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DOE OSTI2026

Mechanistic Insights into Dinitrogen Reduction to Ammonia in Light-Controlled Nanocrystal:Nitrogenase Complexes

Developing systems that can efficiently capture photon energy and convert this energy into fuels and chemicals requires understanding how to assemble molecular components with diverse functions into complete systems possessing selectivity and efficiency in directing charge carriers to catalytic reactions. There are many challenges to achieving this goal. One promising approach is the development of hybrid systems that combine semiconductor nanocrystals (NCs) for light capture and enzymes as efficient catalysts. Such biohybrid systems capitalize on the tunable electronic and optical properties of NCs while leveraging the unmatched specificity and efficiency of enzymes in catalyzing chemical reactions, thereby offering opportunities to surpass the limitations of each component alone. Here, we focus on recent progress in developing a biohybrid system that combines CdS NCs for photon capture with the enzyme nitrogenase to accomplish light-driven dinitrogen (N 2 ) reduction to ammonia (NH 3 ). Integrating light-harvesting materials with biological catalysts requires a deep understanding of NC properties, protein stability, and electron transfer (ET), making it an inherently multidisciplinary problem. The reduction of N 2 to NH 3 is a challenging reaction, with a high demand in both agriculture and industrial chemical production. This reaction is intrinsically energy intensive, due to the need to activate the N≡N triple bond. The current standard industrial approach to N 2 reduction, the Haber−Bosch reaction, obtains the necessary energy input from fossil fuels, whereas biological systems capable of N 2 reduction utilize the hydrolysis of ATP as their energy source. Replacing these costly, energy-intensive inputs with renewable light energy represents a critical step toward sustainable NH 3 production. Recent progress has demonstrated that semiconductor CdS NCs can be coupled to the catalytic component of nitrogenase, the MoFe protein, to form a biohybrid CdS NC:MoFe protein complex, enabling light-driven N 2 reduction rather than energy input from fossil fuels or ATP. This illustrates how inorganic NCs can functionally replace the natural Fe protein partner, yielding a biohybrid catalyst that enables controlled electron delivery and provides not only light-driven NH 3 production but also new approaches for probing enzyme catalytic function. The CdS NC:MoFe protein biohybrid system enables light-initiated electron delivery at ambient temperature, as well as temperatures below freezing, allowing for stabilization and spectroscopic characterization of key reaction intermediates. These findings highlight how photochemical biohybrids can serve as both functional catalysts and mechanistic probes. Beyond studies of the nitrogenase mechanism, studies of the CdS NC:MoFe system reveal how variables such as NC size, electrostatic binding interactions, and sacrificial electron donors (SEDs) govern complex stability, charge transfer efficiency, and catalytic performance. In addition, studies of nitrogenase and the high activation barrier for N 2 reduction are enabling investigations of new and interesting questions regarding the properties and limitations of NC biocatalysis. In this Account, we describe the key features of CdS NC:MoFe protein biohybrids and the parameters for optimal light-driven N 2 reduction, and how controlling ET with light illuminates the path to new insights into the nitrogenase mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Experimental measurement of energy release from an initiating layer in an insensitive explosive

When subjected to a shock of insufficient strength to trigger prompt reaction, heterogenous condensed phase explosives can form regions where significant amounts of the explosive remain unreacted for times much greater than the reaction time of the detonating explosive. This phenomena is observed for the explosive PBX 9502 (95 wt% TATB) both for planar and oblique input shocks. Here, we build on previous results by performing cylinder expansion (CYLEX) tests where the explosive charge is comprised of a faster core of PBX 9501 (95 wt% HMX) inside a slower annulus of PBX 9502. The detonation in the faster PBX 9501 drives an oblique shock into the adjacent PBX 9502, and an annular transverse initiating layer (IL) results. In the test geometry, the IL travels steadily down the length of the test after a short run distance. At radial positions beyond the IL, an annular region of detonating PBX 9502 is observed. Using standard CYLEX test diagnostics, we infer the total energy release of this experiment. By making the assumptions that (1) the combined energy release is comprised of contributions from detonating PBX 9501, detonating PBX 9502, and the IL in the PBX 9502 and (2) mass-specific energy release for the detonating explosives is approximately the same as typically observed for each explosive, the IL energy release and reaction efficiency can be computed. Results are compared to prior results for a similar geometry, and indicate that while shock deadened PBX 9502 does not detonate promptly, it does eventually release a significant portion of its chemical potential energy over longer timescales on the order of 10 µs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Life Cycle Analysis of Greenhouse Gas Emissions of Clean Fuels with the R&D GREET 2024 Model

This document summarizes research on the life cycle greenhouse gas (GHG) emissions rates from the production and use of clean fuels to support a new version of the Research and Development Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (R&D GREET) model, R&D GREET 2024 In this effort, Argonne National Laboratory (ANL) focuses on clean fuel pathways that are readily available in the market or are emerging in the near term. The selected pathways represent clean fuel technologies that convert biomass- and/or waste-based feedstocks to liquid and/or gaseous fuels for the transportation sector and other potential uses. The pathways are configured in R&D GREET 2024 with up-to-date feedstock-to-fuel life cycle inventory (LCI) data. Additionally, a new tab has been added to R&D GREET 2024 called “Clean Fuels” which allows the user to easily change inputs and access LCA results. Argonne does not warrant that the results presented in this report are consistent with the requirements of any particular regulatory or incentive program. Users interested in specific programs that reference GREET are encouraged to review guidance specific to those programs if and when it is available to determine appropriate means of compliance and contact the relevant responsible agencies for those specific policies or programs.

09 BIOMASS FUELS↗

DOE OSTI2024

Accuracy Enhancement of Nuclear Power Plant Simulators Utilizing High Accuracy Simulation Predictions

More recently, reactor core simulators for core designs associated with commercial nuclear power plants that utilize what is believed to be higher fidelity models have been developed. Features such as neutronics models that utilize transport equation solvers with fine spatial meshes and many energy-groups, thermal-hydraulic models that utilize sub-channel solvers with fine spatial mesh and capable of treating a wide range of fluid conditions, and fuel-coolant chemistry interaction models capable of treating CRUD deposition are to be found in these higher fidelity core simulators. These reactor core simulators require access to higher performance computers, characterized by many processors, cores and large memory. So associated with utilization of these simulators is access to high performance computers and ability to accommodate in one’s workflow longer execution times. By contrast, currently used core simulators by the nuclear industry can execute on engineering workstations and have execution times of seconds to minutes. The desirability for having short execution times is not only desired for support of time critical tasks but supports the mental process of decision making by engineers. The goal of the work reported upon here has the objective of retaining the fidelity of higher fidelity models while retaining the ability to utilize engineering workstations. Beyond the core simulator goal, additional goals of this work include incorporating the just described core simulator capability into a Nuclear Steam Supply System (NSSS) simulator, and to incorporate the resulting capability into an environment supportive of design and operational decision making associated with nuclear power stations. The model selected for the core neutronics model is the NESTLE code, for the core thermal-hydraulic model is the CTF code utilizing coarse mesh, and for the NSSS model is the RELAP5-3D code. WSC’s proprietary 3KEYMASTERTM platform is being used to provide software coupling, user interface, visualization, and reporting. The NESTLE core neutronics simulator was first integrated with the CTF core thermal-hydraulic simulator using CTF developed communication commands which are also used for CTF to communicate with RELAP5-3D under WSC’s proprietary 3KEYMASTERTM platform. To assure NESTLE prediction consistency with higher fidelity core neutronic simulators, buffer codes have been created to automatically generate from output files written by the VERA core simulator the NESTLE nodal neutronic parameter’ library, geometry, and pin-power reconstruction input files, thereby avoiding a number of challenges associated with utilizing lattice physics codes and providing consistency with VERA predictions. To treat absorber rod effects a multi-set library is utilized, where a set refers to a specific absorber rod fully inserted pattern. A coarse spatial mesh CTF model was developed with features added that support using CTF as envisioned in the engineering quality simulator. A hybrid meshing approach was implemented to allow for automated construction of models with mixed levels of refinement. Specifically, a core model could resolve some assemblies at a nodal level (4 subchannels per assembly) and others at a pin-resolution (one subchannel per coolant subchannel in the assembly). The intention is that this will allow for better resolution of limiting conditions such as DNBR and PCT, which are based on local rod and subchannel conditions. Further development was done of features that enhance the capabilities for the envisioned engineering quality simulator that has been developed, but now for RELAP-3D. The RELAP5-3D code development includes ability to model more than 999 components and the addition of the cross-channels turbulence mixing model and the void drift model that are implemented in CTF, aiming to achieve closer prediction agreement of the two codes for transient simulations, specifically, more accurate matches of the overall mass, momentum, and energy exchanges of both the liquid and gas phases between the neighboring core assemblies. Graphics were also developed for the Instructor Station for this project under WSC’s proprietary 3KEYMASTERTM platform to facilitate design and operational decision making.

42 ENGINEERING↗

DOE OSTI2023

Jobs and Economic Development Impact (JEDI) Models

The Jobs and Economic Development Impact (JEDI) models (https://www.nrel.gov/analysis/jedi/) are publicly available, user-friendly tools designed to estimate the economic impacts of both construction and operation of power generation and biofuel plants at a local (usually state) level. Based on project-specific and default inputs (from techno-economic analysis data and NREL's expertise), these models estimate the number of jobs and economic benefits to a region that could reasonably be supported by a particular project. Underlying these calculations is an input-output framework that models the local economy as a network of sectors buying and selling to one-another, creating a multiplier effect. Over the last decade, JEDI has been widely used by both the academic and private sectors, serving as the foundations for multiple peer-reviewed publications and impact analysis reports.

economic impact analysis↗

DOE OSTI2024

An Efficient Annual-Performance Model of a Geothermal Network for Improved System Design, Operation, and Control: Preprint

Geothermal district energy systems (DES), and specifically geothermal networks, provide a viable solution for decarbonizing residential and commercial heating and cooling loads. District energy systems of all kinds enable a thermal resource with a relatively high capital cost (such as a geothermal borehole field) to be shared among a large number of users. While district heating and cooling has been studied for many years, geothermal networks, fifth generation DES that utilize water-source heat pumps and an ambient temperature loop to meet heating and cooling loads, have not been implemented extensively and thus require additional technical and economic optimization to obtain maximum benefits. This paper presents a newly developed reduced-order model that captures the flow of energy within the network, including the commercial and residential users' electrical usage, at an hourly rate over a year. The model includes building loads, heat pumps, borehole fields, and auxiliary heat/cool input, all connected with an ambient-temperature thermal loop model. In the model, operational control is possible for the borehole fields, circulation pump, and auxiliary system. For a given system, the model can output the complete state parameters for each component, the thermal loop, and the collective system, such as flow rate over time, average thermal loop temperature over time, and total electricity usage. The model can also be used to optimize the system control for maximizing system efficiency or minimizing system operational cost. For example, one initial assessment of the borehole controller for an example system showed that a controller with on/off operation of the borehole field reduces annual electrical usage by 33%, compared with continuous operation mode. Hence, the model can assist in optimizing a given system's operation to get the most value out of a geothermal network installation. Future work will consider the model's application to a demonstration project, including the model validation against operational data and system operation optimization.

ambient-temperature loop↗

DOE OSTI2020

Performance analysis and tank test validation of a hybrid ocean wave-current energy converter with a single power takeoff

This paper introduces a hybrid ocean wave-current energy converter (HWCEC) that harvests energy from ocean waves and current simultaneously with a single power takeoff (PTO). Specifically, the wave energy is extracted through relative heaving motion between a floating buoy and a submerged second body, while the current energy is extracted using a marine current turbine (MCT). Energy from both sources are integrated by a hybrid PTO whose concept is based on a mechanical motion rectifier (MMR). The hybrid PTO with two one-way clutches converts bidirectional, up-and-down motion from the waves into unidirectional rotation of the generator. Meanwhile a third one-way clutch couples the MCT with the same generator. The wave and current can simultaneously or separately drive the same generator through different engagement and disengagement statuses of the one-way clutches. Time-domain simulation is conducted with hydrodynamic coefficients obtained from computational fluid dynamics analysis and boundary element method. Tank tests were conducted for a HWCEC under co-existing wave and current inputs. For comparison, separate baseline tests of a turbine and a two-body point absorber, each acting in isolation, are conducted. Experimental results validate the dynamic modeling and show that a HWCEC can increase the output power with a range between 29-87% over either current turbine and wave energy converter acting individually, and it can reduce by up to 70% the peak-to-average power ratio compared with the wave energy converter on the tested conditions.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Uncertainty Quantification of Monolithic Tungsten Target Block Fatigue

The current STS spallation target design consists of 21 water cooled, tantalum clad, monolithic tungsten blocks. The target wheel assembly rotates such that each target block receives a direct proton pulse every 1.4 seconds (21 blocks / 15 Hz pulses). The quasi-steady heating induced by the repetitive beam pulses creates a significant mean stress distribution in the target. On top of this, the rapid energy deposition from a proton pulse induces dynamic stress waves that are of the same order of magnitude as the mean stresses. Over the design goal of 5,000 operational hours per year for a decade, each target must withstand over 130 million repetitions of these dynamic stress events. The fatigue life prediction of the target blocks is a complex sequence of simulations that depend upon a set of input parameters. For the tungsten block, the end goal is the fatigue factor-of-safety (FOS). This study documents a new structural analysis routine using software available to U.S. national laboratories (e.g. CUBIT, Sierra Multiphysics, Dakota) that enables more rapid target fatigue analysis. The increased throughput is amenable to parameter sensitivity and uncertainty quantification (UQ) studies. A set of input parameters (ultimate strength, fatigue strength, density, elastic modulus, Poisson’s ratio, thermal conductivity, coefficient of thermal expansion, specific heat, flow rate) with expected ranges are documented based on literature data, recent experimental testing, and engineering judgement. UQ bounds the resulting fatigue FOS at [0.5, 3.4] using epistemic interval analysis. These bounds are expected to be wider due to model effects (discretization) and beam effects (focus, offset) that were not included. Parameter sensitivity underscores the importance of ultimate strength and fatigue strength of irradiated tungsten on the fatigue calculations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

PRIMO – The Oil & Gas Well Plugging Optimizer

This work presents PRIMO’s main capabilities and introduces the PRIMO web application, an intuitive user interface that leverages our sophisticated mathematical optimization model to rigorously optimize P&A priorities and plugging campaign efficiency. The web app simplifies user interaction, provides a powerful data management framework, and supports a broad user base (e.g., state agencies, well owners/operators, and plugging companies) to use PRIMO for decision-making. Specifically, we provide a demonstration of how to input the information on candidate wells, plugging campaign budget, user-defined priority and efficiency criteria to PRIMO. A real-world case study that consists of 1411 oil and gas wells and impact and efficiency priorities (e.g., well age, well proximity to schools/hospitals, well accessibility, distance between wells in projects) is presented to showcase PRIMO’s core capabilities: (i) ranking a candidate well population based on priorities, (ii) recommending high-impact and high-efficiency P&A projects, and (iii) assigning impact and efficiency scores to projects allowing for rigorous quantitative comparison among them.

02 PETROLEUM↗

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗

DOE OSTI2026

A Faster-Than-Real-Time Framework for Reliability-Oriented Simulation of PV Inverters

Physics-of-Failure (PoF) based reliability assessment for photovoltaic (PV) inverters requires long-duration electrical and electrothermal stress histories, yet generating such stress histories with high-fidelity switching models over year long mission profiles is computationally prohibitive. Conventional methods either sacrifice modeling fidelity for speed or require runtimes that are impractical for design iteration and uncertainty studies. To address this bottleneck, this paper presents a High-Performance Computing (HPC) based simulation frame work for faster-than-real-time reliability-oriented simulation. The proposed framework integrates the Average-to-Switching (A2S) method with parallel computing techniques to accelerate switching-level waveform reconstruction. We further introduce optimization strategies, including cluster merging and sensitivity based mission profile screening, to reduce the computational burden. Evaluated using real-world mission profile inputs and a MATLAB/Simulink switching-model reference, the framework reduces the simulation time for a one-year mission from an intractable multi-year duration to approximately 7.3 minutes while maintaining low waveform error. This acceleration provides a practical reliability-oriented simulation engine that can be coupled with component-specific aging models for subsequent PV inverter PoF assessment.

High-performance Computing↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

This study focuses on design parameter sensitivity studies pertaining to a steam generator (SG) model, using both Python and machine-learning tools. The SG model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor (PWR)-type small modular reactor (SMR) system. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system (e.g., heat transfer coefficient [HTC], Nusselt number, heat transfer performance). Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in input parameters. By using machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), detailed design parametric sensitivity studies and model optimization were performed. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (±1% relative changes). The analysis results give valuable insights into SG system performance and optimization, and provide justification for researching optimized sensor placement to effectively monitor and obtain experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Steam Generator Model Design Parameter Sensitivity Study Using Advanced Optimization Tools

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

NWChem: Recent and Ongoing Developments

In this paper we summarize developments in the NWChem computational chemistry suite since the last major release (NWChem 7.0). Specifically, we focus on functionalities, along with input blocks, that are currently accessible in the current stable release (NWChem 7.2) and master branches, interfaces to quantum computing simulators, interfaces to external libraries, the NWChem GitHub repository, and containerization of NWChem executable images. In conclusion, some of the ongoing developments that will be available in the near future are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Characterization of 128 × 128 MM-PAD-2.1 ASIC: a fast framing hard x-ray detector with high dynamic range

Here, we characterize a new x-ray Mixed-Mode Pixel Array Detector (MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using an integrating pixel front-end with dynamic charge removal architecture, the MM-PAD-2.1 ASIC extends the maximum measurable x-ray signal (in 20 keV photon units) to > 10 7 x-rays/pixel/frame while maintaining a low read noise across the full dynamic range, all while imaging continuously at a frame rate of up to 10 kHz. The in-pixel dynamic charge removal mechanism prevents saturation of the input amplifier and proceeds in parallel with signal integration to achieve deadtime-less measurements with incident x-ray rates of > 10 10 x-rays/pixel/s. The ASIC format consists of 128 × 128 square pixels each 150 μm on a side and is designed to be 3-side buttable so large arrays can be effectively tiled. Here we use both laboratory x-ray sources and the Cornell High Energy Synchrotron Source (CHESS) to characterize two single ASIC prototype detectors for both low (single x-ray) and high incident flux detection. In the first detector the ASIC was solder bump-bonded to a 500 μm thick Si sensor for efficient detection of x-rays below 20 keV, whereas the second detector used a 750 μm thick CdTe sensor for x-rays above ~20 keV.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts

As the frequency and intensity of severe weather has increased, its effect on the electric grid has manifested in the form of significantly more and larger outages in the United States. This has become especially true for regions that were previously isolated from weather extremes. In this paper, we analyze the weather impacts on the electric power grid across a variety of weather conditions, draw correlations, and provide practical insights into the operational state of these systems. High resolution computational modeling of specific meteorological variables, computational approaches to solving power system models under these conditions, and the types of resiliency needs are highlighted as goal-oriented computing approaches are being built to address grid resiliency needs. An example analysis correlating outages to 1km day-ahead weather from two historical winter storms, calculated on a large cluster using a combination of interpolated and extrapolated inputs from multiple instrumented sites to workflows that produce primary meteorological outputs, is shown as initial proof of concept.

analysis↗

DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗

DOE OSTI2021

Bamboo Bio-composite Truck/Trailer Decking

Bamboo is one of the fastest growing plants in the world. Massively productive, bamboo will maintain that productivity with limited inputs and minimal management resulting in predictable volume and operating margins and with low overhead. The goal of this project was to develop a bamboo bio-composite trailer decking product that replaces apitong, is lighter weight, stronger and luminescent. The collaboration partners, Fontaine, Resource Fiber and IACMI, worked closely to develop the bamboo bio-composite decking to Fontaine Trailer’s (end-user) specifications. The various technological objectives were (a) To conduct a comprehensive Design of Experiments study for bamboo composites to understand the structure-property relationships for different resin composites and bamboo forms (strips, woven, bulk etc); (b) To establish the nail pull out characteristics of trailer deck geometry bamboo composite form. The nail pull out is a critical test in trailer decks; (c) To design, process and prototype select number of trailer decking planks for Fontaine evaluation; and (d) To conduct life cycle analysis to conduct energy calculations from the various conversion steps of the bamboo from crop to product. The project meets DOE/IACMI metrics of reduced embodied energy, lightweighting and lowering the cost of the end product. It also offers a green solution to a value-added application, i.e. trailer decking. The development goals and benefits of bamboo composite decking included: A. Decreased weight when compared to Apitong and Gen 1, thereby reducing operating costs and petroleum usage due to increased miles per gallon. B. Flexibility to allow camber design into trailers. C. Luminescence to increase safety during low light and dark conditions. D. Composite material to maximize use of bamboo bio-composites as substitute for petroleum-based composites. E. Embedded layer(s) of conditioned bamboo for added strength and stability. F. Improved safety over Apitong by temporary cargo indentation in planks for better stability. G. Cradle-to-cradle design so end-of-life becomes beginning-of-life for other products. Resource Fiber’s bamboo biocomposite trailer decking passed nail pullout tests as compared to apitong (incumbent), was lighter weight than apitong, and used less embodied energy particularly when bamboo is sourced domestically. The planks were optimized in the lab setting to the extent possible and successfully installed on the trailer at Fontaine for field testing. Despite the process not being fully optimized due to through-heat and tooling limitations within the budget constraints, the prototype decking withstood 400 cycles of reverse fatigue loading of a 107,000 lb Caterpillar 349F which was extreme conditions of field testing by Fontaine Trailer. Resource Fiber plans to outsource production of a next round of planks with a commercial pultrusion processor, then to do a re-test with Fontaine Trailers. Tooling specific to the part is required. Long-term commercial plans are to continue outsourcing production while supplying bamboo fiber and mats. Commercial markets include heavy haul trailers, military trailers, and decking for marine, industrial and residential use.

36 MATERIALS SCIENCE↗