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DOE OSTI2021

World Record Demonstration of > 30% Thermophotovoltaic Conversion Efficiency

Thermophotovoltaic (TPV) devices are solid-state heat engines that convert thermal radiation into electricity using semiconductor diodes, and have applications in energy storage, primary power conversion, and waste heat recovery. Higher temperature emitters and wider band gap photovoltaics (PVs) generally afford higher TPV efficiencies. High temperature emitters are accessible in thermal energy storage applications. We have demonstrated a world record 31% +/- 2% TPV conversion efficiency with a 0.9 cm2 GaAs-based PV device under a 2430 °C thermal emitter, producing an electrical output power of 2.23 W. Critical to the result was the cell’s high reflectance of photon energies below the device band gap. Unlike solar PV, for which sub-band gap (SBG) light is lost, a TPV cell can reflect and recycle SBG light to the thermal emitter. The demonstration was made on a custom-built measurement platform in which a ~100 cm2 graphite thermal emitter was heated under vacuum up to 2430 °C. The TPV efficiency was evaluated by comparing the measured electrical output power of the TPV with the calorimetrically measured total net power incident on the cell. The measured TPV efficiency as a function of thermal emitter temperature was corroborated by our full system modeling predictions. As far as the authors are aware, this is the highest TPV conversion efficiency ever measured, and device improvements should yield > 40% efficiency in the near future.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗

DOE OSTI2020

The Potential of Low Molecular Weight Heparin to Mitigate Cytokine Storm in Severe COVID‐19 Patients: A Retrospective Cohort Study

On March 11, 2020, the World Health Organization declared its assessment of coronavirus disease 2019 (COVID‐19) as a global pandemic. However, specific anti‐severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) drugs are still under development, and patients are managed by multiple complementary treatments. We performed a retrospective analysis to compare and evaluate the effect of low molecular weight heparin (LMWH) treatment on disease progression. For this purpose, the clinical records and laboratory indicators were extracted from electronic medical records of 42 patients with COVID‐19 (21 of whom were treated with LMWH, and 21 without LMWH) hospitalized (Union Hospital of Huazhong University of Science and Technology) from February 1 to March 15, 2020. Changes in the percentage of lymphocytes before and after LMWH treatment were significantly different from those in the control group ( P = 0.011). Likewise, changes in the levels of D‐dimer and fibrinogen degradation products in the LMWH group before and after treatment were significantly different from those in the control group ( P = 0.035). Remarkably, IL‐6 levels were significantly reduced after LMWH treatment ( P = 0.006), indicating that, besides other beneficial properties, LMWH may exert an anti‐inflammatory effect and attenuate in part the “cytokine storm” induced by the virus. Our results support the use of LMWH as a potential therapeutic drug for the treatment of COVID‐19, paving the way for a subsequent well‐controlled clinical study.

Shi, Chen↗

DOE OSTI2021

Response to ‘Stochastic and deterministic interpretation of pool models’

Here, we concur with Azizi-Rad et al. (2021) that it is vital to critically evaluate and compare different soil carbon models, and we welcome the opportunity to further describe the unique contribution of the PROMISE model (Waring et al., 2020) to this literature. The PROMISE framework does share many features with established biogeochemical models, as our original manuscript highlighted in Table 1, and our work builds upon model innovations developed by many different groups, including that of Azizi-Rad and colleagues. Yet, the PROMISE framework is distinctive due to where it places mechanistic emphasis, and how these mechanisms are formalized in the mathematical model structure.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluating steam oxidation kinetics of environmental barrier coatings

Environmental barrier coatings (EBCs) are a commercially proven means of protecting SiC-based materials in gas turbine environments. However, there are little specific data in the literature on the impact of coatings like Yb 2 Si 2 O 7 on preventing accelerated SiO 2 growth in the presence of H 2 O. Quantification of reduced rates are necessary for evaluating and comparing EBC effectiveness and incorporation of silica growth rates into future EBC lifetime models. In this study, baseline kinetics of silica formation on bare Si and chemically vapor deposited (CVD) SiC in the 1250–1425 °C range were obtained via 100 h isothermal exposures in dry air and steam environments utilizing a SiC reaction tube to mitigate specimen volatility. An Arrhenius plot of the resulting rates was constructed, representing baseline minimum and maximum rates for Si and SiC oxidation at ambient pressure. Various EBC systems on CVD SiC substrates including air plasma sprayed (APS) EBCs with and without a Si bond coating and with surface roughening to enhance Yb 2 Si 2 O 7 adhesion were subjected to 1-h furnace cycle testing in air with 90vol%H 2 O at 1250–1350°C for up to 500 cycles. After exposure, silica formation rates were measured and compared to the baseline rates to assess EBC effectiveness, where EBC effectiveness is gauged as the propensity to reduce underlying rates of silica formation. With a Si bond coating, a ~180 µm Yb 2 Si 2 O 7 (YbDS) top coating reduced rates over the entire 1250°-1350°C range. Without a Si bond coating, ~60 µm (YbDS) coatings deposited directly onto CVD SiC exhibited poor adhesion, and had to be deposited onto substrates with enhanced roughness at 1350°C. Finally, while exhibiting good adhesion at 1350°C, overall the single layer YbDS coating exhibited a decreasing effectiveness from 1250° to 1350°C.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Control Design for a Marine Hydrokinetic Cycloturbine Vehicle

Abstract Marine Hydrokinetic (MHK) cycloturbines generate sustainable power by exploiting tidal currents. By powering the turbines and using pitching foils for control, a vehicle comprised of MHK cycloturbines also has the ability to station keep and maneuver. The vehicle consists of four counter-rotating cycloturbines, with hydrofoils oriented perpendicular to the flow in a paddlewheel configuration. Lift and drag generated from these foils sum together to produce thrust. An experimentally tuned simulation model that solves the six-degrees-of-freedom rigid body equations of motion for the MHK vehicle subject to hydrodynamic, hydrostatic, and propulsive forces is used to aid the design of vehicle controllers. Global feedback controllers are initially designed by applying classical control methods to an approximate linear model of the system dynamics. A higher performing nonlinear controller is designed using the nonlinear dynamic inversion (NDI) method. NDI accounts for the nonlinearities of the MHK system and therefore is suitable for a wide range of operating conditions. The response of the classical and NDI controllers to speed, depth, roll, pitch, and yaw commands are evaluated and compared in simulation. The classical controller outperforms the NDI controller for small amplitude maneuvers, although the degradation with NDI is minor. However, in the nonlinear operating regime the NDI controller outperforms the classical controller and the classical controller exhibits instability.

Automation & Control Systems↗

DOE OSTI2020

The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.

Ruby, Max↗

DOE OSTI2022

Impact of jet-production data on the next-to-next-to-leading-order determination of HERAPDF2.0 parton distributions

The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA data on inclusive deep inelastic ep scattering together with jet data as published by the H1 and ZEUS collaborations. A perturbative QCD fit, simultaneously of $\alpha _s(M_Z^2)$ and the PDFs, was performed with the result $\alpha _s(M_Z^2)= 0.1156 \pm 0.0011~\mathrm{(exp)}~ ^{+0.0001}_{-0.0002}~ \mathrm{(model}$ $\mathrm{+ parameterisation)}~ \pm 0.0029~\mathrm{(scale)}$. The PDF sets of HERAPDF2.0Jets NNLO were determined with separate fits using two fixed values of $\alpha _s(M_Z^2)$, $\alpha _s(M_Z^2)$ = 0.1155 and 0.118, since the latter value was already chosen for the published HERAPDF2.0 NNLO analysis based on HERA inclusive DIS data only. The different sets of PDFs are presented, evaluated and compared. The consistency of the PDFs determined with and without the jet data demonstrates the consistency of HERA inclusive and jet-production cross-section data. The inclusion of the jet data reduced the uncertainty on the gluon PDF. Predictions based on the PDFs of HERAPDF2.0Jets NNLO give an excellent description of the jet-production data used as input.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Measurements of differential two-particle number and transverse momentum correlation functions in pp collisions at $\sqrt{\textit{s}}$ = 13 TeV

Differential two-particle normalized cumulants (R 2 ) and transverse momentum correlations (P 2 ) are measured as a function of the relative pseudorapidity and azimuthal angle difference (Δη, Δφ) of charged particle pairs in minimum bias pp collisions at $\sqrt{s}$ = 13 TeV. The measurements use charged hadrons in the pseudorapidity region of |η| < 0.8 and the transverse momentum range 0.2 < p T < 2.0 in order to focus on soft multiparticle interactions and to complement prior measurements of these correlation functions in p–Pb and Pb–Pb collisions. The correlation functions are reported for both unlike-sign and like-sign pairs and their charge-independent and charge-dependent combinations. Both the R 2 and P 2 measured in pp collisions exhibit features qualitatively similar to those observed in p–Pb and Pb–Pb collisions. The Δη and Δφ root mean square widths of the near-side peak of the correlation functions are evaluated and compared with those observed in p–Pb and Pb–Pb collisions and show smooth evolution with the multiplicity of charged particles produced in the collision. The comparison of the measured correlation functions with predictions from PYTHIA8 shows that this model qualitatively captures their basic structure and characteristics but feature important differences. In addition, the R$^{CD}_{2}$ is used to determine the charge balance function of hadrons produced within the detector acceptance of the measurements. The integral of the balance function is found to be compatible with those reported by a previous measurement in Pb–Pb collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Kammerdiener, Brandon↗

DOE OSTI2024

Evaluating Emerging AI/ML Accelerators: IPU, RDU, and and NVIDIA/AMD GPUs

As the size and complexity of AI/ML workloads continue to increase, the demand for specialized hardware accelerators has grown rapidly. To better understand the landscape of commercial AI/ML accelerators, we evaluate and compare several popular platforms, including Graphcore IPU, Sambanova RDU, and GPUs, on a range of AI workloads. Our research aims to help researchers and developers understand the unique features of commercial AI/ML accelerators and provide reference design and performance numbers for research prototypes. By shedding light on the current landscape of commercial AI/ML accelerators, we hope to offer insights into the future development of specialized hardware accelerators for AI/ML.

Peng, Hongwu↗

DOE OSTI2025

ASDF: A Compiler for Qwerty, a Basis-Oriented Quantum Programming Language

Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers.

Adams, Austin J [Georgia Tech]↗

DOE OSTI2025

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Exploring the Effectiveness of Carbon Cloth Electrodes for All-Vanadium Redox Flow Batteries

Vanadium redox flow batteries (VRFBs) have shown to be a promising technology for integrating intermittent renewable energy sources into the existing electrical grid. Incorporation of carbon cloth electrodes into VRFB is an area of interest for their enhanced electrochemical performance, however, issues with performance degradation throughout the duration of the experiment persist. This study investigates the performance evolution of carbon cloth electrodes during VRFB cycling to build a hypothesis on possible reasons for the declining performance. Electrochemical impedance spectroscopy and polarization curve measurements are used in conjunction to monitor the electrode degradation and shed light on the effectiveness of carbon cloth electrodes during extended cycling experiments. A detailed investigation into the structure of the carbon cloth electrodes before and after cycling, via several material characterization tests, provides insight needed to determine an explanation for the increasing resistance. The structural integrity and surface morphology of the carbon cloth electrodes are evaluated to compare the electrode before and after cycling, displaying any changes to the electrode due to cycling. Durability of hydrophilicity during RFB cycling is found to be a key feature for future carbon cloth electrode design efforts.

25 ENERGY STORAGE↗

DOE OSTI2022

Investigating the Influence of Ni, ZrO 2 , and Y 2 O 3 from SOFC Anodes on Siloxane Deposition

Siloxanes, as a type of impurity in biogas, can poison the Ni-YSZ anode of SOFCs. However, the influence of individual components of the anode, such as Ni, ZrO 2 , and Y 2 O 3 , on the siloxane deposition process has not been investigated extensively. In this study, Ni, ZrO 2 , and Y 2 O 3 pellets were exposed to H 2 + N 2 + H 2 O + D4 (octamethylcyclotetrasiloxane, 2.5 ppmv) and H 2 + N 2 + D4 (2.5 ppmv siloxane) gas mixtures at 750 °C to investigate their affinity and tolerance for siloxane degradation. Surface morphology analysis and electrochemical analysis including electrochemical impedance spectroscopy (EIS), related distribution of relaxation times (DRT) analysis and equivalent circuit modeling with complex nonlinear least square (CNLS) fitting were conducted. Here, a microstructure parameter—tortuosity factor to porosity ratio $\tau /\varepsilon $ calculated by diffusion polarization resistance was utilized for siloxane deposition evaluation. After comparing pellets surface morphology changes before and after experiments and $\tau /\varepsilon $ change following the contamination, Ni is considered as a major factor in siloxane deposition reactions in Ni-YSZ anode.

25 ENERGY STORAGE↗

DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗

DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

DOE OSTI2025

Deep Design Data Portal (D3P) v0.01

The Deep Design Data Portal (D3P) tool was developed to demonstrate how readily accessible data sources, such as building energy model reports for design and baseline energy performance data for projects, can provide the data required for reporting to an industry initiative (AIA 2030 commitment), as well as more detailed data that makes the industry dataset more valuable to all stakeholders, enabling project level analysis and analysis of BEM industry trends. D3P provides an easier and less time-consuming way for firms to auto-extract data from this data source, compared to the current reporting workflows of the firms. The BEM reports are the first of several data sources that D3P could integrate. D3P also provides the ability for firms to review, compare, and evaluate the performance of their projects to not only their portfolio, but also to the larger anonymized industry dataset created each time a project is added to D3P. The intent of D3P is to become part of a data-sharing ecosystem to assist creating large anonymized industry datasets that are accessible to industry.

Regnier, Cynthia [Lawrence Berkeley National Labor↗