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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Numerical study of advanced solar receiver tubes based on a coupled thermo-mechanical analysis for concentrated solar power tower plant

The search for more sustainable energy to match the growing energy demand begins with finding more dispatchable resources such as solar energy. As one of the promising solar technologies, concentrated solar power (CSP) has a full capacity to store thermal energy for extended operation. Nevertheless, some key components in CSP systems usually face extreme environment, such as uneven solar flux, cyclic thermal expansion, structural degradation on the solar absorber tubes in a Concentrated Solar Power Tower (CSPT) Plant. In this study, we applied Multiphysics simulation to explore the benefits of introducing optimized fins for heat transfer enhancement and uniform temperature distribution, the goal is to improve the thermal efficiency of such advanced solar absorber tubes. The results of this study can supply design guidance for the manufacturing process of absorber tubes, and eventually can benefit the solar energy community for the next generation of molten salt based CSP system.

14 SOLAR ENERGY↗
DOE OSTI2015

Optimization of X-ray tomography through a cooperative computing system in grid

Cooperative Computing implemented as Cooperative Computing System (CCS) in grid has been proved a considerably reliable technique to execute the tasks with real time constraints in a grid environment. This technique can be applied in many high performance distributed computing applications. HPC has a large number of applications in various fields of physics. One such application in radiation physics is X-ray tomography. X-Ray tomography contains numerous applications in various fields of science, technology and research. As the technology is changing from analog to digital in almost all the scenarios, this paper presents an idea towards the attachment of X-ray tomography assembly to HPC environment so as to obtain the highly reliable optimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing

State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. In this paper, to improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding–blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Finally and moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Volume of interest CBCT and tube current modulation for image guidance using dynamic kV collimation

Purpose: The focus of this work is the development of a novel blade collimation system enabling volume of interest (VOI) CBCT with tube current modulation using the kV image guidance source on a linear accelerator. Advantages of the system are assessed, particularly with regard to reduction and localization of dose and improvement of image quality. Methods: A four blade dynamic kV collimator was developed to track a VOI during a CBCT acquisition. The current prototype is capable of tracking an arbitrary volume defined by the treatment planner for subsequent CBCT guidance. During gantry rotation, the collimator tracks the VOI with adjustment of position and dimension. CBCT image quality was investigated as a function of collimator dimension, while maintaining the same dose to the VOI, for a 22.2 cm diameter cylindrical water phantom with a 9 mm diameter bone insert centered on isocenter. Dose distributions were modeled using a dynamic BEAMnrc library and DOSXYZnrc. The resulting VOI dose distributions were compared to full-field CBCT distributions to quantify dose reduction and localization to the target volume. A novel method of optimizing x-ray tube current during CBCT acquisition was developed and assessed with regard to contrast-to-noise ratio (CNR) and imaging dose. Results: Measurements show that the VOI CBCT method using the dynamic blade system yields an increase in contrast-to-noise ratio by a factor of approximately 2.2. Depending upon the anatomical site, dose was reduced to 15%–80% of the full-field CBCT value along the central axis plane and down to less than 1% out of plane. The use of tube current modulation allowed for specification of a desired SNR within projection data. For approximately the same dose to the VOI, CNR was further increased by a factor of 1.2 for modulated VOI CBCT, giving a combined improvement of 2.6 compared to full-field CBCT. Conclusions: The present dynamic blade system provides significant improvements in CNR for the same imaging dose and localization of imaging dose to a predefined volume of interest. The approach is compatible with tube current modulation, allowing optimization of the imaging protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Distribution System State Estimation: A Semidefinite Programming Approach

Distribution system state estimation (DSSE) is one of the vital components in the next-generation distribution management system which allows operators to monitor the entire system's operating conditions. Due to the lack of real-time measurements, DSSE has to process measurements whose quality varies significantly across different sources, which causes a convergence issue to the Gauss-Newton solver. In this paper, a semidefinite programming (SDP) framework is proposed to reformulate the DSSE problem into a rank- constrained SDP problem. One challenge of this technique is the nonconvex rank-one constraint, which is generally relaxed. However, the relaxed SDP-DSSE problem cannot guarantee a rank-one solution and hence loses optimality. Therefore, we propose two solution approaches to obtain rank-one solutions for the SDPDSSE problem: the rank reduction approach and the convex iteration approach. The model and the effectiveness of the proposed solution approaches are numerically demonstrated on the IEEE 13-bus, 34-bus, and 123-bus distribution systems.

convex iteration↗
DOE OSTI2020

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-learning

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

distribution system↗
DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

An 80-W 94.6%-Efficient Multi-Phase Multi-Inductor Hybrid Converter

This paper presents a new Multi-Phase Multi-Inductor Hybrid (MP-MIH) converter that features high efficiency at large conversion ratios, while operating the switches with duty cycles larger than state-of-the-art hybrid topologies. In this converter, the capacitors are soft-charged and soft-discharged through three inductors operated in three interleaving phases. An experimental six-level three-phase converter prototype achieves 94.6% peak efficiency and 425 W/in 3 power density for conversions from 48V to 1V-2V at loads of up to 40A. This multi-phase multi-inductor hybrid converter architecture can be extended to any number of switched-capacitor network levels to support wide range of input and output voltages and load currents in data centers, telecommunication and other high-performance digital systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

SU-E-J-23: Characteristics of X-Rays From ExacTrac and Patient Dose From Imaging Procedures

Purpose: The purpose of this investigation is 1) provide the beam characteristics of x-rays produced by a Novalis TX ExacTrac system; 2) present a method to commission such beams, 3) present radiation dose to patients resulting from the imaging procedures. Methods: The Monte Carlo simulations were used to obtain the characteristics of kV beams and validated by measurements. The calculated beam HVLs, profiles and depth-dose curves were benchmarked against measurements. Twelve different image acquisition protocols were studied. The x-ray tube voltage ranges from 70 - 145 kV and milliampere-second (mAs) ranges from 8 - 80 mAs depending on the selection of Cranium, Head & Neck, Thorax or Abdomen imaging protocols. The beam output of each image acquisition protocol was determined by using an ionization chamber. The air kerma calibration factors of the ion chamber were obtained from an Accredited Dosimetry Calibration Laboratory for specified HVLs. Results: The agreements between measured and simulated results were within the uncertainties for HVLs, dose profiles and depth-dose curves. When %dd was normalized at 1 cm depth, its values at depth of 5 cm ranged from 45% to 66% of in water for kV beams range from 70 kVp to 145 kVp respectively. For head images, a typical dose to eye resulting from single projection ranged from 0.008 cGy to 0.025 cGy depending on the cranial image protocols selected. For a single pelvic image the skin dose could reach up to 0.1 cGy from an abdominal protocol. Conclusion: Although multiple pairs of x-ray images are commonly acquired during a daily patient treatment, the imaging doses to patients resulting from the sum of these projected x-rays are generally much less than 0.5 cGy. The knowledge obtained from this investigation can be used to estimate the image dose and optimize the used of the system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

25 ENERGY STORAGE↗
DOE OSTI2024

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗
DOE OSTI2020

The Future of Renewable Energy Transmission: An Autonomous Energy Grid

The drastic price reduction in variable renewable energy, such as wind and solar, coupled with the ease of use of smart technologies at the consumer level, is driving dramatic changes to the power system that will significantly transform how power is made, delivered, and used. Distributed energy resources (DERs)—which can include solar photovoltaic (PV), fuel cells, microturbines, gensets, distributed energy storage (e.g., batteries, ice storage), and new loads (e.g., electric vehicles (EVs), light-emitting diode (LED) lighting, smart appliances, and electric heat pumps)—are being added to electric grids and causing bidirectional power flows and voltage fluctuations that can impact optimal control and system operation. Residential solar installations, customer battery systems, and EVs are all seeing rapid increases in deployments. With DER seeing such increased use, it is not unreasonable to imagine a residential electricity customer having at least five controllable DERs. In future electric grids, as more DERs are integrated, the number of active control points will be too much for current control approaches to effectively manage.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Optimization Modeling for Advanced Syngas to Olefin Reactive Systems

Reactor designs with mixed catalysts play an important role in transforming a multiple reactor system to single-shot reactors. In addition to savings in capital and ease of implementation, single-shot reactors are useful to break equilibrium limitations, thereby increasing the yield and selectivity of desired product as shown in previous studies. However, the nonlinear and highly exothermic nature of mixed-catalyst systems makes it difficult for commercial process simulation and optimization tools to optimize these systems. This study describes the development and application of optimization strategies for mixed-catalyst, single-shot reactors for syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging and requires advanced solution strategies for singular optimal control problems, which are poorly conditioned and often lead to flat response surfaces. The graded bed and partial-moving finite-element approaches are used to find the optimal catalyst distribution that maximizes the olefins yield. A 1.3% increase in the yield is observed from one zone to three zones. The yield further improves from three zones to the exact infinite dimensional solution by 0.2%. This improvement can be realized in practice by changing only the catalyst distribution, without any extra investment. Lastly, the results suggest that a mixed-catalyst single shot reactor bed can be applied to other reaction mechanisms to increase reactor performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WfBench: Automated Generation of Scientific Workflow Benchmarks

The prevalence of scientific workflows with high computational demands calls for their execution on various distributed computing platforms, including large-scale leadership-class high-performance computing (HPC) clusters. To handle the deployment, monitoring, and optimization of workflow executions, many workflow systems have been developed over the past decade. There is a need for workflow benchmarks that can be used to evaluate the performance of workflow systems on current and future software stacks and hardware platforms.We present a generator of realistic workflow benchmark specifications that can be translated into benchmark code to be executed with current workflow systems. Our approach generates workflow tasks with arbitrary performance characteristics (CPU, memory, and I/O usage) and with realistic task dependency structures based on those seen in production workflows. We present experimental results that show that our approach generates benchmarks that are representative of production workflows, and conduct a case study to demonstrate the use and usefulness of our generated benchmarks to evaluate the performance of workflow systems under different configuration scenarios.

Coleman, Taina↗
DOE OSTI2023

Smart Preprocessing & Robust Integration Emulator

To achieve the desired particle size of biomass feedstocks during preprocessing for trouble-free handling and conversion to produce biofuels and bioproducts, the raw materials must undergo a crucial milling process. The particle size of biomass plays a critical role in subsequent biofuel manufacturing, where a larger area-to-volume ratio facilitates efficient synthesis while balancing the impact of moisture on biomass storage. To optimize biofuel production efficiency and overcome these challenges, it is imperative to accurately predict the particle size distribution (PSD) of the biomass in the design of efficient preprocessing systems. The population balance model (PBM), upon empirical calibration and validation, can provide rapid prediction of post-milling PSD of granular biomass. However, PSD has limitations related to mass conservation and the absence of moisture considerations. To overcome these drawbacks, a deep learning model called the enhanced deep neural operator (DNO+) is implemented in the code. This model not only retains the capabilities of the PBM in handling complex mapping functions but also incorporates additional factors influencing the system. By considering various experimental conditions such as sieve size and moisture content, the trained DNO+ model can effectively predict the PSD after milling for any given feed PSD. To further reduce the reliance on experimental data, the PBM is integrated into the DNO+ model, resulting in a physics-informed DNO+ (PIDNO+). The PIDNO+ model addresses the non-conservation of quality exhibited by the PBM while inheriting the advantages of the DNO+ model in considering multiple influencing factors. Moreover, the PIDNO+ model significantly reduces the amount of data required for model training. Both deep learning models, i.e., DNO+ and PIDNO+, are excellent in predictive performance, offering swift and accurate machine learning-based predictions. The use of this code that contains these models will assist in guiding the proper milling equipment selection and operational conditions to achieve the desired biomass particle sizes, ensuring the efficiency of subsequent biofuel and bioproduct production processes.

Xia, Yidong [Idaho National Laboratory (INL), Idah↗
DOE OSTI2022

A low-cost centralized HVAC control system solution for energy savings, load shedding, and improved maintenance

University campuses rely on centralized controls for managing and optimizing complex HVAC systems in larger buildings. However, most campuses also have many smaller buildings with packaged HVAC systems controlled by a stand-alone thermostat. Even when these distributed and often overlooked systems have modern programmable thermostats, they cannot be centrally monitored or controlled, and they are typically not programmed adequately. This paper describes the implementation of a low-cost centralized control solution for these systems serving smaller campus buildings, mostly under 5,000 sf and representative of light commercial spaces. Thanks to advances in technology spurred by residential and commercial IoT developments, simple networked thermostat solutions exist that can easily replace original thermostats, and, connect these systems to a web-based portal for monitoring and control. We show that, with small customizations, these platforms can be integrated into facility management workflows. Beyond the energy savings potential from improved scheduling and closer management of these systems, there are significant advantages for maintenance crews since these systems can now be monitored on smart phones or tablets. A grid-responsive load-shedding program has also been implemented for additional cost savings. The networked thermostats can also be connected to additional systems such as economizer controls for improved ventilation management and energy savings. With data from these systems integrated centrally, it can also be used for improved analytics and fault detection. A toolkit has been developed to share the program with other campuses, whether for energy savings, improved management of ventilation, or a more proactive maintenance approach.

Fauchier-Magnan, Nicolas↗