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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned

Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e.g., law, healthcare, education, etc. However, only limited efforts have investigated the opportunities and limitations of applying these powerful models to science and security applications. In this work we develop foundation models of scientific knowledge for chemistry to augment scientists with the advanced ability to perceive and reason at scale previously unimagined. Specifically, we build large-scale (1.47B parameter) general-purpose models for chemistry that can be effectively used to perform a wide range of in-domain and out-of-domain tasks. Evaluating these models in a zero-shot setting, we analyze the effect of model and data scaling, knowledge depth, and temporality on model performance in context of model training efficiency. Our novel findings demonstrate that (1) model size significantly contributes to the task performance when evaluated in a zero-shot setting; (2) data quality (aka diversity) affects model performance more than data quantity; (3) similarly, unlike previous work (Luu et al., 2021) temporal order of the documents in the corpus boosts model performance only for specific tasks, e.g., SciQ; and (4) models pre-trained from scratch perform better on in-domain tasks than those tuned from general-purpose models like Open AI’s GPT-2.

Foundation Models, Chemistry↗
DOE OSTI2022

Enhanced convection for higher module and system efficiency (Final Technical Report)

The goal of this work is to develop new solar photovoltaic (PV) modules and solar system-scale designs that promote an increase of the convective heat transfer coefficient of at least 40%, reducing the operating temperature of the solar PV panels, and leading to a boost on the annual energy yield by at least 5%. By achieving this goal, we further foresee decreasing solar panel degradation by +0.3%/year, reducing the LCOE by 2.9-4.5¢/kWh. Currently, this question and/or approach is not considered though it is a relevant factor in performance/efficiency. The technology proposed will be at a mature stage by the end of the project and could be aligned to be proven on a full-field test. PV users, installers and manufacturers will be interested in the technology due to the aforementioned benefits. The work was conducted through a suite of wind tunnel experiments, numerical simulations and field experiments. These were then evaluated through a correlation encompassing the entire data set and implementing findings into NREL SAM. The work resulted in eight peer reviewed publications with three others in preparation. The key results of the project are 1.) Showing the capability of increasing the solar farm power production by increasing the heat transfer coefficient; 2.) Providing strategies for achieving this through large scale arrangement manipulation (heights, spacings, angles), flow deflectors and vortex generators; 3.) Demonstrating scalability of results by comparing numerical simulations, wind tunnel experiments and field experiments; 4.) Providing a correlation to be able to capture the various arrangements while exploiting the heat transfer coefficient; and 5.) Proving the feasibility of such strategies and their capacity through a comprehensive techno-economic analysis. For the heat transfer coefficients, gains of 40% were attained and increases in power in the excess of 5% were observed. For heat mitigation strategies, consideration at the PV array scale were inter-panel spacing, panel height and panel angles while at the module level, wind breaks such as solid walls and selected gaps were evaluated as well as vortex generators on the panel. All proposed strategies saw an increase in heat transfer coefficient. For scalability, results were compared against field experiments and a correlation for the heat transfer quantity was attained leading to a parametric understanding of the variations and impact in the heat transfer coefficient independent of tools implemented (wind tunnel/numerical simulations). The strategies were also further assessed by a technoeconomic analysis finding improvements based on LCOE with respect to location (several states with varies atmospheric conditions/geographic locations) yielded different results. Improvements on this end were also observed especially towards the colder climates and when considering bi-facial modules. The quantitative demonstrations of such conclusions are provided in the main portion of this document with complementary results and their narration. Milestones and go/no-go points were successfully met in the project.

14 SOLAR ENERGY↗
DOE OSTI2022

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

Physics-informed neural networks (PINNs) have become a popular choice for solving high-dimensional partial differential equations (PDEs) due to their excellent approximation power and generalization ability. Recently, extended PINNs (XPINNs) based on domain decomposition methods have attracted considerable attention due to their effectiveness in modeling multiscale and multiphysics problems and their parallelization. However, theoretical understanding of their convergence and generalization properties remains unexplored. In this study, we take an initial step towards understanding how and when XPINNs outperform PINNs. Specifically, for general multilayer PINNs and XPINNs, we first provide a prior generalization bound via the complexity of the target functions in the PDE problem and a posterior generalization bound via the posterior matrix norms of the networks after optimization. Moreover, based on our bounds, we analyze the conditions under which XPINNs improve generalization. Concretely, our theory shows that the key building block of XPINN, namely, the domain decomposition, introduces a tradeoff for generalization. On the one hand, XPINNs decompose the complex PDE solution into several simple parts, which decreases the complexity needed to learn each part and boosts generalization. On the other hand, decomposition leads to less training data being available in each subdomain, and hence such a model is typically prone to overfitting and may become less generalizable. Empirically, we choose five PDEs to show when XPINNs perform better than, similar to, or worse than PINNs, hence demonstrating and justifying our new theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training

As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to the lack of architectural guidance, random recurrent connectivity is often adopted, which does not guarantee good performance. 2. Training of RSNNs is in general challenging, bottlenecking achievable model accuracy. To address these problems, we propose a new type of RSNNs called Skip-Connected Self-Recurrent SNNs (ScSr-SNNs). Recurrence in ScSr-SNNs is introduced in a stereotyped manner by adding self-recurrent connections to spiking neurons. The SNNs with self-recurrent connections can realize recurrent behaviors similar to those of more complex RSNNs while the error gradients can be more straightforwardly calculated due to the mostly feedforward nature of the network. The network dynamics is enriched by skip connections between nonadjacent layers. Moreover, we propose a new backpropagation (BP) method called backpropagated intrinsic plasticity (BIP) to further boost the performance of ScSr-SNNs by training intrinsic model parameters. Unlike standard intrinsic plasticity rules that adjust the neuron's intrinsic parameters according to neuronal activity, the proposed BIP method optimizes intrinsic parameters based on the backpropagated error gradient of a well-defined global loss function in addition to synaptic weight training. Here, based on challenging speech, neuromorphic speech, and neuromorphic image datasets, the proposed ScSr-SNNs can boost performance by up to 2.85% compared with other types of RSNNs trained by state-of-the-art BP methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models with the combined dataset resulted in a classification accuracy of 97.7%. This is a significant improvement over 89.7% to 92.5% accuracy obtained by relying on the field-recorded dataset alone.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Boosting the performance of reversible solid oxide electrochemical cells with a novel hybrid oxygen electrode, Pr 1.39 Ba 0.14 Sr 0.53 Co 1.48 Fe 0.76 O 6- δ -Ba 0.66 Sr 0.34 CoO 3- δ

Solid oxide electrochemical cells (SOECs) stand out as a highly promising clean energy technology that offers several benefits, showing significant potential to play a pivotal role in the transition towards a sustainable and low-carbon energy future. SOECs can efficiently convert the chemical energy stored in fuels to electricity in fuel cell mode, and produce various chemicals from abundant feedstocks (e.g., CO 2 , H 2 O) and intermittent solar/wind-based renewable electricity. Despite extensive efforts that have been devoted to designing novel materials and optimizing SOEC manufacturing processes, aiming to achieve enhanced energy efficiency, the current SOECs still suffer from poor performance, which is mainly due to the sluggish oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) kinetics. To address this challenge, in this work, we have successfully designed an in situ formed hybrid oxygen electrode material (Pr 1.39 Ba 0.14 Sr 0.53 Co 1.48 Fe 0.76 O 6- δ -Ba 0.66 Sr 0.34 CoO 3- δ ), which significantly improves the surface oxygen exchange coefficient and bulk oxygen-ion diffusion coefficient, enhancing the OER and ORR electrocatalytic activities. Further, the SOECs equipped with this newly developed oxygen electrode achieved exceptional performance for power generation using both hydrogen and propane as the fuels. At 750 °C, a peak power density of 2.4 W cm -2 was obtained with H 2 as the fuel. Additionally, the SOECs attain unprecedented performance in steam electrolysis mode. A current density of 4.4 A cm -2 was achieved at 1.3 V and 750 °C, which represents the highest performance among all yttria-stabilized zirconia (YSZ) electrolyte-based SOECs. The SOECs also deliver remarkable stability during the accelerated stability testing, highlighting the great potential of Pr 1.39 Ba 0.14 Sr 0.53 Co 1.48 Fe 0.76 O 6- δ -Ba 0.66 Sr 0.34 CoO 3- δ as a high-performance oxygen electrode for next generation SOECs.

08 HYDROGEN↗
DOE OSTI2022

Tuning the Co/Fe ratio in BaCo x Fe 0.8– x Zr 0.1 Y 0.1 O 3– δ , a promising triple ionic and electronic conducting oxide, to boost electrolysis and fuel cell performance

The triple conducting oxide BaCo 0.4 Fe 0.4 Zr 0.1 Y0.1O 3–δ (BCFZY4411), which accommodates simultaneous transport of protons, oxygen ions, and p-type electronic carriers, has been intensively investigated in recent years as a high-performance positive electrode material for fuel cell and electrolysis applications. The heavy Co and Fe-based transition metal doping in BCFZY4411 ensures adequate electrical conductivity while the multiple oxidation states of Co and Fe assist the electrocatalytic and redox ability. Despite the considerable role of Co and Fe transition metal doping in controlling electrochemical activity, however, the study of alternative BCFZY compositions with varying Co/Fe ratios has not yet been pursued. Here, we evaluate the electrochemical performance of a series of BaCo x Fe 0.8–x Zr 0.1 Y0.1O 3–δ compositions with varying Co/Fe ratio (x = 0.1, 0.2, 0.4, 0.6, 0.7) and use oxygen ion tracer diffusion and in situ high-temperature X-ray diffraction to investigate the effect of Co/Fe ratio on electrocatalytic activity, electronic conductivity, oxygen ion incorporation and transport kinetics, and thermomechanical behavior. We find that Co-rich BCFZY7111 yields the highest performance due to exceptionally high oxygen vacancy diffusion and shows a lower and more linear thermal expansion behavior compared to Fe-rich compositions. A protonic ceramic button cell incorporating a BCFZY7111 positive electrode yields a peak power density of 695 mW cm –2 under fuel cell mode and an electrolysis current density of 1976 mA cm –2 at 1.4 V at 600 °C, underscoring the promise of this new BCFZY electrode composition.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads

Abstract This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and$$\text {Hf}_{0.5}\text {Zr}_{0.5}\text {O}_2$$ Hf 0.5 Zr 0.5 O 2 (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

Science & Technology - Other Topics↗
DOE OSTI2023

Understanding the cold plasma synthesis of ammonia with model metal catalysts through plasma diagnostics (Final Report)

The industrial synthesis of ammonia, which amounts to over 200 million tons annually, is the most energy-intensive chemical process. Therefore, there is a critical need and increased interest in exploring less energetic routes to produce ammonia. Not only does ammonia have a direct impact on the food market, but also it has the potential as a fuel and hydrogen carrier. Recently, plasma catalysis has emerged as a promising alternative for synthesizing ammonia at mild (pressure, temperature and power) conditions. The key to this catalytic process is the synergy between the plasma and the catalyst, where the non-equilibrium plasma allows the generation of excited species, which recombine at the catalyst surface to form ammonia. However, our current understanding of this process is in its infancy. In this respect, model metals are ideal candidates for gaining a basic understanding of this reaction. Moreover, a major roadblock to rationally designing novel effective catalysts for plasma-assisted ammonia production is the need for fundamental aspects of this process. Through a comprehensive plan that integrates model metals as catalysts and world-class diagnostics, the proposed work aims to provide fundamental knowledge about the nature of reactive processes occurring during plasma-assisted catalysis and to demonstrate the selective production of ammonia, catalyzed by employing selected metals under non-thermal plasma conditions. Toward this goal, the central thrust of this proposal was to demonstrate that the synergy between plasma and rationally selected model metals will boost ammonia yields during plasma-assisted ammonia synthesis by delaying hydrogen recombination. Specifically, we aimed to (1) understand the formation and role of gas-phase active species such as NH, N 2 , N 2 + and Ha during the plasma-enhanced synthesis of ammonia through OES and FTIRAS using different reaction configurations: a) only plasma (non-packed DBD reactor) and b) packed DBD reactor with metal nanoparticles. This proposal was awarded/recommended with a run time on the PCRF facility FY20.

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

Thiol‐Functionalized Conjugated Metal–Organic Frameworks for Stable and Efficient Perovskite Photovoltaics

Abstract Metal–organic frameworks (MOFs) have been investigated recently in perovskite photovoltaics owing to their potential to boost optoelectronic performance and device stability. However, the impact of variations in the MOF side chain on perovskite characteristics and the mechanism of MOF/perovskite film formation remains unclear. In this study, three nanoscale thiol‐functionalized UiO‐66‐type Zr‐based MOFs (UiO‐66‐(SH) 2 , UiO‐66‐MSA, and UiO‐66‐DMSA) are systematically employed and examined in perovskite solar cells (PSCs). Among these MOFs, UiO‐66‐(SH) 2 , with its rigid organic ligands, exhibited a strong interaction with perovskite materials with more efficient suppression of perovskite vacancy defects. More importantly, A detailed and in‐depth discussion is provided on the formation mechanism of UiO‐66‐(SH) 2 ‐assisted perovskite film upon in situ GIWAXS performed during the annealing process. The incorporation of UiO‐66‐(SH) 2 additives substantially facilitates the conversion of PbI 2 into the perovskite phase, prolongs the duration of stage I, and induces a delayed phase transformation pathway. Consequently, the UiO‐66‐(SH) 2 ‐assisted device demonstrates reduced defect density and superior optoelectronic properties with optimized power conversion efficiency of 24.09% and enhanced long‐term stability under ambient environment and continuous light illumination conditions. This study acts as a helpful design guide for desired MOF/perovskite structures, enabling further advancements in MOF/perovskite optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Thermodynamics of continuous spin photons

Special relativity allows massless particles to have states of different integer (or half-integer) helicities that mix under boosts, much like the spin states of a massive particle. Such massless particles are known as “continuous spin” particles (CSPs), a term coined by Wigner, and they are notable for their infinite tower of spin polarizations. The mixing under boosts is controlled by a spin scale 𝜌 with units of momentum. Normally, we assume 𝜌 = 0. The interactions of CSPs are known to satisfy certain simple properties, one of which is that the 𝜌 → 0 limit generically recovers familiar interactions of massless scalars, photons, or gravitons, with all other polarizations decoupling in this limit. Thus, one can ask if the photon of the Standard Model is a CSP at small but nonzero 𝜌. One concern about this possibility—originally raised by Wigner—is that the infinite tower of polarizations could pose problems for thermodynamics. To address this question, we study the thermal evolution of a CSP photon gas coupled to isothermal matter, across CSP helicity modes and phase space. We find that the structure of the interactions dictated by Lorentz symmetry implies well-behaved thermodynamics. When the CSP photon’s interactions with charged matter are turned on, the primary ℎ =±1 helicity modes thermalize quickly, while the other modes require increasingly long time scales to thermalize, set by powers of 𝑇/𝜌. In familiar thermal systems, the CSP photon behaves like the QED photon with small 𝜌- and time-dependent corrections to its effective relativistic degrees of freedom. Sizable departures from familiar thermal behavior arise at energy scales comparable to 𝜌 and could have testable experimental consequences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

How the Galaxy–Halo Connection Depends on Large-scale Environment

We investigate the connection between galaxies, dark matter halos, and their large-scale environments at z = 0 with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: explainable boosting machines (EBMs) with simple galaxy environment features and E(3)-invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on 3 Mpc scales. Interpretations via SHapley Additive exPlanations also suggest that in the context of the TNG300 galaxy–halo connection, simple spherical overdensity on ∼3 Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, L, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe (L = 10 Mpc). We conclude that 3 Mpc distance scales are most critical for describing the TNG galaxy–halo connection using the spherical overdensity parameterization, but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Advanced Moderation Module for High-Temperature Micro-Reactor Applications

Development and deployment of micro-reactors that provide competitive efficiency, prevailing compactness, and inherent safety is an immediate focus of the U.S. nuclear industry. For thermal neutron micro-reactors operating at elevated temperature for optimized efficiency, such as micro molten-salt reactors (MSRs), heat-pipe reactors, and very-high temperature reactors (VHTRs), high-performance moderator based on metal hydride can enhance the neutron economy and therefore achieve reduced weight and enhanced portability. As unclad metal hydride inevitably decomposes at elevated temperature, an enclosure is required for hydride moderator to deliver desired performance at elevated temperatures. Conventional moderator enclosure solutions based on high-temperature alloys introduce extraneous neutron penalty into the micro-reactor, affecting the neutronic benefits provided by the hydride moderator. Additionally, the compatibility between the high-temperature alloy enclosure and the micro-reactor matrix is also a potential issue. In this report, we disclose an Advanced Moderator Module (AMM) concept enabled by an innovative enclosure solution combining the advantages of refractory metals, ceramic matrix composites (CMCs), and advanced coating technology to serve as hydrogen permeation barrier up to very-high temperatures. A schematic description of the AMM structure is illustrated in Figure A1. The AMM contains a moderating material core made of metal hydride with high thermal stability, such as YH 2-x . The hydride core is enclosed by an H 2 barrier layer coated on a ductile refractory metal liner to minimize hydrogen loss during high-temperature operation. A ceramic matrix composite (CMC) cladding is adopted to provide further structural strength, especially during power transients. Between the CMC cladding and metal liner, an extra diffusion barrier coating is inserted to suppress the chemical interaction at elevated temperatures. Hence, based on a series of innovative material solutions, the AMM is capable of containing the metal hydride core at elevated temperature (>900oC) inside coated and lined CMC envelop with negligible hydrogen loss throughout the microreactor lifetime. The benefit of the AMM technology was assessed based on a comprehensive multi-stage reactor-physics analysis. Advanced moderation based on hydride metals (such as YH2-x) enables reaching optimum moderation with higher fuel content than traditional VHTR technology, which is required to design compact micro-reactor cores targeting long-life operation. The AMM enclosure technology provides lower thermal neutron absorption rates than traditional solutions (based on stainless steel or Mo-based alloy such as TZM), which reduces the fissile enrichment requirements by 6-8% on a TRISO-fueled design based on the EMPIRE core. Finally, combining the hydride moderator with neutron transparent enclosure solutions provides significant potential to boost neutronics performance of micro-reactors in terms of increased core lifetime or reduced size and weight by 30-50% on a micro-reactor based on the Holos Quad technology. The progress and plans for on-going development and demonstration efforts are also discussed in this report. The current demonstration of AMM is focused on the hydrogen diffusion barrier demonstration under thermal cycling, while radiation tolerance demonstration is planned. The next step of the demonstration plan will be the assembly of miniature AMM for high-temperature testing, which can be used as a prototype for future pilot scale demonstration.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Plasma-Assisted Pre-Chamber Ignition System for Highly Dilute Stoichiometric Heavy-Duty Natural Gas Engines (Final Technical Report)

This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.

03 NATURAL GAS↗
DOE OSTI2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

17 WIND ENERGY↗
DOE OSTI2025

Flat nonlinear optics with intersubband polaritonic metasurfaces

Nonlinear intersubband polaritonic metasurfaces produce some of the strongest second- and third-order nonlinear optical responses reported for condensed matter systems at infrared frequencies. These metasurfaces are fabricated as two-dimensional arrays of nanoresonators from multi-quantum-well semiconductor heterostructures, designed to produce strong nonlinear responses associated with intersubband transitions. By optimally coupling the optical modes of the nanoresonators to vertically polarized intersubband transitions in semiconductor heterostructures, one can boost the nonlinear response associated with intersubband transitions, make intersubband transitions interact with free-space radiation at normal incidence, and hence produce optically thin flat nonlinear optical elements compatible with free-space optical setups. As a result of the strong nonlinear response in these systems, significant nonlinear conversion efficiencies (>0.1 %) can be attained in deeply subwavelength optical films using modest pumping intensities of only 10–100 kW/cm 2 . Subwavelength metasurface thickness relaxes phase-matching constraints limiting the operation of bulk nonlinear crystals. Furthermore, the amplitude and phase of the nonlinear optical response in intersubband polaritonic metasurfaces can be tailored for a specific pump wavelength and a nonlinear process of interest through the co-optimization of quantum engineering of electron states in semiconductor heterostructures and photonic engineering of the metasurface nanoresonators design. Additionally, an applied voltage can dynamically control the amplitude and phase of the nonlinear optical response at a nanoresonator level. Here, we review the current state of the art in this rapidly expanding field, focusing on nonlinear processes supporting second-harmonic generation, saturable absorption, and optical power limiting.

flat optics↗
DOE OSTI2023

2023 Critical Materials Strategy

The global effort to curb carbon emissions is accelerating demand for clean energy technologies and the materials they rely on. Demand for these materials will only continue to grow, especially as some nations aim to achieve net zero emissions by 2050. While some major materials like steel, copper, and aluminum are already powering the fossil fuel economy, others are more minor materials with potential supply risks. These risks could jeopardize the ability to reduce greenhouse gas emissions within the desirable timeframe to avoid significant climate change. In some cases, it may be necessary to take action to improve the resilience of material supply chains and mitigate supply risks. Understanding the importance of individual materials to clean energy and the supply risks associated with them is necessary to identify which materials may serve as potential roadblocks to a clean energy future. The U.S. Department of Energy (DOE) issued a series of 13 supply chain deep dive assessment reports on various energy technologies in 2022 in response to President Biden’s Executive Order on America’s Supply Chains (E.O. 14017). These reports emphasized that supply chain bottlenecks can occur at any stage of the value chain from mining and refining to component and even sub-system manufacturing. The bottlenecks are a combination of factors such as material availability, equipment availability, work force availability and quality, logistics, regulatory framework, and market conditions. These bottlenecks were worsened during the global Covid-19 pandemic. Its lingering impacts have hindered capacity expansion for material supply chains and prevented product lead-time recovery. One approach to reduce supply chain risks for the United States is to have a strong domestic manufacturing sector with a diverse set of producers. Boosting responsible domestic production would require leveraging the latest science not only in material extraction but also in developing substitutes, recycling, reuse, and remanufacturing. This report is an updated analysis of previous Critical Materials Strategy (CMS) reports published by the DOE in 2010, 2011, and 2019 based on national and global priorities, technology advancement, and technology adoption trends. Like the CMS reports, this analysis presents the results of a formal material criticality assessment to identify which materials are critical to the continued deployment of clean energy technologies globally. The analysis in this report leveraged the DOE supply chain deep dive assessments to develop the initial list of materials to evaluate. This DOE Critical Materials Assessment (CMA) is conducted independently of criticality assessments performed by other U.S. government agencies, such as that conducted by the U.S. Geological Survey (USGS). This analysis complements the USGS critical minerals determination in three aspects. First, the DOE assessment is performed from a global perspective, while the USGS analysis focusses on the importance of minerals to the U.S. economy. Second, this report focuses on the importance of materials to clean energy technologies, rather than to the economy in general. Lastly, this study is forward looking to 2035 based on clean energy deployment scenarios, whereas the USGS assessment is retrospective. Materials evaluated in this report that do not appear in the USGS Critical Minerals List include copper, uranium, electrical steel, and SiC. A draft version of this report received ~80 public comments related to supporting data and methodological improvement. Those comments have been incorporated as much as possible where appropriate. Highlights of findings from this 2023 CMA include: Rare earth materials (neodymium, praseodymium, dysprosium, and terbium) used in magnets in electric vehicle (EV) motors and wind turbine generators continue to be critical. While dysprosium (Dy) and terbium (Tb) are both heavy rare earth elements that serve the same function in magnets, the criticality of Tb is slightly lower than that for Dy in the short term due to the widespread use of Dy in high-grade magnets and Tb’s present role as a substitute. Similarly, praseodymium (Pr) is critical in the medium term but only near critical in the short term because it is more substitutable in magnets than neodymium (Nd); Materials used in batteries for EVs and stationary storage are now considered to be critical. While cobalt (Co) was found to be critical in this and previous reports, lithium (Li) becomes critical in the medium term due to its broader use in various battery chemistries and the rampant growth of the EV industry. Natural graphite is a new addition in this assessment and is also found to be critical; Platinum group metals used in hydrogen electrolyzers, such as platinum (Pr) and iridium (Ir), are critical due to an increased focus on hydrogen technologies to achieve net zero carbon emissions, while those used in catalytic converters, such as rhodium (Rh) and palladium (Pd), were screened out due to the decreased importance of catalytic converters in the medium term; Gallium (Ga) continues to be critical due to its use in light-emitting diodes (LEDs). In addition, the use of Ga has increased in magnet manufacturing and in semiconductor in forms such as gallium arsenide (GaAs) or gallium nitride (GaN); Major materials like Aluminum (Al), copper (Cu), nickel (Ni), and silicon (Si) move from noncritical in the short term to near critical in the medium term due to their importance in electrification; Electrical steel is near critical due to its use in transformers for the grid and electric motors in EVs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. Here, this study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

97 MATHEMATICS AND COMPUTING↗