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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A scoping study of far-SOL main-wall protection limiters for steady-state operation of compact pilot plant tokamaks

We present a novel method for handling steady-state heat fluxes incident on the main wall of pilot plant-scale magnetic fusion devices, based on the utilization of protection limiters in the far scrape-off layer (SOL). This method helps avoid large plasma-wall gaps, without excessively compromising blanket performance. We present an optimization algorithm for determining the appropriate size and scale of these protection limiters given (1) probability distributions of SOL plasma parameters and (2) assumed risk tolerance. As part of this optimization, we have developed an analytic description of parallel heat fluxes across limiter shadows, and an objective cost function (the ‘Far-SOL Marginal Cost’) to quantify the impact that different main-wall thermal management design choices have on reactor capital cost. Applying the model to a midscale fusion pilot plant concept shows that making use of far-SOL protection limiters can reduce capital costs on the order of $500 M, relative to naively increasing the plasma-wall gap. Our analysis demonstrates that the far-SOL power decay length is the highest-leverage plasma assumption for thermal loading of the first wall, and the primary cost driver for main wall thermal management. The relative cost efficiency of protection limiters increases as assumptions on the far-SOL heat flux become more pessimistic. The concepts described in this paper motivate the further development of far-SOL protection limiters as part of larger efforts to design economical core-edge-wall compatible solutions for a fusion pilot plant.

Design under uncertainty↗
DOE OSTI2025

Analysis of fast-ion losses measured in MAST-U via infrared thermography and a Fast Ion Loss Detector

Fast-ion losses need to be monitored to avoid damage to plasma facing components. In existing experimental devices, the scintillator-based fast-ion loss detector (FILD) is the most advanced diagnostic for measuring fast-ion losses. However, FILDs provide only local information about the losses. Infrared (IR) thermography can be used as a complementary tool for more global monitoring of the deposition of fast-ion losses on the wall, at the expense of no velocity-space resolution. IR cameras measure the temperature of the plasma facing components. This measurement, determined by a combined effect of the thermal plasma, radiation, neutrons and fast-ion losses, can be decomposed to infer the fast-ion load on the tokamak wall. In this manuscript, a workflow to estimate fast-ion losses via IR thermography is applied to the MAST-U spherical tokamak, using a 1D approximation to extract the experimental heat flux on the FILD front face from IR data. To numerically estimate the different contributions to this total heat flux, the field-line tracing environment SMITER is used to calculate the thermal plasma contribution, the orbit-following Monte-Carlo code ASCOT to estimate the fast-ion losses, and bolometry measurements for the radiation. To validate the workflow, two discharges, L-mode plasmas with low MHD activity, were executed using on and off-axis beams, respectively. The experimentally and numerically estimated heat flux are of the same order of magnitude for the on-axis heated scenario, with a strong dependence of the estimated fast-ion losses contribution on the fit to the kinetic profiles used as input. This is also true for the off-axis heated scenario, where the total numerically estimated heat flux is 2.1 or 1.3 times higher than the maximum experimentally estimated heat flux, depending on the ASCOT input used.

FILD↗
DOE OSTI2026

Disruption modelling for engineering and physics design of ST-E1 fusion power plant

Plasma disruptions represent a critical challenge for high-performance tokamak operations, as they can compromise machine integrity and reduce operational availability. Although future fusion devices essentially need to incorporate strategies to minimise disruption occurrence, complete avoidance remains unattainable. Consequently, assessing and characterising unmitigated disruption consequences is fundamental for the design and qualification of next-generation fusion power plants. This work supports the pre-conceptual design of ST-E1, a low aspect-ratio Tokamak Fusion Power Plant developed by Tokamak Energy Ltd., by presenting a comprehensive disruption modelling approach applied across different design stages. The methodology integrates both physics and engineering considerations to evaluate the impact of disruptions on machine performance and structural integrity. From an engineering perspective, several ST-E1 layout options were analysed to investigate the electromagnetic response of key components under disruption-induced loads, enabling comparison between alternative design solutions. On the physics side, a broad set of disruption scenarios was explored, scanning operational space parameters, plasma-material interactions, and associated thermal loads. Furthermore, the study examined variations in disruption behaviour arising from different reference equilibria, focusing on a range starting from Double Null to Single Null configurations, reflecting the increasing up-down asymmetry consequences. The results reveal significant contrasts in plasma dynamics and structures electromagnetic behaviour between configurations, highlighting the importance of disruption modelling in guiding design choices. These analyses have proven instrumental in shaping ST-E1 development, offering critical insights for mitigating risks and optimising future fusion power plant designs.

Borowiec, Katarzyna [ORNL] (ORCID:0000000335911739↗
DOE OSTI2025

Predicting runtime and resource utilization of jobs on integrated cloud and HPC systems

Recent advances in virtualization technologies used in cloud computing offer performance that closely approaches bare-metal levels. Combined with specialized instance types and high-speed networking services for cluster computing, cloud platforms have become a compelling option for high-performance computing (HPC). However, most current batch job schedulers in HPC systems are designed for homogeneous clusters and make decisions based on limited information about jobs and system status. Scientists typically submit computational jobs to these schedulers with a requested runtime that is often over- or under-estimated. More accurate runtime predictions can help schedulers make better decisions and reduce job turnaround times. Here, they can also support decisions about migrating jobs to the cloud to avoid long queue wait times in HPC systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bat activity at ecovoltaic solar energy developments in the Midwestern United States

As global photovoltaic (PV) solar electricity generation continues to increase, some PV sites are co-prioritizing electricity generation and ecosystem function (“ecovoltaics”) to align renewable energy development with biodiversity conservation. Thus far, positive responses of plant and insect communities to native habitats at ecovoltaic sites have been observed, but there has been little research on bat responses to ecovoltaic designs in the U.S. We conducted passive ultrasonic monitoring in 2023 and 2024 at 12 solar sites and paired reference sites (agricultural fields) to investigate bat activity responses to ecovoltaic facilities in the Midwestern U.S. We found that average weekly overall bat activity was approximately 50?% higher within ecovoltaic sites than reference sites in the first half of the monitoring season. We also found species-specific differences in bat responses to ecovoltaic sites, with Hoary Bats showing higher activity on ecovoltaic sites throughout most of the monitoring season, Big Brown Bats showing higher activity on ecovoltaic sites during the first one-third of the monitoring season, and Silver-haired Bats showing no difference in activity between ecovoltaic sites and reference sites. There were no weeks in which bat activity was statistically greater on reference sites, suggesting that bats in the Midwestern U.S. do not avoid ecovoltaic solar sites. Rather, our results suggest that ecovoltaic sites in this region may provide early season (May-June) habitat at a time of year when resources may be limited in the surrounding landscape. These findings support a growing body of evidence on the positive ecological outcomes of ecovoltaic designs. Further investigations of the types of bat calls being recorded at PV sites and relationships with insect prey abundance are needed to understand the underlying drivers of species-specific responses to PV developments.

14 SOLAR ENERGY↗
DOE OSTI2022

Contributions of anoxic microsites to soil carbon protection across soil textures

Anoxic microsites, zones of oxygen depletion in otherwise oxic soils, may slow soil C turnover. However, the abundance of anoxic microsites and their contribution to soil C protection is yet undefined. In this study, we determine the contribution of anoxic microsites to soil C protection in soils of three distinct textures (clay loam, loam, sandy loam) across a range of soil moistures. We examined the influence of soil oxygen supply by increasing oxygen content in the incubation atmosphere (oxygen enrichment) and through disaggregation. We attributed increases in CO 2 efflux to the aeration of anoxic microsites. The contribution of anoxic microsites to soil C protection increased with decreasing clay content. Clay loam CO 2 efflux was relatively unaffected by aeration. Moderately moist, loam soils had CO 2 effluxes that did not increase with oxygen enrichment but increased by 375% upon disaggregation. Sandy loam soil CO 2 efflux increased by 50–75% with oxygen enrichment and 250% with disaggregation. Geochemical and microbial data reveal that anoxic microsite abundance also increased with decreasing clay content. The proportion of acid extractable Fe present as Fe(II) increased with decreasing clay content, and methanogens were more abundant in loam and sandy loam soils. Our results suggest that oxygen demand, rather than supply, can regulate anoxic microsite formation and that anoxic protection of soil C can be diminished through physical disruption of soil structure. Our findings further illustrate that anoxic microsites should be included in conceptual models of soil C protection to avoid soil C loss and improve predictions of soil C response to disturbance.

58 GEOSCIENCES↗
DOE OSTI2022

A deep learning-based direct forecasting of CO 2 plume migration

Accurate and timely forecasts of CO 2 plume evolution in geological reservoirs are crucial for CO 2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO 2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO 2 plume distributions. In this work, we demonstrate the effectiveness and accuracy of our method in predicting the CO 2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
DOE OSTI2020

An embedded 3D fracture modeling approach for simulating fracture-dominated fluid flow and heat transfer in geothermal reservoirs

In this paper, we describe an efficient modeling approach, named embedded discrete fracture method (EDFM), for incorporating arbitrary 3D, discrete fractures, such as hydraulic fractures or faults, into modeling fracture-dominated fluid flow and heat transfer in fractured geothermal reservoirs. This technique allows 3D discrete fractures to be discretized independently from surrounding rock volume and inserted explicitly into a primary fracture/matrix grid, generated without including 3D discrete fractures in prior. An effective computational algorithm is developed to discretize these 3D discrete fractures and construct local connections between 3D fractures and fracture/matrix grid blocks representing the surrounding rock volume. The constructed gridding information on 3D fractures is then added to the primary grid. This embedded fracture modeling approach can be directly implemented into a developed geothermal reservoir simulator via the integral finite difference (IFD) method or with TOUGH2 technology. This embedded fracture modeling approach is very promising and computationally efficient to handle realistic 3D discrete fractures with complicated geometries, connections, and spatial distributions. Compared with other fracture modeling approaches, it avoids cumbersome 3D unstructured, local refining procedures, and increases computational efficiency by simplifying Jacobian matrix size and sparsity, while maintaining enough accuracy. Several numeral simulations are presented to demonstrate the utility and robustness of the proposed technique. Our numerical experiments show that this approach captures all the key patterns about fluid flow and heat transfer dominated by fractures in these cases. Thus, this approach is readily available to the simulation of fractured geothermal reservoirs with both artificial and natural fractures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Analysis of curtailment at The Geysers geothermal Field, California

Geothermal energy has traditionally been viewed as a baseload energy source, but the rapid growth of intermittent renewable energy has led to a need for more flexibility in power generation to avoid mandatory curtailment imposed by grid operators. This study of curtailment at The Geysers provides insights into the magnitude, duration, frequency, temporal and spatial distribution, and potential causes of curtailment events between 2013 and 2018. Annual levels of curtailment range during this period from 9 to 47 GW h, representing 0.15 to 0.81 % of the net generation. Most curtailments occurred at the power plants connected to a lower capacity transmission line and may result from transmission constriction. There is a clear link between negative pricing and economic curtailment, especially when solar production is higher. Economic curtailment events tend to be only a few hours and vary in magnitude up to almost 300 MW, whereas transmission-related curtailment events can be up to several weeks in duration. It is likely that curtailment of geothermal power will be an increasing concern, and could be mitigated by flexible generation strategies and increases in energy storage. It is critical to know the nature of curtailment events so that flexible generation options can be assessed properly.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

On the computation of moments in the Super-Transition-Arrays model for radiative opacity calculations

In the Super-Transition-Array statistical method for the computation of radiative opacity of hot dense matter, the moments of the absorption or emission features involve partition functions with reduced degeneracies, occurring through the calculation of averages of products of subshell populations. Here, in the present work, we discuss several aspects of the computation of such peculiar partition functions, insisting on the precautions that must be taken in order to avoid numerical difficulties. In a previous work, we derived a formula for supershell partition functions, which takes the form of a functional of the distribution of energies within the supershell and allows for fast and accurate computations, truncating the number of terms in the expansion. The latter involves coefficients for which we obtained a recursion relation and an explicit formula. We show that such an expansion can be combined with the recurrence relation for shifted partition functions. We also propose, neglecting the effect of fine structure as a first step, a positive-definite formula for the Super-Transition-Array moments of any order, providing an insight into the asymmetry and sharpness of the latter. The corresponding formulas are free of alternating sums. Several ways to speed up the calculations are also presented.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

A study on computational fluid dynamics modeling of a refrigerated container for COVID-19 vaccine distribution with experimental validation

A key issue with the distribution of vaccines to prevent COVID-19 is the temperature level required during transport, storage, and distribution. Typical refrigerated transport containers can provide a temperature-controlled environment down to -30 °C. However, the Pfizer vaccine must be carefully transported and stored under a lower temperature between -80 °C and -60 °C. One way to provide the required temperature is to pack the vaccine vials into small packages containing dry ice. Dry ice sublimates from a solid to a gas, which limits the allowable transport duration. This can be mitigated by transporting in a -30 °C refrigerated container. Moreover, because the dry ice will sublimate and thereby release CO 2 gas into the transport container, monitoring the CO 2 concentration within the refrigerated container is also essential. In the present work, a 3D computational fluid dynamics model was developed based on a commercially available refrigerated container and validated with experimental data. The airflow, temperature distribution, and CO 2 concentration within the container were obtained from the simulations. The modeling results can provide guidance on preparing experimental setups, thus saving time and lowering cost, and also provide insight into safety precautions needed to avoid hazardous conditions associated with the release of CO 2 during vaccine distribution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Single Stage PLL-less Decoupled Active and Reactive Power Control for Weak Grid Interactive Inverters

This paper presents a single stage phase locked loop-less (PLL-less) active and reactive power (PQ) control for single-phase weak grid interactive inverters. The absence of the PLL requirement in the proposed PQ control enhances the stability margin comparing to conventional current control approaches in both the stationary or the synchronous reference frame. Additionally, the proposed control scheme enables direct PQ control with a single loop control structure. This paper demonstrates that the proposed PLL-less PQ control scheme is asymptotically stable if the controller gains are positive. The necessary conditions for supplying the rated active power into the weak grid are derived. The provided analysis shows that certain amount of reactive power injection is necessary to avoid point of common coupling (PCC) voltage collapse when the rated active power is injected into the weak grid. Several case studies are provided to demonstrate the performance of the proposed PLL-less PQ control scheme under weak grid condition.

Khan, Ahmad↗
DOE OSTI2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from a small dataset of recorded observations of the perturbed system's dynamics and the control law is optimized offline by interaction with the learned system model. The DPC method is based on two sequential steps, i) system identification using a constrained neural state-space model, and ii) optimization of an explicit control law parametrized by another neural network in closed-loop simulation with the identified neural state-space model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. By incorporating domain knowledge and leveraging established techniques from optimal control, our method leverages deep neural networks as nonlinear function approximators for system identification and control while avoiding concomitant costs of intractably large datasets, and computationally expensive over-parametrized models. The scalability, data efficiency, and constrained optimal control capability of the proposed DPC method are demonstrated in simulation using a multi-zone building emulator.

Drgona, Jan↗
DOE OSTI2023

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Shaw Cortez, Wenceslao E.↗
DOE OSTI2020

Continuous-time look-ahead flexible ramp scheduling in real-time operation

This paper develops a novel continuous-time look-ahead optimization model for co-optimizing balancing energy and flexible ramp products in real-time power systems operation. In addition, a continuous-time day-ahead operation model is developed to commit and schedule an adequate subset of generating units for providing balancing energy and ramping requirements in real-time operation. The up and down flexible ramp products are defined as continuous-time decision trajectories that continuously supply the real-time up and down ramping requirements of the system. The deliverability of flexible ramp products is secured through reserving the respective power requirements in generation capacity constraints of generating units. A function space solution method is proposed to reduce the dimensionality of day-ahead and real-time models through projecting them into finite-dimensional function spaces spanned by Bernstein polynomials, where the real-time operation decisions are modeled with a higher accuracy level than day-ahead decisions. Simulations are conducted on the IEEE-RTS system using CAISO load data, and the numerical results showcase the effectiveness of the proposed model in supplying the energy and flexible ramp requirements and avoiding ramping scarcity in the system, while reducing the operation costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Coordinated operation of pumped-storage hydropower with power and water distribution systems

Small pumped-storage hydropower (PSH) units have gained popularity as distributed energy storage options that can provide flexibility to the operation of power distribution systems. Optimal operation of small PSH units is not only dependent on the energy storage provided to power distribution system, but also on the inflow and outflow of water from and to the water distribution system. Here, in this context, this paper develops an optimization model for coordinated operation of PSH units with power and water distribution systems. The proposed model optimizes the operation of water tanks, variable-speed pumps and PSH in pumping and generating modes to minimize the operation cost of power distribution system, while respecting the power flow constraints of power distribution and hydraulic constraints of water distribution system. Appropriate electricity tariffs are implemented to avoid additional expenses in water distribution system that can be enforced by its coordinated operation in favor of power distribution system. The proposed model is implemented on a 33-bus and a 123-bus test power distribution system connected to a 16-node test water distribution system. Results demonstrate the effectiveness of proposed model in tapping PSH flexibility to reduce the operation cost of power and water distribution systems, while meeting the power and water demands.

13 HYDRO ENERGY↗
DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗