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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

pnnl/openstudio-building-energy-standard-measures-gem

The OpenStudio Building Energy Standards Measure Repository is a comprehensive collection of innovative OpenStudio measures designed to facilitate energy code analysis. OpenStudio is a cutting-edge platform brings together physics-based building energy modeling (BEM), BEM process automation, and large-scale computing capabilities. Contains OpenStudio Standards Performance Rating Method Measure (IPID 32936) and OpenStudio Measure to Generate IPLV-specific Chiller Performance Curves for Chillers (IPID 32940)

Xu, Weili [PNNL]↗
DOE OSTI2021

Sea Surface Salinity Reemergence in an Updated North Atlantic In Situ Salinity Dataset

Monthly sea surface salinity (SSS) fields are constructed from observations, using objective mapping on a 1° x 1° grid in the Atlantic between 30°S and 50°N in the 1970-2016 period in an update of the dataset of Reverdin et al. Data coverage is heterogeneous, with increased density in 2002 when Argo floats become available, high density along Voluntary Observing Ship lines, and low density south of 10°S. Using lag correlation, the seasonal reemergence of SSS anomalies is investigated between 20° and 50°N in 5° x 5° boxes during the 1993-2016 period, both locally and remotely following the displacements of the deep mixed-layer waters estimated from virtual float trajectories derived from the daily AVISO surface geostrophic currents. Although SSS data are noisy, local SSS reemergence is detected in about half of the boxes, notably in the northeast and southeast, while little reemergence is seen in the central and part of the eastern subtropical gyre. In the same period, sea surface temperature (SST) reemergence is found only slightly more frequently, reflecting the short data duration. However, taking geostrophic advection into account degrades the detection of remote SSS and even SST reemergence. When anomalies are averaged over broader areas, robust evidence of a second and third SSS reemergence peak is found in the northeastern and southeastern parts of the domain, indicating long cold-season persistence of large-scale SSS anomalies, while only a first SST reemergence is seen. Finally, an oceanic reanalysis is used to confirm that the correlation analysis indeed reflects the reemergence of subsurface salinity anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Next Generation Durable, Cost Effective, Energy Efficient Tubular SOFC (Final Scientific/Technical Report)

The overall objective of this project is to develop and optimize a YSZ electrolyte-based solid oxide fuel cell (SOFC) technology for low cost, low temperature (~ 650°C), and high energy efficiency operation. The developed technology will be implemented and demonstrated in a high efficiency 2~3kW SOFC with applicability to sub-MW systems. A summary of significant accomplishments is provided below. Key accomplishments: 1. Improved fundamental cell technology demonstrated at single tube and system scale. Improved power output by 54% while operating at the normal temperature of 750°C. And improved power output by 33% while operating at 650°C, 100°C lower than normal temperature. 2. SPS patented internal recycle arrangement was developed and extended to operate on LPG fuel directly in a compact, high-efficiency (> 40%) system. 3. System testing was conducted to prove the long-term durability of cell improvements. Demonstrated over 8000 hours of operation at 0.21 %/1000 hrs degradation. 4. A large-scale, 2.5kW net power system demonstration was completed, which demonstrated 40% net efficiency over 1000 hours. 5. System design and cost analysis of a 1MW system utilizing a 2.5kW bundle was completed. Cost optimization of the bundle showed a reduction of nearly 80% is possible from $\$$5,790/kW to $\$$1250/kW. This lower cost is considered viable for SPS commercialization.

03 NATURAL GAS↗
DOE OSTI2024

Using Parameter Sweep in WaterTAP to Analyze New Water Treatment Technologies

We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Agent-based Modeling for the Multi-objective Optimization of Energy Production Pathways: Integrated Techno-Economics and Life Cycle Assessment

This project developed an integrated modeling framework to evaluate how purpose-grown bioenergy feedstocks could support large-scale sustainable aviation fuel production in the United States. Led by Colorado State University with project partners, the work combined geographically resolved analysis, techno-economic assessment, life cycle assessment, multi-objective optimization, and agent-based modeling to examine feedstock performance, deployment potential, land-use implications, and adoption dynamics. The overall goal was to identify practical and sustainable pathways for producing aviation fuel from feedstocks such as energy crops, woody crops, oilseeds, and algae while helping inform policy, investment, and research needs tied to national SAF goals. In essence, the project provides a systems-level assessment of the opportunities and constraints associated with scaling bioenergy-based aviation fuels in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Next Breakthroughs in Neutrino Physics (LDRD Final Report)

The neutrino is an important fundamental particle, one of the building blocks of the universe. Abetter understanding of the neutrino will answer questions regarding the origin of mass, the matter and anti-matter asymmetry of the universe, and the nature of dark matter. The nation's research community has recognized that the answers to these and other questions are within reach and has assigned neutrino experiments the highest priority for both nuclear and particle physics programs. We are investigating three aspects of neutrino experimentation: detection techniques, target materials, and data analysis. Our efforts targeted multiple applications including experiments to measure neutrinoless double-beta-decay, neutrino-oscillation and neutrino mass. Our research objectives include (1) developing approaches that make detectors scalable to larger sizes and insensitive to background signals; (2) increasing the signal strength and reducing noise from targets; and (3) efficiently distinguishing background noise from signals during analysis. In this LDRD we have advanced all of these areas. We have demonstrated the scale up of a metal organic framework that can adsorb xenon directly from the air that will allow for large neutrino detectors made from xenon. We have studied the use of Cherenkov radiation to reduce the signal backgrounds. We demonstrated the cracking of hydrogen to make atomic tritium for neutrino mass measurements, and lastly we demonstrated the benefits of machine learning techniques to improve signal to noise during analysis

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Demonstration of non-destructive and isotope-sensitive material analysis using a short-pulsed laser-driven epi-thermal neutron source

Neutrons are a valuable tool for non-destructive material investigation as their interaction cross sections with matter are isotope sensitive and can be used complementary to x-rays. So far, most neutron applications have been limited to large-scale facilities such as nuclear research reactors, spallation sources, and accelerator-driven neutron sources. Here we show the design and optimization of a laser-driven neutron source in the epi-thermal and thermal energy range, which is used for non-invasive material analysis. Neutron resonance spectroscopy, neutron radiography, and neutron resonance imaging with moderated neutrons are demonstrated for investigating samples in terms of isotope composition and thickness. The experimental results encourage applications in non-destructive and isotope-sensitive material analysis and pave the way for compact laser-driven neutron sources with high application potential.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Visualizing the SEI formation between lithium metal and solid-state electrolyte

The solid electrolyte interphase (SEI) is regarded as the most important factor affecting the durability of lithium-metal anode in all-solid-state batteries (ASSBs). Despite its significance, the nucleation and growth mechanism of SEI is not yet well understood. Here, we elucidate the thermodynamics and kinetics governing SEI formation at the Li|β-Li 3 PS 4 interface at the atomic scale via thermodynamic phase equilibrium analysis and machine-learning-potential-assisted molecular dynamics (MD) simulations. An accurate moment tensor potential using the machine-learning method is developed for a reactive model of Li|β-Li 3 PS 4 . This potential enabled us to perform large-scale MD simulations with the model size expanded to the experimental dimensions (~40 nm) while maintaining the same level of accuracy as density functional theory calculations. The results reveal a four-stage evolution process at the Li|β-Li 3 PS 4 interface, namely (i) fast ion diffusion, (ii) nucleation, (iii) Li 2 S growth, and (iv) stabilization. Notably, we demonstrate that the SEI can be categorized into crystalline and amorphous regions. The simulated SEI thickness, structure, and composition closely match experimental findings, validating the accuracy of the MD simulations. We further disclose the significant impact of ion diffusion kinetic limitations on the phase formation and crystallization of interfacial products. Furthermore, we shed light on the detailed potential energy (PE) distribution of lithium along the direction perpendicular to the interface. Finally, this information is crucial for better understanding interfacial ion mobility.

25 ENERGY STORAGE↗
DOE OSTI2026

Neutrino flavor instabilities in neutron star mergers with moment transport: Slow, fast, and collisional modes

Determining where, when, and how neutrino flavor oscillations must be included in large-scale simulations of hot and dense astrophysical environments is an enduring challenge that must be tackled to obtain accurate predictions. Here, using an angular moment-based linear stability analysis framework, we examine the different kinds of flavor instabilities that can take place in the context of the postprocessing of a neutron star merger simulation, with a particular focus on the collisional flavor instability and a careful assessment of several commonly used approximations. First, neglecting anisotropies of the neutrino field, we investigate the extent to which commonly used monoenergetic growth rates reproduce the results obtained from a full multienergy treatment. Contrary to the large discrepancies found in core-collapse supernova environments, we propose a simple combination of energy-averaged estimates that reproduces the multienergy growth rates in our representative simulation snapshot. We then quantify the impact of additional physical effects, including nuclear many-body corrections, scattering opacities, and the inclusion of the vacuum term in the neutrino Hamiltonian. Finally, we include the neutrino distribution anisotropies, which allows us to explore, for the first time in a multienergy setting, the interplay between collisional, fast, and slow modes in a moment-based neutron star merger simulation. We find that, despite a dominance of the fast instability in most of the simulation volume, certain regions exhibit only a collisional instability, while others, especially at large distances, exhibit a slow instability that is largely underestimated if anisotropic effects are neglected.

neutrino oscillations↗
DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗
DOE OSTI2021

A Big Problem for Small Earthquakes: Benchmarking Routine Magnitudes and Conversion Relationships with Coda Envelope-Derived Mw in Southern Kansas and Northern Oklahoma

Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (M w ) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally M w <4) earthquakes has meant that magnitudes for these events remain plagued by a patchwork of inconsistent measurement scales. Because of this, magnitudes of small earthquakes and statistics derived from them can be biased. Furthermore, because small earthquakes are much more numerous than large ones, many applications, such as seismic hazard modeling, depend critically on analysis of events characterized by magnitudes other than M w . Therefore, to assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from northern Oklahoma and southern Kansas. Not surprisingly, we find significant differences among M L , m bLg , and M w for M ~2–4 earthquakes examined here. More troublingly, we find that relations designed to convert other magnitudes to M w , which are relied upon for important applications such as seismic hazard analysis, often increase rather than decrease this bias for our dataset. In our case study, we find that converted magnitudes can result in a systematic bias sometimes exceeding 0.5 magnitude units, a difference that typically corresponds to a factor of ~3 in seismicity rate. Moreover, we find a correspondingly large bias in Gutenberg–Richter b-values, controlled primarily by inaccurate magnitude scaling in the conversion relationships. Although this study focuses on a relatively small geographic area, we can expect that similar issues exist with varying severity in other regions. Therefore, magnitudes of small earthquakes and their associated statistics, including seismicity rates and b-values, should be treated with caution.

58 GEOSCIENCES↗
DOE OSTI2020

Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases

Magnetotactic bacteria (MTB) are prokaryotes that possess genes for the synthesis of membrane-bounded crystals of magnetite or greigite, called magnetosomes. Despite over half a century of studying MTB, only about 60 genomes have been sequenced. Most belong to Proteobacteria, with a minority affiliated with the Nitrospirae, Omnitrophica, Planctomycetes, and Latescibacteria. Due to the scanty information available regarding MTB phylogenetic diversity, little is known about their ecology, evolution and about the magnetosome biomineralization process. This study presents a large-scale search of magnetosome biomineralization genes and reveals 38 new MTB genomes. Several of these genomes were detected in the phyla Elusimicrobia, Candidatus Hydrogenedentes, and Nitrospinae, where magnetotactic representatives have not previously been reported. Analysis of the obtained putative magnetosome biomineralization genes revealed a monophyletic origin capable of putative greigite magnetosome synthesis. The ecological distributions of the reconstructed MTB genomes were also analyzed and several patterns were identified. These data suggest that open databases are an excellent source for obtaining new information of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2022

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this paper, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrated protonic ceramic electrochemical cell for sustainable energy economy using water-energy nexus framework

Reliance on fossil fuels will continue for the next decades even though there are global pushes away from it to mitigate the overarching climate challenge, most especially by its highest consumers and availability. While there is a hastening global shift away from fossil fuel, integrating its assets into this technology helps limit the risk and future losses of stranded assets and reduce the cost of investment in the new technologies. Moreover, the generation of electricity from intermittent renewable sources like solar and wind has witnessed a significant surge in recent years, leading to a pressing demand for practical energy storage systems. Electrical energy storage is anticipated to play a pivotal role in the future global energy system, facilitating load-leveling operations to support the greater integration of renewable and distributed generation. Reversible electrochemical cells (RECs) offer a promising option for addressing the fossil fuel assets integration and energy storage challenges through the interconversion between electrical and chemical energy and concurrent utilizing carbon emission. In their electrolysis mode, the RECs convert electricity into durable, storable, and portable valuable chemical fuels such as syngas and methane. Conversely, the produced chemical fuels can be used as reactants in the fuel cell mode to generate electricity on demand with minimal (hydrocarbons) or zero when H2 or NH3 is used emissions. However, a challenging goal for this type of technology remains to achieve optimal operation and high roundtrip efficiencies, which has hindered the deployment of previous electrochemical cells. This dissertation demonstrates how reversible protonic ceramic electrochemical cells (RePCECs) can be integrated with fossil fuel power plants and renewable energy sources as a potential energy storage system. In this work, integrated RePCEC systems are designed and examined using computational modeling at scales to determine appropriate system configurations and operating conditions that achieve high roundtrip efficiencies. Cell level design of the PCEC is the first approach, several cells are assembled for the stack level model that is integrated into combined cycle powerplant and solar photovoltaic for the system level model. After critical literature review, this answered the operational and integration research questions proposed to address these challenges. The designed systems perform two functions, utilizing captured CO2 and storing renewable energy through co-electrolysis of steam and CO2. The co-electrolysis reaction involves endothermic water electrolysis and exothermic methanation reaction. To enhance high roundtrip efficiency, there is a need for thermal balance and management in the electrolysis mode. This involves operating the RePCEC stack under conditions that favor methane production to balance out heat needed by water electrolysis, it crucial for the RePCEC system operation. Methanation is enhanced by low temperatures. Leveraging on fabricated BCZYYb-electrolyte RePCEC, the cell model designed revealed that the optimum temperature for methane production is 450℃ at atmospheric pressure. Thus, to achieve optimum system performance, operating in the temperature range 450-525℃ is recommended at the given configuration, combining between the optimum temperature for methane production and temperature for the optimum stack roundtrip efficiency. Configuration with carbon capture system and purge stream is the optimum configuration from the seven conceptualized and evaluated. The modeling outcomes include a thermodynamic examination of integrated RePCEC systems, calibration of cell and stack level models, and steady-state simulation and integration into a 600MW combined cycle power plant retrofitted with two two-stage membrane-based carbon capture system and a wastewater treatment and recovery unit. At 100% powerplant loading, the stack and system roundtrip efficiencies are 72% and 51.37% respectively. Adding a purge stream for produced hydrogen at the system downstream improves the efficiencies to 74 and 55.48% respectively. At atmospheric pressure and 525℃, the system model suggests that a stack roundtrip of 82% is achievable, and overall system efficiency increases by reducing the energy consumption by the balance of plant components for steam generation and storage. Economic analysis of the process gives levelized cost of methane as $2.24/MMBtu lower than the conventional production route that range between $3.46/MMBtu and $9.85/MMBtu. The lifecycle analysis shows that the global warming potential for the production of methane and hydrogen from the RePCEC system is 3.83 kg CO2 eq which is lower than 9.35 kg CO2 eq emission during steam methane reforming for hydrogen production. This answered both the environmental and economic concerns in the raised research question. The proposed RePCEC configuration and analysis carried out in this dissertation to address the surge in renewable energy and challenges with PCEC technology hold significant potential in achieving large-scale energy storage while simultaneously reducing carbon emissions. These advancements, coupled with suitable governmental policies and incentive programs, have the potential to economically disrupt the natural gas industries by using RePCEC systems for methane production, thereby making them more favorable for eventual implementation and commercialization.

25 ENERGY STORAGE↗
DOE OSTI2020

Quantum Versus Classical Switching Dynamics of Driven Dissipative Kerr Resonators

Here, we report a first-principles study of the driven dissipative dynamics for Kerr oscillators in the mesoscopic regime. This regime is characterized by large Kerr nonlinearity, realized here using the nonlinear kinetic inductance of a large array of Josephson junctions. The experimentally measured nonlinear resonance lineshapes of the junction array modes show significant deviations from steady-state numerical predictions, and necessitate time-dependent numerical simulations indicative of strong measurement-induced dephasing in the system arising from the large cross-Kerr effect between array modes. Analytical and numerical calculations of switching rate corroborate this by showing the emergence of a slow time scale, which is much longer than the linear decay rate and is set by fluctuation-induced switching times in the bistable regime. Furthermore, our analysis shows that the usual quantum-activated escape treatment is inadequate for prediction of the switching rates at large frequency shifts caused by strong nonlinearities, necessitating a quantum treatment that utilizes the full system Liouvillian. Based on our analysis, we identify a universal crossover parameter that delineates the regimes of validity of semiclassical and quantum descriptions, respectively. Our work shows how dynamical switching effects in strongly nonlinear systems provide a platform to study quantum-to-classical transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗