Fresnel Lenses for CSP: Large, Low-Cost Fresnel Lenses in Glass for CSP

The industrial revolution moved society organization from farming communities distributed on the land, self-sufficient in energy and food provided by the sun, to concentrated sources as oil and natural gas. Now there is a reversal of this trend due to the return to the use of solar energy. We produce electricity with PV panels, but we need also heat, and we do not have any way to do so in a distributed way. We propose a model of collecting, storing, and using the energy locally. The first step, collecting, we discuss in this paper. We propose the use of a large Fresnel lens in glass. To mold the lens in glass we use a modified groove design where the vertical step directs the light to the focal point through reflection by a mirror. The angle between facets is large enough to let the glass fill the grooves. We solve the problem of cost by producing the lens in a cylindrical configuration, extruding and calendaring the glass in a commercial plant for textured glass at a cost of $10/m2. The basic unit size can be realized as an assembly of eight 4x8 feet glass panes, collecting what we expect to be 10kW per unit, at a cost of few hundred dollars per unit for the collecting optic. We will capitalize on the know-how on making the receivers for the Parabolic Reflector geometry. This proposal allows solar heating to be affordable by a homeowner, lowers CO2 emissions

Effect of Compressor Inlet Swirl on Solar Gas Turbine Performance

An investigation of the effect of various compressor inlet guide vane (IGV) setting angles on the thermodynamic performance of a simple single shaft solar-hybrid gas turbine power generation system (SHGT) is presented. The formulation of models for the individual system components is described, with emphasis on the generation of compressor characteristics for various IGV setting angles. Matching of the components in the integrated system model is shown, for the standard single shaft turboshaft configuration as well as several variable rotor speed turboshaft configurations. The estimated fuel consumption, solar receiver effectiveness, and thermal efficiency are compared for the reference system, SHGTs with IGVs, and variable rotor speed SHGT configurations. The results of the performance calculations for a 40 kWe gas turbine engine indicate that a 4.3 % reduction in full-load fuel consumption is attainable with compressor IGVs set at 9° in the direction of the rotor blade rotation, and a reduction of 25.3 % is attainable with independent and distinct compressor and turbine rotor speeds

AI-Based Generative Geometrical Design of Concentrated Solar Thermal Tower Receivers

An artificial intelligence (AI) aided generative design workflow for the optimization of cavity receivers for concentrated solar thermal (CST) energy systems is presented. The workflow integrates the Non-dominated Sorting Genetic Algorithm III (NSGA-III) with generative design methodologies and optical evaluation through Monte-Carlo ray-tracing in an interoperable way, to optically optimize the geometry of cavity receivers according to a set of objective functions for a given heliostat field. As a demonstrator test case, the workflow is used to provide an optimal geometrical design of a cavity receiver given the Cyprus Institute’s PROTEAS heliostat field. It is shown that the workflow is able to generate unconventional, non-intuitive and efficient receiver designs in an automated manner, which are often not conceived by traditional design approaches

Dynamic Wind Loading of Heliostats: Efficient Simulation of Resonance Effects for Heliostat Cost-Optimization

A method for the design of heliostats considering dynamic wind loads is presented. The transient FEM simulation is based on a CAD model of the heliostat and a pressure distribution time series measured in a wind tunnel. In order to minimise the calculation times, a simplified FEM model is used first to determine the period in which the maximum deformations occur. The stresses can then be determined for this period using a more precise model

German Renewable Energy Policies and Their Implications for Local Land Use – Maize for Biogas From 2008 - 2018 in Brandenburg

This study investigates the spatiotemporal dynamics of maize cultivation for biogas production in Brandenburg, Germany, from 2008 to 2018, employing a spatially explicit multicriteria analysis. By combining plot-level land-use data from the Integrated Administration and Control System (IACS) with biogas pnt information, we analyze the likelihood of maize cultivation for biogas at the plot level and find that maize for biogas accounts for over 5% of the total arable land in Brandenburg. We identify patterns of high concentration, particularly in the northwest of the region. The analysis also reveals a steady increase in maize cultivation, aligning with regulatory changes in the Renewable Energy Sources Act (EEG). These findings offer valuable insights into the spatial patterns and drivers of biogas maize production, providing a basis for future environmental and economic research. The study highlights the need for plot-level information to evaluate the effects of renewable energy policies on local land use

Luminescent Solar Concentrator Greenhouses for Concurrent Energy Generation and Lettuce Production in the United States

Meeting the needs for both renewable energy production and increased food supply to sustain growing communities remains a global challenge. Agrivoltaic greenhouses can meet these dual needs in one plot of land, mitigating land competition. Luminescent solar concentrators (LSCs) benefit these systems by providing additional design flexibility for crop-specific spectrum modification while allowing sufficient light transmission for crop growth. Silicon quantum dots (Si QDs) have received growing interest as a material candidate for LSC greenhouses as well. We present an investigation into the impact of Si QD film concentration on the energy demands of an LSC greenhouse in Phoenix, Arizona through a comprehensive modelling framework. We then expand upon one Si QD concentration and simulate LSC greenhouses in 48 locations across the United States. We demonstrate LSC greenhouses can supply their annual energy demands in warm climates, where greenhouse heating demands remain low. LSC greenhouses can also be as profitable as the conventional glass greenhouse if the crop yield remains comparable or if the greenhouse can benefit from net metering.

Modeling the Agrivoltaic Potential for Land-Intensive Commodity Crops

Corn and soybean farming use about two-thirds of the agricultural land in the US. To accelerate the large-scale adoption of agrivoltaics, best practices that are compatible with traditional farming operations for corn and soybeans need to be developed. In this presentation, we present the development of a modeling framework to explore the benefits and trade-offs between crop growth and photovoltaic (PV) electricity generation for common commodity crops at the county level. Our model couples a crop growth model, a soil water balance model, and a PV model in one integrated scheme. As an example, we consider corn growth in Renville County, MN. The model suggests that there is a ~0.55% loss in crop yield upon 1% shading because the crop-diminishing effect of reduced radiation is partially offset by increased water retention in the ground

silp_nlp at LLMs4OL 2025 Tasks A, B, C, and D: Clustering-Based Ontology Learning Using LLMs

This paper presents the participation of the silp\_nlp team in the LLMs4OL 2025 Challenge, where we addressed four core tasks in ontology learning: Text2Onto (Task A), Term Typing (Task B), Taxonomy Discovery (Task C), and Non-Taxonomic Relation Extraction (Task D). Building on our experience from the first edition, we proposed a clustering-enhanced methodology grounded in large language models (LLMs), integrating domain-adapted transformer models such as pranav-s/MaterialsBERT, dmis-lab/biobert-v1.1, and proprietary LLMs from Grok. Our framework combined lexical and semantic clustering with adaptive prompting to tackle entity and type extraction, semantic classification, hierarchical structure discovery, and complex relation modeling. Experimental results across 18 subtasks highlight the strength of our approach, particularly in blind and zero-shot scenarios. Notably, our model achieved multiple first-rank scores in taxonomy discovery and non-taxonomic relation extraction subtasks, validating the efficacy of clustering when coupled with semantically specialized LLMs. This work demonstrates that clustering-driven, LLM-based approaches can advance robust and scalable ontology learning across diverse domains

Explainability and Human Supervision in AI Systems in Education: Regulatory Framework

Der Einsatz Künstlicher Intelligenz (KI) in der beruflichen Bildung bietet erhebliches Potenzial, ist jedoch mit ethischen und regulatorischen Herausforderungen verbunden. Dieser Beitrag analysiert die ethischen und regulatorischen Implikationen von KI in der beruflichen Bildung und präsentiert Strategien zur Minimierung potenzieller Risiken. Anhand eines praxisorientierten Beispiels KFZ Elektromotor wird die Implementierung eines KI-Systems in einem beruflichen Bildungszentrum veranschaulicht. Es werden konkrete Lösungsansätze vorgestellt, um Data-Biases, Datenschutzverletzungen und andere ethische Bedenken zu adressieren. Durch die sorgfältige Selektion und Aufbereitung von Trainingsdaten sowie den Einsatz erklärbarer KI-Modelle wird die Entwicklung fairer, transparenter und zuverlässiger KI-Systeme in der Bildung gefördert. Der Beitrag betont die Notwendigkeit der Konformität mit ethischen Prinzipien bei der Entwicklung und Implementierung von KI-Systemen. Im Kontext des EU AI Acts wird insbesondere auf die Kategorie der hochrisikobehafteten KI-Systeme eingegangen, zu denen KI-Systeme in der beruflichen Bildung potenziell zählen können. Trotz der regulatorischen Herausforderungen werden Akteure ermutigt, KI zur Erstellung von Bildungsinhalten einzusetzen. Die implementierte Lösung sieht eine menschliche Aufsicht mit entsprechenden IT-Systemen und Richtlinien vor, die als Middleware und Vertrauensinstanz fungiert.The use of artificial intelligence (AI) in vocational education and training offers considerable potential, but is associated with ethical and regulatory challenges. This article analyses the ethical and regulatory implications of AI in vocational education and training and presents strategies for minimising potential risks. The implementation of an AI system in a vocational training centre is illustrated using a practical example of an electric motor for motor vehicles. Concrete solutions are presented to address data biases, data protection violations and other ethical concerns. The careful selection and preparation of training data and the use of explainable AI models promote the development of fair, transparent and reliable AI systems in education. The article emphasises the need for compliance with ethical principles in the development and implementation of AI systems. In the context of the EU AI Act, particular attention is paid to the category of high-risk AI systems, which may potentially include AI systems in vocational education. Despite the regulatory challenges, stakeholders are encouraged to use AI to create educational content.The implemented solution provides for human oversight with appropriate IT systems and guidelines that act as middleware and a trusted authority

First-Principles Study of Radiation-Induced Defects in Silicon Solar Cells Using Density-Functional Theory Simulation

Displacement damage from high-energy electron and proton irradiation is a critical degradation mechanism in space solar cells, particularly within the Van Allen radiation belts. These energetic particles induce atomic displacements in semiconductor materials, generating lattice defects such as vacancies, di-vacancies, and impurity-related complexes (e.g., BiOi, BiCs and BiHi) that significantly impact the electronic structure of silicon, reducing solar cell efficiency and power output. A fundamental understanding of these defects is critical for designing radiation-resistant photovoltaics. To address this challenge, we employ first-principles Density Functional Theory(DFT) using the SIESTA code with localized orbital basis sets to model the electronic structure of silicon systems with induced defects and impurities. Our study focuses on boron-related defect complexes, including interstitial boron (Bi) and its interactions with oxygen (O) and hydrogen (H), with validation against experimental data and comparative calculations using QUANTUM-ESPRESSO to assess computational robustness. Our simulation identifies key defect energy levels, including BiOi at Ec – 0.23 eV and BiCs at Ev + 0.31 eV, which exhibit strong agreement with experimental data, reinforcing the reliability of our approach. We further analyze the passivating role of interstitial hydrogen (Hi) and its influence on defect neutralization. These findings provide critical insights for defect engineering strategies, enabling optimized doping and thermal processing to mitigate radiation-induced degradation. This research advances the development of next-generation, radiation-tolerant photovoltaics for prolonged space missions by identifying dominant defect configurations and their electronic structure