Search NASA⌕ Search

SEARCH · Search NASA

Results for “Optimal process control”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Utilization of the CW magnetrons as coherent RF sources for superconducting RF accelerators

CW magnetrons designed and optimized for industrial heaters, driven by an injection-locking signal, were suggested to power Superconducting RF (SRF) cavities. However, the CW magnetrons, which are regenerative generators, are intended to operate in self-excitation mode, so, for reliable starting they use a large regenerative gain, causing a large regenerative instability in operation. Thus, power supply ripple, even shot noise, etc., lead to multiple sidebands forming a quasi-continuous noise spectrum at the carrier frequency. Therefore, in the traditionally used mode of operation and control, such magnetrons are inapplicable for powering high Q-factor SRF cavities. A new approach to operation and control of CW magnetrons considering non-stationary processes during start-up and operation makes it possible to find a mode of almost coherent RF generation of tubes with a significant reduction in regenerative instability and noise and increased efficiency. In our work is presented substantiation for the newly developed mode with an analysis of the new approach based on experimental results. •In this article, for the first time, non-stationary, regenerative processes are considered during the start-up and operation of CW magnetrons for powering high-Q superconducting RF cavities.•An analytical study, confirmed by experiments, showed that the traditionally used modes of starting and operating CW magnetrons are not suitable for powering high-Q superconducting RF cavities.•In fact, to make CW magnetrons suitable for such cavities, the physics of starting and operating CW magnetrons must be changed. This has been demonstrated in our work.•In developing the Stimulated Coherent Generation Mode [NIM A 980 (2020) 164366], we have shown the suitability of this mode for starting and operating CW magnetrons for high-Q superconducting RF cavities.•Analytical analysis, verified experimentally, substantiates in our article the suitability of the “Mode of stimulated coherent generation” of CW magnetrons for powering high-Q superconducting cavities in pulsed and continuous modes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Establishment of a Vertically Integrated Domestic Manufacturing Process for Production of Substrates Needed for Manufacture of Gas Diffusion Layers

In this project, AvCarb, LLC evaluated the baseline performance metrics of commercial carbon veils and their corresponding Gas Diffusion Layers (GDLs) with the goal of establishing an optimized, vertically integrated production system for wet-laid nonwoven substrates used in gas diffusion media for electrochemical energy storage and conversion devices. Mechanical testing and microstructural characterization were conducted and used to develop a multiscale computational model capable of simulating and predicting the performance of GDLs in fuel cells. Although the project successfully generated foundational transport and modeling data, it was terminated prior to identifying the critical GDL design parameters necessary for full optimization. The program aimed to improve carbon veil fabrication through enhanced fiber dispersion, fiber-fiber adhesion control, and improved web formation, enabling the production of high-quality, uniform substrates. Simulations were intended to guide mixing and solution delivery system design and process conditions, followed by production-scale trials to evaluate fiber dispersion, web uniformity, and mechanical robustness. At full deployment, the proposed production line would have been capable of producing approximately 650,000 m² of carbon veil annually. This capability remains strategically important, as the United States currently lacks a domestic source of wet-laid nonwoven carbon substrates that satisfy the stringent quality requirements for fuel-cell GDLs and electrolyzers representing an ongoing supply-chain vulnerability. Beyond supply-chain benefits, the project established a robust benchmarking dataset for existing commercial carbon veils while advancing next-generation material concepts targeting improved performance and manufacturing consistency.

Olson, Cynthia Lemay↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

Applied Probability↗
DOE OSTI2024

Development of Stable Solid Oxide Electrolysis Cells for Low-Cost Hydrogen Production

The project objective was to demonstrate a solid oxide cell-based steam electrolysis stack that exhibits robustness, reliability, endurance, hydrogen purity, and produces hydrogen at elevated pressure of 2 to 3 bar. Innovative materials and processing methods were evaluated to improve degradation characteristics. Performance improvement focused on nearly all layers involved in the cell and stack assembly. Primary attention was paid to zirconia-ceria interface resistance control via sintering optimization and decrease in degradation from the oxygen electrode by evaluating low strontium (Sr) or Sr-free composition for both the oxygen electrode and current collection layer. Stack robustness was addressed by validating redox tolerance of fuel electrode, confirming capability of cells to survive repeated thermal cycles, studying the effect of pressure on performance and degradation, evaluating the effect of contamination on fuel and oxygen electrode performance and degradation, and identifying mitigation strategies to improve performance. The characterization included evaluation of electrochemical performance and stability followed by microstructural analysis. At the cell level, performance and stability improvements were achieved by incorporating a Sr-free oxygen electrode and a denser oxygen electrode barrier layer. At the stack level, pressurized operation reduces demand on first stage compression, the redox tolerant fuel electrode mitigates risk from service interruptions, and improvements to interconnect coating alleviate chromium (Cr) contamination effects. The denser barrier layer was achieved by adding a sintering aid to the samaria-doped ceria (SDC) composition that reduced sintering temperature by 150 °C. The resulting density was on par with the baseline SDC barrier layer density and the lower sintering temperature resulted in less resistive phase formation during sintering. Button cell tests did not demonstrate a change in performance when exposed to silicon (Si) or manganese (Mn) impurities to the fuel electrode and Cr impurity to the oxygen electrode. More detailed study however is warranted. The project addressed SOEC performance and stability at the cell and stack levels through a systematic approach to known sources of degradation that were combined and tested in three stack tests using an electrolyte supported cell design to allow for evaluation of a variety of fuel and oxygen electrode compositions. STK-82 and STK-83 had identical compositions. STK-100 incorporated the best materials and processing variables developed under this and concurrent projects, and was tested at elevated pressure in steam electrolysis. • STK-82 recovered performance after redox and thermal cycling, demonstrating the robustness of the stack and seals. It exhibited stable performance in testing for 500 hours in SOEC mode, followed by 300 hours of cycling between SOEC and SOFC tests. Degradation during SOEC operation was 1.8 %/ 1,000 hours. • STK-83 generated hydrogen at >80% steam conversion, and oxygen above 98.5 % purity during pressurized operation. Both hydrogen and oxygen were generated at 3 barg pressure without the use of a pressure vessel. In addition to balanced pressure, electrolysis operation at 1 bar differential pressure across anode and cathode was also demonstrated to substantial the robustness of the cell and seal. • STK-100 measured at initial ambient pressure conditions showed an area specific resistance of 1.1 ohm-cm 2 , and STK-83 had 1.3 ohm-cm 2 .

08 HYDROGEN↗
DOE OSTI2022

Processes controlling helium bubble dynamics at varying temperatures in simulated radioactive materials

Alpha-radiation damage is known to degrade materials exposed to harsh radiation environments via swelling and embrittlement by nano-scale helium bubbles. How these bubbles grow under varying temperature is essential to understanding and predicting material failure and optimization. Using in situ X-ray scattering, we probed ≈1012 bubbles before, during and after annealing at different temperatures in He-implanted aluminum to reveal a critical growth activation temperature that depends strongly on the helium implantation conditions. An analysis of the data aided by our molecular dynamics simulations identifies multiple temperature-dependent bubble growth mechanisms above and below the critical growth activation temperature. Moreover, the irreversible bubble growth that occurs after annealing for ≈4 h was evaluated using dislocation dynamics and found to negligibly affect the yield strength but may affect subsequent bubble growth at temperature. These in situ results provide valuable insight into bubble growth never observed before and reveal, for the first time, bubble shrinkage after cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ML based control systems for nuclear physics experiments

The Experimental Physics Software and Computing Infrastructure (EPSCI) group at Jefferson Lab is leading the use of machine learning (ML) to enhance control systems in nuclear physics experiments. Collaborating closely with domain experts and data scientists, we have developed an ML-based control system that uses a Gaussian process to dynamically adjust the high voltage of the GlueX Central Drift Chamber. This results in stable detector performance by adapting to environmental changes, thereby reducing the offline calibration effort. Furthermore, we are developing ML-driven systems for optimizing the polarization of photon beams and polarized cryotargets. These systems will maintain the optimal microwave frequency in cryogenic targets and make real-time adjustments to diamond radiators for polarized photon sources, tasks traditionally handled by human operators. By automating these functions, we aim to optimize the polarization, reduce downtime, and minimize human error. This talk will highlight the development of reliable ML-based control systems and the policies to ensure they are both effective and trustworthy.

Jeske, Torri↗
DOE OSTI2023

First principles optimization of plutonium electrorefining

Herein this work presents a means of controlling plutonium electrorefining at a maximum rate regardless of equipment setup through the derivation of power supply current and potential governing equations for normal and off-normal operations. The governing equations are demonstrated by electrorefining surrogate materials. A simple linear current sweeping method was used to determine the maximum electrorefining current for the surrogate system. This method can be used to develop autonomous process optimization, real-time online processing monitoring, and real-time process endpoint detection. Ultimately, this research provides the foundation to optimize the liquid metal electrorefining rate to decrease the time needed to the physical limit for the process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Design and analysis of a wake steering controller with wind direction variability

Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

17 WIND ENERGY↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
DOE OSTI2020

Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems

Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions of the detector is presented. Such an adaptive-autonomous detector with both spatial and temporal control requires programmable window of exposure (time frames), ability to switch between readout modes such as full-frame imaging and zero-suppressed data, modification of the number of pixel data bits and independent programmability for distinct detector regions. In this work, a method is presented for seamlessly changing time frames and readout modes without data corruption while still ensuring that the data acquisition system (DAQ) does not need to stop and resynchronize at each change of setting, thus avoiding significant dead time. Data throughput is maximized by using a minimum unique data format, rather than lengthy frame headers, to differentiate between consecutive frames. A data control and transmitter (DCT) synchronizes data transfer from the pixel to the periphery, reconfigures the data to transmit it serially off-chip, while providing optimized decision support based on a DAQ definable mode. Measurements on a test structure demonstrate that the DCT can operate at 1 GHz in a 65 nm LP CMOS process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Printing polymer blends through in situ active mixing during fused filament fabrication

Fused filament fabrication (FFF) enables production of 3D objects over a range of material compositions at low-cost relative to traditional manufacturing approaches. To date, a limited but growing number of materials are able to be used with FFF, however many applications exist where specific mechanical, thermal, or chemical properties are needed that cannot currently be met with the available feedstock selection. Therefore, a need exists to tune these materials for specific chemical or mechanical properties. One common formulation strategy to address these demanding design parameters is to develop composites or polymer blend filaments. Typically, this is a time-consuming and costly optimization process. Here, we have developed hardware for reproducibly mixing two filaments of similar or dissimilar compositions at the time of printing within individual printed layers. This mixing occurs via software-controlled rotating hardware in the chamber of an extruder’s hot-end. The efficiency of mixing within the printed layers has been characterized in detail as a function of the rotational speed and geometry of the blending hardware. These parameters were exploited to program the ratio and distribution of thermoplastic-based filaments blended within printed extrudate. Example printed specimens were produced with thermoplastic polyurethane (TPU) elastomer blended with rigid polylactic acid (PLA) and Nylon blended with PLA. In addition, a conductive carbon nanotube (CNT)-PLA composite was blended as a function of mixer geometry and input feed ratios with non-conductive PLA and resistance values were measured across the resulting printed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Controlling homogenization length scales and microstructure in additively manufactured Ti-Ta functionally graded materials

Materials with smooth compositional gradients or functionally grade materials (FGMs) produced via additive manufacturing (AM), enables joining dissimilar materials and optimizing multiple properties in advanced engineering applications. However, as-printed AM microstructures exhibit micro-segregation and solidification defects which, when combined with controlling macroscale gradient properties, complicates necessary post-processing. Here, we use CALPHAD-informed diffusion modelling to design post-processing heat treatments for lightweight to refractory FGMs. Ti-Ta (0 to 85 at. % Ta) FGMs were fabricated using laser-based directed energy deposition AM. Post-processing heat treatments at 1000° C and 1500° C were designed to promote homogenization across specific length scales and experimentally validated. Investigation of chemical segregation and microstructures demonstrated that the length scale of homogenization is controlled as a function of time, temperature, and local composition. Ta-rich regions exhibited incomplete homogenization compared to Ti-rich layers. Unmelted Ta particles were found to completely dissolve at 1500 °C. By controlling cooling rate (200 °C/min), martensitic structures were produced between 14–36 at. % Ta, consistent with martensite-start temperatures calculations, while furnace cooling (2 °C/min) produced α+β morphologies. This work establishes a validated predictive framework for designing post-processing to tailor microstructure and chemical architecture in AM FGMs, facilitating their deployment in demanding environments.

Materials science↗
DOE OSTI2020

Re-Envisioning RCx: Achieving Max Potential HVAC Controls Retrofits through Modernized BAS Hardware and Software

Most existing buildings have sub-optimal heating, ventilation, and air conditioning (HVAC) controls, resulting in wasted energy and occupant discomfort. Retro-commissioning (RCx) addresses many of these issues, but it is a lengthy and highly customized process. Limited capabilities of existing building automation system hardware restricts the scope of most RCx projects. Incentive programs consider building automation system (BAS) hardware retrofits to be high-capital investments and do not allow them in typical RCx programs. This paper describes work that the authors are leading to facilitate technical and market innovation in the BAS industry to unlock large savings in existing commercial buildings through deep retrofits of BAS hardware and software. California and New York research projects are demonstrating BAS retrofits leveraging the American Society of Heating, Refrigeration, and Air Conditioning Engineers’ (ASHRAE) new Guideline 36 high performance sequences of operation to achieve greater than 20 percent whole building energy savings, while saving costs and reducing risk through streamlined processes and standardization across BAS manufacturer product lines and across implementation practices. This paper describes market barriers that impede achieving deep savings from BAS retrofits in custom incentive and traditional RCx programs and presents a new maximum potential BAS retrofit model that addresses these barriers. The new model leverages the authors’ efforts in market enablement through open standards, BAS industry partnerships, and tools for cost-effective scaling that includes tools for project screening, savings calculations, and measurement and verification (M&V). This approach is widely applicable and will be ready for at-scale implementation within two years.

Paliaga, Gwelen↗
DOE OSTI2024

FARM User Guidance and Instructions

The FARM (Feasible Actuator Range Modifier) module is a component of the RAVEN-based FORCE framework for the analysis of Integrated Energy Systems (IES). FARM aids HERON in the solution of the power dispatch problem by evaluating feasible set-point signals to be issued to the control systems of the different IES unit components. Set-points need to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To enforce all these limits, a two-stage approach is adopted. First, the power dispatcher algorithm in HERON module estimates set-points that meet the constraints on the production variables, e.g., power levels and power ramp rate limits. These constraints are called explicit constraints. Then, if necessary, FARM adjusts these set-points to ensure the respect of the limits on the process variables of interest, given the knowledge of the system dynamics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). In this report, the general workflow and the software requirements of FARM module are summarized, and the detailed instructions for installing FARM software, running built-in example cases, deriving Linear Parameter-Varying (LPV) state-space models, and using FARM for user-defined power dispatch problems are provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance

Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.

3D printing↗
DOE OSTI2020

Efficient Control of Atom Arrangement in Ternary Metal Chalcogenide Nanoparticles Using Precursor Oxidation State

Controlling both the concentration and the distribution of elements in a given material is often crucial to extracting and optimizing synergistic properties of the various constituents. An interesting class of such multielement materials is metal chalcogenide nanoparticles, which exhibit a wide range of composition-dependent optoelectronic properties including both bandgap-mediated processes and localized surface plasmon resonance properties, each of which is useful in applications ranging from energy conversion to sensing. Because metal chalcogenide nanoparticles can support several different metal elements in a variety of chalcogen lattices, this material class has particularly benefited from the ability to control both atom concentration and atom arrangement to tailor final particle properties. The primary method to access complex, multimetallic chalcogenide particles is via a postsynthetic cation exchange strategy. One-pot syntheses have been less explored to access these complex particles, although this route is desirable for economy and scalability. In this work, we compare the composition and morphology outcomes from cation exchange and one-pot preparation approaches using a Cu/Ag/Se system, which is already known to exhibit both binary and ternary metal chalcogenide phases. We show that at similar concentrations of the two metal cations, initial reaction conditions for the one-pot method yield multicomponent nanoparticles, whereas cation exchange yields homogeneous ternary metal chalcogenide structures. We then show that by tuning the precursor oxidation state for the one-pot method, this approach can be used to access homogeneous ternary metal chalcogenide particles that are similar in atom arrangement to the particles obtained using cation exchange. Taken together, our results demonstrate reliable synthetic methods that yield a variety of controlled compositions and composition morphologies in the Cu/Ag/Se system. Importantly, we demonstrate that this entire collection of architectures can all be accessed via a one-pot method simply by modifying metal precursor chemistry. The mechanistic insights gained and the resulting streamlined syntheses outlined indicate pathways to easily scaled, highly tailorable syntheses for rapid translation into downstream technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A holistic framework integrating plant-microbe-mineral regulation of soil bioavailable nitrogen

Abstract Soil organic nitrogen (N) is a critical resource for plants and microbes, but the processes that govern its cycle are not well-described. To promote a holistic understanding of soil N dynamics, we need an integrated model that links soil organic matter (SOM) cycling to bioavailable N in both unmanaged and managed landscapes, including agroecosystems. We present a framework that unifies recent conceptual advances in our understanding of three critical steps in bioavailable N cycling: organic N (ON) depolymerization and solubilization; bioavailable N sorption and desorption on mineral surfaces; and microbial ON turnover including assimilation, mineralization, and the recycling of microbial products. Consideration of the balance between these processes provides insight into the sources, sinks, and flux rates of bioavailable N. By accounting for interactions among the biological, physical, and chemical controls over ON and its availability to plants and microbes, our conceptual model unifies complex mechanisms of ON transformation in a concrete conceptual framework that is amenable to experimental testing and translates into ideas for new management practices. This framework will allow researchers and practitioners to use common measurements of particulate organic matter (POM) and mineral-associated organic matter (MAOM) to design strategic organic N-cycle interventions that optimize ecosystem productivity and minimize environmental N loss.

Environmental Sciences & Ecology↗