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

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
DOE OSTI2022

Controlling magnetic exchange and anisotropy by nonmagnetic ligand substitution in layered 𝑀⁢P⁢𝑋 3 (𝑀 = Ni, Mn; 𝑋 = S, Se)

Recent discoveries in two-dimensional magnetism have intensified the investigation of van der Waals magnetic materials and further improved our ability to tune their magnetic properties. Tunable magnetism has been widely studied in antiferromagnetic metal thiophosphates 𝑀⁢P⁢𝑋 3 . Substitution of metal ions 𝑀 has been adopted as an important technique to engineer the magnetism in 𝑀⁢P⁢𝑋 3 . In this work, we have studied the previously unexplored chalcogen 𝑋 substitutions in 𝑀⁢P⁢𝑋 3 (𝑀 = Mn/Ni; 𝑋 = S/Se). We synthesized the single crystals of MnP⁢S 3−𝑥 ⁢Se 𝑥 (0 ≤ 𝑥 ≤ 3) and NiP⁢S 3−𝑥 ⁢Se 𝑥 (0 ≤ 𝑥 ≤ 1.3) and investigated the systematic evolution of the magnetism with varying 𝑥. Our study reveals the effective tuning of magnetic interactions and anisotropies in both MnPS 3 and NiPS 3 upon Se substitution. Such efficient engineering of the magnetism provides a suitable platform to understand the low-dimensional magnetism and develop future magnetic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Impact of Materials Properties on Higher-Temperature Engine Operation

Here, we examine the effects on materials temperatures and engine efficiency via simulations of engines operating at temperatures which exceed the thermal limits of today’s materials. Potential focus areas include high-speed, high-load operation (in the fuel-enrichment zone) as well as conditions of selective cooling at lower speeds and loads. We focus on a light-duty DISI and a heavy-duty CI engine using GT-Power. Temperature distributions within the head, block, piston, and valves were obtained from 3D FEA simulations coupled with 1D GT-Power representations of the engine’s gas flow and combustion regions. We use experimentally measured thermal properties of current commercial alloys for specific engine components, as well as candidate developmental alloys with improved temperature tolerance, to gauge the effects of materials properties on engine performance, particularly focusing on operating areas where materials which can withstand higher temperatures can enable intensified combustion conditions and increased specific output. The outcome of this work is guidance on materials selection targets and benefits as OEMs meet engine-performance needs in the coming decades, to include Class 8 line-haul freight vehicles with reduced emissions, as well as design of future near net zero-carbon light-duty range extenders for more rapid electrification of medium- and heavy-duty vehicles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of doped barriers on the recombination coefficients of c -plane InGaN/GaN single quantum well light-emitting diodes

Differential carrier lifetime measurements were performed on c-plane InGaN/GaN single quantum well (QW) light-emitting diodes (LEDs) of different QW indium compositions as well as with and without doped barriers. Mg-doped p-type and Si-doped n-type barriers close to the QW were used to reduce the net internal electric field in the QW, thereby improving the electron–hole wavefunction overlap on the LEDs. LEDs with doped barriers show short lifetimes and low carrier densities in the active region compared to the reference LEDs. The recombination coefficients in the ABC model were estimated based on the carrier lifetime and quantum efficiency measurements. The improvement in the radiative coefficients in the LEDs with doped barriers coupled with the blueshift of the emission wavelengths indeed indicates an enhancement in wavefunction overlap and a reduction of quantum confined Stark effect as a result of the reduced internal electric field. However, doped barriers also introduce non-radiative recombination centers and thereby increase the Shockley–Read–Hall (SRH) coefficient, although the increment is less for LEDs with high indium composition QWs. As a result, at high indium composition (22%), LEDs with doped barriers outperform the reference LEDs even though the trend is reversed for LEDs with lower indium composition (13.5%). In conclusion, despite the trade-off of higher SRH coefficients, doped barriers are shown to be effective in reducing the internal electric field and increasing the recombination coefficients.

42 ENGINEERING↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2023

Modular, Multifunction, Multiport and Medium Voltage Utility Scale SiC PV Inverter (Final Report)

This project is to develop and demonstrate a Modular, Multi-function, Multiport and Medium Voltage utility scale SiC solar inverter (M4 Inverter). The M4 Inverter is a next generation utility scale PV + storage inverter that can substantially reduce the levelized cost of energy (LCOE) while providing grid friendly ancillary services. Conventional utility scale solar inverters includes three parts, a megawatt rated solar inverter, a low frequency transformer (LFT) and a medium voltage switch gear. The proposed M4 Inverter directly converts the DC output of solar panels to medium voltage AC, eliminating the bulky and costly LFT. The M4 Inverter also has a DC port to interface with an additional energy storage device. This plus its bidirectional power flow capability, allows multiple functions in addition to PV maximum power tracking be achieved. These additional functionalities include but not limited to reactive power support, peak shaving, fast frequency regulation and synthetic inertia. Predicted LCOE reductions with these added functionalities, improved efficiency and longer lifetime time are more than 30% compared with the 2017 utility scale solar LCOE. A 1MVA SiC M4 Inverter was developed and tested at high power level. The project has also advanced the state of the art in controller hardware in the loop simulation capability. Power electronic innovations introduced in the M4 Inverter includes 1700V SiC MOSFET application in 1500V PV system, soft switching three-port power converters and modular converter architecture.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A charge transfer framework that describes supramolecular interactions governing structure and properties of 2D perovskites

The elucidation of structure-to-function relationships for two-dimensional (2D) hybrid perovskites remains a primary challenge for engineering efficient perovskite-based devices. By combining insights from theory and experiment, we describe the introduction of bifunctional ligands that are capable of making strong hydrogen bonds within the organic bilayer. We find that stronger intermolecular interactions draw charge away from the perovskite layers, and we have formulated a simple and intuitive computational descriptor, the charge separation descriptor (CSD), that accurately describes the relationship between the Pb-I-Pb angle, band gap, and in-plane charge transport with the strength of these interactions. A higher CSD value correlates to less distortion of the Pb-I-Pb angle, a reduced band gap, and higher in-plane mobility of the perovskite. These improved material properties result in improved device characteristics of the resulting solar cells.

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

Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-performance photonic transformers for DC voltage conversion

Abstract Direct current (DC) converters play an essential role in electronic circuits. Conventional high-efficiency DC voltage converters, especially step-up type, rely on switching operation, where energy is periodically stored within and released from inductors and/or capacitors connected in a variety of circuit topologies. Since these energy storage components, especially inductors, are fundamentally difficult to scale down, miniaturization of switching converters proves challenging. Furthermore, the resulting switching currents produce significant electromagnetic noise. To overcome the limitations of switching converters, photonic transformers, where voltage conversion is achieved through light emission and detection processes, have been demonstrated. However, the demonstrated efficiency is significantly below that of the switching converter. Here we perform a detailed balance analysis and show that with a monolithically integrated design that enables efficient photon transport, the photonic transformer can operate with a near-unity conversion efficiency and high voltage conversion ratio. We validate the theory with a transformer constructed with off-the-shelf discrete components. Our experiment showcases near noiseless operation and a voltage conversion ratio that is significantly higher than obtained in previous photonic transformers. Our findings point to the possibility of a high-performance optical solution to miniaturizing DC power converters and improving the electromagnetic compatibility and quality of electrical power.

42 ENGINEERING↗
DOE OSTI2020

The Effects of Absorber Thickness on Reverse-Bias Damage in Cu(In,Ga)Se2 Solar Cells

We studied a variety of absorber thicknesses in Cu(In,Ga)Se 2 devices to examine the ability of the cell to withstand reverse current flow without damage. Absorbers were varied from 2.5 um to 0.4 um, with the expectation that larger reverse current would be allowed to flow at lower voltages as absorbers were made thinner. Our initial experiments showed promise. However, as the efficiency of thin devices was improved, the reverse current allowed at a given voltage decreased. Here we present our negative results on the ability of thin devices to allow higher reverse currents at lower voltages. We also give details on our procedures for reverse-bias testing small-area solar cells. We hope this information will be useful for those who want to study reverse-bias at the cell level.

14 SOLAR ENERGY↗
DOE OSTI2020

Pore-Edge Tailoring of Single-Atom Iron–Nitrogen Sites on Graphene for Enhanced CO 2 Reduction

Hosting atomically dispersed nitrogen-coordinated iron sites (Fe-N 4 ) on graphene offers unique opportunities for driving electrochemical CO 2 reduction reaction (CO 2 RR) to CO. However, the strong adsorption of *CO on the Fe-N 4 site embedded in intact graphene limits current density due to slow CO desorption process. Herein, we report how the manipulation of pore edges on graphene alters the local electronic structure of isolated Fe-N 4 sites and improves their intrinsic reactivity for prompting CO generation. We demonstrate that constructing holes on graphene basal plane to support Fe-N 4 can significantly enhance its CO 2 RR compared to the pore-deficient graphene-supported counterpart, exhibiting a CO Faradaic efficiency of 94% and a turnover frequency of 1630 h -1 at 0.58 V vs RHE. Mechanistic studies reveal that the incorporation of pore edges results in the downshifting of the d-band center of Fe sites, which weakens the strength of the Fe-C bond when the *CO intermediate adsorbs on edge-hosted Fe-N 4 , thus boosting the CO desorption and evolution rates. These findings suggest that engineering local support structure renders a way to design high-performance single-atom catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Theory-Enabled High-Throughput Screening of Ion Dissociation Explains Conductivity Enhancements in Diluted Ionic Liquid Mixtures

The growing demand for room-temperature ionic liquids (RTILs) for energy applications necessitates the development of an efficient screening platform. Here, in this study, we successfully developed a fully automated high-throughput RTIL screening platform specifically designed for assessing ionic conductivity. By utilizing the 96 wells of a microtiter plate as individual electrolysis cells, we measured the ionic conductivity of 22 different RTILs, encompassing various combinations of cations and anions, and benchmarked the values with existing literature. We also employed the screening platform to investigate the conductivities of RTIL mixtures with a nonaqueous solvent, ethylene glycol (EG). Specific combinations of RTILs with EG result in approximately 200% enhancement in the conductivity values compared to the pure RTILs. To understand the underlying mechanisms responsible for this enhancement, we developed a theoretical framework for ionic conductivity that considers factors such as the degree of dissociation, viscous forces, and molal volume of the RTIL-EG mixtures. The optimized electrolyte mixture was then employed in the migration-assisted moisture gradient (MAMG) CO 2 capture process to study the effects of improved ionic conductivity on the energy efficiency of the process. Notably, the enhanced conductivity of the RTIL-EG mixture led to a nearly 50% reduction in energy consumption for capturing CO 2 . These outcomes highlight the effectiveness of our strategy in screening RTILs and improving existing processes. Moreover, this fully automated high-throughput setup, combined with the developed theoretical framework, provides a comprehensive platform for screening and studying RTIL mixtures with different solvents, enabling their application in various fields.

25 ENERGY STORAGE↗
DOE OSTI2021

Symmetrically tuned large-volume conic shell-cavities for axion searches

In an earlier paper [1], a new class of thin-shell cavities were proposed to evade the steep frequency scaling of conventional axion haloscopes. In this follow-up work, we see that a generalized conic geometry enables robust frequency-tuning for these large-volume cm-wave cavities. The frequency-defining dimension of a conic shell-cavity changes symmetrically and uniformly during tuning, maintaining a high axion coupling efficiency (the "form factor") to an external solenoid field. It is further shown that such tunable geometry is not restricted to circular cones. A general prescription for arbitrary volume-filling conic shell-cavities is developed and direct solutions are obtained for the created numerical models. Additionally, the largest of the realized designs is a meandering "brain" cavity that is tunable over a frequency range of 20%. The scan rate of this cavity is three orders of magnitude larger than that of a scaled cylindrical cavity used in the current generation experiments. The prospects for such a large improvement in the scan rate should motivate R& D efforts in fabrication and other implementation techniques. If these engineering challenges can be met, cavity-based axion haloscopes can stay competitive at frequencies higher than a few GHz. We propose an experimental configuration at 20 GHz (~80 μeV) using an array of brain cavities and compare it with other proposals for similar frequencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

HVAC Duct Pressure Optimization System

The US Department of Energy, the California Energy Commission, and other public and private entities including the venture capital community are seeking technical solutions that include “smart, grid-interactive, efficient buildings - to best serve the needs of building occupants and the grid while reducing energy consumption overall.” In this paper, a systems engineering approach is used to examine elements of a prior proof-of-concept project along with new ideas seeking to justify additional research funding to further advance the technology. With additional research and development funding, the technology described here has the potential to achieve success toward development of a commercially viable product. These types of projects provide financial and environmental benefit to American taxpayers and investors. Cost effectiveness and return on investment are key criteria for energy efficiency projects. By evaluating the end-to-end processes including manufacturing, installation, commissioning and comparing various options, we arrived at a proposed implementation concept of a HVAC Duct Pressure Optimization System that saves energy in commercial buildings and improves the return on investment by a factor of 6X compared to previous research projects.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Improving Milling Performance on New Computer Numerical Control Machining Centers with Machining Dynamics

This project is directed at improving the efficiency and productivity of the existing installed US machine tool base by correcting a fundamental system-wide flaw in the existing programmable machine tool infrastructure. The project will catalyze a shift from a geometry-only mindset to a geometry -and-machine -performance mindset. The project will lead to a dramatic overhaul of the existing ecosystem for programming of Computer Numerically Controlled (CNC) machine tools, and will result in energy savings, cost savings, reduced scrap, and improved competitiveness of US manufacturers.

42 ENGINEERING↗
DOE OSTI2021

Co-expression of a β-d-xylosidase from Thermotoga maritima and a Family 10 xylanase from A. cellulolyticus significantly improves the xylan degrading activity of the Caldicellulosiruptor bescii exoproteome

Caldicellulosiruptor species are hyperthermophilic, Gram-positive, anaerobes and the most thermophilic cellulolytic bacteria so far described. They have been engineered to convert switchgrass to ethanol without pretreatment and represent a promising platform for the production of fuels, chemicals and materials from plant biomass. Xylooligomers such as xylobiose and xylotriose that result from the breakdown of plant biomass more strongly inhibit cellulase activity than do glucose or cellobiose. High concentrations of xylobiose and xylotriose, are present in C. bescii fermentations after 90 h incubation and removal or breakdown of these types of xylooligomers is crucial to achieve high conversion of plant biomass to product. In previous studies the addition of exogenous β-d-xylosidase substantially improved the performance of glucanases and xylanases in vitro. β-d-Xylosidases are, in fact, essential enzymes in commercial preparations for efficient deconstruction of plant biomass. In addition, the combination of xylanase and β-d-xylosidase is known to exhibit synergistic action on xylan degradation. In spite of its ability to grow efficiently on xylan substrates, no extracellular β-d-xylosidase was identified in the C. bescii genome. Here we report that the co-expression of a thermal stable β-d-xylosidase from Thermotoga maritima and a xylanase from Acidothermus cellulolyticus in a C. bescii strain containing the A. cellulolyticus E1 endoglucanase significantly increased the activity of the exoproteome as well as growth on xylan substrates. The combination of these enzymes also resulted in increased growth on crystalline cellulose in the presence of exogeneous xylan.

59 BASIC BIOLOGICAL SCIENCES↗