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At least 415 records · Page 23

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DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗

DOE OSTI2025

Retro-Commissioning Sensor Suitcase for Energy Efficiency (CRADA Final Report)

The Sensor Suitcase project aimed to enable cost-effective, data-driven retro-commissioning (RCx) in small commercial buildings through a self-installable toolkit consisting of portable sensors and diagnostic software. Results showed that the Sensor Suitcase could identify common operational faults leading to 5–15% energy savings opportunities, with automated reports requiring minimal user interpretation. The project adhered closely to its Statement of Work (SOW) but evolved the reporting interface and sensor logging duration based on field feedback. The development and validation of diagnostics are documented in PNNL technical reports and internal analysis summaries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015

Abstract. Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. The latest version of our data are available on Zenodo: https://doi.org/10.5281/zenodo.7093997 (Narayan et al., 2022b).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗

DOE OSTI2024

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Chapter 4 - Recent Advances in Identification of Differential Equations from Noisy Data: IDENT Review

Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of recent developments on the topic.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Measuring the robustness of predictive probability for early stopping in two-group comparisons

We report physical experiments are often expensive and time-consuming. Test engineers must certify the compatibility of aircraft and their weapon systems before they can be deployed in the field, but the testing required is time consuming, expensive, and resource limited. Adopting Bayesian adaptive designs is a promising way to borrow from the successes seen in the clinical trials domain. The use of predictive probability (PP) to stop testing early and make faster decisions is particularly appealing given the aforementioned constraints. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. Although PP has been thoroughly studied for binary data, there is less work with continuous data, where many reliability studies are interested in certifying the specification limits of components. A simulation study evaluating the robustness of this approach indicates early stopping based on PP is reasonably robust to minor assumption violations, especially when only a few interim analyses are conducted. The simulation study also compares PP to conditional power, showing its relative strengths and weaknesses. A post-hoc analysis exploring whether release requirements of a weapon system from an aircraft are within specification with desired reliability resulted in stopping the experiment early and saving 33% of the experimental runs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A New High-Impedance-Fault Detection Method to Prevent Power-Line-Induced Wildfires

High Impedance Faults (HIFs) occur when energized power lines come into contact with high impedance ground surfaces, such as tree branches and grassland. HIFs have the potential to cause arcing, leading to vegetation ignition and the initiation of wildfires. The challenge in detecting HIFs comes from the high impedance of the partially conductive materials in contact with the power lines. They create a fault current of low magnitude and traditional protective devices struggle to detect such faults. This paper proposes a novel HIF detection algorithm based upon the analyzed arcing signatures associated with HIFs. The algorithm is evaluated using the Australian Public Bushfire Safety Program (PBSP) dataset. For comparative analysis, a state-of-the-art commercial HIF detection product is also evaluated using the same dataset. The proposed algorithm demonstrates higher detection accuracy over the commercial products with fewer false flags and undetected faults.

grasslands↗

DOE OSTI2024

Options for Meeting Data Center Demand in Virginia: Focus on GETs and Reconductoring [Slides]

Data center demand growth in Virginia will need to met with many options - GETs and/or reconductoring have the potential to be part of the solution suite in the short-term and medium-term. Almost all GETs and reconductoring technologies have more favorable economics than transmission wires investments considering relatively low levels of existing wide-scale deployment. Deployment of GETs and/or reconductoring is not a one size fits all - requires detailed analysis. The complementary nature of GETs and/or reconductoring impacts have the potential to be synergistic (more congestion relief, more transfer capability, more cost savings - when implemented together).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Quantifying the Temperature Dependence of the Multi-Species, Multi-Reaction Model: Part II. Estimation of Entropy Coefficient for Meso-Carbon Micro-Bead Graphite

In Part 1 of this paper, the temperature impact on the Multi-Species, Multi Reaction (MSMR) model was studied. This was accomplished by acquiring data from slow rate lithiation and delithiation of a meso-carbon micro-bead (MCMB) graphite. Through this analysis, the temperature impact on the total fraction of available host sites in a particular MSMR gallery (X j ), the impact on the reference potential (U$^o_j$), and the impact on the parameter detailing the deviation from Nernstian behavior (ω j ) was determined. Here, the intercalation material is discussed, compared to traditional methods of acquiring the entropy coefficient, and comparison is made to previous mathematical estimates. Some of the challenges in using temperature dependent constant rate charge and discharge data as compared to the potentiodynamic entropy coefficient calculation method are also discussed, and a recommendation for future applications is proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

CFD modeling of turbulent air flow in self-heated gyroid TPMS structures: Thermal-hydraulic performance and validation

The application of mathematically derived geometries, such as triply periodic minimal surface (TPMS) lattices, has garnered significant interest across various fields, including the nuclear sector, due to their superior thermal-hydraulic characteristics for heat transfer compared to traditional plain or finned tubes. Here, this study validates a computational fluid dynamics (CFD) model, evaluates different turbulence models and CFD model settings, and performs uncertainty quantification to provide a comprehensive analysis. Despite extensive research on CFD modeling of TPMS lattices, such as gyroid and diamond geometries, there is a notable lack of publicly available literature providing comprehensive details on numerical analysis aspects, including convergence and methodological best practices. This study embarks on a benchmark analysis of a gyroid geometry to evaluate its thermal-hydraulic performance under turbulent flow conditions and scrutinize various CFD model configurations. The main contributions of this work include validating the CFD model, assessing and comparing different turbulence models, and enhancing pressure drop and temperature prediction capabilities. The results aim to support the development of methodologies needed to benchmark and enhance numerical analysis techniques for TPMS lattices. This work seeks to complement the existing body of knowledge, support the development of TPMS reactor concepts, and improve best practices for CFD modeling of TPMS lattices, ultimately advancing methodologies to support future applications in this domain.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Essential barrier height and a probabilistic approach in characterizing potential landscape

In this work we propose a probabilistic approach to investigate the shape of landscapes of multi-dimensional potential functions. Under a suitable coupling scheme, two copies of the overdamped Langevin dynamics associated with the potential function are coupled, and the coupling times are collected. Assuming a set of intuitive yet technically challenging conditions on the coupling scheme, it is shown that the tail distributions of the coupling times exhibit qualitatively different dependencies on the noise magnitude for single-well versus multi-well potential functions. More specifically, for convex single-well potentials, the negative tail exponent of the coupling time distribution is uniformly bounded away from zero by the convexity parameter and is independent of the noise magnitude. In contrast, for multi-well potentials, the negative tail exponent decreases exponentially as the noise vanishes, with the decay rate governed by the essential barrier height, a quantity introduced in this paper to characterize the non-convex nature of the potential function. Numerical investigations are conducted for a variety of examples, including the Rosenbrock function, interacting particle systems, and loss functions arising in artificial neural networks. These examples not only illustrate the theoretical results in various contexts but also provide crucial numerical validation of the conjectured assumptions, which are essential to the theoretical analysis yet lie beyond the reach of standard technical tools.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Mapping Local Dissipation and Entropy Production in Complex and Active Fluids

While global entropy production provides a measure of irreversibility, its partitioning into contributions from local regions is key to understanding the mechanisms underlying time-reversal symmetry breaking in complex systems and active matter. Here, by analyzing local heat flows and fluxes, we propose a framework that enables the mapping of local dissipation and entropy production in a nonequilibrium system. We test this approach in simulations of fluids driven through complex environments and active systems. We connect the results across the local and global scales by showing that local dissipation and entropy production satisfy a local version of the usual (global) fluctuation theorem, which accounts for the correlations between the local region and its surroundings. Interestingly, in the case of the active fluid, our analysis reveals that these correlations are of opposite signs for the active (stochastic) and passive (deterministic) contributions to local dissipation.

Entropy↗

DOE OSTI2026

Draft Feasibility Assessment for Use of AI in Preparing Transportation Safety Analysis Reports

Preparing transportation safety analysis reports for microreactors is time and labor intensive, requiring extensive cross referencing to Federal regulations, previously approved documents, and expert review comments across structural, thermal, criticality, shielding, containment, and security. These burdens are magnified by the novelty of microreactor technologies and the evolving regulatory landscape, as well as current workforce constraints. Generative AI and supporting machine learning tools present an opportunity to accelerate drafting timelines, lift generalized writing burdens, and systematically enforce regulatory adherence through retrieval augmented generation and other knowledge retrieval and mapping methods. This draft report presents a preliminary feasibility assessment of the use of AI to expedite the preparation of microreactor transportation safety analysis reports and proposes an initial methodology for doing so.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

alfalfa↗

DOE OSTI2024

A computational analysis of effective R-values of buried ducts – the dynamic performance of buried ducts

Here, this paper evaluates the thermal performance of ducts partially or fully buried in loose-fill attic insulation. The overall thermal resistance between the ducts and the attic is referred to as an effective R-value. This paper shows a strong dependency of assumed attic temperature on the effective R-value. Based on the results, the effective R-value can be about twice as much with an attic temperature of 130 °F [54.4 °C], compared to when the attic temperature is 80 °F [26.7 °C]. Thus, this paper provides a polynomial regression equation based on a large set of simulations to determine the effective R-value of buried ducts depending on attic temperature and whether the HVAC system runs in cooling or heating mode. Further, the work presented in this paper investigated the potential impact of convective airflow within the attic insulation, particularly around the ducts. The analysis was based on computational fluid dynamics (CFD) and indicated that convectional forces are presented around the exterior surface of the ducts, but with negligible impact on the overall heat balance between the duct and the attic space.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

FRAM Version 7.1’s Bias

The Fixed-Energy Response-Function Analysis with Multiple Efficiency (FRAM) code was developed at Los Alamos National Laboratory to measure the gamma-ray spectrometry of the isotopic composition of plutonium, uranium, and other actinides. For FRAM versions 4 and earlier, the reported uncertainties of the results come from the propagation of the statistics in the peak areas only. No systematic error components are included in the reported uncertainties. For FRAM versions 5 and 6, we examined the FRAM analytical results of both the archival plutonium data and the data specifically acquired for the isotopic uncertainty analysis project and found the relationship between the bias and other parameters. We worked out the equations representing the biases of the measured isotopes from each measurement using internal spectral parameters, such as peak resolution and shape, region of analysis, and burnup (for plutonium) or enrichment (for uranium). The resulting biases were included in the reported uncertainties of FRAM v.5 and v.6.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

A Review and Comparison of Different Sensitivity Analysis Techniques in Practice

There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question – How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? – different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desired sensitivity analysis method judiciously, making sure to match the selected approach to the specifics of their problem and to their desired objectives. In this chapter, we provide a practical overview of a collection of widely used, widely available sensitivity analysis methods. We focus on global sensitivity approaches, which seek to characterize how uncertainty in the model output may be allocated to sources of uncertainty in model inputs across the entire input space. Generally, this will require the practitioner to specify a probability distribution over the input space. On the other hand, methods for local sensitivity analysis do not require this specification but they have more limited utility, providing insight into sources of uncertainty associated only with a particular, specified location in the input space. Our hope is that this chapter may serve as a decision-making tool for practitioners, helping to guide the selection of a sensitivity analysis approach that will best fit their needs. To support this goal, we have selected a suite of approaches to cover, which, while not exhaustive, we believe provides a flexible and robust sensitivity analysis toolkit. All methods included are widely used and available in standard software packages.

97 MATHEMATICS AND COMPUTING↗