Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “embedded systems”

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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CRADA Final Report: CRADA Number NFE-24-10036 with ThermaMatrix, Inc.

ThermaMatrix, Inc provides novel vision inspection solutions for a wide range of manufacturers and industries, providing and implementing the leading technologies for nondestructive inspection (NDI) and material characterization. Many other inspection solutions are either not adequate or are not approachable due to implementation barriers needing expert level operators, excessive inspection time, and high cost. ThermaMatrix’s advanced vision inspection technology addresses all of these limitations. The Lab Embedded Entrepreneurial Program (LEEP) opportunity by the Department of Energy (DOE) allows small-business start-ups to leverage national laboratory capabilities and skilled scientists to rapidly develop their technology that aligns with DOE goals. ThermaMatrix, Inc. was positioned in the Innovation Crossroads program at Oak Ridge National Laboratory to further develop the novel Watson Vision Inspection System to support manufacturing quality control efforts. The research goals were (1) explore fundamental parameters that would improve preexisting capabilities, (2) full-scale industrial setup for demonstration, and (3) capability testing and verification. Manufacturing is demanding more NDI implementation to support their quality control needs, which this technology development would support.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗
DOE OSTI2021

Power allocation for visible light communication

A system and method for providing optical multiple input and multiple output data communication using optical signals includes a plurality of light sources, a plurality of photodetectors, and at least one controller. The plurality of light sources are configured to emit optical signals to communicate data. The plurality of photodetectors are configured to sense the optical signals, and are embedded in at least one receiver. At least one of the plurality of photodetectors is configured to receive the optical signals from two or more of the plurality of light sources. The controller is configured to assign a transmit power to at least some of the plurality of light sources based on parameters of the plurality of photodetectors.

Brandt-Pearce, Maite↗
DOE OSTI2020

Microgrid Energy Management System Integration with Advanced Distribution Management System

The Integrated Distribution Management System (IDMS) project was initiated to demonstrate the interactive operation of microgrid systems and the distribution systems with which they interconnect. The key technologies for this are the microgrid management system and the utility distribution management system. The IDMS project successfully demonstrated that a utility’s advanced distribution management system/distributed energy resources management system (ADMS/DERMS) could effectively manage microgrids to provide visibility and control functionalities as well as use the microgrid as a dispatchable resource to support the utility grid. The DERMS accomplishes this by determining active and reactive power needs at the point of common coupling (PCC) using advanced applications like volt/VAR watt optimization (VVWO) and load relief (LR) with the underlying core applications state estimation (SE) and load flow (LF). The ADMS/DERMS can use the microgrid as a resource to resolve and prevent violations in the grid and to optimize the operational working state of the grid. The IDMS project demonstrated that a utility-operated ADMS with embedded DERMS functionality can flexibly manage a variety of microgrids and other aggregated distributed energy resources (DER) in concert with the wider distribution grid. Microgrids can provide grid services in any number of different ways to meet the operational needs of the distribution utility. The manner of aggregation — microgrid or virtual power plant — is not necessarily relevant to the utility as long as the grid services from the aggregated DER are available and can be managed by its ADMS/DERMS for the stability and reliability of the grid. The IDMS project demonstrated this integrated ADMS concept by combining hardware/software-in-the-loop testing with commercial products from different vendors and a utility’s network model. The project integrated the Schneider Electric EcoStruxure™ ADMS with DERMS with the Schweitzer Engineering Laboratories (SEL) POWERMAX ® Microgrid Control System, and the simulated resources with energy company PECO’s model of a utility-owned microgrid, establishing an operational relationship in which the utility manages the operational functionality of a microgrid at the PCC and that provides the utility with the capability to control the comprehensive power system, inclusive of the macrogrid and microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Unraveling the Nature of Excellent Potassium Storage in Small-Molecule Se@Peapod-Like N-Doped Carbon Nanofibers

The potassium-selenium (K-Se) battery is considered as an alternative solution for stationary energy storage because of abundant resource of K. However, the detailed mechanism of the energy storage process is yet to be unraveled. In this work, the findings in probing the working mechanism of the K-ion storage in Se cathode are reported using both experimental and computational approaches. A flexible K-Se battery is prepared by employing the small-molecule Se embedded in freestanding N -doped porous carbon nanofibers thin film (Se@NPCFs) as cathode. The reaction mechanisms are elucidated by identifying the existence of short-chain molecular Se encapsulated inside the microporous host, which transforms to K 2 Se by a two-step conversion reaction via an "all-solid-state" electrochemical process in the carbonate electrolyte system. Through the whole reaction, the generation of polyselenides (K 2 Se n , 3 ≤ n ≤ 8) is effectively suppressed by electrochemical reaction dominated by Se-2 molecules, thus significantly enhancing the utilization of Se and effecting the voltage platform of the K-Se battery. This work offers a practical pathway to optimize the K-Se battery performance through structure engineering and manipulation of selenium chemistry for the formation of selective species and reveal its internal reaction mechanism in the carbonate electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2025

Temperature-dependent calibration procedures for the silicon photomultiplier readout of the cosmic ray veto detector for the Mu2e experiment

The cosmic ray veto detector for the Mu2e experiment consists of scintillation bars embedded with wavelength-shifting fibers and read out by silicon photomultipliers (SiPMs). Here, in this manuscript, the calibration procedures of the SiPMs are described including corrections for the temperature dependence of their light yield. These corrections are needed as the SiPMs are not kept at a constant temperature due to the complexity and cost of implementing a cooling system on such a large detector. Rather, it was decided to monitor the temperature to allow the appropriate corrections to be made. The SiPM temperature dependence has been measured in a dedicated experiment and the calibration procedures were validated with data from production detectors awaiting installation at Fermilab.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Probing multiscale dissolution dynamics in natural rocks through microfluidics and compositional analysis

Mineral dissolution significantly impacts many geological systems. Carbon released by diagenesis, carbon sequestration, and acid injection are examples where geochemical reactions, fluid flow, and solute transport are strongly coupled. The complexity in these systems involves interplay between various mechanisms that operate at timescales ranging from microseconds to years. Current experimental techniques characterize dissolution processes using static images that are acquired with long measurement times and/or low spatial resolution. These limitations prevent direct observation of how dissolution reactions progress within an intact rock with spatially heterogeneous mineralogy and morphology. We utilize microfluidic cells embedded with thin rock samples to visualize dissolution with significant temporal resolution (100 ms) in a large observation window (3 × 3 mm). Here we injected acidic fluid into eight shale samples ranging from 8 to 86 wt % carbonate. The pre- and postreaction microstructures are characterized at the scale of pores (0.1 to 1 µm) and fractures (1 to 1,000 µm). We observe that nonreactive particle exposure, fracture morphology, and loss of rock strength are strongly dependent on both the relative volume of reactive grains and their distribution. Time-resolved images of the rock unveil the spatiotemporal dynamics of dissolution, including two-phase flow effects in real time and illustrate the changes in the fracture interface across the range of compositions. Moreover, the dynamical data provide an approach for characterizing reactivity parameters of natural heterogeneous samples when porous media effects are not negligible. The platform and workflow provide real-time characterization of geochemical reactions and inform various subsurface engineering processes.

58 GEOSCIENCES↗
DOE OSTI2022

Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

Phasor Measurement Units (PMUs) are critical for the Wide Area Measurement System (WAMS), especially for real-time grid monitoring, disturbance location, and situation awareness. Once they are damaged, the stable operation of the power system will be affected directly and greatly. Therefore, it is urgent and necessary to study their immunity to electromagnetic pulse (EMP) events. In this paper, an effective impedance measurement scheme is proposed for port impedance measurement of PMU. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then circuit of pulsed current generator is established to generate damping sinusoid and double exponential wave applied to the port. Finally, by using measured impedance as a load of the generator, the voltage and current responses of different ports are calculated in the pulsed current injection (PCI) testing simulation. Results show that common-mode impedance is large and differential mode impedance is small. The critical condition will appear in the common mode for voltage and differential mode for current.

Zhang, Liang↗
DOE OSTI2023

Rationally Engineered Vertically Aligned β ‐Ga 2− x W x O 3 Nanocomposites for Self‐Biased Solar‐Blind Ultraviolet Photodetectors with Ultrafast Response

Abstract With the astonishing advancement of present technology and increasing energy consumption, there is an ever‐increasing demand for energy‐efficient multifunctional sensors or transducers based on low‐cost, eco‐friendly material systems. In this context, self‐assembled vertically aligned β ‐Ga 2− x W x O 3 nanocomposite (GWO‐VAN) architecture‐assisted self‐biased solar‐blind UV photodetection on a silicon platform, which is the heart of traditional electronics is presented. Utilizing precisely controlled growth parameters, the formation of W‐enriched vertical β ‐Ga 2− x W x O 3 nanocolumns embedded into the W‐deficient β ‐Ga 2− x W x O 3 matrix is reached. Detailed structural and morphological analyses evidently confirm the presence of β ‐Ga 2− x W x O 3 nanocomposite with a high structural and chemical quality. Furthermore, absorption and photoluminescence spectroscopy explains photo‐absorption dynamics and the recombination through possible donor–acceptor energy states. The proposed GWO‐VAN framework facilitates evenly dispersed nanoregions with asymmetric donor energy state distribution and thus forms build‐in potential at the vertical β ‐Ga 2− x W x O 3 interfaces. As a result, the overall heterostructure evinces photovoltaic nature under the UV irradiation. A responsivity of ≈30 A/W is observed with an ultrafast response time (≈350 µs) under transient triggering conditions. Corresponding detectivity and external quantum efficiency are 7.9 × 10 12 Jones and 1.4 × 10 4 %, respectively. It is believed that, while this is the first report exploiting GWO‐VAN architecture to manifest self‐biased solar‐blind UV photodetection, the implication of the approach is enormous in designing electronics for extreme environment functionality and has immense potential to demonstrate drastic improvement in low‐cost UV photodetector technology.

Das, Debabrata↗
DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗
DOE OSTI2023

Ab initio structural dynamics of pure and nitrogen-containing amorphous carbon

Abstract Amorphous carbon (a-C) has attracted considerable interest due to its desirable properties, which are strongly dependent on its structure, density and impurities. Using ab initio molecular dynamics simulations we show that the sp 2 /sp 3 content and underlying structural order of a-C produced via liquid quenching evolve at high temperatures and pressures on sub-nanosecond timescales. Graphite-like densities ( $$\lesssim$$ ≲ 2.7 g/cc) favor the formation of layered arrangements characterized by sp 2 disordered bonding resembling recently synthesized monolayer amorphous carbon (MAC), while at diamond-like densities ( $$\gtrsim$$ ≳ 3.3 g/cc) the resulting structures are dominated by disordered tetrahedral sp 3 hybridization typical of diamond-like amorphous carbon (DLC). At intermediate densities the system is a highly compressible mixture of coexisting sp 2 and sp 3 regions that continue to segregate over 10’s of picoseconds. The addition of nitrogen (20.3%) (a-CN) generates major system features similar with those of a-C, but has the unexpected effect of reinforcing the thermodynamically disfavored carbon structural motifs at low and high densities, while inhibiting phase separation in the intermediate region. At the same time, no nitrogen elimination from the carbon framework is observed above $$\simeq$$ ≃ 2.8 g/cc, suggesting that nitrogen impurities are likely to remain embedded in the carbon structures during fast temperature quenches at high pressures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Helical spin structure in iron chains with hybridized boundaries

In this paper, we have compared the magnetic properties of well-controlled ultra-short (≤50 nm) atomic iron (Fe) chains embedded in Fe-phthalocyanine films with those in Fe–hydrogen (H2) phthalocyanine superlattices. Surprisingly, we found that the coercivity of the atomic chains with free boundary conditions is independent of the chain length, whereas the one subject to hybridization of the chain ends exhibits an unexpected length dependence. These findings suggest that ferromagnetism in the free-boundary condition system is caused by an intrinsic indirect exchange. On the other hand, controlled boundary conditions produce a helical spin structure due to an extrinsic indirect exchange, which arises from the interaction between iron atoms at the ends of the chain and the hydrogen in the H 2 phthalocyanine spacer. As a consequence, during magnetic reversal, ultra-short iron chains subject to boundary clamping develop a helical spin structure, leading to increased coercivity. These findings suggest unique insights and ideas for the design of atomic-scale ultra-dense magnetic storage nanodevices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware

The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Enhancement of Thermal Conductivity of Bentonite Buffer Materials with Copper Wires/Meshes for High-Level Radioactive Waste Disposal

In high-level radioactive waste disposal, a heat-generating waste canister is generally encased with a layer of bentonite-based buffer material acting as an engineered barrier to limit water percolation and radionuclide release. The low thermal conductivity of bentonite (~0.5 W/m∙K) combined with a high thermal loading waste package may result in a high surface temperature on the package that can potentially impact the structural integrity of the package itself as well as the surrounding buffer material. We show in this paper that the thermal conductivity of bentonite can be effectively enhanced by embedding copper wires/meshes across the buffer layer to form fully connected high heat conduction pathways. A simple calculation based on Rayleigh’s model shows that a required thermal conductivity of 5 W/m∙K for effective heat dissipation can be achieved simply by adding ~1 vol % of copper wires/meshes into bentonite. As a result, the peak surface temperature on a large waste package such as a dual-purpose canister can be reduced by up to 300°C, leading to a significant reduction in the surface storage time for waste cooling and therefore the overall cost for direct disposal of such waste packages. Because of the ensured full thermal percolation across the buffer layer, copper wires/meshes turn out to be much more effective than any other materials currently suggested (such as graphene or graphite) in enhancing the thermal conductivity of buffer material. Furthermore, the embedded copper wires/meshes can help reinforce the mechanical strength of the buffer material, thus preventing the material from a potential erosion by a possible intrusion of dilute groundwater.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗
DOE OSTI2022

Resilient Hierarchical Networked Control Systems: Secure Controls for Critical Locations and at Edge

Integration of information and communication technology (ICT) offers new opportunities in improving the management and operation of critical infrastructures such as power systems as it allows connection of different sensors and control components via a communication network, leading to the so-called networked control systems (NCS). However, the use of open and pervasive ICT such as the Internet or wireless communication technologies comes at a price of making NCS vulnerable to cyber intrusions/attacks which may cause physical damage. Here, this chapter presents control algorithms to ensure resilient and safe operation of NCS under unknown cyberattacks. Specifically, a variant of dynamic watermarking strategies is presented by embedding encoding/decoding components of chaotic signals into the NCS for secure control for critical locations where the measurement/control signals are transmitted to/from the control center via a communication network. In addition, resilient cooperative control algorithms are discussed to ensure safe operation at edge of the NCS which consists of a large number of distributed controllable devices. Several numerical examples are provided to illustrate the proposed control strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2022

Design of a Combined Proportional Integral Derivative Controller to Regulate the Temperature Inside a High-Temperature Tubular Solar Reactor

Solar fuels are proven to be promising candidates for thermochemical energy storage. However, the transient nature of solar radiation is an obstacle to maintaining a stable operational temperature inside a solar reactor. To overcome this challenge, the temperature of a solar reactor can be regulated by controlling the incoming solar radiation or the feedstock flowrate inside the reactor. In this work, a combined proportional integral derivative (PID) controller is implemented to regulate the temperature inside a high-temperature tubular solar reactor with counter-current flowing gas/particles. The control model incorporates two control systems to regulate incoming solar radiation and gas flow simultaneously. The design of the controller is based on a reduced-order numerical model of a high-temperature tubular solar reactor that is vertically oriented with an upward gas flow and downward particle flow. The reactor receives heat circumferentially through its wall over a finite segment of its length. Formulation of the heat transfer model is presented by applying the energy balance for the reactor tube and considering heat and mass transfer inside. A set of governing differential equations are solved numerically by using the finite volume method to obtain reactor wall, particles, and gas temperatures along the reactor length with various boundary conditions. Simulation results are used to tune the PID controller parameters by utilizing the Ziegler–Nichols tuning method. Both the simulation results and the controller performance are visualized on the LABVIEW platform. The controller is challenged to track different temperature setpoints with different scenarios of transient solar radiation. Here, the performance of the PID controller was compared to experimental results obtained from an industrial PID controller embedded in a 7 kW electric furnace. Results show that the combined PID controller is successful in maintaining a stable temperature inside the reactor by regulating the incoming solar radiation and the flowrate via small steady-state error and reasonable settling time and overshoot.

14 SOLAR ENERGY↗