Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies.

Sand-based thermal energy storage systems represent a paradigm shift in sustainable energy solutions, leveraging Earth’s most abundant mineral resource through advanced nanocomposite engineering. This review examines sand-based phase change materials (PCM) systems with emphasis on integration with human-powered energy generation (HPEG). Silicon-based hierarchical pore structures provide multiscale thermal conduction pathways while achieving PCM loading capacities exceeding 90%. Carbon-based nanomaterial doping enhances thermal conductivity by up to 269%, reaching 3.1 W/m·K while maintaining phase change enthalpies above 130 J/g. This demonstrated cycling stability exceeds 1000 thermal cycles with <8% capacity degradation. Thermal energy storage costs reach ~$20 kWh⁻¹—60% lower than lithium-ion systems when normalized by usable heat capacity. Integration with triboelectric nanogenerators achieves 55% peak mechanical-to-electrical conversion efficiency for direct pathways, while thermal-buffered systems provide 8–12% end-to-end efficiency with temporal decoupling between intermittent human power input and stable electrical output. Miniaturized systems target off-grid communities, offering 5–10× cost advantages over conventional batteries for resource-constrained deployments. Levelized storage costs remain competitive despite efficiency penalties versus lithium-ion alternatives. Critical challenges, including thermal cycling degradation, energy-power density trade-offs, and environmental adaptability, are systematically analyzed. Future directions explore biomimetic multi-level pore designs, intelligent responsive systems, and distributed microgrid implementations.

This protocol presents a Gaussian Process Regression (GPR) optimization framework for human-powered electricity generation systems to address technological unemployment and renewable energy demands simultaneously. Seven participants performed 112 trials across 16 configurations combining four battery voltages (12 V, 24 V, 36 V, 48 V) with four electrical loads (10 W, 30 W, 50 W, 70 W) using a modified stationary bicycle generator. The GPR model incorporated automatic relevance determination (ARD) squared exponential kernels to map pedaling speed, pressure, voltage, and load parameters to generation efficiency while quantifying prediction uncertainty. Real-time data acquisition at 2 Hz captured mechanical and electrical parameters through Hall-effect sensors, strain gauges, and power analyzers with Modbus communication protocols. The optimized system achieved 80%-90% theoretical maximum efficiency with coefficient of variation (CoV) below 15%, compared to 60%-70% efficiency and 25%-35% variability in commercial systems. GPR predictions demonstrated R² = 0.713 with sub-10ms latency through sparse approximation techniques, enabling real-time control. Optimal operational parameters varied systematically: low loads required 150-180 revolutions per minute (RPM) at 50-70 N pressure, while higher loads peaked at 100-120 RPM with 100-150 N pressure. The 48 V configuration improved efficiency by 68% over 12 V baseline (measured as relative efficiency index, REI, normalized to baseline = 100%). Phase-specific monitoring identified fatigue onset at 35-40 min, triggering risk-aware control adjustments based on posterior uncertainty. The protocol establishes quantitative methods for balancing energy generation with physiological constraints, providing a reproducible framework for deploying human-powered systems in fitness facilities where break-even occurs at $0.18/kWh electricity pricing.

This protocol presents a Gaussian Process Regression (GPR) optimization framework for human-powered electricity generation systems to address technological unemployment and renewable energy demands simultaneously. Seven participants performed 112 trials across 16 configurations combining four battery voltages (12 V, 24 V, 36 V, 48 V) with four electrical loads (10 W, 30 W, 50 W, 70 W) using a modified stationary bicycle generator. The GPR model incorporated automatic relevance determination (ARD) squared exponential kernels to map pedaling speed, pressure, voltage, and load parameters to generation efficiency while quantifying prediction uncertainty. Real-time data acquisition at 2 Hz captured mechanical and electrical parameters through Hall-effect sensors, strain gauges, and power analyzers with Modbus communication protocols. The optimized system achieved 80%-90% theoretical maximum efficiency with coefficient of variation (CoV) below 15%, compared to 60%-70% efficiency and 25%-35% variability in commercial systems. GPR predictions demonstrated R² = 0.713 with sub-10ms latency through sparse approximation techniques, enabling real-time control. Optimal operational parameters varied systematically: low loads required 150-180 revolutions per minute (RPM) at 50-70 N pressure, while higher loads peaked at 100-120 RPM with 100-150 N pressure. The 48 V configuration improved efficiency by 68% over 12 V baseline (measured as relative efficiency index, REI, normalized to baseline = 100%). Phase-specific monitoring identified fatigue onset at 35-40 min, triggering risk-aware control adjustments based on posterior uncertainty. The protocol establishes quantitative methods for balancing energy generation with physiological constraints, providing a reproducible framework for deploying human-powered systems in fitness facilities where break-even occurs at $0.18/kWh electricity pricing.

Household-scale sand-based thermal energy storage (TES) integrated with thermoelectric generators (TEGs) is constrained by insufficient radial thermal transport, interfacial heat transfer losses, and uncertain cyclic durability. This study addresses all three issues through a finite-element parametric investigation of thirty composite configurations spanning five dopant candidates, three volume fractions, and two spatial architectures. The 30 vol% functionally graded Solar Salt formulation produced a well-defined isothermal plateau near 493 K at the TEG interface, stabilizing the hot-side temperature to within 10 K through a spatially coherent phase-change front. No purely conductive dopant reproduced this temporal stability despite achieving higher peak temperatures. Interfacial analysis revealed that a 0.1 mm thermal interface material layer imposed a 40 K temperature discontinuity comparable to the TEG operating differential. Thermomechanical screening across nine insulation-shell material combinations demonstrated that compatibility among the coefficients of thermal expansion governed long-term reliability, with zirconia paired with Inconel 718 achieving a fatigue safety factor exceeding 2.0 over the 20-year service horizon projected by the power-law ratcheting extrapolation. Furthermore, coupled parametric mapping of interface conductivity and contact pressure identified an optimized design point capable of delivering 82% of the thermal energy to the TEG surface. This interfacial optimization secured a projected thermal interface material (TIM) fatigue life of 6,200 cycles (approximately 17 years) and ultimately yielded an end-to-end electrical efficiency of approximately 5%. These findings establish quantitative design criteria for transitioning sand-based thermal storage to compact household-scale modules.

This paper investigates a high-precision, magnetic-field-based position sensing scheme for an electromagnetic maglev system. Accurate air-gap estimation is challenging due to strong nonlinearities, sensor noise, and the indirect observability of velocity and acceleration. Reliable state sensing is critical for the stability and performance of maglev platforms used in precision motion, manufacturing, and transportation. To address these issues, a low-cost multi-sensor fusion approach is developed that combines a Hall-effect sensor, an inductive displacement sensor, and an accelerometer. Nonlinear measurement models are constructed from COMSOL-derived magnetic-field data together with a calibrated flux-density–gap relationship, and an extended Kalman filter (EKF) is designed for real-time estimation of air gap, velocity, and acceleration. The algorithm is implemented in MATLAB and evaluated using deterministic simulations and statistical consistency checks. The fusion EKF achieves an airgap RMSE of 0.12 mm, corresponding to a 33% reduction relative to a 0.18 mm single-sensor baseline. The innovation sequences remain approximately zero-mean with most residuals within the 3 σ bounds, indicating good filter consistency. The proposed methodology offers a practical and economical sensing solution that is readily adaptable to other contactless positioning systems with nonlinear measurements.