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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

spammR: an R package designed for analysis and integration of spatial multi-omic measurements

Spatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance profiles for multiple different omics types and associated microscopy imaging data, as well as their analysis in a spatial context. However, most analytical tools are designed for spatial transcriptomics platforms and are challenging to use in other contexts such as mass spectrometry-based measurements or metagenomics. To this end we present spammR (spatial analysis of multi-omics measurements in R), an R package that enables end-to-end analysis with a specific focus on mass-spectrometry derived spatial omics datasets with (1) smaller sample sizes and spatial sparsity of samples, (2) considerable missingness, and (3) no a-priori knowledge about proteins or genes of interest, relying on a fully data-driven approach.

spammR↗
DOE OSTI2025

Can Restoring Tidal Wetlands Reduce Estuarine Nuisance Flooding of Coasts Under Future Sea‐Level Rise?

Wetland restoration is an increasingly popular nature‐based method for flood risk mitigation in coastal communities. In this study, we present a novel method using hydrodynamic modeling and harmonic analysis to quantify wetlands' ability to reduce future nuisance flooding. The method leverages a hydrodynamic model calibrated to present day data and was run for a range of future sea‐level rise (SLR) and wetland restoration scenarios to quantify changes to tidal harmonic amplitudes and phases. The harmonic constituents are used to generate water surface elevations over a time period of interest (e.g., one year) and compared to critical exceedance thresholds such as levee elevations. Then, changes to nuisance flooding are calculated by counting the number of hours critical thresholds are exceeded under different SLR and wetland restoration scenarios. We applied the method to Coos Bay, Oregon, USA as a test case. We found restoration reduces the number of hours nuisance flooding occurs in downtown Coos Bay from 15 hr (present day conditions) to 0 hr (fully restored condition) under median SLR (82 cm by 2100). Restoration had spatially variable impacts on reducing peak flood elevations with minimal impacts near the estuary mouth and greatest impact 32 km inland. The effectiveness of restoration was heavily dependent on future SLR. Restoration was maximally effective in 2050 under all SLR scenarios, less effective in 2100 under median SLR, and not effective under high SLR. Modeling results suggest increased tidal prism and accommodation space are driving restoration‐associated reductions in tidal amplitudes.

Brand, Matthew W. [Louisiana State Univ., Baton Ro↗
DOE OSTI2024

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Frequentist cosmological constraints from full-shape clustering measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.

Bayesian reasoning↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗
DOE OSTI2026

Cation Data for the East River Watershed, Colorado (2014-2025)

This data package contains mean values for cation concentration for water samples taken from the East River Watershed in Colorado. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of elements of interest simultaneously for the East River Watershed, Colorado groundwater and surface water samples to inform insights on the biogeochemistry processes within the watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. For samples collected prior to 06-16-2021, the instrumentation, Elan DRC II, PerkinElmer SCIEX, automatically switches among the three models necessary to analyze all 37 elements. These 37 elements include: (1) Lithium (Li), Beryllium (Be), Boron (B), Sodium (Na), Magnesium (Mg), Aluminium (Al), Silicon (Si), Phosphorus (P), Titanium (Ti), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Caesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th), Uranium (U) using standard model, argon Ar as reaction gas, (2) Potassium (K), Calcium (Ca), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe) using dynamic reaction cell (DRC) model, ammonia NH3 as reaction gas, and (3) Phosphorus (P) and Selenium (Se) using DRC model, oxygen O2 as reaction gas. Note for the samples with higher concentrations of chloride (Cl-), asenic (As) concentrations were analysed with DRC model (oxygen O2 as reaction gas) to avoid the interference of chloride. For samples collected on and after 06-16-2021, an advanced Agilent 8900 triple quadrupole inductively coupled plasma mass spectrometry system (Agilent 8900 QQQ ICP-MS, Agilent Technologies) has been used to measure the concentrations of interested 36 elements simultaneously for environmental samples, including (1) Lithium (Li), Beryllium (Be) and Boron (B) using standard no gas mode, (2) Sodium (Na), Magnesium (Mg), Aluminium (Al) Phosphorus (P), Potassium (K), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Cesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th) and Uranium (U) using standard helium (He) collision mode, (3) Titanium (Ti) and Vanadium (V) using high Energy (HEHe) helium (He) collision mode, and (4) Silicon (Si), Calcium (Ca) and Selenium (Se) using standard H2 reaction mode. All samples were prepared/diluted with 2% (v/v) ultrapure nitric acid in Milli-Q water (18.2 mega ohm-cm), and analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (cation_data_2014_2025.zip) containing a total of 5,849 files: 5.848 data files of cation data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v6_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v6_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (4) PDF and docx files for the detemination of Method Detection Limits (MDLs) for ICP-MS PerkinElmer DRC II instrumentation (Detemination_of_Method_Detection_Limits__MDLs__for_ICP_MS__PerkinElmer_Elan_DRC_II__LBL_Bldg74_Lab214D) for samples before November 2021; (5) PDF and docx files for the determination of MDLs for ICP-MS Agilent 8900 QQQ instrumentation (ICP_MS_Analysis_detection_limits_and_QA_QC_WenmingDong_updated_2026-08-06) for samples November 2021 and onward. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 113 locations containing cation data. Update on 2021-04-11: Added Detemination of Method Detection Limits (MDLs) for ICP-MS document, which can be accessed as a PDF or with Microsoft Word. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated cation data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) removed suffix and prefix on two variables (“aqberylliumion_asberyllium” and “aqlithiumion_aslithium”), (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-10-16. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Updated versions of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-06, of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The anomaly of the CMB power with the latest Planck data

Abstract The lack of power anomaly is an unexpected feature observed at large angular scales in the maps of Cosmic Microwave Background (CMB) produced by the COBE, WMAP andPlancksatellites. This signature, which consists in a missing of power with respect to that predicted by the ΛCDM model, might hint at a new cosmological phase before the standard inflationary era.The main point of this paper is taking into account the latestPlanckpolarisation data to investigate how the CMB polarisation improves the understanding of this feature. With this aim, we apply to the latestPlanckdata, both PR3 (2018) and PR4 (2020) releases, a new class of estimators capable of evaluating this anomaly by considering temperature and polarisation data both separately and in a jointly way. This is the first time that the PR4 dataset has been used to study this anomaly. To critically evaluate this feature, taking into account the residuals of known systematic effects present in thePlanckdatasets, we analyse the cleaned CMB maps using different combinations of sky masks, harmonic range and binning on the CMB multipoles.Our analysis shows that the estimator based only on temperature data confirms the presence of a lack of power with a lower-tail-probability (LTP), depending on the component separation method, ≤ 0.33% and ≤ 1.76% for PR3 and PR4, respectively. To our knowledge, the LTP≤ 0.33% for the PR3 dataset is the lowest one present in the literature obtained fromPlanck2018 data, considering thePlanckconfidence mask. We find significant differences between these two datasets when polarisation is taken into account most likely due to a different level of systematics. Especially, the analysis with PR3 data, unlike that with PR4, seems to point towards a lack of power at large scales also for polarisation.Moreover, we also show that for the PR3 dataset the inclusion of the subdominant polarisation information provides estimates that are less likely accepted in a ΛCDM cosmological model than the only-temperature analysis over the entire harmonic-range considered. In particular, at ℓ max = 26, we found that no simulation has a value as low as the data for all the pipelines.

Astronomy & Astrophysics↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2024

Identifying Topological Defects in Lamellar Phases through Contour Analysis of Complex Wave Fields

Lamellar phases frequently contain structural imperfections that significantly affect their behaviors and properties. Our previous research successfully reconstructed real-space configurations of defective lamellar phases from diffuse scattering patterns, indicating the presence of phase vortices as a potential method for identifying topological defects disrupting the smectic ordering. Here, this report presents a mathematical framework using regularized wave fields to represent defective lamellar structures in real space. Phase singularities, resulting from the interference of random waves and indicating lamellar order disruption, are identified through a contour integral. These wave fields, derived from coherent scattering in reciprocal space, were validated via computational benchmarks analyzing small-angle neutron scattering data from AOT surfactant solutions, facilitating further statistical analysis of the defects. Our study highlights the potential to extract meaningful information about topological defects in lyotropic phases by inversely analyzing experimentally measured two-point static correlations. Our method allows for detailed structural analysis of various lyotropic phases, both particulate and nonparticulate, in their quiescent states and facilitates quantitative investigation of defects’ role in phase transitions. By integrating small-angle scattering, deep learning, and vortex tangle analysis, our comprehensive approach shows promise in addressing complex challenges in the structural analysis of soft matter systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning aided line intensity ratio method for helium–hydrogen mixed recombining plasmas

The helium line intensity ratio (LIR) with the help of a collisional radiative (CR) model has long been used to measure the electron density, n e , and temperature, T e , and its potential and limitations for fusion applications have been discussed. However, it has been reported that the CR model approach leads to deviations in helium–hydrogen mixed plasmas and/or recombining plasmas. In this study, a machine learning (ML) aided LIR method is used to measure n e and T e from spectroscopic data of helium–hydrogen mixed recombining plasmas in the divertor simulator Magnum-PSI. To analyze mixed plasmas, which have more complex spectral shapes, the spectroscopy data were used directly for training instead of separating the intensities of each line. Finally, it is shown that the ML approach can provide a robust and simpler analysis method to deduce n e and T e from the visible emissions in helium–hydrogen mixed plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2025

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Chemical Physics (physics.chem-ph)↗
DOE OSTI2025

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2024

Normalizing flows for domain adaptation when identifying Λ hyperon events

Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗
DOE OSTI2024

Open‐Source Anaerobic Digestion Modeling Platform, Anaerobic Digestion Model No. 1 Fast (ADM1F)

An open‐source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open‐source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady‐state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two‐phase co‐digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.

anaerobic co-digestion↗