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DOE OSTI2026

Organic Rankine Cycle Integration and Optimization for High Efficiency CHP Genset Systems (Final Technical Report)

This project successfully advanced the integration of Organic Rankine Cycle (ORC) technology with reciprocating engine–based combined heat and power (CHP) systems to improve electrical efficiency, total CHP efficiency, and grid-responsive operation. Over three budget periods, the work progressed from high-temperature ORC component development and thermodynamic model validation to next-generation system design, working fluid transition, and techno-economic analysis. Key technical accomplishments include development and validation of a thermodynamic model capable of accurately predicting ORC performance across an expanded temperature and pressure envelope; successful identification and validation of low-global-warming-potential (GWP) working fluids—most notably R1233zd(E)—as viable replacements for R245fa; and demonstration of scalable ORC architectures suitable for integration with 1–20 MW class reciprocating engines. These advances enable flexible CHP configurations that can increase electrical output while maintaining high overall utilization of available thermal energy. The project also produced a clean-sheet design for a next-generation ORC system targeting substantially higher power output per unit, supported by detailed component selection, heat exchanger evaluation, and system-level modeling. Techno-economic analyses indicate that ORC-enabled flexible CHP systems can meet or exceed Department of Energy (DOE) efficiency targets while providing value to both facility operators and the electric grid.. Late-stage testing of the largest next-generation ORC prototype identified limitations related to pump net positive suction head (NPSH) requirements and condenser flooding under certain operating conditions. Although these issues constrained full validation of that configuration within the project timeframe, they provided clear and actionable design guidance for future system refinements. Importantly, validated modeling, smaller-scale testing, and working fluid evaluations confirmed the technical viability of the overall approach. In aggregate, this project met its core objectives by establishing validated design tools, de-risking key ORC technologies for CHP applications, and defining a credible pathway toward commercialization of flexible, high-efficiency CHP systems. The results form a strong foundation for continued development and deployment beyond the conclusion of the DOE-funded effort.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2016

Enhancements to the RAVEN code in FY16

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN has currently reached a good level of maturity in terms of deployed state-of-art and advanced capabilities. The main subject of this report is to show the activities that have been recently accomplished: • Implementation of ensemble modeling for time-series, and • initial implementation of model validation for surrogate models, and • advanced visualization capability for topology based data analysis The development of ensemble modeling for time-series has been performed in order to begin tackling the needs of those RISMC applications that need to communicate 1-Dimensional information (e.g. power histories, etc.) among different models. In this document the implementation details and an application example is reported. The second subject of this report is about the initial development of methods, within the RAVEN framework, to assess the validity of the predictive capabilities of surrogate models. Indeed, after the construction of a surrogate tight to a certain physical model, it is crucial to assess the goodness of its representation, in order to be confident with its prediction. In this initial activity, a cross-validation technique has been employed. This report will highlight the implementation details and proof its correct implementation by an application example. The final subject of this report is about the implementation of advanced visualization capability in RAVEN, for interactive data analysis. Indeed, RAVEN offers several post-processing capabilities that can structurally decompose data extracted from experimental results offering both data clustering/partitioning and dimensionality reduction techniques. A disadvantage of the workflow available in RAVEN is that it treats these as black box operations and the user is expected to know specific information about their data including the number of partitions to expect in some cases or the “correct” parameter settings for a particular algorithm. In order to overcome this limitation, it has been added an interactive user interface that can be run in RAVEN to explore different parameter settings on the fly specifically for the topological post-processor and allows the user to explore the data interactively. The design of this user interface is generalizable to other algorithms and can be used as a model to generate more dynamic and user-friendly visualization capabilities within RAVEN.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗

DOE OSTI2026

Comparing ICME simulations with scaled laboratory experiment

In stellar physics and astrophysics, numerical simulations and laboratory experiments are often compared to observational data to support their representation of the real world. However, there is also merit in comparing numerical simulations to properly scaled experiments, especially when the experiment and the simulation are both emulating the solar phenomena. Confirming the credibility of scaled experiments and their scaling with well-validated models is important to expand our knowledge of the associated physical phenomena. This is significant because experiments and simulations can be performed frequently, whereas observations may be limited by location, field of view, and missing data. In this work, we use the Alfvén Wave Solar atmosphere Model, a well-validated magnetohydrodynamic model, to simulate an interplanetary coronal mass ejection (ICME) and compare it to an experiment which provides a scaled analog to a physical ICME. The experiment was performed on the Big Red Ball facility and scaled using dimensionless parameters such as plasma β and magnetosonic Mach number to reproduce the main structure of an ICME. We compare the model-simulated temperature, density, and magnetic field to those from the experiment, as well as the scaling parameters used in the experiment, to those calculated from the simulation. This comparison is performed to further justify the scaling arguments made by the experiment. Additionally, the comparison would lead to the development of stronger scaling arguments for future experiments.

Bryant, K. [University of Michigan, Ann Arbor, MI ↗

DOE OSTI2019

High-Resolution Regional Wave Hindcast for Hawaii

This report summarizes modeling efforts for hindcasting of wave climate within the exclusive economic zone (EEZ) around the state of Hawaii. Specifically, the report provides detailed description of model mesh development, sensitivity analysis of the configuration of model parameters, data used for model forcing and validation, model skill assessment, and model outputs for resource characterization. The unstructured, nested-grid modeling approach incorporates a regional high-resolution SWAN (Simulating WAves Nearshore) model within the global-regional WAVEWATCH III® (WW3) wave model. The models were configured to comply with the International Electrotechnical Commission (IEC) Technical Standards for wave energy resource characterization. The SWAN model domain covers the entire EEZ with a spatial resolution of approximately 300 m in the nearshore region. Two levels of WW3 nested grids, from 0.5 arc-degree at global to 7.5 arc-minute at regional scale, were used to provide boundary conditions that drive the high-resolution nearshore SWAN model. The WW3 and SWAN models were forced by 0.5 arc-degree Climate Forecast System Reanalysis wind and 5 km regional Weather Research and Forecasting wind, respectively. Wave resource and bulk wave parameters were simulated for a 32-year period from 1979 to 2010, and were subsequently validated using wave buoy data within the model domain. Model performance was evaluated using a set of error statistics. Overall, the model shows good skills in reproducing the wave climate in the Hawaii EEZ in both spatial and temporal domains. Comparisons of time series and scatter plots for the six IEC resource parameters and the associated error statistics tables, and horizontal two-dimensional plots of annually averaged and monthly averaged IEC parameters are provided.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Los Alamos National Laboratory High Performance Computing Overview [Slides]

We need such big computers because without testing, we don't understand the health of the AGING stockpile. Our big computers store a wealth of test data. We model weapons as they were tested and see if we can match the results to validate models, and we then use validated models along with dismantlement information on aging to certify the stockpile. Sometimes the results of tests can’t be explained well, so even that part requires massive computations, but applying the model to future use is an astonishing amount of computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Long-Term Experimental Determination of Solubilities of Micro-Crystalline Nd(III) Hydroxide in High Ionic Strength Solutions: Applications to Nuclear Waste Management [A Pitzer Model for Am(III)/Nd(III) hydroxide solubility in NaCl-H 2 O at 298.15 K to high ionic strengths: Experimental validation and model applications]

In this paper, the experimental results from long-term solubility experiments on micro crystalline neodymium hydroxide, Nd(OH) 3 (micro cr), in high ionic strength solutions at 298.15 K under well-constrained conditions are presented. The starting material was synthesized according to a well-established method in the literature. In contrast with the previous studies in which hydrogen ion concentrations in experiments were adjusted with addition of either an acid or a base, the hydrogen ion concentrations in our experiments are controlled by the dissolution of Nd(OH) 3 (micro cr), avoiding the possibility of phase change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

RAAW

High-fidelity wind turbine modeling advances necessitate model validation datasets of equally high fidelity. The Rotor Aerodynamics, Aeroelastics & Wake (RAAW) project was designed to address this need. This three-year project runs from October 2021 through October 2024 starting with a field data collection period followed by model validation activities. RAAW is a partnership between GE Renewable Energy and the United States Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories. The field experiment is designed to make detailed measurements of the inflow, wind turbine response, and the resulting wake. The experimental dataset will be suitable for validating wind turbine models across the fidelity spectrum from actuator disk methods to blade-resolved codes.

17 WIND ENERGY↗

DOE OSTI2020

Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite

This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. Here, to address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.

42 ENGINEERING↗

DOE OSTI2017

Mathematical Calibration of Modular Plastic Scintillators for Various Assay Applications - 17322

Plastic scintillators provide an inexpensive option to assay and characterize radioactive material. Due to the low cost it is generally possible to an array of large numbers of plastic scintillators to achieve high measurement efficiencies. The beta only thin plastic scintillators used in the Argos contamination monitors provide a convenient and reliable scintillator package that can be grouped together to form an assay system. Mathematical calibrations, using MCNP, have been produced for this detector that allow for rapid construction of various custom measurement geometries. The calibrations were constructed by comparing a series of measurements made with a number of beta and beta/gamma emitting nuclides. Characterization of the beta scintillators utilized beta emitting nuclides that included spectra from low average energy, e.g. C-14, to relatively high average energy, e.g. Sr/Y-90. In order to perform the calibration, custom versions of the standard Argos scintillators were produced that provide an MCA output for spectroscopic investigation. The spectra were then compared to detailed MCNP models in an iterative process to energy calibrate and efficiency calibrate each detector type. The validated models and energy calibrations were then tested against a number of volumetric geometries to confirm that the model would translate well to any other geometry. These validated models can then be used to rapidly construct custom measurement geometries for material measurement and screening. Two examples of measurement applications include a beta screening system for dirt on a conveyor belt and a flow through beta liquid assay system. The mathematical calibration process, validation results, and a number of application examples are discussed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Open‐source photovoltaic model pipeline validation against well‐characterized system data

Abstract All freely available plane‐of‐array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib‐python and pvpltools‐python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853‐1 and 61853‐2 testing, and the input data for each model were sourced from these system‐specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system‐specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system‐specific data.

14 SOLAR ENERGY↗

DOE OSTI2017

Experimental Validation of a Spectral-Based Structural Analysis Model Implemented in the Design of the VolturnUS 6MW Floating Offshore Wind Turbine

The following work presents a spectral-based structural analysis method implemented in the design of the VolturnUS 6MW Floating Offshore Wind Turbine hull developed to increase system optimization. To begin, a description of the methodology is provided which details how transfer functions relate the hull's structural response to first order wave kinematics and wind turbine aerodynamics. Following the model definition, focus is placed on model validation using results from an experimental 1:52 scale model test of the VolturnUS system (shown in Fig. 1) conducted at the University of Maine's Alfond W2 Ocean Engineering Laboratory. Using data acquired from load cells installed at various locations in the hull the accuracy of the model is assessed through the use of power spectra and statistics. The performance of the structural model is considered over a range of environmental conditions representing design load cases prescribed by the American Bureau of Shipping for class certification of floating offshore wind turbines.

artificial intelligence, offshore projects plannin↗

DOE OSTI2026

Data for Selective Oxidation of 3-Hydroxypropionic Acid to Malonic Acid over Pd/C: Mechanistic and Kinetic Study

Malonic acid (MA) is a high-value dicarboxylic acid with strong industrial demand, yet its current production heavily relies on petrochemical feedstocks. Here, we report the first systematic study for the sustainable production of MA via oxidation of 3-hydroxypropionic acid (3-HP) with a Pd/Carbon catalyst. The effects of oxidant type (O2 and H2O2), pH, and temperature on the reaction chemistry were comprehensively evaluated. Guided by experimental observation and DFT-calculated thermochemical energetics, reaction networks for 3-HP oxidation with both oxidants are proposed and validated through kinetic modeling. MA was identified as the primary oxidation product, while further oxidation yields acetic acid and oxalic acid. The kinetic model validated the network, displaying excellent agreement (R2 > 0.95). Kinetic observations also enabled a direct comparison between O2 and H2O2 and revealed their distinct behaviors. The model was further developed into a temperature-time map, providing insight into conditions that maximize MA production. Malonic acid selectivity of 56.9 % and yield of 50.5 % were achieved at 3 bar oxygen, equimolar NaOH:3-HP ratio, and 50 °C.

Bioproducts↗

DOE OSTI2026

Data and code from: Selective oxidation of 3-hydroxypropionic acid to malonic acid over Pd/C: Mechanistic and kinetic study

3-Hydroxypropionic Acid↗

DOE OSTI2026

Selective oxidation of 3-hydroxypropionic acid to malonic acid over Pd/C: Mechanistic and kinetic study

Malonic acid (MA) is a high-value dicarboxylic acid with strong industrial demand, yet its current production heavily relies on petrochemical feedstocks. Here, we report the first systematic study for sustainable production of MA via oxidation of 3-hydroxypropionic acid (3-HP) with a Pd/Carbon catalyst. The effects of oxidant type (O 2 and H 2 O 2 ), pH, and temperature on the reaction chemistry were comprehensively evaluated. Guided by experimental observation and DFT-calculated thermochemical energetics, reaction networks for 3-HP oxidation with both oxidants are proposed and validated through kinetic modeling. MA was identified as the primary oxidation product, while further oxidation yields acetic acid and oxalic acid. The kinetic model validated the network, displaying excellent agreement (R 2 > 0.95). Kinetic observations also enabled a direct comparison between O 2 and H 2 O 2 and revealed their distinct behaviors. The model was further developed into a temperature-time map, providing insight into conditions that maximize MA production. Malonic acid selectivity of 56.9 % and yield of 50.5 % were achieved at 3 bar oxygen, equimolar NaOH:3-HP ratio, and 50 °C.

3-hydroxypropionic acid↗

DOE OSTI2021

The role of global agricultural market integration in multiregional economic modeling: Using hindcast experiments to validate an Armington model

Not Available

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Process Model-Based Validation of the Intensification of Biomass Fast Pyrolysis in a Fluidized Bed via Autothermal Operation

A model for the fluidized-bed pyrolysis of biomass is extended to enable the simulation of intensified autothermal operation. In this system, partial oxidation of char and pyrolysate species replaces an external heat source to supply the enthalpy of pyrolysis, greatly increasing the throughput of a given fast pyrolysis reactor. Oxidation reactions are compiled from CRECK and other literature sources and extended to cover the species found in pyrolysis products, including a derivation of the catalytic effect of ash on char combustion from experimental studies at Iowa State University (ISU). Results indicate a roughly 3-fold increase in biomass throughput for a given reactor volume at the pilot scale with minimal loss of valuable products, in agreement with published data from the 3 × 10 -2 m 3 reactor operated by ISU. Additionally, a proposal for a 250 ton/day commercial-scale biomass pyrolysis reactor is analyzed, showing that the same size reactor operated by heating the fluidizing gas externally would have only one-tenth the capacity; if operated by heating the sand externally, the sand would need to circulate at an impractical 0.7 bed volume per minute rate to maintain a 250 ton/day biomass throughput.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations for solvent-based carbon capture. Part 2: Chemical absorption across a wetted wall column: Original Research Article: Hierarchical calibration and validation framework of bench-scale computational fluid dynamics simulations

Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO 2 ) capture. In this study, to generate data for WWC model validation, CO 2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO 2 in MEA. In addition, the overall mass transfer coefficient predicted using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO 2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N 2 O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO 2 reaction rate constants after using the N 2 O/CO 2 analogy method. Finally, the calibrated model can be used to predict the CO 2 mass transfer in a WWC for a wider range of operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗