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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 OSTI2021

Factors controlling pore network development of thermally mature Early Palaeozoic mudstones from the Baltic Basin (N Poland)

Understanding the formation of pore space, especially in low porosity shales (as source rocks and as unconventional resources), is critical to the oil and gas industry, since pores control the space available for hydrocarbon and participate in hydrocarbon transport. We examined 87 Ordovician and Silurian mudstone samples collected from four wells located in the Pomeranian part of the Baltic Basin (northern Poland), one of the primary Polish targets for hydrocarbon exploration. These samples represent the Pelplin, the Paslek, the Jantar, the Prabuty, and the Sasino Formations, which still requires more detailed porosity studies. Our study aimed to identify factors controlling porosity development, by applying bulk techniques (organic petrology and TOC analyses, quantitative mineralogy, and porosimetry) as well as nano- to microscale techniques (thin section petrography, electron microscopy). The studied samples are mainly argillaceous mudstones. The results of porosimetry measurements, combined with image analysis, indicate that the pores of all studied rocks are dominated by micropores (pores < 2 nm in diameter), mesopores (2-50 nm in diameter) and small macropores. The SEM images showed three main pore types: a) voids related to clay mineral aggregates, b) pores inside organic matter particles, and c) pores between other mineral grains. In the Jantar and Sasino mudstones, the organic matter content and its thermal maturity control porosity. The occurrence of solid bitumen in the rocks from these formations reduces samples’ mesoporosity because of the pore-clogging effect. In contrast, in the Paslek and Prabuty Formations, there is low organic matter content and specific surface area and the volume of mesopores increase with clay minerals content. In the Pelplin mudstones, there are no prevailing factors controlling porosity. As a result, we suggest that a combination of SEM image analysis and dual liquid porosity (DLP) measurements is a powerful method to assess porosity available for petroleum flow in mudstones.

03 NATURAL GAS↗
DOE OSTI2018

Assessing Constitutive Models for Prediction of High-Temperature Flow Behavior with a Perspective of Alloy Development

The utility of different constitutive models describing high-temperature flow behavior has been evaluated from the perspective of alloy development. Strain compensated Arrhenius model, modified Johnson–Cook (MJC) model, model D8A and artificial neural network (ANN) have been used to describe flow behavior of different model alloys. These alloys are four grades of SS 316LN with different nitrogen contents ranging from 0.07 to 0.22%. Grades with 0.07%N and 0.22%N have been used to determine suitable material constants of the constitutive equations and also to train the ANN model. While the ANN model has been developed with chemical composition as a direct input, the MJC and D8A models have been amended to incorporate the effect of nitrogen content on flow behavior. The prediction capabilities of all models have been validated using the experimental data obtained from grades containing 0.11%N and 0.14%N. The comparative analysis demonstrates that ‘N-amended D8A’ and ‘N-amended MJC’ are preferable to the ANN model for predicting flow behavior of different grades of 316LN. The work provides detailed insights into the usual statistical error analysis technique and frames five additional criteria which must be considered when a model is analyzed from the perspective of alloy development.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

Here in this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly nonintrusive since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate and can be used as a robust predictive tool for nonintrusive model order reduction of complex fluid flows.

42 ENGINEERING↗
DOE OSTI2024

Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids

The increasing deployment of distributed energy resources (DERs) and microgrids (MGs) in power distribution systems has enabled the adjustment of reactive power consumption as seen at the substation, which can be used to provide voltage support for the bulk power system (BPS). Leveraging this new capability will provide greater resiliency to the power system as a whole. Here, the goal of this paper is to develop and compare three different algorithms, namely distributed optimal power flow, distributed consensus algorithm, and fully decentralized collaborative autonomy for unbalanced distribution systems for microgrid coordination. These algorithms use networked MGs to support the BPS voltage when a contingency at the bulk grid results in abnormally low voltages, which may be a precursor to voltage collapse. Our comparative analysis includes both qualitative and quantitative assessments of the three algorithms and a discussion of the trade-offs between the decentralized and distributed methods in normal and disrupted conditions. Each algorithm was evaluated on the modified IEEE 13-bus system and a real power distribution system at Chattanooga, Tennessee, that encompasses more than 4500 buses. Each algorithms excels differently and may be suited for different scenarios depending on the condition, operations, and priorities of the power and communication systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1976

A higher order panel method for general analysis and design applications in subsonic flow

A higher-order panel method is described for numerical solution of boundary-value problems relating to steady inviscid irrotational incompressible subsonic fluid flow in a domain. Both Neumann and Dirichlet boundary conditions are treated; two types of auxiliary conditions are used to remove the degrees of freedom that arise from specifying only the derivative of the perturbation velocity potential. Four general network types and two expansions of the induced potential kernel are employed in the numerical solution. Some results are presented which illustrate the modeling options and numerical characteristics of the method.

Johnson, F. T.↗
DOE OSTI2021

The role of gas flow distributions on CO 2 mineralization within monolithic cemented composites: coupled CFD-factorial design approach

The carbonation kinetics of monolithic cementing composites are strongly affected by gas transport which is, in turn, influenced by microstructural resistances and the presence of liquid water within pore networks. The non-uniform gas flow distribution within the CO 2 mineralization reactor can impart mass transfer resistance in the monolith microstructure, which affects the uptake of CO 2 (“carbonation”) of the cementing composites. This paper demonstrates how the gas spatial distribution (velocity and flow rate; quantified by CFD analysis) and processing conditions (temperature, relative humidity, and flow rate; quantified by factorial design) affect drying and carbonation, and in turn, the engineering properties of a representative ‘monolithic’ carbonate-cemented concrete component (i.e., herein concrete masonry unit: CMUs, also known as concrete block). It is shown that the gas flow distribution affects drying front penetration and results in moisture and carbonation gradients within the monolith. Particularly, variations in drying kinetics caused by non-uniformity of the contacting gas velocity impose gradients in moisture saturation, which results in increasing microstructural resistance to CO 2 transport. The resultant non-uniform carbonate-mineral formation (i.e., carbonate cementation), if not controlled, can produce gradients in mechanical properties and may alter failure patterns upon loading. Finally, these insights inform the optimal design of gas flow distribution systems and processing conditions within CO 2 mineralization reactors for the manufacturing of low-CO 2 concrete components using CO 2 -dilute industrial flue gas streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Predicting departure from nucleate boiling with advanced data- and physics-driven approaches

The subcooled and low-quality saturated flow boiling critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) is one of the major limiting factors in the design and operation of pressurized water reactors (PWRs). Due to lack of agreement on its physical mechanisms, development of an accurate DNB-type CHF model has been elusive. The various steady-state (SS) predictive tools that have been proposed can be grouped into two categories: (1) data-driven methods—namely best-fit empirical correlations and look-up tables that result in relatively good agreement with specific experimental datasets but often fail to extend beyond their ranges of validity, and (2) the physics-based mechanistic models that rely on reasonable yet limited understanding of the underlying physics and apply constitutive relations to close the conservation equations. On one hand, with recent advances in computational capabilities and optimization techniques, machine learning (ML)-based methods constitute an alternative and a more advanced, data-driven approach. With enough data, they can be particularly useful in engineering fields where the physical phenomena are complex and challenging to model. On the other hand, a state-of-the-art physics-driven approach would not only provide more accurate prediction of SS CHF, but it would also help better understand and model transient applications. The data-driven regression models use deep feed-forward neural networks (NNs) and random forests to cross-validate with 1,865 CHF test cases, covering a wide range of flow conditions and channel geometries. The best-estimate ML-based predictors compare favorably with the widely used look-up table for annulus and plate, and sensitivity analysis has confirmed their effectiveness. The key advantage of ML-based methods is their online extensibility of applicability domain. The proposed physics-driven approach combines key assumptions and parameters in the relatively well-accepted theories of liquid sublayer dryout and near-wall bubble crowding. A more realistic understanding of local mechanisms has been modeled. The new model has been optimized and validated against 1,439 tube data, and it shows considerably improved performance when compared to a recent mechanistic model, demonstrating unbiased close agreement with measurements over a wide range of operating conditions. Future work will extend the improved physics-driven model to non-tube geometry applications and will couple the model with ML via a hybrid approach. The mechanistic SS work will also serve to improve understanding and to model transient CHF scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2016

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗
DOE OSTI2016

The Medical Science DMZ

Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbpsþ) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational and numerical analysis of AC optimal power flow formulations on large-scale power grids

We report that alternating current optimal power flow (AC-OPF) is a fundamental tool in electric utilities to determine optimal operation of the various resources. Typically, the AC-OPF problem uses power balance formulation containing voltages and power equations. Yet, there is no comprehensive comparison of the different AC-OPF formulations, especially for large-scale networks. This paper presents a detailed comparative evaluation of different formulations of the AC-OPF problem on networks ranging from 9-bus to 25,000 buses. Three different formulations: 1) power balance with polar voltages, 2) power balance with Cartesian voltages, and 3) current balance with Cartesian voltages are discussed in detail by comparing their characteristics, and numerical and computational performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data

The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.

Distribution network↗
DOE OSTI2022

Design of an Additively Manufactured Recuperator With 800 °C Inlet Temperature for sCO2 Power Cycle Application

Abstract Recuperators with design temperatures at and above 800 °C can further increase the thermal efficiency of supercritical CO2 power cycles by enabling higher turbine exhaust temperatures. Mar-M247 is a well-suited nickel-based superalloy for high temperature service due to its high creep strength that prevents excessive material thickness being required for pressure containment. Additive manufacturing using a high-speed laser-directed energy deposition (L-DED) process presents a promising solution, with build trials demonstrating the ability to produce nonconventional flow channels for enhanced heat transfer. A design process is presented that includes aerothermal and mechanical evaluation to maximize performance within the constraints of the manufacturing process. A 2-D heat transfer network and pressure drop code allows prediction of flow distribution and its effect on overall thermal performance. Established literature correlations, along with CFD simulation, inform the prediction of heat transfer coefficients and friction factors for the flowpaths and enhancement features in the heat exchanger core. Mechanical evaluation using finite element analysis (FEA) modeling with the intent of the ASME Boiler & Pressure Vessel Code (BPVC) Section VIII, Div. 2 assesses the operational safety of the design. The detailed design features annular finned passages that take advantage of helical flow paths to distribute the flow from separated headers to shared heat transfer surfaces. Performance predictions for the recuperator at a 50 kW scale provide insights into the feasibility of the additively manufactured (AM) process to produce recuperators on a commercial scale that extend existing operating envelopes.

Engineering↗
NASA NTRS1999

Near-Surface Flow Fields Deduced Using Correlation Tracking and Time-Distance Analysis

Near-photospheric flow fields on the Sun are deduced using two independent methods applied to the same time series of velocity images observed by SOI-MDI on SOHO. Differences in travel times between f modes entering and leaving each pixel measured using time-distance helioseismology are used to determine sites of supergranular outflows. Alternatively, correlation tracking analysis of mesogranular scales of motion applied to the same time series is used to deduce the near-surface flow field. These two approaches provide the means to assess the patterns and evolution of horizontal flows on supergranular scales even near disk center, which is not feasible with direct line-of-sight Doppler measurements. We find that the locations of the supergranular outflows seen in flow fields generated from correlation tracking coincide well with the locations of the outflows determined from the time-distance analysis, with a mean correlation coefficient after smoothing of bar-r(sub s) = 0.840. Near-surface velocity field measurements can used to study the evolution of the supergranular network, as merging and splitting events are observed to occur in these images. The data consist of one 2048-minute time series of high-resolution (0.6" pixels) line-of-sight velocity images taken by MDI on 1997 January 16-18 at a cadence of one minute.

DeRosa, Marc↗
DOE OSTI2025

Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for onset of thermal barrier leakage in reactor coolant pumps. It incorporates uncertainty quantification to enhance the reliability and robustness of pre- dictions. Using synthetic data generated by the Generic Pressurized Water Reactor simulator, realistic degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using machine learning framework. The fully connected neural network models demonstrated exceptional performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10-2 gallon per minute (gpm) for the three stages of the lifecy- cle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds during steady-state operations and appropriately wider bounds during transitional phases, reflecting the physical behavior of the system. This work addresses important gaps in real-time condition moni- toring and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Status Report on the Water-Based NSTF RCCS Capability: Preparations and Design for the Transformation of the Natural Convection Shutdown Heat Removal Test Facility (NSTF) From Air to Water-based Cooling

The following report provides an overview of the current status of the water-based transformation, including details of the design, geometry, and fabrication of the test section and network piping, for the ½ scale water-based NSTF program at Argonne National Laboratory. In early FY2015, efforts towards a water-based design began in parallel with on-going air-based work, and began by identifying top level objectives of the transformation. Scaling studies and preparation tasks with computational modeling were carried out to guide the design decisions, minimize distortions, and ensure relevant data among scales. This entailed close collaboration with AREVA, whose water-based RCCS (included as part of their 625 MWt SC-HTGR) served as the primary design basis for incorporation into the NSTF. A literature review of previous works was made, with an emphasis placed on similar facilities, relevance to passive decay heat removal, and details pertaining to two-phase flow and measurement techniques. The study focused on input from key designers and a thermal analysis of existing cooling panels (e.g. at TAMU and UW-Madison) and two proposed AREVA cooling panels. A base case was used as a reference point for parametric studies of effects from varying tube spacing (pitch), tube diameter, fin thickness, and materials. Then, a structural analysis was performed to ensure safe material stresses during high temperature operation. Details of the pipe network geometry and guidelines for design of the water storage tank are finally presented, followed by engineering drawings in the appendix. The results of this study have identified a suitable configuration to support both the ANL/DOE project goals and the DOE vision to provide AREVA with data suitable for characterizing the RCCS of their full scale HTGR design. The dimensions were not selected for optimal RCCS performance, but instead to serve as a representative yet bounding configuration for future implementation into a full scale design. Scaling distortions are unavoidable; however, they can be accurately predicted based on earlier works of derived similarity solutions. Flexibility remained a primary design philosophy, and will allow the test facility to easily accommodate future alterations. The final design for the test section and network piping is as follows: riser tubes: 1.5” Schedule 160, 5.91” (150-mm) pitch, 304L stainless, heat transfer panels: 5/16” plates, 4.01” (102-mm) width, full penetration weld to risers, 304L stainless, split into 4 panels per riser separated by horizontal 1/8” gaps, test section: Eight (x8) riser tubes and nine (x9) heat transfer panels, fabricated into banks of two (x2) riser tubes and three (x3) fins, joined to form single section, and network geometry: 4.0” Schedule 40, 304L stainless. Details of the water storage tank will reflect competing effects such as thermal hydraulic phenomena during single and two-phase operation. These considerations include mixing during single-phase jet penetration, condensation of rising bubbles during two-phase discharge, bubble entrainment into the liquid outlet, and liquid carry-over into the steam outlet.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2016

The Freight Analysis Framework Verson 4 (FAF4) - Building the FAF4 Regional Database: Data Sources and Estimation Methodologies

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns the flows to the transportation network, and projects freight flow patterns into the future. The FAF4 is the fourth database of its kind, FAF1 provided estimates for truck, rail, and water tonnage for calendar year 1998, FAF2 provided a more complete picture based on the 2002 Commodity Flow Survey (CFS) and FAF3 made further improvements building on the 2007 CFS. Since the first FAF effort, a number of changes in both data sources and products have taken place. The FAF4 flow matrix described in this report is used as the base-year data to forecast future freight activities, projecting shipment weights and values from year 2020 to 2045 in five-year intervals. It also provides the basis for annual estimates to the FAF4 flow matrix, aiming to provide users with the timeliest data. Furthermore, FAF4 truck freight is routed on the national highway network to produce the FAF4 network database and flow assignments for truck. This report details the data sources and methodologies applied to develop the base year 2012 FAF4 database. An overview of the FAF4 components is briefly discussed in Section 2. Effects on FAF4 from the changes in the 2012 CFS are highlighted in Section 3. Section 4 provides a general discussion on the process used in filling data gaps within the domestic CFS matrix, specifically on the estimation of CFS suppressed/unpublished cells. Over a dozen CFS OOS components of FAF4 are then addressed in Section 5 through Section 11 of this report. This includes discussions of farm-based agricultural shipments in Section 5, shipments from fishery and logging sectors in Section 6. Shipments of municipal solid wastes and debris from construction and demolition activities are covered in Section 7. Movements involving OOS industry sectors on Retail, Services, and Household/Business Moves are addressed in Section 8. Flows of OOS commodity on crude petroleum and natural gas are presented in Sections 9 and 10, respectively. Discussions regarding shipments of foreign trade, including trade with Canada/Mexico, international airfreight, and waterborne foreign trade, are then discussed in Section 11. Several appendices are also provided at the end of this report to offer additional information.

97 MATHEMATICS AND COMPUTING↗