (PF). Herein, by investigating thermoelectric performance of a polar side-chain bearing CP, poly(3- (methoxyethoxyethoxy)thiophene) (P3MEET), vapor doped with fluorinatedderivative of tetracyanoquinodimethane FnTCNQ (n = 1, 2, 4), we show that using strong dopants can in fact have detrimental effects on the thermoelectric performance of CPs. Despite possessing higher electron affinity, doping P3MEET with F4TCNQ only results in a σ (27.0 S/cm) comparable to samples doped with other two weaker dopants F2TCNQ and F1TCNQ (26.4 and 20.1 S/cm). Interestingly, F4TCNQ-doped samples display a marked reduction in the Seebeck coefficient (α) compared to F1TCNQ- and F2TCNQ-doped samples from 42 to 13 μV/ K, leading to an undesirable suppression of the PF. Additionally, structural characterizations coupled with Kang-Snyder modeling of the α–σ relation show that the reduction of α in F4TCNQ-doped P3MEET samples originates from the generation of low mobility carrier within P3MEET's amorphous domain. Our results demonstrate that factors such as dopant distribution and doping efficiency within the crystalline and amorphous domains of CPs should play a crucial role in advancing rational design for organic thermoelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Green’s functions on a renormalized lattice: An improved method for the integer quantum Hall transition

Highlights: • Speed up of numerical calculations by exact renormalization group steps. • Practical approach to handle delicate scaling analysis. • Absence of evidence to prefer marginal corrections over power-law corrections. We introduce a performance-optimized method to simulate localization problems on bipartite tight-binding lattices. It combines an exact renormalization group step to reduce the sparseness of the original problem with the recursive Green’s function method. We apply this framework to investigate the critical behavior of the integer quantum Hall transition of a tight-binding Hamiltonian defined on a simple square lattice. In addition, we employ an improved scaling analysis that includes two irrelevant exponents to characterize the shift of the critical energy as well as the corrections to the dimensionless Lyapunov exponent. We compare our findings with the results of a conventional implementation of the recursive Green’s function method, and we put them into broader perspective in view of recent development in this field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Direct regeneration of degraded LiFePO4 cathodes via a separator-enabled prelithiation strategy

A persistent challenge in lithium-ion batteries is the loss of active lithium due to the solid electrolyte interphase (SEI) formation and associated side reactions. While prelithiation employing lithium replenishment separator (LRS) has been proven effective in compensating for lithium loss, previous studies have largely been accompanied by gas evolution or solid residue formation during the prelithiation process. To surmount this challenge, we present a LRS based on 4-fluoro-1,2-dihydroxybenzene lithium salt (LiDF), capable of mitigating lithium loss while producing decomposition products that integrate directly into the electrolyte as functional additives which can assist with the stability of the SEI, free from gas or solid formation, thus establishing a sustainable and environmentally benign strategy for lithium compensation. Incorporation of the LRS enables the pristine LiFePO4||graphite (Gr) full cell to achieve 10.8% higher capacity than the cell with a polypropylene separator (PPS) after 200 cycles at 0.5C. Remarkably, the degraded LiFePO4 (D-LFP)||Gr full cell with the LRS exhibits a 135.8% capacity improvement over the PPS-based cell after 500 cycles. These findings establish the LRS as a powerful approach for both boosting high-performance lithium-ion batteries and recovering the capacity of degraded batteries.

Tao, Fujun↗
DOE OSTI2021

Co-simulation of transactive energy markets: A framework for market testing and evaluation

The proliferation of distributed energy resources (DER)—and the ability to intelligently control these assets—is re-defining the electrical distribution system. As the number of controllable devices rapidly expands, grid operators must determine how to incorporate these assets while delivering reliable, equitable, and affordable electricity. One possible approach is to establish distribution-level electricity markets and allow devices/aggregations of devices to participate in price establishment. While this approach purports some of the same benefits as the highly successful wholesale electricity markets (i.e., open competition, efficient price discovery, reduced communication overhead), this needs to be researched and quantified via an analysis platform that models distribution-level markets at the appropriate fidelity. Specifically, the simultaneous evaluation of market performance, DER performance, DER bidding approaches, and distribution feeder power quality requires modeling that spans multiple technical areas. Co-simulation has emerged as a powerful tool in addressing this type of problem, where outputs depend on a range of underlying areas of expertise and associated models. In this paper we describe a solution, as implemented in the HELICS co-simulation platform, where we include (1) high fidelity house models, (2) intelligent bidding agents, (3) a modular market integration/design, and (4) a distribution feeder model. We then present a case study where we test two different market designs: (1) a pseudo-wholesale double-blind auction, and (2) an asynchronous matching market. In this work, the markets are run under two DER penetration levels and economic results are compared to full retail net energy metering and avoided cost net metering scenarios that bookend current approaches to remuneration of DER participation. We show the potential for transactive markets to provide increased value for most customers relative to net metering (and all customers relative to avoided cost scenarios) while decreasing costs for the utility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A reversible perovskite air electrode for active and durable oxygen reduction and evolution reactions via the A-site entropy engineering

The performance of reversible solid oxide electrochemical cells (R-SOECs) is largely hindered by the insufficient electroactivity and poor durability of the bifunctional air electrodes, where the oxygen reduction and evolution reactions (ORR and OER) occur. Here, we report our findings in boosting the electrochemical activity and durability of an air electrode with Pr 0.2 Ba 0.2 Sr 0.2 La 0.2 Ca 0.2 CoO 3-δ (PBSLCC) via an A-site entropy engineering. The PBSLCC electrode shows enhanced oxygen reaction activity and excellent durability compared to binary and ternary double perovskites (PrBaCo 2 O 5+δ and Pr 0.8 Ba 0.8 Ca 0.4 Co 2 O 5+δ , respectively). A low and nearly unchanged area-specific resistance of 0.042Ωcm 2 is achieved at 750 °C during the 225-h stability test. La 0.9 Sr 0.1 Ga 0.8 Mg 0.2 O 3-δ electrolyte-supported cells with the PBSLCC air electrode show remarkable performance at 800 °C, demonstrating a peak power density of 1.2Wcm -2 in the fuel cell mode, and a current density of -1.1 Acm -2 at 1.3 V in the electrolysis mode while maintaining the excellent cycling durability of 228 h at ±0.5 Acm -2 under humidified H 2 (10% H 2 O). A bulk oxygen p-band center model is applied to verify that tailoring of the A-site entropy strongly influences the surface exchange coefficients (k* chem ), leading to higher oxygen reaction activity of PBSLCC than the binary and ternary perovskites. Finally, this study opens a new class of high-entropy perovskites for the rational design of air electrodes for R-SOECs with high activity and durability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Efficacy of vibro-impact energy harvesting absorbers on controlling dynamical systems under vortex-induced vibrations and base excitation

Here this study investigates improving the efficacy of an energy harvesting absorber's ability to control a structure under vortex-induced vibrations, base excitation, and a combination of the two by including mechanical amplitude stoppers. The nonlinear reduced-order model is developed through modifying trilinear spring models to represent the impact forces, a modified van der Pol oscillator to represent the forcing due to the vortex-induced vibrations and using the Euler-Lagrange principle to express the equations of motion. It is seen that a soft stopper stiffness and a 5mm gap performs the most effectively of increasing the power generated from the absorber while still greatly reducing the primary structure's amplitude. By changing the stopper's location towards the middle of the energy harvesting absorber, the large effects of the impact forces are reduced and improves the efficacy of medium and hard stopper stiffnesses to generate near the amount of power the soft stopper does, while greatly improving the control of the primary structure. When the system is under combined loadings, the large oscillations of the synchronization region cause the effective configuration to be that of a 27.5 mm gap with soft stiffnesses. The results shows that medium stiffness stoppers with small gaps generate large aperiodic regions due to the high impact force. When the oscillations are close to the stoppers, the beating phenomenon is observed and is not overpowered by the vibro-impact force.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Revitalizing interface in protonic ceramic cells by acid etch

Protonic ceramic electrochemical cells hold the promise to be operated at intermediate temperatures below 600 °C. Although the high proton conductivity of the bulk electrolyte has been demonstrated, it cannot be fully utilized in electrochemical full cells due to unknown causes. A practical solution is thus urgently needed. Here we showed that it comes from poor contacts between the low-temperature processed oxygen electrode-electrolyte interface. We demonstrated that a simple acid treatment can effectively rejuvenate the high-temperature annealed electrolyte surface, resulting in reactive bonding between the oxygen electrode and the electrolyte and improved electrochemical performance and stability. This enables exceptional protonic ceramic fuel-cell performance down to 350 °C, with peak power densities of 1.6 W cm -2 at 600 °C, 650 mW cm -2 at 450 °C, and 300 mW cm -2 at 350 °C, as well as stable electrolysis operations at large current densities above 3.9 A cm -2 under 1.4 V applied voltage at 600 °C. Furthermore, our work highlights the critical role of interfacial engineering in ceramic electrochemical devices and offers new understanding and practices towards sustainable energy infrastructure.

08 HYDROGEN↗
DOE OSTI2022

Critical role of acceptor dopants in designing highly stable and compatible proton-conducting electrolytes for reversible solid oxide cells

Proton-conducting electrolytes are receiving increasing attention due to their high ionic conductivity at intermediate temperatures, enabling the operation of solid oxide cells with high energy efficiency at low cost. However, the effect of B-site dopants on the properties of doped barium hafnate-cerate electrolyte materials, especially in single cells under operating conditions, has not been systematically studied. Here we report our findings in the development of a series of proton-conducting electrolytes with a general formula of BaHf 0.1 Ce 0.7 R 0.2 O 3–δ (BHCR172, R = Yb, Er, Y, Gd, Sm). Here, the results reveal that electrical conductivity, ionic transference number, chemical stability against steam and CO 2 , and compatibility with NiO during sintering are all closely correlated with the dopant size. In particular, the reaction with NiO is found to strongly affect the properties of the electrolytes and hence cell performance. Among all tested compositions, BaHf 0.1 Ce 0.7 Yb 0.2 O 3–δ (BHCYb172) shows excellent chemical stability and minimal reactivity towards NiO, as predicted from density functional theory (DFT)-based calculations and confirmed by experimental results. In addition, proton-conducting reversible solid oxide cells (P-ReSOCs) based on the optimized electrolyte composition, BHCYb172, demonstrate exceptional performance and stability, achieving a remarkable peak power density of 1.74 W cm –2 (O 2 as the oxidant) at 600 °C in the fuel cell mode and a high current density of 2.0 A cm –2 at 1.3 V and 600 °C in the steam electrolysis mode while maintaining excellent durability for over 1000 h.

25 ENERGY STORAGE↗
DOE OSTI2020

A multi-wavelength streaked optical pyrometer for dynamic shock compression measurements above 2500 K

This work presents a concept and implementation of a calibrated streaked spectral pyrometer (SSP) temperature diagnostic used in dynamically driven shock experiments on a two-stage gas gun. This system relies upon measuring the total system response using a tunable monochromator, a NIST-traceable calibrated power meter, and a SSP. The diagnostic performance is validated against previously measured temperatures of shock driven z-cut quartz at 99 GPa and 93 GPa. The results are found to agree with the literature to within 5% and are discussed in this manuscript. The experimental setup utilizes measurements from multiple SSP systems per sample, providing several independent measurements and substantially increasing confidence in the extrapolated shock driven sample temperature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Illumination-induced modulation of conductivity and Gunn oscillation properties in epitaxial GaAs

In this work, we illuminate a gallium arsenide (GaAs) Gunn device and study the light-induced changes of Gunn oscillation properties. We observe that illumination leads to the modulation of the Gunn threshold voltage, the Gunn oscillation magnitude, and the coherency of Gunn oscillation, with the nature of the modulation being closely related to the position of illumination on the device. These effects are attributed to the generation of optically excited carriers, which results in the modulation of conductivity and the electric field profile along the device. The finite element method is used to simulate the change of the field profile of the Gunn device caused by illumination. We also report an unexpected phenomenon of Gunn oscillation property manipulation with an optical chopper. In addition, wavelength-dependent, power-dependent, and pulsed illumination measurements are performed to help with further understanding the observations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Gate control of electron correlations towards Mott field effect transistors (MottFET)

The modulation of channel conductance in field-effect transistors (FETs) via metal-insulator-semiconductor structures has revolutionized information processing and storage. However, the limitations of silicon-based FETs in electrical switching have driven the search for new materials capable of overcoming these constraints. Electrostatic gating of competing electronic phases in a Mott material near its metal-to-insulator transition (MIT) offers the prospects of substantial modulation of the conducting electrons and electrical resistivity through small changes in band filling. While electrostatic control of the MIT has been previously reported, the advancement of Mott materials toward novel Mott-based transistors, MottFETs requires the realization of their unique physical properties in a solid-state device. In this study, we present gate control of electron correlation using a solid-state device utilizing the oxide Mott system La 1-x Sr x VO 3 as a correlated FET channel. We report on a gate resistance response that cannot be explained in a purely electrostatic framework. This behavior suggests an enhancement in effective mass and a reduction in effective carrier density as the system approaches its insulating state, consistent with theoretical predictions for Mott systems, suggesting at least 100× charge gain originating from the correlated behavior. These preliminary results pave the way toward the development of highly efficient, low-power electronic devices that could surpass the performance bottlenecks of conventional FETs by leveraging the electronic phase transitions of correlated electron systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗