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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Potential annual daylighting performance of a high-efficiency daylight redirecting slat system

While the primary role of window attachments is often to moderate glare and solar heat gains, they are also able to provide additional daylight to interior spaces. For this purpose, a variety of daylight-redirecting window systems have been developed over the past 150 years. Fixed reflective systems (slats/light shelves) or prismatic systems that rely on total internal reflection work well under specific solar conditions, but generally sacrifice performance over a much wider range of incident solar angles and sky conditions. Dynamic systems - typically reflective slats - are more responsive to sun angles but have not been able to achieve optimal performance for glare and daylight redirection efficiency. A previous investigation into an adjustable, reflective blind concept first conceived of in the late 1970s showed promise but was not reduced to practice due to lack of adequate simulation and analysis tools. In this paper, this concept is further developed and its energy and visual comfort performance evaluated for four mid-latitude, temperate climates using ray-tracing simulation techniques. Overall, results indicate significant potential lighting energy savings when compared with conventional automated reflective blinds (2.1–4.9 kWh/(m 2 ·a), or 14%–42%, depending on climate and orientation) or, especially, manually-operated matte white venetian blinds (1.4–7.9 kWh/(m 2 ·a), or 9%–54%, depending on climate and orientation), while maintaining acceptable or better visual comfort conditions throughout the interior space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A sensitivity analysis to predict the neutronics behavior of samples irradiated in the VTR rabbit system

We report a low-order neutronics model is developed to carry out hundreds of simulations efficiently and investigate the neutronics behavior of samples being irradiated in a test reactor setting under different geometrical constraints. The low-order model allowed for simulations that yield the expected neutronics behavior of any irradiated sample in any environment and allows for the calculation of highly accurate spatially averaged statistics and idealized spatial distributions in the neutron flux. Several benchmarks are performed to evaluate the performance and limitations of the low-order model revealing many important findings. The low-order model predicted the LHGR in the EBR-II driver fuel to within 2.34% by only simulating the fuel rod by itself, which served as a validation for the model. Sensitivity studies investigated 3% enriched UO 2 and U-10Zr being irradiated in the Versatile Test Reactor rabbit system. The analyses investigated a range of combinations of 15 radii and 5 heights for each sample in the rabbit system. Similar data sets are also provided for irradiations in the Advanced Test Reactor’s B-10 irradiation position, which is a thermal neutron spectrum environment. Generalized fits and fit coefficients are obtained for sample heating, reaction rate densities, and local multiplication rate characteristics, allowing the predictions of the neutronics behavior of the samples based on their geometrical constraints. The analyses and fits laid the groundwork for developing a user-end Multiphysics analysis framework to assist and accelerate irradiation experiment design and optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2025

CFD modeling of natural circulation in LiCl-KCl molten salt closed loop

Characterizing flow within a molten salt closed-loop system is crucial for assessing system requirements, evaluating performance, and identifying potential flaws. Direct flow measurement using instrumentation is challenging due to extreme environmental conditions and the limitations associated with measuring molten salt flow under natural convection. Here, this study aims to provide comprehensive insights into the thermal-hydraulic behavior of a closed loop, with a particular focus on temperature distribution and velocity prediction. The Computational Fluid Dynamics (CFD) model demonstrated the capability to effectively simulate and predict both temperature distributions and flow velocities within the molten salt loop. The CFD model's predictive capability was validated by its ability to replicate temperature measurements under varying boundary conditions. The analysis revealed that the CFD model tends to underpredict temperatures in the cold leg and overpredict them in the hot leg, highlighting the need for continuous model refinement and acknowledging the limitations of using a steady-state approach. Furthermore, the potential of using external temperature measurements to estimate internal molten salt temperatures and predict flow velocity was explored, revealing that this approach could introduce up to a 5.5% error in flow velocity calculations. Line probes mapping temperature distributions across the tube's cross-section and molten salt provided valuable insights into temperature gradients, emphasizing the need for a thermal conductivity equation for molten salt with lower uncertainty to achieve more accurate temperature predictions of the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗
DOE OSTI2021

Design and testing of a free floating dual flap wave energy converter

With a wide variety of wave energy device archetypes currently under consideration, it is a major challenge to ensure that research findings and methods are broadly applicable. In particular, the design and testing of wave energy control systems, a process which includes experimental design, empirical modeling, control design, and performance evaluation, is of interest. This goal motivated the redesign and testing of a floating dual flap wave energy converter. As summarized in this paper, the steps taken in the design, testing, and analysis of the device mirrored those previously demonstrated on a three-degree of freedom point absorber device. The method proposed does not require locking WEC degrees of freedom to develop an excitation model, and presents a more attainable system identification procedure for at-sea deployments. The results show that the methods employed work well for this dual flap device, lending additional support for the broad applicability of the design and testing methods applied here. The aim of this paper is to demonstrate that these models are particularly useful for deducing areas of device design or controller implementation that can be reasonably improved to increase device power capture.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Computational Experiment Design for Operations Model Simulation

Computer simulations that demonstrate the value of novel approaches are crucial to developing more flexible and robust power systems operations with high penetrations of renewable energy at multiple geographic and temporal scales. However, optimization-based simulations that depend on forecast data often face challenges in evaluating performance, reproducing results, and testing under realistic simulation scenarios. In this paper, we develop scientific computing best-practices for the validation and reproduction of power systems operational models. We then employ two case studies to demonstrate the proposed validation and reproduction framework.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
DOE OSTI2026

DTLMod: A simulation framework for in situ workflow optimization

In situ processing workflows have become essential for coping with the explosion in data volume and velocity in large-scale scientific computing, providing domain scientists with early insights at runtime. Multiple frameworks implement this paradigm through a data transport layer (DTL), offering different data access modes and deployment schemes, but researchers currently lack the appropriate tools to assess design and deployment options before committing to costly real experiments. We introduce DTLMod, an open-source simulated DTL that enables performance evaluation of in situ workflow configurations at scale. Built on SimGrid, it links into any SimGrid-based simulator and is available in C++ and Python. We evaluate DTLMod along four axes: scalability (tens of thousands of simulated processes across interconnected clusters in seconds, with linear memory scaling), versatility (three implementation variants trading fidelity for speed), accuracy (simulated times faithfully reflecting real behavior), and practical utility (two use cases demonstrating evidence-based workflow design decisions).

Suter, Fred [ORNL] (ORCID:0000000319021955)↗
DOE OSTI2025

Adaptive Algebraic Derivative Estimation for Battery Electric Buses Energy Consumption Forecasting

The limited service life of onboard batteries for EVs is a challenge, underscoring the need for real-time battery usage prediction. This paper proposes an adaptive Algebraic Derivative Estimation (ADE) approach for forecasting the energy consumption of battery electric buses. By dynamically adjusting the sliding window length, the adaptive ADE retains the fixed-length ADE’s key advantage—namely, operating online without reliance on extensive historical datasets—while substantially bolstering forecast accuracy by actively trading estimation bias off estimation variance. Comparative experiments against both the conventional ADE with a fixed length and a representative machine learning algorithm, XGBoost, were conducted, with performance evaluated via root mean square error, mean absolute error, and the coefficient of determination. The results demonstrate that the proposed approach significantly outperforms baseline methods.

Cui, Tianyang [The University of Texas at Dallas]↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2024

Investigation of precooling unit design options in hydrogen refueling station for heavy-duty fuel-cell electric vehicles

Precooling gaseous hydrogen fuel to a cold temperature before refueling a heavy-duty (HD) hydrogen fuel cell electric vehicle (HFCEV) is essential to avoid overheating the vehicle tank, as well as achieving a high state of charge (SOC). Because a large volume of hydrogen is dispensed during each fill, the need for a shorter fill time amplifies the need to precool each load for refueling a HFCEV. Thus, the design and operation of a precooling unit (PCU), as well as the associated capital and operating costs, plays a pivotal role in any plans for heavy-duty hydrogen refueling stations. Here, in this paper, we present a thermodynamic and technoeconomic analysis of a PCU in a gaseous hydrogen refueling station (HRS) for HD HFCEVs. By employing Argonne National Laboratory's hydrogen station cost optimization and performance evaluation (H2SCOPE) model, the refueling of 50 kg of hydrogen on-board type IV tank at ambient temperatures of 15–45 °C and varying fill rates is simulated. The required degree of precooling temperature to obtain either 100% or the maximum possible SOC is obtained from the simulation. Additionally, the simulation results demonstrate that the average flow rate of hydrogen is approximately 40% lower than the maximum flow rate during a typical fill; which motivates further evaluation of the instantaneous hydrogen mass flow rate profile and suggests the scope of improving precooling unit design. Accordingly, a hybrid strategy of precooling hydrogen has been proposed to address the cooling load by sizing the refrigeration unit for the average flow rate of hydrogen, while supplementing the above average peak hydrogen flow cooling load through thermal buffering. The combined technique enables the downsizing of the original PCU capacity by 25–40% and demonstrates a potential cost reduction of the PCU by approximately 30%, which translates to an installed cost reduction of ∼$125,000 per dispenser.

25 ENERGY STORAGE↗
DOE OSTI2022

Evaluating the Rheo-electric Performance of Aqueous Suspensions of Oxidized Carbon Black

Here, the macroscopic properties of carbon black suspensions are primarily determined by the agglomerate microstructure built of primary aggregates. Conferring colloidal stability in aqueous carbon black suspensions should thus have a drastic impact on their viscosity and conductivity. Carbon black was treated with strong acids following a wet oxidation procedure. An analysis of the resulting particle surface chemistry and electrophoretic mobility was performed in evaluating colloidal stability. Changes in suspension microstructure due to oxidation were observed using small-angle X-ray scattering. Utilizing rheo-electric measurements, the evolution of the viscosity and conductivity of the carbon black suspensions as a function of shear rate and carbon content was thoroughly studied. The carboxyl groups installed on the carbon black surface through oxidation increased the surface charge density and enhanced repulsive interactions. Electrostatic stability inhibited the formation of the large-scale agglomerates in favor of the stable primary aggregates in suspension. While shear thinning, suspension conductivities were found to be weakly dependent on the shear intensity regardless of the carbon content. Most importantly, aqueous carbon black suspensions formulated from electrostatically repulsive primary aggregates displayed a smaller rise in conductivity with carbon content compared to those formulated from attractive agglomerates.

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

Orientation-selected micro-pillar compression of additively manufactured 316L stainless steels: Comparison of as-manufactured, annealed, and proton-irradiated variants

In this work, irradiation response and deformation mechanisms of additively manufactured (AM) 316L stainless steel were studied by atomic scale characterization and micro-pillar compression. The AM 316L stainless steels were fabricated by direct energy deposition, a laser-based additive manufacturing process. Irradiation with 2 MeV protons at 360°C was performed to create ~1.8 displacements-per-atom (dpa) damage in AM 316L. Deformation behaviors of the as-manufactured, annealed, and proton-irradiated variants were studied, focusing on the effects of manufacturing-induced pores, residual stress, and irradiation-introduced defects (dislocation loops and voids). Micro-pillars were prepared from grains of pre-selected orientation, avoiding contributions of grain boundaries and allowing determination of resolved shear stress on {111} glide planes. Transmission electron microscopy was used to characterize the pre- and post-deformation microstructure. It was found that in the as-manufactured alloy variant, moving dislocations were the major deformation carrier, with noticeable blocking by fabrication-induced pores, In the annealed variant, hardness was reduced, and deformation was also accomplished by dislocation gliding. In the proton-irradiated variant, significant twinning was observed. Comparing measured resolved shear stress and predicted critical stress for dislocation dissociation, we conclude that irradiation hardening became high enough to activate twinning. Therefore, the deformation mechanism changes from dislocation gliding to twinning. The study is important for both processing optimization and performance evaluation of AM alloys for reactor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characterization of the radial microstructural evolution in LWR UO 2 using electron backscatter diffraction

Studies on high burnup UO 2 subjected to loss-of-coolant accident conditions have shown that restructured regions of the fuel are susceptible to pulverization and eventual dispersal. Due to a lack of pre-test characterization, the distinct microstructural features rendering the fuel prone to fragmentation remain ambiguous. Four samples of commercially irradiated light-water reactor UO 2 have been characterized utilizing electron backscatter diffraction to assess the susceptible microstructure. The microscopy focused on determining the burnup and temperature conditions responsible for the formation of the different microstructural regions where the regions were denoted as the high-burnup structure (HBS), HBS transition, mid-radial, restructured central, and central region. Previous works have outlined the specific conditions required for the restructuring of the microstructure into the HBS, but the conditions responsible for the restructuring in the central region of the fuel are not well understood. The four analyzed samples confirm a burnup threshold of 61 GWd/tU, and an unknown temperature range is needed to facilitate the formation of the restructured central region. In conclusion, additional fuel performance evaluations are needed to quantify the temperature range promoting restructuring in the central region.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Electrical edge effect induced photocurrent overestimation in low-light organic photovoltaics

In many literatures, the short-circuit current (J SC ) of indoor organic photovoltaics is overestimated, leading to severely wrong device performance evaluation and analysis. In this work, based on the equivalent circuit model, we demonstrate that electrical edge effect is sensitive to both transverse surface resistance and light intensity. At low light intensity, the electrical edge effect could lead to J SC being significantly overestimated, i.e., by 100% and even more. We show that for a PM6:Y6 device capped with a MoOX layer, when measured under 0.01 sun, the usually overlooked interface doping mechanism would lead to J SC and PCE overestimation by 51% and 15%, respectively. Besides, we show that the magnitude of J SC overestimation drastically increases with high photoactive-layer surface roughness. This work emphasized the significant electrical edge effect on J SC evaluations for low-light solar cells and is conducive to understanding the intrinsic mechanism of edge effect, promoting a healthier development of organic photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2022

Optically connected memory for disaggregated data centers

Recent advances in integrated photonics enable the implementation of reconfigurable, high-bandwidth, and low energy-per-bit interconnects in next-generation data centers. We propose and evaluate an Optically Connected Memory (OCM) architecture that disaggregates the main memory from the computation nodes in data centers. OCM is based on micro-ring resonators (MRRs), and it does not require any modification to the DRAM memory modules. We calculate energy consumption from real photonic devices and integrate them into a system simulator to evaluate performance. Here, our results show that (1) OCM is capable of interconnecting four DDR4 memory channels to a computing node using two fibers with 1.02 pJ energy-per-bit consumption and (2) OCM performs up to 5.5× faster than a disaggregated memory with 40G PCIe NIC connectors to computing nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. In this work, we present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.

97 MATHEMATICS AND COMPUTING↗